esa-sceva/satcom-chunk-collection huggingface dataset:
# Metamodelling for Design of Mechatronic and Cyber-Physical Systems
## 2 Modelling Mechatronic Systems
### Modelling Tools  
Nowadays, there are many software tools for systems modelling and engineering available on the market. The most popular are listed as follows:  
* Cameo Systems Modeler (formerly known as: MagicDraw with _SysML_ plugin); Vendor: NoMagic; Web: [[http://www.nomagic.com/products/cameo-systems-modeler.html](http://www.nomagic.com/products/cameo-systems-modeler.html)]([http://www.nomagic.com/products/cameo-systems-modeler.html](http://www.nomagic.com/products/cameo-systems-modeler.html)),
* Innsolate; Vendor: SPEC Innovations; Web: [[https://www.innsolate.com/](https://www.innsolate.com/)]([https://www.innsolate.com/](https://www.innsolate.com/)),
* Enterprise Architect with SysML plugin;Vendor: SparxSystems; Web: [[http://www.sparxsystems.com/products/mdg_sysml.html](http://www.sparxsystems.com/products/mdg_sysml.html)]([http://www.sparxsystems.com/products/mdg_sysml.html](http://www.sparxsystems.com/products/mdg_sysml.html)),
* Modelio; Vendor: ModelioSoft; Web: [[http://www.modeliosoft.com/en/products/solutions/system-architect-solution-overview.html](http://www.modeliosoft.com/en/products/solutions/system-architect-solution-overview.html)]([http://www.modeliosoft.com/en/products/solutions/system-architect-solution-overview.html](http://www.modeliosoft.com/en/products/solutions/system-architect-solution-overview.html)),
* Papyrus 4 SysML, Web: [[http://www.eclipse.org/modeling/mdt/papyrus/](http://www.eclipse.org/modeling/mdt/papyrus/)]([http://www.eclipse.org/modeling/mdt/papyrus/](http://www.eclipse.org/modeling/mdt/papyrus/)),
* IBM Rhapsody; Vendor: IBM; Web: [[http://www.ibm.com/software/rational/products/hapsody/sysarchitect/](http://www.ibm.com/software/rational/products/hapsody/sysarchitect/)]([http://www.ibm.com/software/rational/products/hapsody/sysarchitect/](http://www.ibm.com/software/rational/products/hapsody/sysarchitect/)),
* ARTiSAN Studio; Vendor PTC; Web: [[http://www.atego.com/products/artisan-studio/](http://www.atego.com/products/artisan-studio/)]([http://www.atego.com/products/artisan-studio/](http://www.atego.com/products/artisan-studio/)).  
These tools are the most popular due to their advanced features and support for team-oriented development.  
Modelling software architecture and simulation research of new products including control systems is possible due to the features present in the following applications:  
* Matlab/Simulink; Vendor: Mathworks,
* MapleSim; Vendor: Maplesoft,
* LMS Imagine.Lab Amesim; Vendor: Siemens,
* Scilab, open source project, initially started by INRIA, France,
* HOPSAN, from Linkoping University of Technology, Sweden,
* OpenModelica, open-source Modelica-based modelling and simulation environment, supported mainly by the Open Source Modelica Consortium (OSMC),
* Dymola.  
Most of the integrated development environments (IDE) for industrial control systems [37] compliant with the IEC 61131-3 standard [38] allows validation of the designed algorithms by the means of offline or real-time simulation before it is compiled to the production code.
# Computational Methodology for the Development of Wrinkled Tubes by Plastic Deformation
## 3 Materials and Methods
### Mathematical Model
#### 3.1.1 Pre-Processing  
A structural analysis was defined for the problem under investigation. The chosen finite element was Plane 183, a 2D element with 8 nodes and two degrees of freedom per node: translations occur in the x and y nodal directions. The proposed element is suitable for axisymmetric analyses and has sufficient degrees of freedom to represent the deformation effects to which the structure is subjected. The model geometry was pre-designed in two dimensions, considering the parts of the assembly with an axisymmetric shape, as the model has symmetry throughout its extent. This approach reduced simulation time and simplified the analysis, optimising the process.  
