esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Thermal Comfort Analysis
Table 5 shows the results of the three comfort indicators for a comfort temperature equal to 25 \({}^{\circ}\)C, distinguishing between the four summer months and for the whole summer. Each of the measures (1-3) demonstrates the percentage improvement in comparison to Case 0. The same analysis can be performed by setting the comfort temperature in accordance with the Spanish regulations pertaining to energy conservation. Table 6 illustrates the outcomes obtained with comfort temperatures set at 27 \({}^{\circ}\)C. It can be observed that with a mere 2 \({}^{\circ}\)C rise in the indoor perception, the enhancement in comparison to the initial scenario is amplified. This demonstrates that merely elevating the comfort temperature by 2 \({}^{\circ}\)C with the objective of curbing energy consumption in dwellings concurrently augments the number of comfort hours in hot climates through the incorporation of night ventilation. \begin{table}
\begin{tabular}{l c c c c c c c c c c c c} \hline \hline
**Modes** & \multicolumn{3}{c}{**0**} & \multicolumn{3}{c}{**1**} & \multicolumn{3}{c}{**2**} & \multicolumn{3}{c}{**3**} \\ \hline
**Comfort** & **[h]** & [\({}^{\circ}\)C-h] & [\({}^{\circ}\)C-h-ppil] & **[h]** & [\({}^{\circ}\)C-h] & [\({}^{\circ}\)C-h-ppil] & **[h]** & [\({}^{\circ}\)C-h-ppil] & **[h]** & [\({}^{\circ}\)C-h-ppil] \\ \hline June & 367 & 807 & 359 & 299 & 1064 & 814 & 333 & 747 & 347 & 324 & 745 & 367 \\ July & 744 & 4100 & 3944 & 737 & 3045 & 2528 & 709 & 3420 & 3059 & 665 & 3129 & 2766 \\ August & 744 & 4920 & 4920 & 662 & 3297 & 2946 & 712 & 4047 & 3915 & 664 & 3611 & 3450 \\ September & 720 & 1818 & 1081 & 310 & 817 & 360 & 579 & 1365 & 742 & 567 & 1323 & 712 \\ Summer & 2575 & 11,644 & 10,303 & 2008 & 8223 & 6649 & 2333 & 9579 & 8063 & 2220 & 8809 & 7295 \\ \hline Improvement & - & & 37\% & 52\% & 58\% & 27\% & 44\% & 50\% & 31\% & 49\% & 54\% \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Evaluation of the impact on thermal comfort in different modes with 27 \({}^{\circ}\)C as comfort temperature (For Spain). \begin{table}
\begin{tabular}{l c c c c c c c c c c c} \hline \hline
**Modes** & \multicolumn{3}{c}{**0**} & \multicolumn{3}{c}{**1**} & \multicolumn{3}{c}{**2**} & \multicolumn{3}{c}{**3**} \\ \hline
**Confort** & **[h]** & [\({}^{\circ}\)C-h] & [\({}^{\circ}\)C-h-ppil] & **[h]** & [\({}^{\circ}\)C-h] & [\({}^{\circ}\)C-h-ppil] & **[h]** & [\({}^{\circ}\)C-h-ppil] & **[h]** & [\({}^{\circ}\)C-h-ppil] \\ \hline June & 674 & 1838 & 1
## 4 Modified Weight Update Method by LS-CMA and SD-CMA
### Effectiveness of Proposed Method Verified through Computer Simulations  
The effectiveness of proposed method is verified through computer simulations. Figure 9 shows the SINR by weight update of the LS-to-SD-CMA versus iteration. The parameters are the same as those in Figure 8. As can be seen from Figure 9, in the LS-to-SD-CMA method, a high SINR is maintained after the SINR is improved with the first few updates. Because the weight stabilizes without increasing the smoothing sizes as in LS-CMA, it is possible to reduce the number of symbols.  
Figure 8: SINR by weight update of LS-CMA and SD-CMA versus iteration.  
