esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Model Selection
#### 2.4.6 Random Forest  
RF is an effective ensemble learning method derived from the bagging approach [48]. The decision tree is the essential structural unit in RF: each tree is built on a random sample of data with replacement. Prediction outcomes are identified by aggregating multiple decision trees' predictions. Each tree is independently trained with randomly selected data and features. Thus, RF tends to be robust to overfitting and is well-suited to large-scale and high-dimensional data. These inherent advantages have earned RF recognition in diverse fields involving classification, regression, feature selection, anomaly detection, and data compression.  
In brief, the above-mentioned tree-based regression models fall into two common types of ensemble strategies: (1) boosting, including AdaBoost, GBoost, XGBoost, LightGBM, and CatBoost; and (2) bagging (e.g., RF). The essential idea behind the boosting method is that it iteratively combines weak learners into a single strong prediction by taking the mistakes of the previous learners when training a new weak learner. Unlike boosting, the bagging methods use the bootstrap sampling technique to train several weak learners separately and aggregate learners' outputs for final prediction. Because of the difference in ensemble strategies, their final predictions differ accordingly. Therefore, the final prediction results for the boosting methods can be written as Equation (1), where \(X\) is the training dataset, \(M\) is the number of weak learners, \(f_{i}^{B}(X)\), and \(\alpha_{i}\) is the weight of the \(i\)-th weak learner. The bagging methods yield prediction results through Equation (2), where \(N\) is the number of weak learners, \(f_{j}^{b}(X)\).  
\[\hat{F_{B}}(X) =\sum_{i=1}^{M}\alpha_{i}f_{i}^{B}(X) \tag{1}\] \[\hat{F_{b}}(X) =\frac{1}{N}\sum_{j=1}^{N}f_{j}^{b}(X) \tag{2}\]
## 4 Discussion
### Clinical Utility of BRAF V600E Detection  
In 2011, a clinical trial conducted on 675 patients with previously untreated, metastatic melanoma with the BRAFV600E mutation concluded that the BRAF kinase inhibitor venurafenib produced improved rates of overall and progression-free survival in this category of patients [17]. In an extended follow-up analysis of patients older than 18 years, with treatment-naive metastatic melanoma and whose tumor tissue was positive for BRAF(V600) mutations, it was also observed that an inhibition of BRAF with veumarafenib improved survival [20]. A retrospective study revealed that veumarafenib appears to have predictable and manageable adverse events (rash, squamoproliferative growths, photosensitivity and squamous cell carcinoma or keratoacanthoma). Proactive management can limit the impact of adverse effects (AEs) on patients, allowing for treatment to continue despite toxicities [26].  
The therapeutic benefits of combination of veumarafenib and IFN-\(\alpha\) in patients with BRAFV600-mutated melanoma were found in a recent 2021 study [27].  
All these factors suggest that we need a more optimized screening of melanoma of the skin in our country, using molecular methods. The targeted therapy is also new for our area, but by proving the presence of BRAF mutations, we may advise the use of these new drugs.  
A limitation of the study was the limited number of melanoma cancers that were analyzed. This was mainly due to the fact that only the analyzed archival melanoma cancers were available in the moment of FFPE selection from the hospital archive. The results of this study warrant the implementation of BRAF testing as routine in the case of melanoma patients.  
In conclusion, we detected a 60% prevalence of BRAF V600E mutations in this pilot study group of melanoma patients using a sensitive, accurate and fast QuASAR real-time PCR method. These data can be used for the selection of patients with melanoma, as candidates for target therapy.  
[PERSON], [PERSON], and L.S.I. were responsible for the research design. V.P., I.-D.C., D.C., C.D. and D.F. resources, original draft preparation, I.I., C.T. and D.C. composed and analyzed the datasets. All authors have read and agreed to the published version of the manuscript.  
This work was developed within the project \"Program of excellence in multidisciplinary doctoral and postdoctoral research regarding chronic diseases\" (POSDRU 159/1.5/S/133377), [PERSON] University of Medicine and Pharmacy, Iasi.  
Not applicable.  
All data and materials supporting the results of the present study are available in the published article.  
[PERSON] and [PERSON] are supported by \"[PERSON]\" University of Medicine and Pharmacy Iasi (research contract 6983 from 21.04.2020).  
The authors declare no conflict of interest.