To obtain the most accurate results possible, the mechanical properties of the tube and die material were provided in both linear and nonlinear deformation regimes. For elastic behaviour, the mechanical properties adopted for the aluminium tube were Young's Modulus and Poisson's Ratio, respectively, 69 GPa and 0.3. For the nonlinear, elastoplastic regime, yield stress and tangent modulus values were 280 MPa and 100 MPa, respectively [33]. Concerning the die, although modelled as a deformable structure, a Young's Modulus value of 69,000 GPa was adopted to ensure that it was rigid. The yield stress was also significantly increased to 600 MPa. The Poisson's Ratio and tangent modulus values were like those applied to the tube. Initially, a mesh sensitivity analysis and result convergence were conducted. The element sizes created for the analyses were 2 mm, 1.5 mm, and 1 mm, always meeting the tube thickness value of 3.2 mm.  
Figure 1: Geometry of the tube-die assembly: (**a**) the drawing of the die and the tube to the numerical study, (**b**) the initial geometry and the assembly of the die and tube before deformation.
## 5 Conclusions  
This paper proposed a practical co-design method with heterogeneous processors to parallelize and accelerate a part of the multiple convolutional layer operations on an FPGA for onboard real-time routing in dynamic low-orbit satellite networks. Previous work has considered reinforcement learning, dynamic environments, and satellite link outages, but none has considered parallelization. This paper is different in that it includes the previous topics and proposes an FPGA-accelerated CNN with parallelized sum pooling for an onboard real-time routing method. In the sum-pooling structure of conventional CNNs, convolution and ReLU operations require excessive execution time, which is not suitable for the real-time requirements of dynamically changing low-orbit satellite network environments. The proposed method solved this problem by accelerating the algorithm through the co-design of CPU and FPGA, specifically by changing the computation structure of CNN. Experimental results with an OBC with heterogeneous processors including an FPGA showed that the proposed method was about 3.10 times faster than the conventional method while achieving the same routing results.  
All authors contributed to the present paper with the same effort in describing the problem, finding the available literature, and writing the paper. [PERSON], [PERSON], and [PERSON] designed and implemented the proposed method to resolve the problem. [PERSON] and [PERSON] addressed the topic and proposed the idea to resolve it. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Agency for Defense Development, funded by the Korean government (Defense Acquisition Program Administration) (UI220033 VD). Data Availability StatementData is unavailable due to the restrictions of the organization supporting this work.  
Authors [PERSON] and [PERSON] were employed by the company LIGNEX1 and TelePIX, respectively. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
# Crop Yield Prediction Based on Agrometeorological Indexes and Remote Sensing Data
## 4 Results  
### Trend Yield  
The correlation between climate yield and agricultural disaster data obtained by the moving average method, HP filtering method and exponential smoothing method was analyzed by using the gray correlation degree. The results are shown in Figure 3. As shown in the figure, compared with the other methods, the climate yield obtained by the moving average method in most municipalities of Jilin Province and Liaoning Province shows a better degree of correlation with the agricultural disaster area, with the average value of the correlation degree reaching 0.9181 and 0.9038, respectively. Among them, the gray correlation degree of six municipalities in Jilin Province exceeded 0.9500, that of four municipalities in Liaoning Province exceeded 0.9400 and that of another seven cities exceeded 0.9000. Therefore, the trend yield obtained by the moving average method was used in subsequent analysis and modeling.  
Figure 3: Gray correlation degree between the climate yield and the agricultural disaster data in Jilin (**a**) and Liaoning (**b**).
# Thermal Stability of Ionic Liquids: Effect of Metals
## 4 Conclusions  
The structural characterization of the ionic liquid [TBuMA][NTF\({}_{2}\)], both fresh and after thermal treatment, with or without different metals, was carried out by HRMAS NMR and FTIR spectroscopic analyses, while HS-GC-MS was employed to reveal the formation of volatile compounds. The degradation products of [TBuMA][NTF\({}_{2}\)] were characterized after thermal treatment at 200 \({}^{\circ}\)C for 4, 24 and 168 h in contact with AISI 304 steel, copper or brass, and without metals as a comparison. The combination of the above-mentioned techniques evidenced the occurrence of degradation processes of the cation. The data suggested a degradation mechanism compatible with the Hoffman elimination of one or two alkyl chains from the cation, with 1-butene being one of the degradation products after thermal treatment, both in the absence or presence of metal plates. The proposed multi-technique approach was revealed to be suitable for the characterization of the degradation compounds of [TBuMA][NTF\({}_{2}\)] after thermal treatment in the presence of metals, thus proving to be a promising method for the selection of IL compounds that possess high stability and a suitable lifetime to meet the durability requirements of commercial and industrial solar thermal applications.  
