esa-sceva/satcom-chunk-collection huggingface dataset:
# A Multi-Criteria Assessment of Manufacturing Cell Performance Using the AHP Method
## 4 A Practical Example  
### Description of Manufacturing Cell Designs Alternatives  
This section aims to introduce five manufacturing cell alternatives for their mutual comparison in order to determine the optimal design. The CM alternative designs along with basic input data are taken from a case study by [PERSON] and [PERSON] [65]. The essential data from this case study include routing of parts (P) through machines (M), sequencing of parts by way of recording, operational times in minutes, and batch sizes for individual parts. The parts routings and operational times for all the parts are shown in Figure 4 (e.g., part P1 is firstly processed for 96 min on machine M1. It is subsequently machined for 36 min on machine M4, processed for 36 min on machine M8, and finally, it is machined for 72 min on machine M9).  
The alternative designs that are depicted in Figure 5 can be divided into two groups: two-cell solutions (three design alternatives, i.e., Cell Designs 1-3) and three-cell solutions (two design alternatives, i.e., Cell Designs 4-5). The sequence of machines for each job is not violated in all the alternatives.  
As can be seen in this figure, Cell Design 1 consists of two cells: 11 machines are located in the first cell, and 12 machines are located in the second cell. Parts P1-P4 and P7-P11 are processed in the first cell, and the rest of the parts marked as P12-P19 are processed only in the second cell. Parts P5 and P6 are partially processed in the second cell and finalized in the first cell.  
Cell Design 2 contains 25 machines. There are 16 machines in the first cell and 9 machines in the second cell. Parts P1-P11 and P18 are machined in the first cell, and the rest of the parts are produced in the second cell.  
Cell Design 3 is quite similar to Cell Design 2, except that machine M1 is eliminated in the second cell, and parts P15 and P16 are first produced in the first cell and subsequently finished in the second cell.  
Cell Design 4 includes three cells with 8 machines in the first cell, 10 machines in the second cell, and 8 machines in the third cell. Parts marked as P1, P3, P7-P9, and P11 are machined in the first cell, while P3 is completed in the second cell. Parts P2, P4-P6, P10, P15, P16, and P18 are produced in the second cell, but P15 and P16 are finished in the third cell. Parts P12-P14, P17, and P19 are machined in the third cell.  
Figure 4: Sequences of machines for each job/part.  
Cell Design 5 is also divided into three cells. The first cell consists of 5 machines, 15 machines are located in the second cell, and 4 machines are placed in the third cell. P1, P3, P7-P9, and P11 are machined in the first cell, while P1, P3, and P7-P9 are completed in the second cell. P2, P4-6, P10, P14-P16, and P18 are produced in the second cell, while P15 and P16 are finalized in the third cell. P12, P13, P17, and P19 are machined only in the last cell.
## 8 Operational Tools--From EC-JRC to TsuComp with Enhanced Products
It should be noted that prior to NEAMWave14, on 1 October 2014, the US NOAA Pacific Tsunami Warning Center (PTWC) commenced issuance of new Enhanced Tsunami Products for all Pacific countries, culminating a 7-year intergovernmental process coordinated by the IOC through its Intergovernmental Coordination Group for the Pacific Tsunami Warning and Mitigation System (ICG/PTWS). As a result of this significant progress, the 12 th session of the ICG/NEAMTWS in 2015 recommended that NTWCs, in consultation with their CPAs, evaluate the need to provide enhanced products in the NTWC messages, such as maps, and to present and make proposals for discussion and adoption at its next session. No decision was taken at the 13 th session of ICG/NEAMTWS in 2016, although the NEAMTWS Working Group on Public Awareness, Preparedness and Mitigation (WG4) recommended keeping the requirement for enhanced TSP products, even if its implementation is not foreseen in the short term. In NEAMWave17, national messages with enhanced products were sent to the Turkish CPA (AFAD) using KOERI's TsuComp (formerly TsuMessenger) software developed within the framework of EC/FP-7 Project ASTARTE. NEAMWave17 was the final full-scale testing of TsuComp prior to its operationalization by KOERI as of 1 January 2018. At its 14 th session in 2017, ICG/NEAMTWS WG4 recommended analysis of and improvements to the NEAMTWS national tsunami messages. In the meantime, KOERI's intention to disseminate enhanced products to its subscribers in 2018 for a period of one year for the purpose of testing and evaluation was noted. So far, KOERI still remains the only NEAMTWS TSP disseminating such products to its subscribers. Examples of these products are given in Figures 6 and 7.
