esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion  
### Effect of Input Parameters on Specific Energy  
SPE increased in the ranges of 0.19 to 0.69 kWh ha\({}^{-1}\) at the lowest test range of the \(u/v\) ratio and CI, and from 0.34 to 0.73 kWh ha\({}^{-1}\) for the highest range of the \(u/v\) ratio and CI, when DO varied from 20 to 60 mm, respectively. The effect was caused by the increased soil strength brought on by increased DO and the tool interacting with a larger volume of soil, also causing larger draft forces being measured. This was evident in a study by [PERSON] and [PERSON] [43] where the draft force increased with the increased forward speed as well as the DO. In this study, SPE also increased with the increase in CI (from 0.44 to 0.73 kWh ha\({}^{-1}\) for the \(u/v\) ratio of 1.7, from 0.41 to 0.70 kWh ha\({}^{-1}\) for the \(u/v\) ratio of 2.2, and from 0.37 to 0.66 (SE: \(\pm\)0.07) kWh ha\({}^{-1}\) for the \(u/v\) ratio of 3 at the highest DO of 60 mm). This was due to the increase in draft force as the CI increased; since SPE is the function of the draft and torque, it also increased [29; 44; 45; 46; 47; 48; 49; 50]. A previous study also reported that the draft force requirements of active and passive tillage equipment increased with the increase in CI, \(u/v\) ratio, DO, and width of implementation. While the torque requirements only increased when the implement width and CI increased, it decreased with the increase in the \(u/v\) ratio and DO [50]. The effect of the \(u/v\) ratio and its interaction with DO significantly impacted SPE requirements. When the \(u/v\) ratio increased from 1.7 to 2.2, SPE reduced by 35 to 10%. This could be explained by increased accelerating forces due to the rise in \(u/v\) which increased draft forces, thereby increasing forces on the soil-engaging tool surface [51; 52]. A prior study reported that forward-speed standalone did not significantly impact SPE of tillage implements [30], although it did increase drawbar power requirements as observed in this study.
## 3 Experiment and Analysis
### Performance Measures  
For the input data, there are four different recognition states following model prediction: True Positives (TP), False Positives (FP), True Negatives (TN) and False Negatives (FN). The specific meanings for these recognition states are provided in Table 3. The output labels are compared with the ground truth. When a correct prediction is made, the number of correct predictions incremented by \(1\) and the proportion of corresponding recognition state is changed. When a wrong prediction is made, the number of wrong predictions is incremented by \(1\) and the proportion of corresponding recognition state is changed.  
In this paper, four prevalent performance measures were selected to evaluate the model, including accuracy (ACC), precision (PR), recall (RE), and F1-score (F1). The derivations and definitions of the performance measures are demonstrated in Table 4.
# Demystifying MLOps and Presenting a Recipe for the Selection of Open-Source Tools
## 4 Workflow Stages in MLOps
### Machine Learning Preparation Phase
#### 4.3.3 Data Cleaning and Labeling  
Next to the elimination of existing errors in the input data, procedures for feature engineering, carried out by data scientists (in cooperation with domain experts), are necessary for other domain-specific ML operations. Data cleaning can be split into three parts [43], where error detection like duplicate data, violations of logical constraints, or incorrect value recognition is the first task. Moreover, solving every detected error is a second operation, and the data imputation supplements the missing and incomplete data as the last step.
# Influence of Porosity on Drag Coefficient of Perforated Road Signs
## 4 Conclusions  
In this study, the drag coefficient of perforated road signs with porosity less than 30% was analyzed using CFD analysis. The analysis was conducted using the aspect and clearance ratios of perforated road signs as parameters, and the drag coefficient was analyzed according to the porosity of the perforated road sign and the wind speed. Based on the analysis results, the following conclusions were drawn.  
1. As the porosity of perforated road signs increased, the drag coefficient was found to decrease. This effect can be attributed to the fact that the area of the separation region on the rear surface of the perforated road sign decreased as the porosity increased. In addition, the influence of porosity on the drag coefficient of perforated road signs was analyzed. The perforated road sign with a porosity of 30% showed a drag coefficient reduction effect of up to 0.85 times. Moreover, the drag coefficient reduction rate of perforated road signs was found to be more affected by the clearance ratio than the aspect ratio. The drag coefficient reduction effect of the perforated road sign with a clearance ratio of 0.2 was 0.9 times, while that of signs with clearance ratios of 0.3 and 0.5 was 0.85 times, indicating that the perforated road signs closer to the ground have a higher drag coefficient reduction effect.
