esa-sceva/satcom-chunk-collection huggingface dataset:
# Measuring Polarization in Online Debates
## 6 Conclusions and Future Work
\begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline
**Subreddit** & **DebateID** & \(|\mathcal{C}|\) & \(|\mathcal{E}|\) & _BipPol_ & _LC_ & _RC_ & _SWeight_ \\ \hline \multirow{3}{*}{Halloween} & qdg24n & 27 & 27 & 0.0692 & 0.0370 & 0.9246 & 2.0219 \\  & qg6q98 & 84 & 39 & 0.4156 & 0.5765 & 0.3294 & 2.1890 \\  & q71 wxm & 41 & 4 & 0.5626 & 0.3780 & 0.5935 & 2.5074 \\ \hline \multirow{3}{*}{travel} & n1 uxcn & 16 & 25 & 0.1019 & 0.6771 & 0.0625 & 2.4072 \\  & muovcd & 34 & 15 & 0.4379 & 0.4706 & 0.3725 & 2.4979 \\  & mu25 ze & 27 & 16 & 0.6857 & 0.5556 & 0.4296 & 2.8730 \\ \hline \multirow{3}{*}{movies} & q7g2 fh & 135 & 156 & 0.3400 & 0.4547 & 0.3728 & 2.0054 \\  & q1 vw6l & 125 & 111 & 0.4809 & 0.4680 & 0.4320 & 2.3786 \\  & q8975v & 87 & 86 & 0.5939 & 0.5594 & 0.4103 & 2.5874 \\ \hline \multirow{3}{*}{Bitcoin} & mmx2 og & 32 & 23 & 0.2953 & 0.7585 & 0.1875 & 2.0761 \\  & mwglkl & 110 & 62 & 0.4912 & 0.4818 & 0.4498 & 2.2668 \\  & mtux4x & 43 & 12 & 0.6107 & 0.5349 & 0.4651 & 2.4546 \\ \hline \multirow{3}{*}{politics} & qhm2 di & 139 & 153 & 0.4318 & 0.5454 & 0.3120 & 2.5374 \\  & qcrq1v & 216 & 201 & 0.5082 & 0.4961 & 0.3997 & 2.5630 \\  & qerwa6 & 217 & 141 & 0.5927 & 0.5668 & 0.3944 & 2.6513 \\ \hline \multirow{3}{*}{worldnews} & morqje & 78 & 91 & 0.3754 & 0.4983 & 0.2949 & 2.5553 \\  & mq580o & 195 & 257 & 0.5175 & 0.4521 & 0.4434 & 2.5816 \\ \cline{1-1}  & morpw5 & 59 & 68 & 0.6393 & 0.5036 & 0.4669 & 2.7186 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Results for the min/median/max values of _BipPol_ of the same debates of Table 1.  
**Author Contributions:** Conceptualization, T.A., J.A., R.B. and S.M. ; methodology, T.A., J.A., R.B. and S.M. ; software, T.A., J.A., R.B. and S.M. ; validation, T.A., J.A., R.B. and S.M. ; formal analysis, T.A., J.A., R.B. and S.M. ; investigation, T.A., J.A., R.B. and S.M. ; data curation, T.A., J.A., R.B. and S.M. ; writing--original draft preparation, T.A., J.A., R.B. and S.M. ; writing--review and editing, T.A., J.A., R.B. and S.M. ; visualization, T.A., J.A., R.B. and S.M. ; supervision, T.A., J.A., R.B. and S.M. ; project administration, T.A., J.A., R.B. and S.M. ; funding acquisition, T.A., J.A., R.B. and S.M. All authors have contributed equally to the work. All authors have read and agreed to the published version of the manuscript. **Funding:** This research was funded by Spanish Project PID2019-111544 GB-C22 (MINECO/FEDER), by the European Union's Horizon 2020 Research and Innovation Program under Grant Agreements 723596, 768824, 764025 and 814945, and by 2017 SGR 1537. **Institutional Review Board Statement:** We choose to exclude this statement since the study do not involve humans and animals. **Informed Consent Statement:** We choose to exclude this statement since the study do not involve humans. **Data Availability Statement:** We choose to exclude this statement since the study did not report any data. **Acknowledgments:** The authors would like to thank the anonymous reviewers for providing helpful comments. **Conflicts of Interest:** The authors declare no conflict of interest.
# Knowledge Graph Recommendation Model Based on Feature Space Fusion
## 4 Experiments
### Study of KGRFSF
#### 4.5.2 Influence of the Fusion Method  
This section verifies the effectiveness of the proposed feature space fusion method through comparative experiments.  