# GPS Multipath Analysis Using Fresnel Zones
## 2 Multipath Theory
### Multipath Propagation
Expressing \(\gamma\) as a function of the azimuth and elevation angles of the satellite and the reflection point leads to  
\[\Delta\Phi_{M}\left(t\right)=\frac{2\pi}{\lambda}\frac{d_{h}}{\cos el^{R}} \left[1-\cos el^{S}\left(t\right)\cos el^{R}\cos\left(az^{S}\left(t\right)-az ^{R}\right)-\sin el^{S}\left(t\right)\sin el^{R}\right]. \tag{7}\]  
In the special case of a horizontal reflector (see Figure 3), the satellite-reflection-antenna configuration simplifies. Here, the satellite and the points \(P_{R}\) and \(P_{A}\) form a vertical plane, leading to \(az^{R}=az^{S}\). Furthermore, the elevation angle of the reflection point \(el^{R}\) can be replaced by \(-el^{S}\). Since the horizontal antenna-reflector distance \(d_{h}\) changes due to the satellite motion, it can be replaced by a function of the satellite elevation \(el^{S}\) and the vertical antenna-reflector distance \(h\), as follows:  
\[d_{h}=\frac{h}{\tan el^{S}\left(t\right)}. \tag{8}\]  
Figure 2: Geometrical satellite-reflector-antenna configuration for a single reflector with arbitrary orientation in space. After integrating these simplifications into Equation (7), the geometrical path delay \(\delta\) and the multipath relative phase can be expressed as  
\[\delta\left(t\right) =2h\sin el^{S}\left(t\right) \tag{9}\] \[A_{C}^{2}=(A_{D}+\alpha A_{D}\cos\Delta\Phi_{M})^{2}+(\alpha A_{D}\sin\Delta \Phi_{M})^{2} \tag{4}\]0
## 2 Method
### Preprocessing Module for Dehazing Design
#### 2.2.3 Quadtree Search Algorithm for Atmospheric Light Value  
The correctness of the atmospheric light value directly impacts the performance of the dehazing module. Generally, the method for obtaining the atmospheric light value is as follows: first, select the top 0.1% darkest pixels from the dark channel image of the hazy picture, and then find the maximum value among the corresponding pixels in the R, G, and B channels of the original hazy image as the atmospheric light value [36]. However, there are often white objects or bright areas in the image whose dark channel values are generally higher, resulting in inaccurate estimates of atmospheric light values. Therefore, this algorithm adopts the quadtree search method [37] to derive the atmospheric light value. The flowchart illustrating the quadtree method is depicted in Figure 5, as follows:As shown in the diagram, the first step is to divide the input image \(I(x)\) into four regions, following the procedure outlined in the accompanying Figure 6:  
Subsequently, the mean square deviation of each of the four regions is calculated, and the region with the smallest mean square deviation is selected. A threshold is set by the user. If the threshold is not met, the region with the previously selected smallest mean square deviation is considered as input, and the aforementioned steps are iterated until the threshold is reached. The quadtree operation is illustrated in the following, Figure 7:  
Figure 5: Quadtree search algorithm for atmospheric light value flowchart.  
Figure 6: Quadtree illustration.  
After obtaining the atmospheric light value using the quadtree method, the accuracy of the atmospheric light value is improved, effectively mitigating issues such as black spots and overexposure distortion caused by inaccurate estimation. This enhances the suitability of the dehazed image for semantic segmentation processing, thereby improving the accuracy and effectiveness of semantic segmentation.
# A Hybrid Degradation Modeling and Prognostic Method for the Multi-Modal System
###### Abstract  
Engineering systems typically go through complicated degradation processes, partially due to their multiple operating modes. Therefore, how to accurately estimate their remaining useful life is a critical issue. To address this challenge, a hybrid degradation modeling and prognostic method for the multi-modal system is proposed. Firstly, the cumulative dynamic differential health indicator is constructed for the multi-modal switching system using a multi-objective optimization approach. The long-term cumulative degradation assessment model is constructed based on the gated recurrent unit. Then, considering that the damage in the latest stage has a significant impact on the remaining useful life, the time window is used to extract local features of the sequence, including energy features and statistical features. The latest stage degradation is predicted based on the light gradient boosting machine. Finally, model averaging is used to integrate the two predicted results, which is expected to improve the prognostic robustness. The proposed model is evaluated with synthetic analysis and NASA turbofan aero-engine datasets. Extensive experimental results demonstrate the proposed method provides a better characterization of the degradation status of the system and provides a higher estimation accuracy than existing methods.  
remaining useful life; multi-modal system; prognostic; health indicator; local feature +
Footnote †: journal: Applied Sciences
## 7 Experimental Results
### Live Human Activity Recognition on Smart Glasses  
The ResNetv1-6 model with 48 filters per convolution, 16-bit fixed-point quantization and CMSIS-NN optimizations, has been trained using the UCA-EHAR dataset. This network has been then integrated onto the smart glasses firmware to perform live human activity recognition. Data are collected from the accelerometer and the gyroscope of the smart glasses when worn by a subject. The smart glasses' microcontroller performs the classification and sends the label of the recognized activity to a computer for visualization through a Bluetooth Low Energy communication. Additionally, the accelerometer and gyroscope data are also sent for visualization, even though the classification is not performed on the computer. A 30-second sample of such a live recognition has been extracted and can be seen in Figure 10. In this extract, the following sequence of activities has been performed by the subject: walking downstairs, walking upstairs, walking, stopping in a standing position and finally drinking a slip of water.  
