esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Chemical Composition of Extracts Volatile Fraction  
The SPME/GC-MS characterization data of the volatile fraction of the extracts of the three plants are shown in Table 2.  
In thyme extracts 22 compounds were found, 21 monoterenes, and one sesquiterpene. Thymol has generally been reported to be the main component of _T. vulgaris._ However, this cultivar contains carvacrol, the isomer of thymol, that has the same biological activity. Linalool was the most abundant volatile compound in all the extracts, but using Soxhlet for two hours, a much lower quantity was found. Both carvacrol and linalool are natural effective antimicrobials used to control the growth of spoilage microorganisms in food as demonstrated in some studies in literature [33; 34]. They have been reported to have also therapeutic properties (e.g., vs. Alzheimer's disease) [35]. A total of 25 compounds were identified in hemp extracts, 13 monoterenes, and 12 sesquiterpenes. The predominant compounds were: \(\beta\)-myrcene and carryophyllene within the monoterenes and sesquiterpenes, respectively. \(\beta\)-myrcene is known to possess anti-inflammatory, analgesic, and anxiolytic properties [36; 37]. Caryophyllene has been reported as anti-inflammatory compound in some cannabis preparations because of the interaction with the cannabinoid receptors and a gastric cytoprotective activity has been also found [38; 39; 40]. Interestingly, Caryophyllene oxide seems to be a multi-target molecule, known for its anticancer and analgesic properties [37]. \begin{table}
\begin{tabular}{c c c c c c c} \hline  & **N2h** & **N6h** & **S2h** & **S6h** & **UAE** & **M** \\ \hline
**Thyme** & 2.30 \(\pm\) 0.06 d & 2.45 \(\pm\) 0.09 c & 9.25 \(\pm\) 0.02 a & 8.65 \(\pm\) 0.05 b & 1.62 \(\pm\) 0.02 e & 1.78 \(\pm\) 0.04 e \\
**Hemp** & 6.00 \(\pm\) 0.03 b & 5.81 \(\pm\) 0.05 b & 5.70 \(\pm\) 0.01 b & 10.00 \(\pm\) 0.07 a & 0.71 \(\pm\) 0.09 c & 0.95 \(\pm\) 0.04 c \\
**Coriander seeds** & 0.57 \(\pm\) 0.09 f & 0.73 \(\pm\) 0.08 e & 1.18 \(\pm\) 0.06 d & 1.63 \(\pm\) 0.09 c & 2.36 \(\pm\) 0.07 a & 2.17 \(\pm\) 0.08 b \\ \hline \end{tabular} Results followed by the same case-letter are not different according to [PERSON]’s HSD post-hoc test (\(p>0.05\)).  
\end{table} 
Table 1: Yields of extracts (% _w/w_). N2h, RSLDE 2 h; N6h, RSLDE 6 h; S2h, Soxhlet 2 h; S6h, Soxhlet 6 h; UAE, ultrasound assisted extraction; M, maceration.
## 5 Discussion
### Sentinel-2 MSI and Landsat-8 OLI
Sentinel's superiority in temporal resolution could not be adequately exhibited for the present case study, as the cloud free images for the two sensors are comparable in both number and temporal span. Nevertheless, the sensors' difference in temporal resolution is significant and even larger performance differences are expected in most of relevant scenarios.
# Suitability Evaluation of Urban Underground Space Development: A Case Study of Qingdao City
## 2 Overview of the Research Area  
Qingdao is located in the eastern coastal region of Shandong Province, China, with geographical coordinates ranging from approximately 36\({}^{\circ}\)03\({}^{\prime}\) to 37\({}^{\circ}\)25\({}^{\prime}\) north latitude and 119\({}^{\circ}\)30\({}^{\prime}\) to 121\({}^{\circ}\)00\({}^{\prime}\) east longitude. It borders the Yellow Sea to the east, Jiaozhou Bay to the west, Qingdao Bay to the south, and the Laoshan Mountains to the north. The scope of this study encompasses the primary urban zone of Qingdao, comprising five districts: Shinan District, Shibei District, License District, Laoshan District, and Chengyang District, spanning a total land area of 669.767 km\({}^{2}\). Given Qingdao's recent rapid growth, the strategic development of urban underground space has emerged as a pivotal approach for achieving sustainable progress and enhancing urban living standards [32]. The precise research scope is depicted in Figure 1.  