In the experiment, the behavioral feature module and content feature module of KGRFSF remain unchanged. This section compares the feature space fusion method with other fusion methods:  
* Sum aggregator: The behavioral feature and content feature are directly added and then fused by a multilayer perceptron. \[agg_{sum}=\sigma(W_{sum}(e_{cf}+e_{kg})+b_{sum})\] (31) where \(W_{sum}\) and \(b_{sum}\) are parameters to be learned. \(e_{cf}\) and \(e_{kg}\) represent behavioral features and content, respectively.
* Concat aggregator: The behavioral feature and content feature are spliced and then fused by a multilayer perceptron. \[agg_{concat}=\sigma(W_{concat}(e_{cf}||e_{kg})+b_{concat})\] (32) where \(W_{concat}\) and \(b_{concat}\) are parameters to be learned.  
The other parameters of the experiment were set the same, and the experimental results are shown in Table 4. In the table, KGRFSF-\(agg_{sum}\) uses the Sum aggregator, KGRFSF-\(agg_{concat}\) uses the Concat aggregator, and KGRFSF-\(agg_{project}\) uses the feature space fusion  
Figure 5: Influence of content feature layers on AUC.  
method. The observation results show that KGRFSF-_aggproject_ has obtained the optimal values of all indicators in the two datasets. In addition, compared with the suboptimal results, the AUC and the F1 of the KGRFSF-_aggproject_ on the Last.FM dataset increased by 1.8 and 1.6 percentage points. The AUC and the F1 on the MovieLens-20 M dataset improved by 0.3 and 0.6 percentage points, respectively. The experimental results show that the presented feature space fusion method is able to effectively fuse behavioral features and content features and avoid the \"noise\" problem caused by simple fusion of the two. Finally, the accuracy of the recommendation results is improved.
## 2 Direct Shear Test of Concrete-Granite Interfaces
### Materials and Methods
#### 2.1.2 Sample Preparation  
The three types of roughness of interest for this research project are: a smooth surface, bush-hammered surface, and natural granite surface (Figure 1). The samples with smooth surfaces were prepared using a saw cut. The samples with bush-hammered surfaces were obtained from samples with smooth surfaces, which were bush-hammered. Samples with natural granite surfaces came from a natural rock joint.  
The bush-hammered and natural granite surfaces were cleaned and scanned using a laser profilometer with a vertical resolution of 0.05 mm. After this process, the granite block was put into the lower half shear box, then adjusted to make the mean shear plane parallel to the shear load, and after this adjustment, concrete was cast to complete the lower half shear box. To complete the sample, concrete was poured into the upper half shear box positioned on top of the lower half shear box containing the granite block (Figure 2). The concrete-granite assembly was left to cure for 28 days at ambient temperature. The granite blocks have a volume of about 10 \(\times\) 10 \(\times\) 5 cm\({}^{3}\).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Component** & **Quantity (kg/m\({}^{3}\))** \\ \hline Cement CEM 152.5R & 280 \\ Sand / 4 mm & 650 \\ Sand / 2 mm & 150 \\ Aggregate 11.2/22.4 mm & 780 \\ Aggregate 4/11 mm & 330 \\ Plasticizer-OPT 203 & 2.8 \\ Water & 157 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Concrete formula.  
Figure 1: Types of roughness: (**a**) smooth surface; (**b**) bush-hammered surface; (**c**) natural granite surface.  
Most dams in France are mid-height dams with heights usually between 10 and 60 m [47], which leads the case of gravity dams to normal stress below 1.5 MPa at the concrete-rock interface. With this consideration, all the samples were tested using three different normal stresses: 0.5, 1.0, and 1.5 MPa.