Finally, by using the LS-to-SD-CMA, the effectiveness of the proposed method is verified for the downlink. Figure 10 shows the average bit rate versus AoA at the UTs when the LS-CMA or LS-to-SD-CMA is applied. Table 2 shows the simulation conditions of the smoothing size and the iteration. Moreover, in order to reduce the amount of calculations and stabilize the CMA, the LS-to-SD-CMA is not applied when a high SINR has already been obtained by only analog beam patterns. As can be seen in Figure 10, in the case of LS-CMA, the bit rate decreases to a value less than the value before applying CMA, when the number of smoothing size is small. However, by applying the proposed method, even with the same number of smoothing size, the performance is improved than when using only beam control. In addition, because this result is approaching the ideal performance securing a sufficient number of symbols and iterations, this control method in the downlink is effective with realistic data sizes.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Smoothing Size** & **Iteration** \\ \hline LS-CMA & 50 & 20 \\ least-square to steepest-descent-CMA (LS-to-SD-CMA) & 50 & 20 \\ Ideal performance & 500 & 20 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Simulation conditions of the smoothing size.  
Figure 10: Average bit rate versus AoA at UTs using LS-to-SD-CMA.  
Figure 9: SINR by weight update of least-square to steepest-descent-CMA (LS-to-SD-CMA) versus iteration.
## 2 Materials and Methods
### Characterization  
The surface morphology and elemental contents of the HWWC were observed using a scanning electron microscope-energy dispersive X-ray spectrometer (SEM-EDS) (JSM-7900F, JEOL, Tokyo, Japan). The X-ray diffraction (XRD) pattern of the catalyst was analyzed using an XRD system (D/max-2500V, Rigaku, Tokyo, Japan). The point of zero charge (pH\({}_{\rm{pzc}}\)) of HWWC was determined by titration method with slight modification [27]. HWWC (0.04 g) was suspended in 20 mL of 0.01 M NaNO\({}_{3}\) for 24 h. Then the pH of solution was adjusted using 0.1 M HNO\({}_{3}\) or NaOH solution. To reach the equilibrium, the solution was agitated for 1 h, then the pH\({}_{\rm{initial}}\) was measured. After measuring the pH\({}_{\rm{initial}}\), 0.6 g of NaNO\({}_{3}\) was added to the suspension. After 3 h, the pH\({}_{\rm{final}}\) of the solution was measured. The pH\({}_{\rm{pzc}}\) value was determined as \(\Delta\)pH (pH\({}_{\rm{final}}\)-pH\({}_{\rm{initial}}\)) was 0 when plotting \(\Delta\)pH against pH\({}_{\rm{final}}\). As shown in Figure 1a, pH\({}_{\rm{pzc}}\) of HWWC was 7.4. The magnetic property of HWWC was measured using vibrating sample magnetometer (VSM) (Model 7404, Lake shore cryotronics, Westerville, OH, USA).
## 3 Material and Methods
### Data Analysis  
To attain an overview of the research content of the collected data, the abstracts of the collected literature were processed and analysed at the word level. First, we converted all the text to lowercase for consistency. Stop words, which deliver little meaning and punctuation, were filtered, and then identical terms (e.g., \"natural language processing\" and \"nlp\"; \"chatbots\" and \"chatbot\") were combined. Generic keywords associated with abstract formatting requirements (such as \"method\" and \"conclusion\") were removed. Verbs, adjectives, adverbs, and nouns were converted into their prototypes. As a result, 19,146 terms were extracted from the dataset in total by using word frequency counting. The minimal number of occurrences for a term in the word extraction process was set to 25, and 614 of the 19,146 words satisfied the inclusion criterion. However, among the 614 words generated, not every word was consistent with the research objectives, so we screened useless words and extracted related words manually. After several rounds of screening and extraction, the result contained 143 keywords in total. The above tasks were performed with the Python 3.9, NLTK 3.7 [51], NetworkX 2.8 [52], and word_cloud 1.9 [53] packages.  
Within these keywords, several topics regarding government management could be seen. Therefore, we performed a manual check to ensure a more accurate classification. Moreover, to explore the relationship between different NLP techniques and different research topics, we first labelled the words related to NLP techniques by following the guide of an expert in the area, including: \"nlp\", \"sentiment analysis\", \"machine learning\", \"classification\", \"deep learning\", \"artificial intelligence\", \"nlp technique\", \"identification\", \"extraction\", \"text mining\", \"data mining\", \"topic modeling\", \"prediction\", \"recognition\", \"information retrieval\", \"text classification\", \"chatbot\", \"opinion mining\", and \"question answering\". The remaining words are mainly distributed in the following topics based on the collected articles: governance and policy, citizens and public opinion, medical and healthcare, economy and environment.  
In addition to a great proportion of words having clear meanings, the authors then traced back to the collected literature to understand their potential meanings and used such additional information to classify the words that could not be clearly classified. For instance, \"quality\" referred to the quality of the information [54; 55; 56], \"online\" was related to the online discussion of the public [57; 58; 59; 60], \"agency\" meant the government agency [61; 62; 63], etc.
