esa-sceva/satcom-chunk-collection huggingface dataset:
# Performance-Based Analysis of Single-Layer Cylindrical Steel Reticulated Shells in Fire
## 5 Structural Response Analysis in Different Fire Scenarios
### Structural Model
#### 5.1.2 Material Properties
The coefficient of thermal conductivity refers to the ability of material to conduct heat directly, which is the heat transferred per unit area in unit time when unit temperature is increased. The coefficient of thermal conductivity \(\lambda\) is 45 W/(m.\({}^{\circ}\)C).
###### Abstract  
Recently, indoor positioning systems have attracted a great deal of research attention, as they have a variety of applications in the fields of science and industry. In this study, we propose an innovative and easily implemented solution for indoor positioning. The solution is based on an indoor visible light positioning system and dual-function machine learning (ML) algorithms. Our solution increases positioning accuracy under the negative effect of multipath reflections and decreases the computational time for ML algorithms. Initially, we perform a noise reduction process to eliminate low-intensity reflective signals and minimize noise. Then, we divide the floor of the room into two separate areas using the ML classification function. This significantly reduces the computational time and partially improves the positioning accuracy of our system. Finally, the regression function of those ML algorithms is applied to predict the location of the optical receiver. By using extensive computer simulations, we have demonstrated that the execution time required by certain dual-function algorithms to determine indoor positioning is decreased after area division and noise reduction have been applied. In the best case, the proposed solution took 78.26% less time and provided a 52.55% improvement in positioning accuracy.  
Keywords: indoor positioning system; visible light; machine learning classification; machine learning regression; multipath reflections; signal pre-processing +
Footnote †: journal: Applied Sciences
# A Multi-Fidelity Successive Response Surface Method for Crashworthiness Optimization Problems
## 6 Results and Discussion
### Engineering Use Case: Crash Box Design
#### 6.3.3 High- and Low-Fidelity Models  
Before diving into the optimization, we need to distinguish the two fidelities for the MF-SRS method. To determine an optimal mesh size for the high-fidelity model, we performed a mesh sensitivity analysis by varying the average element size. As shown in Figure 14, reducing the mesh size below 2 mm has minimal impact on the response functions considered. However, this size reduction significantly increases the simulation time. For example, on a 2\(\times\) AMD EPYC 7601 (32 cores, 2.2 GHz) per node of our HPC cluster, the time changes from 20 min to 1 h and 26 min. Based on this analysis, we chose an average mesh size of 2 mm for the high-fidelity model.  
Beyond mesh size, the presence of the damage model, which defines the failure criterion for element deletion, is another significant factor that affects the duration of a simulation. Disabling this feature in our aluminum material card results in approximately a 25% reduction of simulation time for the 2 mm finite element model. In addition, increasing the mesh size to 5 mm dramatically drops the simulation time to approximately 3 min and 10 s. While this coarser finite element model is obviously less accurate and deviates from the response of the high-fidelity model, we propose its utility as a low-fidelity model. We believe that it can capture significant global information that, when integrated with high-fidelity runs, contributes significantly to the prediction of response functions. The two models are illustrated in Figure 15.  
For a more thorough understanding of the use case, we carried out additional simulations to provide a broader view of the correlation between the high-fidelity and low-fidelity models. This additional analysis serves to stress the complex relationship between the two models, allowing for a more sound interpretation of the results. Detailed results and discussions from these additional simulations are provided in Appendix C.3.  
Figure 14: Mesh sensitivity analysis to assess the impact of the element size on the response functions.