The terrain of the study area generally slopes from east to west, with the western areas such as Cangkou and Chengyang being relatively flat, belonging to the marginal area of the Jiaolai Alluvial Plain, with elevations mostly ranging from 10 to 60 m above sea level. The eastern part comprises low mountains and hills, with elevations mostly between 400 and 800 m. The geological structures in the study area include several brittle faults, among which Cangkou, Pishikou, and two northeast-oriented faults are the most typical. The engineering mechanical properties are relatively poor, with soft soil layers characterized by silt and fine sand mainly distributed in the northwest part of the study area (around Luting) in the floodplain areas of Baisha River and Moshui River, as well as in the river valley sections of Beizhai and Qinjiatuzhai. The core urban areas, such as Shinan District, Shibei District, and Licang District, have high population densities and abundant commercial, cultural, and educational resources. Core commercial areas along the southern coast, such as Xinhaoshan Commercial District and the surrounding areas of May Fourth Square, also boast high commercial land prices.
## 2 Materials and Methods  
For the cladding process (hardfacing), a low-energy version of the [PERSON] (Austria) CMT (Cold Metal Transfer) [26] welding method was used. The unique control system of the CMT process, which detects short circuits and assists the droplet detachment by retracting the filler wire, resulted in a reduced amount of heat introduced into the treated material during the process and the possibility of precise control of coating-substrate dilution [27]. The heat is transferred into the deposited material only during the impulse arc phase, which is very short-lived. This feature allows for the application of surfaced layers, characterized by a low-dilution ratio, with the substrate (D), which should be from 1 to 10%, which significantly affects the properties of the deposited welds. In this study four protective coating materials, Inconel 718 (according to EN-ISO 18274--SNi7718/NiFe19 CrNb5 Mo3), Inconel 625 (according to EN-ISO 18274--SNi6625/NiCr22 Mo9 Nb), Alloy 33 (EN 1.4591/X1 CrNiMoCuN33-32-1) and Stellite 6 (according to AWS A5.21/ERCCoCCr-A) were deposited on 16 Mo3 (according to EN 10028) steel tubes, with a diameter of 60.3 mm, a wall thickness of 8 mm, and 150 mm-long samples. Metallographic tests were carried out via an optical microscope produced by the Olympus BX51M, equipped with a camera for digital recording of the image. The microstructure of the coating was investigated by scanning electron microscopy (SEM),using JEOL JSM-7600F with an EDX Oxford Instrument detector X-MaxN typu SDD (Silicon Drift Detector) microscope.  
As a supplement to the metallographic research, hardness measurements in the cross-section, perpendicular to the longitudinal axis of the substrate and coating system, were performed using the Vickers method HV 0.2 using the Leitz Wetzlar (Germany) 8375 microscope. The wear resistance of the CMT cladded coatings, as well as of 16 Mo3 steel (for comparison), was determined in accordance with the dry sand/rubber wheel test, according to ASTM G65, procedure B. In that test, the abrasive medium (50-70 mesh silica sand) had been continuously fed between the investigated surface and the rotating rubber wheel. The testing schematic is shown on Figure 1a. In Figure 1b the test stand, home-made at the Warsaw University of Technology (Faculty of Mechanical and Industrial Engineering) is shown. That test is often used for the wear resistance evolution of different materials in the mining, mineral, oil processing and power generation industry [28].  
The working chamber is equipped with a pneumatic lance with a replaceable tip (pneumatic nozzle) and a sample holder Figure 2a. The design of the holder enables the adjustment of the angle of the samples in relation to the blasting stream axis. The tank with the feeding system (B) has been equipped with a valve that allows adjustment of the pressure of the abrasive stream. The used abrasive goes to the tank (D), located below the working chamber. The control system is equipped with a controller that allows for the regulation of the process duration and an emergency switch (E).