## 3 Materials and Methods
### EDAS Method
The EDAS method shows considerable applicability across various domains, including engineering, social, ecological, and economic issues, spanning a wide range of application areas. It is mainly employed as part of integral MCDM frameworks. For instance, [PERSON] [109] employs a fuzzy AHP-EDAS integrated model to select third-party logistics (3 PL) providers, while [PERSON] et al. [110] introduced the combination of IF, DEcision MAking Trial and Evaluation Laboratory (DEMATEL), and EDAS methods for analyzing barriers in reverse logistics. [PERSON] et al. [111] developed the Multi-Objective Optimization by Ratio Analysis plus the full MULTlplicative form (MULTlIMOORA)-EDAS integrated model aimed at examining the obstacles in adopting renewable energy sources. [PERSON] et al. [112] presented the Weighted Aggregates Sum Product Assessment (WASPAS)-COmplex PROportional Assessment (COPRAS)-EDAS model for selecting locations for hydrogen mobility collection. [PERSON] et al. [113] integrated the CRiteria Importance Through Intercriteria Correlation (CRITIC) and LPF EDAS methods to address the issue of industrial solid waste management. [PERSON] et al. [114] proposed a PROMETHEE-EDAS model incorporating Covering-based Variable Precision Fuzzy Rough Set (CVPFRS). The literature shows a growing trend of studies focused on improving the conventional EDAS model to address some of its inherent limitations. In this context [PERSON] et al. [115] proposed several approaches, including integrating system dynamics and simulation modeling for more reliable solutions, especially in complex decision-making structures; incorporating the concept of stratification to consider uncertainties in decision-making; and applying various normalization techniques within the EDAS process, among others. These studies specifically address the issue of vagueness and ambiguity inherent in decision-making problems. In this context, multiple models that effectively incorporate various forms of fuzzy logic into the EDAS process have been established, including q-rung orthopair fuzzy [49], spherical fuzzy sets [46], Pythagorean probabilistic hesitant fuzzy [116], trapezoidal bipolar fuzzy [117], interval-valued intuitionistic fuzzy [118], and interval-valued Pythagorean fuzzy [119]. This paper proposes integrating IT2F theory into the conventional EDAS model to effectively address vagueness and ambiguities in preferences. This model has been utilized in only a handful of published studies. Initially introduced by [PERSON] et al. [120], it was subsequently implemented as a part of the supplier selection methodology framework, which considers both environmental and economic factors [121]. [PERSON] and [PERSON] [122] employed the IT2 FEDAS model as an integral part of the service-level improvement methodology for public transportation, while [PERSON] and [PERSON] [123] adopted the same model for the selection of a cloud service scheme for new electric vehicles.
## 4 Computational Results
### A Real Case Study
Similarly, jobs 1 and 2 are to be inserted into period 7. At this stage, the remaining idle time of each period is as follows: \(I_{1}=3.5\), \(I_{2}=1.8\), \(I_{3}=4.5\), \(I_{4}=0\), \(I_{5}=0\), \(I_{6}=3.2\), \(I_{7}=2.2\), \(I_{8}=4.2\), \(I_{9}=0\), \(I_{10}=0\). An illustration is given in Figure 9(c). \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Period** & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** & **10** \\ \hline Duration (h) & 3.5 & 7 & 4.5 & 8 & 1 & 3.5 & 7 & 4.5 & 8 & 1 \\ Price (CNY/kwh) & 1.2473 & 0.8451 & 1.2473 & 0.443 & 0.8451 & 1.2473 & 0.8451 & 1.2473 & 0.443 & 0.8451 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: TOU tariffs for the hypothetical instance. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Product Model** & **Average Power Consumption Rate (kW)** & **Processing Time (h)** & **The Number of Parts** \\ \hline
40 & 4.4 & 2.4 & 15 \\
70 & 4.7 & 2.6 & 35 \\
100 & 5.3 & 3.1 & 10 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The measurement data of the real-life instance. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Product Model** & **Average Power Consumption Rate (kW)** & **Processing Time (h)** & **The Number of Parts** \\ \hline
40 & 4.4 & 2.4 & 15 \\
70 & 4.7 & 2.6 & 35 \\
100 & 5.3 & 3.1 & 10 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The measurement data of the real-life instance. Finally, jobs 3 and 4 are assigned to high-price layer 3, and they both satisfy Condition 6. A final complete scheduling diagram is given in Figure 9(c). Returning to the real-life instance, let us now talk about the efficiency of the MILP model and the proposed algorithm. Currently, the company plans to produce 70-Model, 40-Model, and 100-Model rectangular parts in 7, 3, and 2 days respectively. That is, five parts are to be produced every day from 8:00 to 24:00. This suggests that it needs 12 days to process all the parts and the total electricity cost (\(TEC\)) can be computed as follows:  