## 1 Introduction
Our new study investigates exotic wave effects predicted by Bloch-Floquet dispersion surfaces and explicit time-domain finite element (FE) simulations within pass bands at higher frequencies (i.e., above the band gaps investigated by [19]). Phenomena such as negative refraction, cloaking, wave-trapping, and long-wavelength enveloped wave modes (with wavelengths much larger than those for the resonant subunits at the microscopic scale) are demonstrated using transient, accelerated GPU (graphics processing unit) implementation [24]. The time-domain approach described here uses modelling methods similar to those applied for other high-fidelity FE studies of elastic wave propagation [25; 26; 27], for which experimental validations were also performed. Future studies of the model described here are likely to use experimental methods to reproduce the predicted wave behaviours. In order to manufacture the metamaterial, additive manufacturing methods may be used, which will result in microscopically rough versions of the cross-shaped inclusions. Additional FE studies are presented for perturbed versions of the crosses, using roughness statistical parameter values typically recorded for metal parts created by additive manufacturing methods [28; 29; 30]. The article is organised as follows: Section 2 reviews the governing equations, underlying assumptions, and dispersion properties. The FE model generation is also described in detail. Section 3 discusses the examples showing transient wave phenomena for the periodic and perturbed periodic structures. Concluding remarks and the future outlook for the model are drawn together in Section 4.
# Unsupervised Learning Approach for Anomaly Detection in Industrial Control Systems
## 3 Proposed Method
### Data Preparation
#### 3.3.3 Design the Model Framework  
This study applied a composite autoencoder model to detect anomalies in industrial control systems. The composite autoencoder evaluates anomalies by comparing the anomaly score of the output with a pre-generated threshold of the input and classifying it as an anomaly if it exceeds the threshold. The CNN layer filters the input data to activate specific features and then progressively abstracts them to reduce data complexity. The Rectified Linear Unit (ReLU) activation function introduces nonlinearity by setting negative values to zero. This allows the model to learn more complex functions, mitigating the problem of neuron deactivation and improving computational efficiency. Long Short-Term Memory (LSTM) networks, a type of Recurrent Neural Network (RNN), are particularly useful for detecting anomalies in time series data because they can remember long-term dependencies in sequential data. By combining CNNs and LSTM networks, composite autoencoders can identify and highlight important features in the data while encoding and decoding temporal variations. The encoder compresses the input data to extract significant features, and the decoder reconstructs the original data based on these features. Composite autoencoders can effectively detect anomalies, even if they are small or occur consistently over time, by capturing these important features and identifying anomalous patterns. Figure 7 shows the structure of the composite autoencoder used in our study.  
* Input layer: In this study, each tag obtained by clustering was used as input data. It was assumed that all tags had no explanation, but they can be processed simultaneously because different data such as temperature, pressure, and flow were clustered.
* Output layer: The output layer of the model was designed to detect abnormal behavior. The output layer reconstructed the input data, compared it to the original data, and generated an abnormal behavior score. At this point, if the abnormal behavior score exceeded a certain threshold, it was considered abnormal and would inform you of the name, value, and time of occurrence of the tag.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Value** & **Mean** & **Std** & **Gradient** & **Intercept** & **Cluster** \\ \hline
0 & 0.0299 & 0.0299 & 0.0000 & 0.0000 & 0.0000 & 1 \\
1 & 0.0282 & 0.0291 & 0.0011 & 0.0000 & 0.0000 & 1 \\
2 & 0.0293 & 0.0292 & 0.0008 & 0.0000 & 0.0000 & 1 \\
3 & 0.0317 & 0.0307 & 0.0023 & 0.0000 & 0.0000 & 1 \\ \(\cdots\) & \(\cdots\) & \(\cdots\) & \(\cdots\) & \(\cdots\) & \(\cdots\) & \(\cdots\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: The values of the statistical property collection tag.  
Figure 6: Statistical characteristics of input data.  
* Activation functions and singularities: ReLU was used in the model to increase the nonlinearity of the model and to increase computational efficiency. Loss used the Mean Squared Error and the learning speed of the Adam optimizer was generally set to 0.001.
## 4 Results
The reason for this is that the algorithm for time window selection can reflect the effects of technical or operational developments, new requirements, or specific arrangements on the vessel traffic being analyzed. According to Table 1 and Figure 2, the vessel traffic risk declines overall from the year 2000 until 2016. Thus, the vessel traffic risk may be overestimated without considering the time window selection.