Figure 10: Live human activity recognition on smartglasses.  
No quantitative evaluation of the live recognition performance has been done so far. However, it can be said that qualitatively the performance follows the results presented in the confusion matrix. Activities such as WALKING, WALKING_DOWNSTAIRS, WALKING_UPSTAIRS and DRINKING are generally recognized properly, while the STANDING and SITTING activities cannot be distinguished properly.
## 4 Experiments and Results
### Sentiment Analysis Model: Training Setup  
We describe in this subsection the training setup of the aspect extraction and the aspect polarity detection models. We used LIBLINEAR ([[https://www.csie.ntu.edu.tw/~cjlin/liblinear/](https://www.csie.ntu.edu.tw/~cjlin/liblinear/)]([https://www.csie.ntu.edu.tw/~cjlin/liblinear/](https://www.csie.ntu.edu.tw/~cjlin/liblinear/))) to build all the linear classifiers. First, we define all the features used in our system, and then we show the configuration of features for each model. After that, we present the evaluation metrics and the performances of the models, and finally we discuss the results on the Tarragona case study.
## 3 Methods
### Accuracy Evaluation  
Two criteria were used here to evaluate model performance, including the determination coefficient (R\({}^{2}\)) and root mean square error (RMSE). In addition to the validation with observed GPP from FLUXNET sites, the performances of the PAR-LUE model in GPP estimation were compared with that of the MOD17 algorithm, EC-LUE and D-VPM from multiple dimensions, such as overall accuracy (8-day and annual), accuracy of each vegetation type and the seasonal dynamics in typical sites.
## 2 Materials and Methods
### Desorption Studies  
It is crucial to consider the regeneration of the adsorbent material, as it is significant in economic development. The current study was intended to accomplish phenol (adsorbate) elution from an aqueous solution through the use of 0.1 M NaOH, taking into account identical settings of a flow rate of 8 mL-min\({}^{-1}\), adsorbent bed height of 8.5 cm used for preloading of adsorbent, and a concentration of phenol feeding of 1000 mg:L\({}^{-1}\). After the elution, distilled water was conveyed via the column for bed washing purposes until the wash effluent's pH settled at nearly 7. The column was loaded once again with a phenol concentration of 1000 mg:L\({}^{-1}\) within conformable regeneration conditions. Studies on regeneration of the column, as well as reuse, were applied for three sequences of adsorption-desorption. The efficiency of regeneration was determined by Equation (4), as follows [39]:  
\[Regeneration\ efficiency\ (\%)=\frac{(q_{e,exp})_{r}}{(q_{e,exp})_{o}}\times 100 \tag{4}\]  
where \((q_{e,exp})_{r}\) is the adsorptive capacity of the regenerated column (mg:g\({}^{-1}\)) and \((q_{e,exp})_{o}\) is the original capacity of the virgin adsorbent (mg:g\({}^{-1}\)).
## 2 Method
### Numerical Simulations
#### 2.2.5 Meshing  
A polyhedral mesh is commonly adopted to reduce the calculation time and the numerical diffusion of the mesh (eliminating discretization errors providing a coarse mesh) [37]. In this study, a polyhedral unstructured mesh was used to make a preliminary calculation, where the base mesh size was set to 0.10 m [43]. Base mesh size refers to the reference mesh of the whole computational domain. Besides the custom boundaries and areas, all the surface and volume in the computational domain were divided into the base mesh size. The custom meshes were set with reference to the ratio of the base mesh size [52].  
According to the simulation results, the mesh was refined in the areas where the wind speed changed greatly. The mesh close to the building had relatively high density, and the mesh far from the building had relatively low density. Mesh size could be controlled by \"Volumetric control\" in Star CCM+. Based on the distribution of test points and changes in wind speed in the wind tunnel experiment, three blocks were added around the building, as shown in Figure 4. The sizes of three blocks and the size ratio of the mesh within blocks to the base mesh are shown in Table 1.  
\begin{table}
\begin{tabular}{c c c c c} \hline  & **Length (m)** & **Width (m)** & **Height (m)** & **Block Mesh Size/Base Mesh Size** \\ \hline Block 1 & 0.16 & 0.16 & 0.2 & 10\% \\ \hline Block 2 & 0.64 & 0.24 & 0.26 & 15\% \\ \hline Block 3 & 0.72 & 0.4 & 0.32 & 20\% \\ \hline \end{tabular}
\end{table}
Table 1: Block size and size ratio.  