# Research Needs for Realization of Zero-Carbon Power Grids with Selected Case Studies
## 2 Critical Research Agenda
### Inverters as VRE Interfaces with Grids
#### 2.2.6 A List of Research Challenges  
The establishment of future power grids with high proportions of VRE poses specific challenges (see Table 2) in the sub-areas discussed above. These challenges apply at both the transmission and distribution levels. The latter poses additional challenges in terms of decentralization.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Challenges** & \multicolumn{3}{c}{**Research Questions and Requirements**} \\ \hline Desynchronization & \(\bullet\) & How can the change from a synchronous to a non-synchronous system be _seamless while maintaining reliability?_ \\ Desynchronization & \(\bullet\) & Existing synchronous generators and network infrastructures should become _more adaptive and co-operative to meet the increased flexibility, stability, and control needs of VRE-dominated grids_. \\ \hline Modeling and analysis & \(\bullet\) & How can _inverter design and the functionality of VRE-dominated power grids be optimized?_ \\ \hline \multirow{6}{*}{Desynchronization} & \(\bullet\) & It is necessary to _implement various forms of VRE inverter control_ that reference local conditions, parameter settings, and controller tuning, and to _ensure that these are incorporated with grid-level control_. \\ \cline{1-1}  & \(\bullet\) & It is necessary to _understand the limitations of existing simulation models and tools for power electronic converters_ and to _develop new models and tools that support the planning and interconnection of VRE-dominated grids_. \\ \cline{1-1}  & \(\bullet\) & _Existing, positive-sequence fundamental-frequency simulation tools must be updated_ to represent faster controllers with advanced functionality and limiting conditions, focusing on accurate VRE inverter controller representation. \\ \cline{1-1}  & \(\bullet\) & _Analytical simulation models_ that are needed to handle the characteristics of inverter-based generators, storage, and loads with real-world, complex, non-linear operational characteristics. \\ \cline{1-1}  & \(\bullet\) & _The fidelity of generic and manufacturer-specific EMT-level simulation models and tools_ for large-scale network studies must be enhanced. \\ \hline \hline \end{tabular}
\end{table}
Table 2: List of research challenges in VRE inverter interfacing.
## 4 Experiment
### Public Datasets  
We chose to use four datasets, including the Singapore Safe Water Treatment dataset; all selected datasets are detailed below and in Table 1. For each dataset used, we selected multiple data subsets on which we trained and evaluated the models in our experiments.  
**SMAP.** Soil collected by satellite comprises remote sensing information data [34]. Satellite-derived active and passive level 3 soil moisture observations were integrated into a modified two-layer Palmer model.  
**MSL.** Similar to SMAP datasets. It consists of data collected by the Mars Science Laboratory [34] rover.  
**SwaT.** Safe Water Treatment Dataset. Real-world industrial water treatment plants collect during the production of filtered water. It contains data from 7 days of regular operation and 4 days of abnormal function [35].  
**SKAB.** A dataset is involved in evaluating anomaly detection algorithms [36]. The benchmark currently includes over 30 datasets and Python modules for evaluating algorithms. Each dataset represents a multivariate time series collected from sensors installed on the testbed.  
Table 1 shows how many instances are included in each dataset, the ratio of training to testing, the number of features in each dataset, and the proportion of anomalies. The four open datasets are commonly used in anomaly detection, whose training/testing partition varies from dataset to dataset, and the number of features and anomaly ratios also vary among different datasets. We validate the effectiveness and robustness of our approach using various datasets with different proportions of anomalies, different partitions of training/test divisions, and different feature numbers.
## 1 Introduction
The reactions of 8-quinolinesulfenyl halides (chloride and bromide) with cycloalkenes (cyclopentene, cyclohexene, and cyclooctene), depending on the nature of the halogen and  
Figure 1: Known biologically active compounds structurally related to the 2\(H\)3\(H\)-[1,4]thiazino[2,3,4-_ij_]quinolin-4-ium scaffold, which have the quinoline ring condensed with six-membered cycles (fluoroquinolone antibiotics [17; 18; 19], compounds with antibacterial [22] and anti-tuberculosis [23] activity). the reaction conditions, gave products of electrophilic addition or annulation products in high yields [33]. The reactions of 8-quinolinesulfenyl chloride with cycloalkenes afforded 8- [(2-chlorocycloalkyl)]sulfanyl]quinolines in quantitative yields, while condensed tetracyclic compounds were synthesized in 90-100% yields from 8-quinolinesulfenyl bromide and cycloalkenes. Thus, the reaction of 8-quinolinesulfenyl chloride with cyclopentene at room temperature in methylene chloride gave 8-[(2-chlorocyclopentyl)sulfanyl]quinoline in a quantitative yield, whereas condensed tetracyclic compound, 8,9,10,10a-tetrahydro-7a\(H\)-cyclopenta[5,6][1,4]thiazino[2,3,4-_i_]quinolin-11-ium bromide **(1)**, was obtained in a 98% yield by the annulation reaction of 8-quinolinesulfenyl bromide with cyclopentene in chloroform under reflux (Scheme 1) [33]. Although some synthetic methods for the preparation of [1,4]thiazino[2,3,4-_i_] quinolin-4-ium derivatives have been developed [32; 33; 34; 35; 36; 37; 38; 39; 40], the annulation reactions of 8-quinolinesulfenyl halides with natural products such as eugenol, isoeugenol, methyleugenol, anethole as well as with 1\(H\)-indene, styrene derivatives (4-methylstyrene and \(\alpha\)-methylstyrene) and simple alkenes (1-hexene and 1-heptene) have not been described in the literature. The development of efficient selective methods for the preparation of new [1,4]thiazino quinolin-4-ium derivatives with promising biological activity is an urgent task. The aim of this research is the development of the regioselective synthesis of new derivatives of [1,4]thiazino[2,3,4-_i_]quinolin-4-ium based on annulation reactions of 8-quinolinesulfenyl halides with natural products (eugenol, isoeugenol, methyleugenol, _trans_-anethole) as well as with 1-hexene, 1-heptene, 1\(H\)-indene and styrene derivatives (4-methylstyrene and \(\alpha\)-methylstyrene) and the evaluation of their antimicrobial activity.