\[TEC_{70}=4.7\times(3.5\times 1.2473+7\times 0.8451+2.5\times 1.2473)\times 7=440.8\text{~{}CNY};\]  
\[TEC_{40}=4.4\times(3.5\times 1.2473+7\times 0.8451+1.5\times 1.2473)\times 3=160.4\text{~{}CNY};\]  
\[TEC_{100}=5.3\times(3.5\times 1.2473+7\times 0.8451+4.5\times 1.2473+0.5\times 0.4 430)\times 2=170.8\text{~{}CNY};\]  
\[TEC=TEC_{70}+TEC_{40}+TEC_{100}=772.0\text{~{}CNY}.\]  
Figure 11 is the scheduling result diagram of the real-life instance. It can be seen from Figure 11 that with our scheduling, the total electricity cost for processing all the parts is 447.9 CNY, which can be reduced by 42.0%. Figure 10: Illustration of the computational process of GIH-F.  
Figure 11: The scheduling result of the real-life instance.
## 4 Experimental Dataset and Simulation Results
### Hand Gesture Segmentation  
For the model, we did not select the original image as the direct input, because the process completion time would be huge, and the results of classification would be affected by the redundant background. To avoid this situation, the hand gesture image was segmented, then used as the input of the model. In this study, we used hybrid segmentation methods for preprocessing dataset images. To identify the hands, we analyzed the Cr components of the YCbCr color space and identified the pixels that matched the color of the hand with a certain frame of HSV analysis of the SkinMask process. Therefore, the segmented images were fed into the feature extraction process. Figures 9 and 10 show the example of the segmented images of the YCbCr and SkinMask segmentation method. Furthermore, we considered the Sebastien Marcel Static Hand Posture Database [23] for comparison to our proposed method. Therefore, we applied our method to segment hand gestures and then transferred them to feature extraction and classification. Figure 11 shows the sample images of hand gestures in the dataset of [23] and segmented images using the YCbCr and the SkinMask segmentation method.  
Figure 8: Example of the dataset images.  
Figure 9: Example of the YCbCr segmented images from the datasets.
### New Contribution  
The new contribution of this paper is the proposal of a combination of 36 straight and curved calibration trajectories for systematic odometry error evaluation and correction in a three-wheeled omnidirectional mobile robot. This procedure has been empirically applied and validated in a real human-sized three-wheeled omnidirectional mobile robot of 1.760 m and 30 kg (Figure 1). These 36 calibration trajectories have been proposed as a representative test-bench of the infinite trajectories that can be performed by an omnidirectional mobile robot, which together depict a characteristic flower-shaped figure. The calibration procedure implemented requires the registering of the real odometry and ground truth trajectories generated while performing each calibration trajectory. Finally, the odometry of each calibration trajectory is recomputed offline to iteratively adjust the effective values of the kinematic parameters of the mobile robot in order to match the odometry with the ground truth trajectory. This paper has evaluated different matching strategies using different sets of calibration trajectories. The best matching between the odometry and ground truth trajectories has been obtained using genetic algorithms and 5 repetitions of each one of the proposed 36 calibration trajectories. The fitting results of the kinematic parameters have been validated by performing 5 additional repetitions of the 36 calibration trajectories.  
This new contribution was inspired in [PERSON] et al. [17], who proposed the use of four curved calibration trajectories, and in [PERSON] et al. [11], who calibrated the odometry with straight paths but concluded that the percentage of error correction depends on the type of the path. The new contribution is the proposal of a complete set of straight and curved calibration trajectories which together depict a characteristic flower-shaped figure. This new contribution is also inspired in the work of [PERSON] et al. [15] that used genetic algorithms to adjust the kinematic matrix of a three-wheeled mobile robot although without comparing the results obtained with other minimization alternatives. This proposal will apply the same methodology proposed by [PERSON] et al. [12], based on the comparison of the initial and final positions of the mobile robot through N experiments to directly evaluate the odometry improvement achieved. Finally, as an alternative to [PERSON] et al. [15] and [PERSON] et al. [12], this new proposal adjusts the value of the kinematic parameters of the mobile robot (radii of the wheels, distance from the wheel to the center of the mobile robot and angular orientation of the wheels) instead of directly adjusting the values of the kinematic matrix, allowing a direct physical interpretation of the fitting results obtained.