# Hybrid Fuzzy Regression Analysis Using the \(F\)-Transform
## 5 Conclusions  
In this paper, we have confirmed that the response function for the center and spread of the dependent variable in the fuzzy regression model may not match, and proposed the hybrid estimation algorithm for independently estimating the response function for the center and the response function for the spread. The proposed hybrid estimation algorithm is a modified and extended new version of the algorithm introduced in [PERSON] et al. [16]. We also applied the discriminant analysis for categorical data to construct the fuzzy regression model when the size of the set of spreads of the dependent variable is very small than the number of samples. In addition, \(F\)-transform was used to categorize the spread of the dependent variable. Then, we combined the LAD estimation method for the center of the dependent variable with \(F\)-transform and discriminant analysis for the spread of dependent variable to estimate the value of the dependent variable.  
Two examples have confirmed that the proposed fuzzy regression model estimated using the \(F\)-transform, Fisher discriminant function, and LAD estimation method can be more efficient than the existing other methods. This means that when the number of spreads of the dependent variable is much less than the sample size or the number of centers of the dependent variable, the proposed hybrid estimation algorithm can provide more efficient estimation results.  
In future studies, we plan to check whether the proposed hybrid algorithm is robust to the number of basic functions and the type of membership function. In addition, we will apply our algorithm to fuzzy regression model with fuzzy coefficients.  
Conceptualization, S.H.C.; methodology, H.-Y.J. and W.-J.L.; software, W.-J.L.; validation, W.-J.L.; formal analysis, W.-J.L.; investigation, H.-Y.J.; resources, H.-Y.J.; data curation, W.-J.L.; writing--original draft preparation, H.-Y.J.; writing--review and editing, H.-Y.J. and S.H.C.; visualization, H.-Y.J.; supervision, S.H.C.; project administration, S.H.C.; funding acquisition, S.H.C. and H.-Y.J. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2017R1D1A1B03039559, No.2019R1 IIA1A01046810) and Institute of Information & communications Technology Planning & Evaluation(IITP) grant funded by the Korea government(MSIT) (No.2020-0-01343, Artificial Intelligence Convergence Research Center(Hanyang University)).  
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.
## 5 Implementation Technicalities  
Q1 Synth was developed as a Web application. First introduced in 2011, the Web Audio API provides a powerful system for handling audio on the Internet, turning any Web browser into a widely available, and potentially versatile, musical tool [20].  
Q1 Synth's sound synthesis engines were implemented with the Web Audio framework Tone.js [21]. The user interface is built with React.js [22] and written in TypeScript [23]. The Bloch sphere visualisation is rendered using P5.js [24].  
The instruments were networked using OSC-Qasm, a Python package developed at ICCMR, University of Plymouth, as part of QuTune Project [25]. OSC-Qasm connects music programming environments with quantum hardware [26]. It is based on the OpenSound-Control (OSC) communication protocol, which is widely used by the music technology community [27].  
OSC-Qasm includes a server and client applications. For _Spinnings_, the OSC-Qasm Client was developed with Max/MSP [28] and QuTune's QAC Toolkit package [29]. The client builds the quantum circuits from the Q1 Synth coordinates in OpenQASM (Quantum Assembly Language) [30]. The OSC-Qasm Server is a cross-platform, Python-based application, that reads quantum circuits written in OpenQASM and runs them on quantum backends, either simulators or real hardware. It is powered by the Python-based quantum programming package Qiskit [31].  
Finally, note that the Bloch sphere coordinates captured by Q1 Synth's interface use the _ZYZ_ convention [32] for defining the Euler angles passed to the quantum circuit. However, for visualisation, P5.js contains a different definition for its coordinate system. To achieve the desired visual result, it is necessary to use the coordinates in _YZY_ convention for rotating the sphere.
## 4 Data Analysis and Results
### Reliability Enhancement  
From the system reliability plot, the preventive maintenance schedule for the unloading system is specified in order to maintain the reliability of the system above a certain level. In the previous sections, the critical components of the systems were identified. Therefore, preventive maintenance of these critical items will have a huge impact on the total system reliability. In this study, preventive maintenance of the strainer, positive displacement pump, and positive displacement meter will be considered due to their design nature. Practically, rotating part failures are relatively easier to detect before they occur, compared to static part failures. For example, if there is a slight alignment deviation in the pump coupling it will remain functional for a small period of time before the breakdown. While, for static components like valves, the failures happen within seconds without prior signals. In addition, the strainer is considered as a critical component because of the cleaning process that should take place during the maintenance, which will improve the system reliability.  