# DDC Control Techniques for Three-Phase BLDC Motor Position Control
## 3 DDC Control System Design
### Design of MFAC
So, by applying \(\hat{\phi}(\kappa)\) rather than the actual value of \(\phi(\kappa)\), the system's past value is \(\hat{\phi}(\kappa)\) and the parameter estimations are as follows:  
\[\delta_{\hat{\phi}}(\kappa)=\hat{\phi}(\kappa-1)-\hat{\phi}(\kappa-2).\]  
Moreover, Equation (14) becomes as follows:  
\[y(\kappa+1)=\big{(}y(\kappa)+\big{(}\hat{\phi}(\kappa)+\hat{\phi}(\kappa-1)- \hat{\phi}(\kappa-2)\big{)}\Delta u(\kappa)\big{)} \tag{15}\]  
\(\lambda\) is a vital regulating parameter for the MFAC method. The numerical simulation and theoretical analysis confirms that the suitable value of \(\lambda\) will guarantee the stability of the proposed system. The small value of the weighting factor means a lower rise time. More features regarding this parameter are shown in [19; 20], respectively. The simulated results show that the appropriate value of \(\lambda\) that can enhance the general performance of the control system. The main block diagram of MFC is present in Figure 13.  
By selecting appropriate parameter, the following result is obtained using the MFAC scheme. Figure 14 shows the response of the BLDC motor using MFAC. It can be seen that the result is better than the traditional PID method. The overshoot value and settling time is reduced but still needs improvement.
# Deep Learning Semantic Segmentation for Water Level Estimation Using Surveillance Camera
## 3 Results
### Inference Phase  
During the inference phase, the trained models were tested on several images captured from four surveillance cameras located at different spots in Selangor. In general, both trained networks could perform semantic segmentation tasks when introduced to new images but DeepLabv3+ could separate water elements from the background better than SegNet. DeepLabv3+ obtained more accurate water segmentation and smooth water regions delineation as compared to SegNet as shown in Figure 11.  
Figure 10: Comparisons between segmentation results and ground truth data are illustrated by overlay columns. (**a**) The original images (**b**) DeepLabv3+ prediction and ground truth (**c**) SegNet prediction and ground truth.
# On the Use of the Multi-Agent Environment for Mobility Applications
## 2 Environment as a Spatio-Temporal Reference
### Space-Time Environment for Planning in Mobility Applications
#### 2.3.2 Coordination of Action Zones
We launch the process that we just described ten times with every problem file, creating ten different versions of every problem file. We implemented two MAS with almost the same behaviour; the only difference concerns the measure used by vehicle agents to compute the insertion cost of a traveller. For the first implemented MAS, it relies on the Solomon measure (noted \(\Delta\) Distance). The second relies on the space-time model (noted \(\Delta\) Space-Time). We chose to run our experiments with the problems of class R and C, of type 1, which are the instances that are very constrained in time (narrow time windows). For each problem class and type, we considered different travellers numbers to verify the behaviour of our model with respect to to the problem size. To this end, we considered the 25 first customers, the 50 first customers, and finally, all the 100 customers in each problem file. Table 2 summarises the results. Each cell contains the best-obtained results with each problem class (the sum of all problem files). The results show, with the two classes of problems, that the use of the space-time model mobilises fewer vehicles than the traditional model (\(53<64,31<34,92<107,53<60,150<181,108<121\)). This result validates the intuition of the model, which consists of maximizing the future insertion possibilities for a vehicle agent. The results show the superiority of this method, in terms of execution times and response times [20], and in terms of number of mobilised vehicles [21], compared to traditional methods.