## 7 Conclusions and Further Work  
As we have seen, the problem raised throughout this work offers us an efficient way of managing a series of \(K\) qRobots to collect a set of orders, optimizing the number of robots used. The provided approach applies to a \"central computer\" capable of carrying out all the calculations and then giving each of the robots' orders. However, when we begin to deal with very large problems both in the number of products and in the number of robots, the number of qubits required will tend to grow too large. A possible solution is to distribute the calculation of a central computer to each of the robots in such a way that each one has to calculate its route given a list of products to be collected. In this case, the equations of the problem would not change; just take \(K=1\) for each qRobot and apply the technique mentioned at the beginning of the discussion. Although it may not be possible to reach the best solutions, this process of distribution of the calculation would suppose a significant computational cost reduction despite the need to create the batches beforehand. This search for batch creation will be studied in future projects. On the other hand, it is important to note that the problem dealt with has a QUBO-type formulation, which allows it to be executed in annealing-type quantum computers. This makes a big difference in today's era (NISQ) as we have managed to work with 200 qubits versus the 30 qubits that we would have with a gate-based quantum computer. Finally, note that the defined problem seeks to minimize the total distance traveled by the robots, making it worthwhile for not all the robots to come out. For future lines, we will address the same problem. Still, We will continue to try to reduce the total times instead of the distance traveled (as done in this previous work [82]) since this situation is also very important in warehouse logistics.  
Conceptualization, P.A.-A.; Methodology, P.A.-A. and G.A.-L. and J.A.-C.; Software, P.A.-A. and G.A.-L.; Validation, P.A.-A., G.A.-L., J.A.-C. and D.C.-F.; Formal Analysis, P.A.-A., G.A.-L. and J.A.-C.; Investigation, P.A.-A.; Resources, P.A.-A.; Data Curation, P.A.-A.; Writing-Original Draft Preparation, P.A.-A.; Writing-Review and Editing, P.A.-A., J.A.-C. and G.A.-L.; Visualization, P.A.-A., G.A.-L., J.A.-C. and D.C.-F.; Supervision, P.A.-A.; Project Administration, J.A.-C. All authors have read and agreed to the published version of the manuscript. This research received no external funding. This article does not contain any studies with human or animal subjects. Informed Consent Statement: Informed consent was obtained from all individual participants included in the study. Data Availability Statement: Data sharing not applicable. No new data were created or analyzed in this study. Data sharing is not applicable to this article  
The authors greatly thank the AWS-Braket and IBM team, mainly [PERSON] and [PERSON], respectively. P.A. thanks [PERSON] for her support and comments on the manuscript. The authors declare no conflict of interest.
## 4 Results and Discussion  
### Methane Adsorption in Coal Models  
To verify the correctness and applicability of the models and the applied force field, the adsorption of methane on the coal was fitted by the Langmuir equation. Figure 2 shows the methane isotherm adsorption curves and Langmuir fitting parameter, a, of the anthracite and coking coal at 273.15-313.15 K. When the adsorption temperature is constant, the adsorption capacity of methane molecules in the coal molecular structure model rapidly increases and then stabilises with increasing adsorption pressure, which is consistent with the Langmuir adsorption equation. When the adsorption pressure is constant, the amount of methane adsorption decreases with increasing temperature. In the molecular structure model of anthracite, the saturated adsorption amount of methane is 5.80 N/uc at 273.15 K; it decreases to 4.85 N/uc at 313.15 K. In the molecular structure model of coking coal, the methane adsorption amount is 5.23 N/uc at 273.15 K; it decreases to 4.78 N/uc at 313.15 K. Regarding the methane adsorption amount in the molecular structure model of coking coal under different temperature and pressure conditions, [PERSON] et al. built a coking coal model of the Chiyu coal mine and also found that the methane adsorption amount decreased with the increase in temperature [46]. A probe with a diameter equal to the methane molecular diameter (0.38 nm) was used to detect and count the micropores of the two coal molecular structure models. The results show that the free volumes of methane adsorbed by the anthracite and coking coal are 16.25 \(\times\) 10\({}^{-3}\) and 8.98 \(\times\) 10\({}^{-3}\) nm\({}^{3}\)/uc, respectively, and that the accessible surface areas of methane are 0.45 and 0.56 nm\({}^{2}\)/uc, respectively. The simulation results are consistent with Ref [47], and [PERSON] et al. reported that the micropores in coal are the main places where methane occurs, that is, the larger the micropore volume, the greater the methane adsorption capacity.  