## 5 Conclusions  
In this paper, the estimation of the source radial velocity is realized based on the synthetic aperture beamforming method. Then, the least squares criterion is used to estimate the range and time of CPA and the source velocity. Finally, source range localization is accomplished. The proposed method is validated to be effective by both simulation data and SWellEx-96 experimental data. This paper presents a method for estimating the radial velocity in both narrowband and broadband forms. The broadband form is more stable in the presence of interference in the environment or low SNR case. Because the FFT algorithm is used in the calculation of the horizontal wavenumber spectrum, the running speed of the method can be increased and a shorter running time can be obtained. Since only the sound speed in water is needed as a priori information, the proposed method is robust to the environmental uncertainty. It should be noted that for the case of no CPA present, after the radial velocity is obtained, the estimation of the source range can still be realized based on the waveguide invariant method. In this case, some broadband signal information is needed, and the specific process can be found in [14].  
Conceptualization and methodology, J.-Y.D. and Z.-WL.; validation, J.-Y.D., Z.-W.L. and L.-G.L.; formal analysis, L.-G.L.; investigation, J.-Y.D.; resources, Z.-WL.; data curation, J.-Y.D.; writing--original draft preparation, J.-Y.D.; writing--review and editing, Z.-W.L. and L.-G.L.; funding acquisition, Z.-W.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key R&D Program of China, grant number 2016 YFC1400200, National Natural Science Foundation of China, grant number 41706113, and open foundation of Laboratory for Regional Oceanography and Numerical Modeling, Qingdao National Laboratory for Marine Science and Technology, grant number 2017A04.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript or in the decision to publish the results.
## 3 Experimental Results and Discussion
### Engineering Problems  
The application of metaheuristics to the solution of engineering problems is one important area of research. In this section, the ability of GSLEO-AEFA to solve engineering problems was evaluated. Four common engineering problems with low and high variables from CLEC 2011 Real World Optimization Problems were used for this test [54]. The evaluation is based on 30 independent runs with 30 populations and a maximum of 1000 iterations. For comparison, the best solution from each optimization algorithm was used.
## 2 Optimization Problem
### Gearbox Mass Calculation
#### 2.1.3 Calculation of Shaft Mass  
The shaft mass can be found by:  
\[m_{s}=\sum_{i=1}^{3}m_{si} \tag{23}\]  
where \(m_{si}\) is the mass of ith shaft of the gearbox (kg), which is determined by:  
\[m_{si}=\pi\cdot p_{s}\cdot\left(\sum_{j=1}^{n}d_{j}^{2}\cdot l_{j}+\sum_{k=1}^ {2}d_{bk}^{2}\cdot B_{k}\right) \tag{24}\]  
in which \(d_{j}\) and \(l_{j}\) are the diameter and the length of jth shaft part (mm); \(d_{bk}\) and \(B_{k}\) are the diameter and the width of kth shaft part on which the bearing is installed. The values of \(d_{j}\) and \(d_{bk}\) also are determined by [23]:  
\[d_{j}=\sqrt[3]{\frac{M_{e}}{0.1\cdot[\sigma_{s}]}} \tag{25}\]  
wherein \([\sigma_{s}]\) is the allowable shaft stress (MPa), which can be determined by the material and size of the shaft [23]. \(M_{e}\) is the equivalent moment (Nmm), which is found by [23]:  
\[M_{e}=\sqrt{M_{y}^{2}+M_{x}^{2}+0.75\cdot T^{2}} \tag{26}\]  
where \(M_{x}\) and \(M_{y}\) are the bending moment in x and y directions (Nmm); T is the torque (Nmm). These parameters can be defined based on the diagram for finding shaft dimensions. Figure 2 describes this diagram for calculation of the first shaft of the gearbox.  