Figure 9: Relative intensity (counts) of 9 main compounds identified in samples B, 4B168, 1B168, 2B168 and 3B168 samples by HS-GCMS analysis.  
**Supplementary Materials:** The following supporting information can be downloaded at [[https://www.mdpi.com/article/10.3390/app12031652/s1](https://www.mdpi.com/article/10.3390/app12031652/s1)]([https://www.mdpi.com/article/10.3390/app12031652/s1](https://www.mdpi.com/article/10.3390/app12031652/s1)): Figure S1: comparison of \({}^{1}\)H HRMAS spectrum of 4B168 (a,d) with 1D selective \({}^{1}\)H TOCSY obtained by irradiating H1,1 (9.16 ppm, b), H1,2 (8.31 ppm, c) and H1a,3 (5.13 ppm, e).; Figure S2: \({}^{1}\)H-\({}^{13}\)C HSQC spectrum of 4B168; Table S1: assignment of \({}^{1}\)H and \({}^{13}\)C NMR signals of the ionic liquid B; Table S2: assignment of \({}^{1}\)H and \({}^{13}\)C NMR signals of the degradation compounds 1, 2 and 3.  
Conceptualization, A.F., S.P., E.B. ([PERSON]) and A.L.; methodology, A.F., S.P., E.B. ([PERSON]) and A.L.; investigation, F.N., S.P., E.B. ([PERSON]), B.C., A.L. and E.B. ([PERSON]); writing--original draft preparation, A.F., S.P., E.B. ([PERSON]); writing--review and editing, F.N., C.F., A.L., E.B. ([PERSON]), S.P.; supervision, A.F., C.F.; funding acquisition, A.F., A.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the FELIX project (Fotonica ed Elettronica Integrate per l'Industria, project code no. 6455) and project MIUR Cluster CTN02_00018 <<Energia>> Codice progetto CTN02_00018_10016852 \"NeMESi\".  
The authors declare no conflict of interest.
## 4 Conclusions  
It is important to predict peak-period demand accurately to optimize the resource and operation in logistics industries. Korea Post, the national postal service provider, needs to predict short-term changes in parcel volume in order to optimize its operations, especially during periods of sharp changes in parcel volume.  
This study proposed the prediction method for the demand of logistics services during special periods in holidays using deep learning models. The proposed method improved  
Figure 8: Comparison of MAPE results of weighted ensemble methods based on initial and VAE-based enriched datasets.  
Figure 7: Average performance of compared methods on 25 mail center datasets.  
the prediction performance by two steps. In the first step, the training dataset was enriched by synthetic data generated from the VAE model. It decreased the average MAPE of the baseline MLP model that was learned from the daily training datasets of 25 mail centers by 3.7% (see the results of baseline MLP and IF-MLP in Table 2). In the second step, the proposed method combined two MLP models that were trained on the enriched daily training dataset using calendar and internal volume-derived features and the dataset using external features of the bulk mailing volume and contract customer data. The final prediction result was estimated by the weighted average of the outputs of these EF-MLP and IF-MLP models. The weighted ensemble of EF-MLP and IF-MLP models reduced the MAPE of the baseline MLP models by 5.0% successfully.  
The experimental results showed how the proposed method improved the prediction performance step by step and compared the forecasting results of the proposed method with machine learning-based models on 25 mail center datasets. The proposed method outperformed the compared models on most datasets and achieved a performance improvement of up to 59.6%. The experimental results confirm that the proposed weighted ensemble model is acceptable for peak-period prediction, and it is highly possible to expand the range of its applications.  
Conceptualization, E.K.; methodology, E.K. and T.A.; software, E.K. and T.A.; validation, E.K., T.A. and H.J.; resources, E.K.; data curation, E.K. and T.A.; writing original draft preparation, E.K. and T.A.; supervision, E.K. and H.J.; funding acquisition, E.K. and H.J. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Institute of Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (No. 2018-0-01664, Development of Logistics Infrastructure Technology for Postal Service).  