2. For perforated road signs, the drag coefficient does not show a clear correlation with the wind speed. It is found that the difference in wind pressure between the front and rear of the perforated road sign shows a constant trend, owing to the air flow through the hole in the perforated road sign. Furthermore, the drag coefficient reduction rate of perforated road signs according to the wind speed was analyzed. The analysis results showed that the drag coefficient range according to the wind speed increased as the aspect and clearance ratios of perforated road signs increased.  
This study provides a foundation for further research to prepare a drag coefficient calculation formula that considers the porosity of perforated road signs and wind speed.  
H.S.: Writing--Reviewing and Editing, Conceptualization, Project administration. K.C.: Data curation, Investigation. W.C.: Writing--Original draft preparation, Writing--Reviewing and Editing. All authors have read and agreed to the published version of the manuscript.  
This study was funded by \"Road Sign Management Center Operation\" from Korea Institute of Civil Engineering and Building Technology, 2022.  
Not applicable.  
The authors declare no conflict of interest.
## 4 Performance Analysis
### SINR Comparison of All Beamformer with Respect to Disparity Angle
#### 4.2.1 SINR Comparison of the FDL-Based Beamformer with Respect to the Disparity Angle  
Figure 3a provides an illustration of this. With \(1^{\circ}\), \(2^{\circ}\), and \(3^{\circ}\) disparity, the SINR of the LCA beamformer is 4.65 dB, 1.69 dB, and 0.52 dB, respectively. The SINR of  
Figure 2: (**a**) Performance analysis of all beamformer applying optimal technique. (**b**) Output power of all beamformer applying optimal technique.  
the LCPAA beamformer is 3.42 dB, 1.15 dB, and 0.53 dB for a 1\({}^{\circ}\), 2\({}^{\circ}\), and 3\({}^{\circ}\)differences, respectively. The SINR LRAA beamformer is 3.44 dB, 1.13 dB, and 0.53 dB for a 1\({}^{\circ}\), 2\({}^{\circ}\), and 3\({}^{\circ}\) difference, respectively. With 1\({}^{\circ}\), 2\({}^{\circ}\), and 3\({}^{\circ}\) difference, the SINR LCCA beamformer is 3.49 dB, 1.15 dB, and 0.53 dB, respectively. All beamformers with no disparity have an SINR of 40.45 dB. According to the discussion above, the LCCA beamformer has a greater SINR performance.  
Figure 3: \(Cont.\)
## 4 Application-Based Performance  
To evaluate the potential of using the 433 MHz band for remote monitoring in naturally complex environments, an underground salt mine in Khewa, Pakistan was used to conduct the customized experimentation. The mine consisted of 19 floors, with 12 underground floors and 7 floors on and above ground level. For the experiments, the third underground level was utilized and tunnel and room-pillar areas were chosen. Stationary sensor nodes and miner positions were placed in both areas, as depicted in Figure 8a,b, respectively. To validate the trilateration technique for miner localization, five different positions of randomly moving miners were utilized. The experiments were conducted in both mine tunnels and room-pillar mining, where the mobile nodes (miner) were randomly moved up to a maximum of 50 m of Tx-Rx separation in order to calculate the RSSI at three stationary nodes. The stationary nodes were positioned at the ceiling and side walls of the mine to avoid ground reflections. Nevertheless, repeated experiments showed random fluctuations in RSSI, with an irregular trend ranging from \(-\)60 to \(-\)84 dBm, possibly caused by human (miner) and metal object movements and multi-path effects in the propagation range. Additionally, it was noted that the RSSI was also affected by increased concentrations of gases present in the atmosphere. The measured RSSI values for miner nodes at one of the three stationary nodes are displayed in Figure 9. Due to the influence of multiple factors, the localization accuracy was limited to 5-10 m during the experiments, which is considered sufficient for safety assurance applications.  
Figure 8: Localization of miners in different scenarios: (**a**) tunnel and (**b**) room and pillar scenarios.  
Over the span of three weeks, multiple experiments were conducted, and the results showed an average variation of 3.5%. The RSSI values for the localization setup in the mine tunnel are depicted in Figure 9a. Similarly, in room and pillar mining, RSSI showed a uniform pattern for all the miner node (MN) positions, except for a sudden increase at P2, which might have been due to reflected waves at the receiver nodes. The observed pattern remained consistent across all the stationery and miner node positions, with an average variation of 2.8% in repeated experiments conducted on different days, which had random temperature and gas concentrations. Figure 9b presents the analysis of RSSI values for the moving miner in the mine room-pillar configuration.