The adsorption heat of methane in coal is an important thermodynamic parameter for characterizing the adsorption behaviour of methane in coal; it serves as a quantitative indicator to evaluate the adsorption affinity of coal to methane [48]. The isothermal adsorption heat of methane in coal is generally calculated by the Clausius-Clapeyron equation [46; 48]. Based on the methane adsorption as an exothermic process, [PERSON] et al. used negative values to characterize the methane adsorption heat [48]. The simulation results show that the adsorption heat is expressed as a positive value. Figure 3 shows the isothermal adsorption heat simulation results of methane in the anthracite and coking coal at 273.15-313.15 K. The methane adsorption heat of anthracite is 28.13-28.57 Kj/mol, and that of coking coal is 26.23-26.48 Kj/mol. As the adsorption temperature increases from 273.15 K to 313.15 K, the methane adsorption heat of both coals shows an increasing trend, which is consistent with the results of [PERSON] et al. and [PERSON] et al. [48; 49].
## 3 DCS Scheme Families
### Similarity Search Algorithm  
The **Similarity Search Algorithm (SSA)**[22] was proposed by [PERSON], _et al._ based on the Hilbert Curve over a DCS structure. SSA is successful in searching similar data without collecting data from all of the network sensors. Being motivated from the Hilbert Curve concept, the authors divide the network recursively into \(4^{l}\) square quadrants where \(l\) denotes the number of levels. The center (indexing node) of each square quadrant (cell) is denoted by \(I\). It is important to select the indexing nodes to avoid performance degradation due to too many indexing nodes and on the other hand a lack of enough storage space due to too few indexing nodes. If the total memory space for storing data is \(A\) and the memory size of each sensor is \(z\), then the number of indexing nodes \(n\) can be defined as: \(n\geq[A/Z]\). So, the number of levels \(l\) is defined by: \(l=log_{4}n\geq[A/Z]\). The entire data range of an event is referred to by \(R\) where \(R_{L}\) and \(R_{U}\), respectively, denote the lower bound and upper bound. \(R\) is divided into \(n\) equal sub-ranges each being equal to \(r\)_i.e._, \(n.r=R\). So, the data sub-range for which \(I_{ID}\) is responsible is defined as \(\left[R_{l}\rightleftharpoons,R_{U}\rightleftharpoons]=\left[R_{L}+(I_{p}-1) \cdot r,R_{L}+I_{p}\cdot r)\). Figure 2 illustrates level 1 and level 2  
Figure 1: Geographic Hash Table [1].  
assuming that the data range \(R\) of an event is (0, 1). Detected events are mapped to a particular segment if the event falls in the range of that cell. Two parameters \((v_{min}{}^{1D},v_{max}{}^{1D})\) are used to record the minimum and maximum values of each segment. Initially these two parameters are set to 0. When data is inserted into an index node, the values are updated accordingly. For example, if a sensor detects an event with a value of 0.2 then the data is sent to \(I_{0}\) as it belongs to the range \([0,\ 0.25]\) and both parameters \((v_{min}{}^{1D},v_{max}{}^{1D})\) of this cell will be updated to \((v_{min}{}^{1D}=0.2,v_{max}{}^{1D}=0.2)\). Being a mini-repository of an entire distributed database, each sensor has knowledge of its local data and hence lacks global knowledge of the entire sensor database. The distributed nature of the data throughout the sensor network is one of the major challenges when processing similarity search queries. To overcome this challenge, the adjacent index nodes in SSA along the Hilbert curve have data of similar values and thereby avoid the need to collect data from all of the sensors in the network. This data mapping based on the concept of the Hilbert curve is simple and easy to implement. However, when storing multidimensional attributes it is not clear whether SSA maintains a separate Hilbert curve or not and in this case how it responds to the multidimensional queries.