Not applicable.  
The authors declare no conflict of interest.
# Mechanical Properties and Microstructure of Polyvinyl Alcohol (PVA) Modified Cement Mortar
## 3 Results and Analysis  
### Flow Properties  
Fluidity is an important index that reflects the working performance of a cement mortar. The flow properties of cement mortar can be evaluated by using a flow table test. Figure 4 shows the test results of the slump value of all mortars, in which the w/c ratio was fixed at 0.4. Obviously, the incorporation of PVA greatly reduced the flow properties of mortars, as shown in Figure 4, the slump value of fresh mortars decreased from 219.4 mm to 108.5 mm with the PVA content increased from 0% to 2.0%. The incorporation of 0.2%, 0.6%, 1.0% and 2.0% PVA reduced the slump value by 13.9%, 31.5%, 41.1%, and 50.5%, as compared to the control one, respectively. This reduction possibly attributed to the strong, cohesive force of PVA, which limits the movement of cement particles during the mixing process. Moreover, the -OH bonds in the PVA matrix may react with the -OH bonds of water, which reduce the water participating in dispersing of cement particles. The working performance impacts the mechanical properties of cement mortar, and generally, a higher slump value leads to a better uniformity for the fresh mortar, which further results in a higher mechanical properties. Thus, the content of PVA for actual engineering applications should be as small as possible.
# Application of Soft Computing Techniques for Predicting Thermal Conductivity of Rocks
## 2 Establishment of Dataset  
In rock mechanics, each test induces costs, and a long period is needed for each phase of the experimental process. Therefore, it is crucial to reach the desired accuracy by testing a minimum number of samples [54]. [PERSON] [55] sought to analyze this problem using a statistical technique called the \"decision of the sample number\". He tested three different kinds of igneous rocks to evaluate their compressive strength and found that a 90% confidence level could be acquired by using only ten samples. This study showed that, due to the high similarity in their physical properties, the mechanical properties of each primary type of rock (i.e., sedimentary, metamorphic, and igneous) could be similar to each other. Therefore, testing a small number of samples would be sufficient to obtain results with a high confidence level [55].  
In our study, 50 datasets, including TC, UCS, density, porosity, and P-wave velocity, were taken from the literature [2; 3; 4; 24]. The type of these samples was not reported, but most of them were sedimentary rocks (i.e., by referring to their P-wave velocity and porosity values). The basic descriptive statistics of the datasets are presented in Table 1.  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Variable** & **Max** & **Min** & **Mean** & **Median** & **St. Deviation** \\ \hline TC (W/m K) & 3.01 & 0.186 & 1.395 & 1.2628 & 0.762 \\ UCS (MPa) & 116.9 & 3.43 & 61.32 & 63.258 & 24.151 \\ P-wave (m/s) & 6300 & 1800 & 4486.436 & 4457.5105 & 1096.66 \\ Density (kg/m\({}^{3}\)) & 2970 & 500 & 2508.30 & 2575.3305 & 422.851 \\ Porosity (\%) & 84 & 0.83 & 5.91 & 2.354 & 12.755 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Descriptive statistical distribution of datasets.
## 2 Existing Routing Methods for the DTN
### Destination Area  
We define a destination area in order to specify a multi-point address. A destination area is a polygon, which is identified by its location. When a bundle is carried to a destination area, all nodes within the destination area become receiving nodes. Typical shapes of a destination area are a circle and a rectangle, as shown in Figure 3. The sending node needs only to specify a destination area rather than individual receiving nodes.