# High-Level Smart Decision Making of a Robot Based on Ontology in a Search and Rescue Scenario
## 3 Methods
### The Update of Ontology
The probability correlation of the geometric information of the grid and the target object in the grid can be expressed by prior knowledge \(\mathrm{P}(\mathrm{M}(G)\big{|}L)\). After obtaining the semantic information of the QR code and the geometric categories of the grid, the posterior probability of the detected target object of the grid can be updated by Bayesian reasoning. \[P_{G,t}(L|O_{t},M(G))=\frac{\mathrm{P}(O_{t},\mathrm{M}(G)\big{|}L)\mathrm{P}_{ G,t-1}(L)}{P(O_{t},M(G))} \tag{4}\]  
Since the information of the two channels is independent, we can deduce  
\[P_{G,t}(L|O_{t},M(G))=\frac{\mathrm{P}(O_{t}|L)\mathrm{P}(M(G)\big{|}L)P_{G,t- 1}(L)}{P(O_{t})P(M(G))} \tag{5}\]  
In Equations (4) and (5), \(\mathrm{P}_{G,t-1}(L)\) is based on the priori knowledge of detecting object information on grid \(G\) at all times before time \(t-1\). We abbreviate the posterior probability \(P_{G,t}(L|O_{t},M(G))\) to \(P_{G,t}(L)\) as the prior probability of the next iteration update. Therefore, at time \(t\), event \(O\) occurs in a grid \(G\), that is, an object is detected, and the probability value of the grid is updated by using Equation (5). Similarly, for the inverse event \(\overline{O}\), Equation (6) is used to update. \[P_{G,t}(L|\overline{O}_{t},M(G))=\frac{\mathrm{P}\Big{(}\overline{O}_{t}|L) \mathrm{P}(\mathrm{M}(G)\Big{|}L)P_{G,t-1}(L)}{P(\overline{O}_{t})P(M(G))} \tag{6}\]  
Finally, at the end of robot exploration, whether the final posterior probability of grid \(G\) is greater than the set threshold can be used to determine whether the target object exists in the grid or not.
## 3 Optical Wireless Satellite Channel Model
### Atmospheric Power Losses: Absorption and Scattering Modeling
In such critical weather conditions, the use of very high power lasers (1550 nm) with special mitigation techniques is the only option to maximize the chances of optical feeder link connectivity. As an alternative method to the Mie scattering theory, the attenuation due to fog for different wavelengths can be estimated using empirical models that use as the input parameter the visibility in km measured on the VL region (550 nm). For a comparison of the fog attenuation at different wavelengths (850 nm and 950 nm), please refer to [41]. Note that in extreme cases, where the visibility due to fog is reduced to about 50 m, atmospheric attenuation can be as high as 350 dB/km [42]. **Power losses due to rain:** The impact of rain in the propagation of optical signals is not as pronounced as fog, because the size of the rain droplets are significantly larger in size (from 100 \(\upmu\)m to 1000 \(\upmu\)m) when compared to operating wavelengths of optical feeder links. For example, the attenuation loss in light rain (2.5 mm/h) to heavy rain (25 mm/h) ranges from 1 dB/km to 10 dB/km for 850 nm and 1550 nm operating wavelengths, respectively [43]. Note that the low clouds, which usually accompany rainy weather, are the source of strong attenuation in most optical feeder links. In order to combat the huge power loss that takes place in such conditions, it is recommended to include a few tens-of-dB margin (e.g., 30 dB) when designing the link budget of the optical wireless link. Moreover, optical feeder link designers can also implement adaptive coding and modulation schemes to address the varying weather conditions in the geographical area around the ground station [26]. **Power losses due to snow:** Finally, since the size of snow droplets is between the size of rain and fog droplets, the atmospheric attenuation for dry/wet snow conditions is usually stronger than the one in the presence of rain, but not as severe as the one in case of fog. However, during heavy snow storms, the path of the optical feeder link can be completely blocked for the presence of densely-packed snow flakes in the propagation path. In such cases, the attenuation is similar to the one observed in foggy weather (30-350 dB/km) and, as expected, can seriously put at risk the optical feeder link connectivity.