In Equation (22), \(B_{k}\) is the bearing width (mm). In this work, radical ball bearings with angular contact were used. From the data in [24], the following regression was proposed to calculate the width of the bearings (with \(R^{2}=0.9951\)):  
\[B_{k}=0.0017\cdot d_{bk}^{2}+0.0277\cdot d_{bk}+13.869 \tag{27}\]
## 2 Materials and Methods
### Beams' Characteristics
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Beam Codified Name** & \(\rho_{l}\) & \(\rho_{l}f_{yl}\)/\(f_{c}\) & \(\rho_{v}\) & \(\rho_{v}f_{yv}\)/\(f_{c}\) & \(f_{c}\) & \(f_{ct,spl}\) \\  & & & & & **(MPa)** & **(MPa)** \\ \hline B500 & 1.76\% & 0.340 & - & - & 28.5 (0.53) & 2.60 (0.26) \\ B200a & 1.76\% & 0.334 & 0.24\% & 0.018 & 29.0 (0.98) & 2.32 (0.14) \\ B200b & 1.76\% & 0.328 & 0.24\% & 0.018 & 29.5 (0.70) & 2.40 (0.20) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Geometrical and mechanical reinforcement ratios for initial beam. Figure 1: Geometrical and reinforcement details of initial and retrofitted beams. Furthermore, the main purpose of the jacket was to cover the added reinforcement, which consists of straight longitudinal bars and U-shaped stirrups, each with a diameter of 5.5 mm (\(\O\)5.5) and a yield tensile strength of 225 MPa. The three retrofitted RC beams, distinguishable from the original ones, henceforward will be referred to as \"B-J\". Each of them is still 1.2 m long and has the following dimensions: width (\(b\)) = 150 mm, height (\(h\)) = 225 mm, effective depth (\(d\)) = 203 mm, and shear span (\(\alpha\)) = 500 mm. This results in a shear span-to-depth ratio of \(\alpha/d\) = 2.46. For further clarification, please refer to Figure 1, which illustrates the mentioned geometric and reinforcement details. \"B-J\" beams were reinforced with two \(\O\)5.5 mm-diameter bottom bars, two \(\O\)5.5 mm side bars, and two \(\O\)5.5 mm top bars. Further, L-shaped dowels were epoxy-bonded into 7 mm drilled holes in the vertical sides of the beams. The drilled holes were cleaned and filled with epoxy resin for successful anchorage. Additionally, U-shaped transverse reinforcement with varying spacings (60 mm, 85 and 85 mm) was used for beams labeled \"B500-J\", \"B200a-J\", and \"B200b-J\", respectively. Finally, the jacket was cast in steel molds with cementitious self-compacting mortar (Figure 3). More details on the mechanical reinforcement and the retrofitting process can be found in the research conducted by [PERSON] et al. [2]. Figure 3: Retrofitting process for beams “B-J”. Figure 2: Preparation of the surface.
## 3 Results and Discussion
### Headspaces Chemical Composition
#### 3.2.2 Beverages  
Beer Sample  
The complete composition of the HSs of the beer samples, both the control and the treated beers, is reported in Table 4. A total of 23 compounds were identified, covering 100.0% and 99.9% of the total composition, respectively. \begin{table}
\begin{tabular}{l c c c c} \hline \hline \multirow{2}{*}{**Compounds**} & \multirow{2}{*}{**1.r.i. 1**} & \multirow{2}{*}{**Class.**} & \multicolumn{2}{c}{**Relative Abundance (\%) \(\pm\) SD**} \\ \cline{3-5}  & & & **SPME Hop** & **SPME Hibiscus** \\ \hline _trans_-\(\gamma\)-cadinene & 1514 & sh & 0.3 \(\pm\) 0.04 & 0.9 \(\pm\) 0.11 \\ \(\delta\)-cadinene & 1524 & sh & 0.4 \(\pm\) 0.12 & 1.9 \(\pm\) 0.26 \\ \(\alpha\)-cadinene & 1537 & sh & - & 0.4 \(\pm\) 0.03 \\ spathulenol & 1577 & os & - & 1.0 \(\pm\) 0.02 \\ carophyllene oxide & 1582 & os & - & 1.0 \(\pm\) 0.06 \\ humulene oxide II & 1608 & os & - & 0.9 \(\pm\) 0.02 \\ \(\gamma\)-eudesmol & 1631 & os & - & 0.2 \(\pm\) 0.01 \\ hexahydroafaresylacetone & 1845 & ac & - & 4.9 \(\pm\) 0.23 \\ \hline Total identified (\%) & & & 99.5 \(\pm\) 0.05 & 99.7 \(\pm\) 0.03 \\ \hline Monoterpene hydrocarbons (mh) & & & 71.6 \(\pm\) 2.2 & 8.4 \(\pm\) 0.73 \\ Oxygenates monoterepenes (om) & & & 0.7 \(\pm\) 0.05 & 28.7 \(\pm\) 1.98 \\ Sesquirepre hydrocarbons (sh) & & & 23.7 \(\pm\) 2.14 & 28.6 \(\pm\) 2.21 \\ Oxygenated sesquiterpenes (os) & & - & 3.0 \(\pm\) 0.01 \\ Apocarotonoids (ac) & & - & 4.9 \(\pm\) 0.23 \\ Pyrazine (pyr) & & - & 0.6 \(\pm\) 0.02 \\ Other non-terpene derivates (nt) & & 3.5 \(\pm\) 0.03 & 25.4 \(\pm\) 0.75 \\ \hline \hline \end{tabular} 1  
\end{table} 
Table 3: _Cont._