Thus, the need for effective energy storage, demand response mechanisms, or supplementary power sources becomes imperative to bridge this temporal disparity effectively. Additionally, mitigating the consequences of intermittency hinges crucially on developing energy storage systems, most notably batteries, to accumulate surpluses during periods of excess generation for utilization during times of diminished output. This effort, while indispensable, is fraught with difficult technical and economic challenges, requiring innovation and efficiency improvements. One of the most remarkable issues arises during Dunkelflauten (DF) events, which refers to extended periods of low solar and wind generation, often worsened by weather conditions such as overcast skies or calm winds. These events pose a serious obstacle to maintaining a reliable energy supply, particularly in regions heavily reliant on renewables. The DF effect highlights the need for supplementary systems such as energy storage, demand response, or complementary power sources to ensure a consistent energy flow during such periods of reduced generation. Addressing these challenges requires innovative solutions in both storage technologies and grid management to mitigate the impact of these extended low-generation events, ensuring energy security even during challenging weather conditions. Integrating substantial volumes of renewable energy into preexisting power grids exists an additional challenge on grid infrastructure [10]. Grid operators are compelled to adeptly manage energy flows and sustain stability, particularly when confronted with abrupt fluctuations in renewable generation brought about by meteorological vagaries. The logistical complexities extend to the transmission and distribution of renewable energy, often situated far from densely populated urban centers or regions characterized by heightened demand. The expansion and modernization of transmission and distribution networks are formidable and costly endeavors that are indispensable for efficient energy transport from generation facilities to consumers. Furthermore, the management of renewable energy centers on precise production forecasts [24; 25]. The inherent fluctuations in solar and wind generation mandate deploying sophisticated forecasting techniques to ensure grid stability and a reliable energy supply. Geographical considerations also exert a profound influence on renewable energy feasibility. The uneven distribution of renewable resources across regions necessitates meticulous planning to optimize the siting of renewable energy projects, striking a balance between resource abundance and proximity to demand centers. The regulatory landscape presents yet another layer of complexity, with policies and regulations often misaligned with the unique characteristics of renewable energy generation. Establishing supportive frameworks that incentivize investment, grid integration, and innovation is paramount in surmounting these challenges.
## 3 Test Operations in the Pilot CO\({}_{2}\) Capture Process  
### Description of the Pilot Process  
The pilot CO\({}_{2}\) capture process (PCCP) was built at the site of the 2 MW coal-fired power plant of the Korea Institute of Energy Research and could be tested with actual real flue gas or with a simulated gas. The PCCP was designed to normally treat 150 Nm\({}^{3}\)/h of flue gas with a CO\({}_{2}\) capture rate exceeding 90% using the MEA 30 mass% solvent, though it can also be operated with various other solvents. In consideration of corrosion, all major equipment and piping were manufactured with stainless steel (SUS316). The stripper column and reboiler were designed to be able to operate at up to 7 barg, as some solvents can be regenerated under pressure. A schematic of the PCCP is shown in Figure 3, which also presents a photograph of the PCCP.  
Key units in the PCCP are described in the following:  
Pre-scrubber: This unit removes SO\({}_{2}\) together with dust in the flue gas. It was designed as a packed column with CMR(CASCADE MINI-RINGS)(r) random packing materials. An aqueous solution of NaOH is used to reduce the concentration of SO\({}_{2}\) to less than 2 ppm. The gas treated in the pre-scrubber passes through a dust removal drum for further removal of dust. The bed dimensions and packing materials of the packed columns are described in detail in Table 4.  
Figure 3: (**a**) Process flow diagram and (**b**) photograph of the PCCP(Pilot CO\({}_{2}\) Capture Process).  
Absorber columns: Two absorber columns were installed for a flexible configuration. The inner diameter of each column is 25 cm and the total height is 835 cm with two 250 cm packing beds. An intercooler was installed in the middle of each column. The two columns were each filled with different packing materials of TPT 350X structure packing, similar to Mellapak 350X and CMR random packing. The absorber columns can be operated in series by changing the order, or each absorber column can be operated independently.  
Stripper column: The inner diameter of the stripper column is 20 cm, and the height is 790 cm, of which 410 cm is accounted for by the packing bed. The bed was filled with CMR random packing materials. The top of the stripper column was equipped with gauze-type structure packing to reduce the entrainment during feed flash. The exhausting vapor from the column is cooled to recover the solvent and water vapor by a shell and tube condenser. The reboiler was designed as a kettle-type.  
Cross-heat exchanger: A plate heat exchanger was installed to efficiently recover the thermal energy in the hot lean loading solvent to the cold rich loading solvent.  
Analysis: Table 5 shows on-line gas sensors installed in the PCCP. In addition to these sensors, gas and liquid samples were taken periodically and analyzed in the lab to measure CO\({}_{2}\) loading, solvent decomposition products, heat stable salts, and selected components in the absorber exhaust gas.