# Use Electroencephalogram Entropy as an Indicator to Detect Stress-Induced Sleep Alteration
## 2 Materials and Methods
### Phase Entropy  
The phase entropies between the control and footshock groups were compared in Figure 5. The phase entropy reflects the shape of EEG waveforms; that is, smooth sine-wave-like oscillations result in an even phase distribution and a higher phase entropy (Figure 5A). By analyzing the phase entropy, we noticed that the footshock enhanced the phase entropies during NREM sleep (Figure 5B,C), REM sleep (Figure 5D,E) and wakefulness (Figure 5F,G). The 15-min mean phase entropies were significantly higher after the footshock stimuli  
Figure 4: Comparison of the frequency entropy after the footshock stimuli. (**A**) Uniform frequencies of EEGs result in a low entropy, whereas the variety of frequencies causes an uncertainty of the occurrence rate and results in a high entropy. (**B**) Distribution of frequency entropy for NREM sleep. The solid line and light gray shadow represent the values of means \(\pm\) SEMs for every 15 min during the light period, obtained from the control group. The dashed line and dark gray shadow are the results acquired from the footshock group. (**C**) Statistical significant test for the frequency entropy. The white bar is the value of mean \(\pm\) SEM for 15 min bins across the total 12 h light period, obtained from the control group, and the gray bar represents the frequency entropy acquired from the footshock group. (**D,E**) Represented the data for REM sleep. (**F,G**) Demonstrated the results for wakefulness. ** Indicates the difference reaches statistical significance of \(p<0.01\).**(Figure 5C, NREM sleep: control vs. footshock: 3.5791 \(\pm\) 0.0001 vs. 3.5801 \(\pm\) 0.00003, F(1,1394) = 86.21, \(p<\) 0.01; Figure 5E, REM sleep: control vs. footshock: 3.5793 \(\pm\) 0.0001 vs. 3.5807 \(\pm\) 0.00004, F(1,1226) = 169.02, \(p<\) 0.01; Figure 5G, wakefulness: control vs. footshock: 3.5795 \(\pm\) 0.0001 vs. 3.5805 \(\pm\) 0.00003, F(1,1473) = 77.72, \(p<\) 0.01). These results suggest that the waveforms became smoother after receiving the footshock stimuli when compared with those obtained from the control group. In summary, the footshock stimuli profoundly affected the duration, EEG amplitude, frequency and oscillation phase during the distinct vigilance states.
## 4 Research and Project Aimed at Developing Modules of a Classification and Expert System
### Final Classification After Treatment Module
#### 4.3.2 The Process of Data Preparation and Model Training
\begin{table}
\begin{tabular}{l l} \hline \hline
**Model** & **Parameters/Settings** \\ \hline Random forest (RF) & n\_estimators = 100, max\_depth = 10, min\_samples\_split = 5, \\  & min\_samples\_leaf = 2, bootstrap = True, random\_state = 42 \\ \hline SVC & C = 1.0, kernel = ”rbf”, gamma = ”scale”, probability = True, \\  & random\_state = 42 \\ \hline Logistic regression & max\_iter = 1000, penalty = 72, solver = ”bfgs”, random\_state = 42 \\ \hline XGBost & n\_estimators = 100, max\_depth = 6, learning\_rate = 0.1, \\  & subsample = 0.8, colsample\_bytree = 0.8, random\_state = 42, \\  & eval\_metric = ”logloss” \\ \hline K-nearest neighbors (KNN) & n\_neighbors = 5, weights = ’distance’, metric = ’minkowski’, p = 2 \\ \hline Decision tree (DT) & max\_depth = 7, criterion = ’entropy’, min\_samples\_split = 4, \\  & random\_state = 42 \\ \hline Gradient boosting (GB) & n\_estimators = 100, max\_depth = 5, learning\_rate = 0.1, \\  & subsample = 0.8, random\_state = 42 \\ \hline AdaBoost & n\_estimators = 50, learning\_rate = 0.1, random\_state = 42 \\ \hline Extra Trees (ET) & n\_estimators = 50, max\_depth = 7, min\_samples\_split = 4, \\  & random\_state = 7 \\ \hline Bagging & estimator = DecisionTreeClassifier (max\_depth = 7, criterion = ’gin’, \\  & random\_state = 42), n\_estimators = 50, max\_samples = 0.8 \\ \hline LightGBM & n\_estimators = 100, max\_depth = 7, learning\_rate = 0.1, \\  & feature\_fraction = 0.8, bagging\_fraction = 0.8, random\_state = 42 \\ \hline CatBoost & iterations = 500, learning\_rate = 0.1, depth = 6, random\_seed = 42, \\  & verbose = 0 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Learning methods and parameters for each model. The training data were based on predefined thresholds specified in Table 5 and Figure 6 for initial data labeling and validation of system performance against clinical standards. Specific model performance results and algorithm evaluation are presented in the next section. The detailed results of the model and algorithm performance evaluation are presented later in the paper. In Section 5, a detailed evaluation of the integration of this classification model into the broader framework of the proposed decision support system in FSGS is presented.