# A Survey on Bug Deduplication and Triage Methods from Multiple Points of View
## 6 Evaluation Methods and Results
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Work** & **Methods** & **Effects** & **Dataset** \\ \hline TopicMiner & Multiple-topic model & 68.7\% and 90.8\% for top-one and top-five & 27 k from CCG, 42 k from OpenOffice, 46 k from NetBeams, 82 k from Eclipse, and 86 k from Mozilla \\ \hline Triage for non-reproducible bug & Time analysis, priority assignment, and & \(\sim\)70\% precision & Mozilla and Eclipse \\ \hline En-LDA & LDA and entropy calculation & 84\% RR85 for IDT and 58\%; RR807 for Firefox & 3 k from Mozilla and 2 k from Eclipse \\ \hline \multirow{2}{*}{Deduplication by continuous querying} & Continuous querying & Over 42\% duplication prevention & 222.4 k from Android, App Inventor, Bazaar, Cyanagomomol, Eclipse, K9 Mail, Mozilla, MyTay, OpenOffice, Openstack, Osmand, and Tempest \\ \hline \multirow{2}{*}{Unified triage framework} & Information gain, chi-square statistics, TF-IDE, LDA, and SVM & 49.22\%, 85.99\%, and 74.89\% precision for Eclipse, Baldiquipt, and Mooretest, respectively & 2 k reports from Eclipse and 0.2 k reports from Bastiquipt \\ \hline Triage based on expertise score & Jaccard and cosine similarity & 89.49\% accuracy, 89.83 \% precision, 89.42\% & 41 k reports from Mozilla, Eclipse, Netbeams, recall rate, and 89.49\% F-score & Firefox, and Freedesktop \\ \hline RSFH & LDA and graph classification & 0.732 accuracy, 0.871 precision, 0.732 recall & 135 k from Buggilla \\ \hline \multirow{2}{*}{Feature extraction model for triage} & \multirow{2}{*}{TF-IDF and heuristic feature detection} & 2\% precision, 4.5\% recall rate, and 5.9\% & Not mentioned \\  & & F-score improvement & \\ \hline Triage based on principal component analysis & Principal component analysis and entropy-based keyword extraction & 90\% top-100 team precision and 67\% individual & 43 k from a private dataset \\ \hline \multirow{2}{*}{Intuitionistic fuzzy-set-based triaging} & \multirow{2}{*}{Intuitionistic fuzzy sets (IFS)} & 15\%, 903, 050, and 0.88 precision for Eclipse, Moilla, and NetBeams, respectively & 32 k from Eclipse, Mozilla, and NetBeams \\ \hline \multirow{2}{*}{Quality-based classifier} & \multirow{2}{*}{Multiple-feature extraction} & 76\% precision, 70\% recall rate, and & 5 k from Jira and Buggilla \\ \hline \multirow{2}{*}{Intuitionistic fuzzy-set-based triage} & \multirow{2}{*}{LDA and IPSim} & 0.894\% accuracy, 0.897 precision, 0.893 recall & Eclipse \\  & & rate, and 0.896 F1 score for Eclipse & \\ \hline TM-FBT & Topic modeling and fuzzy logic & 0.903, 0.887, and 0.851 precision for Eclipse, Mozilla, and NetBeams, respectively & Eclipse, Mozilla, and NetBeams \\ \hline CTED8 & Word2 Vec, TextRank, SBERT, and DeBERTaV3 & 66 k from eclipse and 230 k from from moilla & Over 98\% accuracy, \(\sim\)96\% precision, 96\% recall rate, and 96\% F1 score \\ \hline \hline \end{tabular}
\end{table} 
Table 4: _Cont._
## 3 Proposed Method
### Parameter Analysis  
In this section, we analyze the impact of parameters. Our method was based on one pre-fixed parameter, \(\alpha\), and eight learned parameters, four weight values for channel preferences, \(w_{U}^{u}\), \(w_{D}^{u}\), \(w_{H}^{u}\), and \(w_{T}^{u}\), and four weight values for genre preferences, \(v_{U}^{u}\), \(v_{D}^{u}\), \(v_{H}^{u}\), and \(v_{T}^{u}\).  