Undoubtedly, non-terpene derivatives constituted the main class of components of the aroma of both the samples, representing 96.4% of the whole volatilome in the control beer and 99.0% in the hibiscus one. Within this chemical class, esters were the most abundant compounds, being significantly higher in the treated sample (77.6%) than in the control (68.4%). Ethyl acetate, isopentyl acetate, ethyl hexanoate, ethyl octanoate and ethyl decanoate were the most represented volatiles. Esters are considered the main chemical class contributing to beer aromas and flavors [12], as they are responsible for fruity and flowery notes, even if in small amounts, thanks to their low odor threshold [12; 38]. In particular, isopentyl acetate, which confer sweet, banana flavor characters, is a desirable acetate ester derivatives originating from the yeast metabolism [39]. However, high amounts of this VOC are related to high content of ethyl acetate, which, instead, presents solvent-like scents. Ethyl hexanoate, ethyl octanoate and ethyl decanoate are aliphatic esters relevant for the beer aroma: the first two confer apple-like notes, whilst the latter is responsible for fruity and flowery notes, even if in small amounts, thanks to their low odor threshold [12; 38].
## 4 Discussion
### Limitations  
This study is not without limitations, which we accept. First, we did not question participants about their place of exercise due to the gathering data rapidly. However, we assumed participants in this experiment would not be able to take part in team training and matches inside training facilities, but might use their home and/or backyard for individual training purposes due to lockdown restrictions.  
The main limitation in this study however, as with all self-reporting, is the possibility of reporting bias of PA (overestimation of PA), which is common among the respondents of a self-reported questionnaire. However, we have asked the same questions in the survey about the two different periods (before and during home confinement by COVID-19). Thus, we speculate that the degree of bias is similar based on internal consistency of respondents. It is also possible that recall bias was manifest, and respondents were unable to correctly recall the amount of PA before confinement. This is an innate problem with all self-reporting and recall data. Without the power of foresight, it is impossible to avoid recall bias if asking participants about a previous time period. However, one way to ameliorate the influence of recall and reporting bias is to use the athletes' 'wearables' to objectively and retrospectively determine changes in PA over specific time periods. However, as described in our recent article (REF), there are still significant hurdles to this approach, namely skills in computer science and web protocols, approval for data access from multiple stakeholders, between-device variation, and resource commitment. Thus, we feel our approach in the present study was justified at time of writing. We are hopefulthat in the future, objective, valid, population level PA patterns will be more easily accessed through consumer-grade wearable devices.  
Additionally, data concerning mode of exercise (i.e., cycle or running, resistance training), and confinement nuances based on country of residence would add depth to this study and should be considered in future studies. Additionally, although our sample was exclusively handball players, the tool for PA data collection was not specifically designed for this population, and was intended for the general population, so more nuanced data are not possible.  
Reporting satisfaction (LS) regarding life before and during the pandemic may be affected by recall biased due to different problems (e.g., stress) or diseases (e.g., depression). This impairs the meaningfulness of the results.  
Finally, data collection was via questionnaires with subjective ratings and this has not been validated by objective indicators. Therefore, we strongly recommend conducting an indirect calorimetry or doubly labelled water to measure physical activity during 24 h to enhance validity of the results.
## 3 Design and Implementation  
The system uses gestures on or manipulation of an ID tag to generate a haptic passcode (Table 1). These gesture or manipulation patterns include tapping, shaking, and flipping of the ID tag. The signals acquired through the acceleration sensor are processed on five different axes (x, y, z, y-z, x-z), and passcodes are generated from various combinations of signals from each axis. Currently, seven different patterns of haptic manipulations can be differentiated after a probabilistic inference analysis on the signals acquired from the five axes is performed. The passcode in haptic format is then sent to the automated security system via a Bluetooth communication module for a personal authentication check. The automated security system then matches the passcode patterns with the pre-stored personal passcode patterns and decides whether to allow access.  
To generate a passcode by combining haptic gestures such as tapping, flipping, or shaking an ID tag, an interval of approximately half a second between each haptic manipulation is required.