Figure 5 shows the system reliability when preventive maintenance for the critical components is conducted every 45 days. It is noted that preventive maintenance of the three critical components significantly enhances the reliability of the unloading system. In other words, the probability that any of these components are functional for the next one and a half months is constant throughout the lifecycle of these components.  
Figure 5 shows the improvements reflected on the system reliability due to conducting preventive maintenance. It is clear that the preventive maintenance has a large impact on the total system reliability improvement.  
The analysis shows that the reliability of the unloading system reached approximately zero after 5 years of continuous operation, while after conducting the preventive maintenance, system reliability significantly improved. In addition, the results indicated that implementing preventive maintenance improves the system reliability by 80% in the first year, and this ratio increases dramatically as the period increases.
# Novel Real-Time Compensation Method for Machine Tool's Ball Screw Thermal Error
## 2 Thermal Equilibrium Analysis  
A three-axis drilling machine (powered by an HNC-848 controller board, Huazhong CNC, China) adopting a typical semi-closed control loop is used as the research object in this study. A 3-D model and picture of the machine are shown in Figure 1. The X-, Y-, and Z-axes are driven by a ball screw feed system, fixed on only one end (one-ended installation). Therefore, the position accuracy of the linear axis is highly sensitive to the screw's thermal error.
# Towards Agent Organizations Interoperability: A Model Driven Engineering Approach
## 2 Organizational Models for MAS
### Models Review
#### 2.2.4 Moise+  
MOISE+ (Model of Organization for multl-agent SystEms) is a model that explicitly divides the specification of an agent organization in three parts: The _structural_, the _functional_ and the _deontic_ specifications. The structural specification defines the internal structuring of agents through the notions of _roles_, _roles relations_ and _group specifications_. A role defines a set of constraints the agent has to accept to enter in a group. Role relations are _links_ (_communication_, _acquaintance_ and _authority_) and _compatibilities_ from a source role to a target role. A group specification consists in role definitions, subgroup definitions (group decomposition), links and compatibilities definitions, role cardinalities and subgroup cardinalities. The functional specification describes how an agent organization usually achieves its _global goals_, i.e., how these goals are decomposed (by _plans_) and distributed to the agents (by _missions_). Global goals, plans and missions are specified by means of a _social scheme_. A social scheme can be seen as a goal decomposition tree where the root is a global goal, the internal nodes are _plan operators_ (sequence, choice, parallel) to decompose goals into subgoals, and the leaves are atomic goals that can be achieved by an individual agent. Missions are coherent sets of goals; hence, an agent that is committed to a mission is responsible for the satisfaction of all its component goals. Finally, the deontic specification associates roles to missions by means of _permissions_ and _obligations_.  
Like STEAM, MOISE+ addresses both the functional and structural dimensions of modeling. However, MOISE+ goes further and provides concepts for modeling normative aspects (deonticspecification). The deontic concepts allow a flexible coupling between the functional and structural specifications that is not seen in STEAM.
## 5 Effectiveness of Enlarging Dataset  
### Experiment Design  
The aim of this section is to evaluate the effectiveness of enlarging the dataset and to quantify how many additional data are needed to offset the effects of data corruption. For the NLP-SL experiment, the model is tested on datasets of different sizes and varying data corruption ratios. The variable \"subset ratio\" represents the proportion of the GLUE dataset used for fine-tuning. The results are shown in Figure 11a. The Signal-RL task experiments with different numbers of training episodes and data-missing ratios, as shown in Figure 11b.  
Figure 11: The effectiveness of enlarging the training dataset. (**a**) NLP-SL. The \(x\)-axis represents the subset ratio, and the \(y\)-axis indicates model score. (**b**) Signal-RL. The \(x\)-axis represents number of training episodes, and the \(y\)-axis indicates model score. Data corruption leads to a decline in model performance, which cannot be fully recovered by increasing the sample size. To achieve the same level of performance (if achievable by the corrupted model), the number of samples required, and hence the training time, increases exponentially with the data corruption level.  