Figures 3 and 4 show the performance with different values of the weight parameter \(\alpha\) in Equation (8) from 0 to 1. \(K\) is the number of recommended TV programs. In all cases of \(K\)  
Figure 2: The optimization time as the size of logs increases.  
the recommendation performance was the best when both channels and genres were considered together. In terms of HR, the performance was high when \(\alpha\) was 0.6-1.0 and tended to improve as \(\alpha\) increased for any value of \(K\). This meant that both the channels and the genre preference were necessary, but the channel preference was more effective for better HR. In terms of AR, the values of \(\alpha\) for the best performance were 0.1-0.6 and tended to improve as \(\alpha\) decreased for any value of \(K\). This implied that the genre preference was effective to improve AR.  
When \(K\) = 5, HR was the best under \(\alpha\) = 1.0. This may be related to users' watching patterns. In Table 1, the top five channels that a user frequently watched cover more than 80% of TV watching. So, there was little improvement using genre features.  
Figure 5 shows the average values of weight parameters of channel preferences and genre preferences. The blue bars were weight values for channel preferences, \(w_{U}^{u}\), \(w_{D}^{u}\), \(w_{H}^{u}\), and \(w_{T}^{u}\) in Equation (3), and the red ones for genre preferences, \(v_{U}^{u}\), \(v_{D}^{u}\), \(v_{H}^{u}\), and \(v_{T}^{u}\) in Equation (5). For both channel preferences and genre preferences, the weight value of usual context was larger than that of other time contextual factors. So, it could be inferred that most of users were likely to be influenced by the usual context when they selected a program. In other words, users tended to select TV programs that they usually watch regardless of time. Hour context was the second influential time-contextual factor, followed by day context and day-hour context. Overall, it was inferred that users selected a program mainly based on their usual preference and other time contextual preferences that function as a variation on users' usual preference.  
Figure 4: Average rank as the weight parameter changed.  
Figure 3: Hit rate as the weight parameter changed.
## 2 Results and Discussion
### Quantitative Analysis and OAV of Volatile Compounds
Nevertheless, 2, 4-dithiopentane, a typical aroma in Italy famous white truffle _Tuber magnuturn_[32], was not found in Chinese white truffle. In T3, dimethyl sulfide (1089), 2-methylbutanal (580), 3-methylbutanal (4573), 2-methylbutanol (3879), 3-octanone (950), octanal (436), 1-octen-3-ol (566) and benzeneacetaldehyde (403), had higher concentration. This result was similar to another major truffle species _Tuber indicium_ in China. Researchers also found that the highest content compound was dimethyl sulfide, followed by 3-methylbutanal, 2-methylbutanal, 2-butanol and 1-octen-3-ol in _Tuber indicium_[25]. Of the above-mentioned compounds, the same characteristic compound of three samples was dimethyl sulfide, which has been described as responsible for the detection of such fungi by animals, such as wild boar and trained dog [33]; it had also been detected in different species of truffles, such as _T. melanosporum_, in which it was also the only quantitatively important sulfur volatile organic compound (VOC) [22; 34; 35]. Simultaneously, these compounds had high AIs and OAVs, which could be regarded as active-odor compounds in truffles. From Table 3, volatile components like dimethyl sulfide (3630-4200), 3-methylbutanal (15-508), 3-octanone (672-4846), octanal (61-1247), 1-octen-3-one (102-1293), 1-octen-3-ol (437-5849), benzeneacetaldehyde (34-576), dimethyl disulfide (3-163) bis(2-methyl-3-furyl)disulfide (129) showed relatively higher OAVs than other compounds, indicating that critical influence to the aroma of truffle. Although some compounds were present at relatively low concentration (\(<\)100 \(\mu\)g/kg), the OAVs were above 1 because of their lower thresholds, such as hexanal (33.56-53.63 \(\mu\)g/kg), (_E_)-2-nonenal (8.76-23.59 \(\mu\)g/kg), (_E_)-2-octenal(7.68 \(\mu\)g/kg), nonanal(38.75-65.38 \(\mu\)g/kg), bis(methylthio)methane (273-658), 3-methylbutanoic acid (6.32-9.45 \(\mu\)g/kg), limonene(3.61-45.79 \(\mu\)g/kg), 4-isopropyltoluene(4.81 \(\mu\)g/kg), so these compounds also played significant roles in the aroma of truffle. The compounds had high OAVs showed that the aroma of the three varieties of Chinese truffles were less strong, pungent sulfurous odor, plenty of sweetness of flowers and fatty notes. This conclusion also had many similarities with other related Chinese truffle aroma studies [15; 26]. The unique aroma of Chinese truffles might also be related to the plant environment, soil conditions, such as host tree and complex bacterial colonies in soils [36; 37]. GC-O sensory evaluation combined with OAVs could provide a better assessment of key aroma compounds.