# Analysis of a Preliminary Design Approach for Conformal Lattice Structures
## 2 Materials and Methods
### Beam Approach for Linear Structural Analysis
The lattice structure wireframe information needed to reconstruct the model are the following:  
* The _node coordinates_ list contains the cartesian coordinates of all the nodes of the structure;
* The _beam connection_ list contains the indexes of the starting and ending node of each beam of the structure;
* The _beam size_ list contains the diameter of the cross-section of each beam of the structure. * The load, constraints, and material are added inside the CAE environment and correspond to the boundary conditions of the structural size optimization (Figure 4):
* Two load cases: 7.5 kN (traction) and -10 kN (compression) applied to the nodes of the beams connected to the big rod's end;
* The displacements along the X- and Y-direction of the nodes of the beams connected to the big rod's end are locked;
* The displacements and rotations of the nodes of the beams connected to the small rod's end are locked;
* Material: AlSi10 Mg (Table 2). The FE models set up in the three software are presented in Figure 4. The structural analysis of the case study was simplified, and only the results regarding the displacement and direct stress were investigated. Direct stress is defined as the stress component due to the axial load encountered in a beam element, and the axial load is the force acting on an object parallel to its axis. For a better understanding of the behavior of the piston rod, a detailed investigation should be performed considering, for instance, failure criteria such as the [PERSON] criterion [73; 74].
###### Abstract  
Various studies on latency in remote mixed reality collaborations (remote MR collaboration) have been conducted, but studies related to interaction latency are scarce. Interaction latency in a remote MR collaboration occurs because action detection (such as contact or collision) between a human and a virtual object is required for finding the interaction performed. Therefore, in this paper, we propose a method based on interaction prediction to reduce the time for detecting the action between humans and virtual objects. The proposed method predicts an interaction based on consecutive joint angles. To examine the effectiveness of the proposed method, an experiment was conducted and the results were given. From the experimental results, it was confirmed that the proposed method could reduce the interaction latency compared to the one obtained by conventional methods.  
remote collaboration; mixed reality; interaction prediction; human-virtual object interaction; interaction latency; latency reduction
# Source Symbol Purging-Based Distributed Conditional Arithmetic Coding
## 5 Conclusions  
A new DSC implementation named source symbol purging-based distributed conditional arithmetic coding is proposed. At the encoder of this scheme, some source symbols are purged from the source sequence to obtain additional compression gain, and a forbidden symbol is added to the source alphabet to ensure a better bit error rate performance at the decoder. The main feature of this scheme is that the purged symbols still have to be used as the context of the subsequent symbols to be encoded, so the decoder can utilize the correlation between the adjacent symbols in the source sequence to improve the decoding performance. Based on this feature, an improved calculation method for the posterior probability is proposed for the decoding process. The simulation results show that SPDCAC exhibits a better coding performance and encoding complexity than the DSC scheme based on expanding probability intervals. Compared with some source and channel code-based distributed source coding schemes, the proposed algorithm exhibits a better performance under the same compression ratio, especially when the internal correlation strength of the source sequence is strong. Therefore, SPDCAC is very suitable for application scenarios such as distributed video coding and wireless sensor networks. A limitation of this study is that the purging rate is fixed. This scheme should be extended to the adaptive purging rate in the future.  
Conceptualization, J.L.; methodology and software, J.L.; validation, M.Z.; formal analysis, H.M.; investigation, J.L.; resources, H.M.; data curation, W.W.; writing--original draft preparation, J.L.; writing--review and editing, J.C.; visualization, W.W.; supervision, J.C.; project administration, J.C. All authors have read and agreed to the published version of the manuscript.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{\(N\) and \(M\) Size} & \multicolumn{3}{c}{**Average Encoding Time (s)**} & \multicolumn{3}{c}{**Average Decoding Time (s)**} \\ \cline{2-5}  & **SPDCAC** & **DJSCAC** & **SPDCAC** & **DJSCAC** \\ \hline \(N=128\) & & & 0.059 & 0.046 \\ \(M=128\) & & & 0.00138 & & \\ \(N=128\) & & & 0.12 & 0.098 \\ \(M=256\) & & & & \\ \hline \(N=1024\) & & & 0.43 & 0.39 \\ \(M=128\) & & & & \\ \hline \(N=1024\) & & & 1.11 & 0.91 \\ \(M=256\) & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: The average encoding and decoding time (s) of SPDCAC and DJSCAC.  
**Funding:** This research was funded by the National Natural Science Foundation of China under Grant 61861045.  
**Conflicts of Interest:** The authors declare no conflict of interest.