# Thermal and Physical Properties of Crude Palm Oil with Higher Oleic Content
## 4 Conclusions  
This study determined the physicochemical, physical, and thermal properties of hybrid palm oil obtained from an interspecific hybrid cultivar (_E. oleifera_\(\times\)_E. guinensis_), which was compared to the palm oil obtained from the African cultivar (_E. guinensis_). The oils presented chemical differences; however, both showed an appropriate chemical profile required for commercial exploitation. Moreover, small differences in density and viscosity were found between the oils, with no relevance for the materials' processing design. Nevertheless, the thermal analysis demonstrated that the hybrid species' oil presented distinguished melting/crystallization behavior, affecting industrial operations, such as fractionation andlein and stearini yields. Furthermore, besides the higher content of unsaturated fatty acids, HOPO was more stable than APO due to a higher antioxidant content. The results presented here are useful to establish operation conditions for processes using palm oil from hybrid oil palm, a cultivar that is arousing the industry's interest due to its resistance to pests.  
Conceptualization, E.S.d.A.; Methodology, E.S.d.A. formal analysis, D.d.S.D., L.C., P.A.V., R.M.d.P. and P.V.d.A.P.; Supervision, M.C.-F., K.A.S. and S.M.; Writing--original draft preparation, E.S.d.A.; Writing--Review and Editing, S.M.; Funding Acquisition, K.A.S. and S.M. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Conselho Nacional de Desenvolvimento Cientifico e Tecnologico--CNPq, grant number 421852/2018-6; 429873/2018-2; Fundacao de Apoio a Pesquisa do Distrito Federal--FAPDF, grant number 224/2019; Fundacao de Amparo a Pesquisa do Estado de Sao Paulo--FAPESP, grant number 2014/21252-0; Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior, grant number 001.  
Not applicable.  
The authors thank Embrapa Agroenergia for providing some facilities that were necessary for the development of this work. The authors also acknowledge Denpasa for kindly donating essential material.  
The authors declare no conflict of interest.  
Figure 3: TGA curves of palm oil APO and HOPO.
## 3 Problem Formulation and Objectives
### Communication Model
\begin{table}
\begin{tabular}{c c} \hline \hline
**Symbol** & **Description** \\ \hline \(I\) & Set IIoT tasks \(I=(1,2,3,\ldots,i),\ \forall i\in I\) \\ \hline \(N\) & Set IIoT devices \(N=(1,2,3,\ldots,n),\ \forall n\in N\) \\ \hline \(T\) & Set time slots \(T=(1,2,3,\ldots,t),\ \forall t\in T\) \\ \hline \(M\) & Set MEC server \(M=(1,2,3,\ldots,m),\ \forall m\in M\) \\ \hline \(S^{i}\) & Offloading decision from IIoT device to MEC, whether 1 or otherwise \\ \hline \(S_{n}\) & Data size of the computation task \(n\)-\(th\) \\ \hline \(D_{n,m}\) & Data transmission rate from IIoT \(n\)-\(th\) to MEC server \(m\)-\(th\) \\ \hline \(B_{n,m}\) & Transmission power device-n to MEC server \(m\)-\(th\) \\ \hline \(H_{n,m}\) & Channel bandwidth \\ \hline \(\psi\) & Ground interference power consumption \\ \hline \(P_{v}\) & Processing power required by _VNF v-\(th\)_ \\ \hline \(U_{v}\) & Utilization of VNF v-\(th\) \\ \hline \(B_{m}\) & Total bandwidth of MEC server \(m\)-\(th\) \\ \hline \(L_{max}\) & Satisfaction of latency \\ \hline \(C_{max}\) & Upper bound of total resource usage of the capacity of each MEC server. \\ \hline \(X_{MIC}^{n,m}\) & Execution at MEC server with task \(n\)-\(th\) \\ \hline \(L_{i}^{n}\) & Time accepted \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The notation of network system model.