## 4 Experimental Results and Analysis  
### Evaluation Indicators  
The velocity vector \(\widetilde{v}(\widetilde{v}_{x},\widetilde{v}_{y})\) predicted by the RBCN-Net is converted to the estimated coordinates \(\widetilde{p}_{i}(\widetilde{x}_{i},\widetilde{y}_{i})\), and the resulting trajectory is the estimated trajectory. As described in the previous section, real trajectories are generated from Google Tango data, and data frame \(i\) corresponds to real coordinates as \(p_{i}(x_{i},y_{i})\). The evaluation benchmarks include root mean square error (RMSE) and average positioning accuracy (RTE). The RMSE provides a visual representation of the global agreement between the estimated and true trajectories [29], and the calculation formula is shown in Equation (11).  
\[RMSE=\sqrt{\frac{1}{n}\sum_{i=1}^{n}{[(x_{i}-\widetilde{x}_{i})^{2}+(y_{i}- \widetilde{y}_{i})^{2}]}} \tag{11}\]  
In Equation (11), \(n\) represents the total number of data frames. The RTE is calculated by adding the difference between \(\widetilde{p}_{i}(\widetilde{x}_{i},\widetilde{y}_{i})\)and \(p_{i}(
## 3 Results and Discussion  
Another important result is the digital database of oil wells in northwest Romania, which does not exist freely. From their analysis it can be seen that they are predominantly concentrated in Bihor county, most of them being distributed at the level of territorial administrative units: Suplacu de Barcau with several 430 wells (this area is also recognized as the most important Petrom on-shore extraction area at national level), U.A.T. Piscolt with 166 operational wells, U.A.T. Abram with 36 wells, U.A.T. Abramut with 25 wells, etc. (Figure 1).  
The distribution of functional boreholes at the level of geological structures shows that 341 (representing 49% of the total number of boreholes analysed) of them are located  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Petroleum** &
\begin{tabular}{c} **Normal** \\ **Values** \\ \end{tabular} & \multicolumn{2}{c}{**Alert Thresholds**} & \multicolumn{2}{c}{**Intervention Thresholds**} \\ \cline{2-6}
**Hydrocarbons** & \(<\)100 & \begin{tabular}{c} Sensitive \\ 200 \\ \end{tabular} & \begin{tabular}{c} Less sensitive \\ 1000 \\ \end{tabular} & \
\begin{tabular}{c} Sensitive \\ 500 \\ \end{tabular} &
\begin{tabular}{c} Less sensitive \\ 2000 \\ \end{tabular} \\ \hline \end{tabular}
\end{table}
Table 1: Alert and intervention thresholds for TPH.  
in the area of marly clays, sands, gravels (pn), 37% of them (254 boreholes) are in the area of gravels, clayey sands and sands (qh2), 8% (53 wells) are located in the area of alluvial-proluvial deposits (qh1), 4% (30 wells) in the area of deluvial deposits (qp3/3), 2% (13 wells) are located in the area of alluvial-proluvial deposits qp2/3 and only 1% (4 wells) are in the area of eolian deposits (Q).  
The analysis determined that 49.6% of the functional wells are located in the area of brown-luvial soils (podzolite), 26.5% of the functional wells (184 wells) are located in the area of alluvial soils, 69 of them in the area of brown clayey-louvial soils, 67 wells on albic Luvisols (podzolite clayey-louvial).  
Bioremediation of hydrocarbon-polluted soils is the objective when pollution limits are exceeded, including all the biological and chemical processes necessary for the microorganisms developed in the soil to break down hydrocarbons (into CO\({}_{2}\) to be released into the atmosphere and inert, humic matter). The aim is to ensure that the bioremediation of polluted soils will meet the requirements in terms of moisture, pH, C/N ratio and temperature. In all areas where oil resources are exploited, the aim is to identify the contamination of soils with oil and to channel financial resources and interdisciplinary research into developing alternative technologies for removing these contaminants. From the category of technologies used to achieve bioremediation objectives, the use of biosurfactants (derived from agro-industrial wastes) has been noted due to their high degree of biodegradability, implying low toxicity and low cost [32].