Figure 10: Illustration of context-filling imputation in the Signal-RL experiment. This represents a type of “general imputation”, where each element of the state vector is evaluated for potential correction. Road occupancy cells with a value of zero are imputed as one if sufficient surrounding vehicles are detected.
###### Abstract  
The increasing attraction of crude resources on the ocean floor leads to the development of risers, which are an indispensable component of an offshore oil and gas exploitation structure. High-grade steel is normally utilized for risers, however, fiber reinforced polymer (FRP) composite risers have become a feasible and economical alternative due to the desirable properties of the material. A comparative study of vortex-induced vibration (VIV) of FRP composite risers with two designed geometries were conducted and an X80 steel riser was used as the benchmark. The length to diameter ratios of all three risers were set to 100 and their VIV responses under different environmental situations, including the natural frequencies, global displacements, global stresses and the stress distributions in every composite layers, were obtained using computational fluid dynamics (CFD) simulation with coupled fluid-structure interaction. The VIV characteristics of both FRP composite risers and their distinction compared with that of steel risers were analyzed and discussed. From this paper, the dynamic characteristics in different environmental situations of VIV for FRP composite risers can be further understood.  
FRP composite riser with large length to diameter ratio; vortex-induced vibration; 3D finite element modelling; CFD simulation; fluid-structure interaction +
Footnote †: journal: Applied Sciences
## 5 Discussion
The number of used 1D-CNN or LSTM layers significantly affected the model's performance. However, it was fascinating to observe that increasing the number of layers did not effectively enhance the outcome. It was found that the best RMSE of 0.024016 was obtained for three stacked layers of 1D-CNN and LSTM networks. The application of the loss function is vitally essential for a competent deep learning model. The influence of different loss functions was assessed on the proposed model's performance. The Huber [38] and LogCosh loss functions demonstrated better performances relative to the mean squared error. Table 5 presents the results of the performance comparison of the models under study in terms of MSE, MAPE, and LogCosh loss functions. In terms of statistics, the utilisation of LogCosh led to a 26.7% improvement relative to the MSE loss function with regard to the MAE. Similarly, on the traffic flow data from the PeMS dataset, the LogCosh function provided the best output. Table 6 presents the obtained MAE values by the different models under study using different loss functions. It was interesting to observe that CNN-GRU outperformed the proposed model while using the MSE as the loss function. The number of trainable parameters of each baseline under consideration, i.e., of the LSTM, GRU, and CNN-GRU models, were equal to 516 K, 389 K, and 468 K, respectively. The proposed algorithm needed more parameters for training relative to the baselines and demanded more computational resources. Consequently, this study also addressed the trade-off between computational costs and performance. The demanded computational time increases in proportion to data sample volume. An average of 67 s per epoch was required to train the proposed model on the traffic speed dataset containing 1,855,589 data \begin{table}
\begin{tabular}{c c c c} \hline
**1D-CNN Layers** & **LSTM Layers** & **Parameters** & **RMSE** \\ \hline
5 & 5 & 599 K & 0.02919 \\
4 & 4 & 569 K & 0.02839 \\
3 & 3 & 562 K & 0.0266 * \\
2 & 2 & 174 K & 0.863 \\ \hline \end{tabular} Here, * represents the best found result among the models under comparison.  
\end{table} 
Table 4: Ablation studies of the proposed model on the traffic speed dataset. \begin{table}
\begin{tabular}{c c c c c} \hline
**Loss Function** & **LSTM** & **GRU** & **CNN–GRU** & **CNN–LSTM** \\ \hline MSE & 0.0229 * & 0.0234 & 0.0245 & 0.024 \\ MAPE & 0.0293 & 0.0287 & 0.067 & 0.025 * \\ LogCosh & 0.0218 & 0.0204 & 0.0206 & 0.019 * \\ \hline \end{tabular}
\end{table} 
Table 5: MAE obtained by the models under study in terms of the used loss function on the traffic speed dataset. samples, which was almost twice the time required for the traffic flow dataset: 36 s per epoch over 99,359 data samples. The findings also suggest that the volume of data samples significantly influences model performance. The traffic speed dataset used contains 1,855,589 data samples, almost twice as many as the flow dataset. The results demonstrated a significant improvement in predicting future speed values over flow values. Hence, this suggests the requirement of many data samples to find the model's best output.