To achieve good computational efficiency, a mesh sensitivity analysis was conducted as described below.
## 3 Results and Discussion
### Influence of Atomizing Gas Type  
#### 3.3.1 Extinguishing Time  
In Figure 10, the extinguishing times of nitrogen and air are shown for HRR conditions of 1.17 and 5.23 kW. For the low HRR case in Figure 10a, at the only gas discharge conditions, the extinguishing times shortened with increasing the gas flow rates. Additionally, the extinguishing times of nitrogen were shorter than those of air. Under the water mist discharge conditions, the extinguishing times were shorter and their differences between the nitrogen and air became smaller than those under the only gas discharge conditions. As the water and gas flow rates became smaller, the effects of the atomizing gas type on the extinguishing time appeared to be larger.  
For the high HRR case in Figure 10b, increases in gas and water flow rates led to a shortening of the extinguishing times. Under the low gas flow rate condition (e.g., 10 L/min), the nitrogen appeared to slightly decrease the extinguishing times when compared with those of air. However, with increasing gas flow rate conditions, the differences between the extinguishing times of nitrogen and air decreased.  
Based on Figure 10a,b, the high HRR case exhibited shorter extinguishing times than the low HRR case, particularly under low water (including no water) and gas flow rate conditions. This is due to the fact that the high HRR case consumes oxygen more quickly in the enclosed chamber than the low HRR case, which contributes to a reduction in the extinguishing times.  
Figure 10: Extinguishing time for nitrogen and air (note that the scales of \(y\)-axis in both figures are different): (**a**) HRR = 1.17 kW and (**b**) HRR = 5.23 kW.  
Figure 9: Comparison of differences in measurement parameters between large and small atomizers: (**a**) extinguishing time, (**b**) peak surface temperature, and (**c**) minimum oxygen concentration.  
extinguishing time. Conversely, under higher water and gas flow rate conditions, the differences in extinguishing times between both HRR conditions became smaller. This implies that the higher flow rates of water and gas became dominant parameters for extinguishing both fires.
# MBHAN: Motif-Based Heterogeneous Graph Attention Network
## 2 Related Work  
### Graph Neural Networks  
Graph representation learning, also known as graph embedding, aims to represent nodes in a network as low-dimensional dense vectors with real values, and the results can then be stored in a vector space. The resulting vector representations can be easily and conveniently used as the input for machine learning models, which can then be applied to common social network applications, such as visualization, node classification, link prediction, and community discovery [32]. The impressive results of neural networks in solving Euclidean space data (e.g., for images [33] and text [34], etc.) have resulted in proposals to extend deep neural networks to the processing of data with graph (network)structures [4; 5]. However, applying graph neural network techniques to heterogeneous graphs and distinguishing the heterogeneity of nodes and links in the graph structure has consistently proved challenging.  
The first attempt to model graph structure data with multiple correlations using GNNs involved Relational Graph Convolutional Networks (R-GCNs) [6], which maintain unique linear mapping weights for each different type of link. They also decompose relationship-specific parameters into linear combinations of several elementary matrices, so as to be able to deal with networks with a large number of relationships. To handle the structure and node properties of heterogeneous graphs, [PERSON] et al. [35] began by introducing a random walk with a restart strategy that can extract a fixed number of strongly correlated heterogeneous neighbors for each node and group them according to node types. They then used a type-specific Recurrent Neural Network (RNN) to encode the vertex features of each type of neighbor. The encoded representations of different types of neighbors were then aggregated using another RNN. To address the problem of heterogeneous graph GNNs still needing to artificially set meta-paths, with a consequent loss of accuracy in downstream tasks, [PERSON] et al. [21] proposed a graph transformation network (GTN) that is capable of generating new network structures. It can identify useful interactions between unconnected nodes in the original graph, while learning valid node representations in the transformed graph in an end-to-end fashion. A similar approach that does not involve setting meta-paths is the Heterogeneous Graph Transformer (HGT) [36], which has parameters relating to different types of nodes and links, so as to be able to characterize the heterogeneous attention for each link. This enables HGT to generate specialized representations for different types of nodes and links.  
Most existing graph neural networks based on heterogeneous graphs tend to use meta-paths to transform the heterogeneous graphs into homogeneous graphs for representation learning. This inevitably results in a lack of non-vertex information in the meta-paths. At present, most methods cannot perform global node representation learning.
# MAC/PHY Comprehensive Visible Light Communication Networks Simulation
## 5 Methodology
### Data Analysis  
The metrics obtained from the simulation were collected as follows.  
* **Throughput**. It is the aggregated traffic that arrives to the coordinator, and calculated as the amount of payload bits obtained from data frames divided by the simulation time;
* **Queued Packet Delivery Probability (QPDP)**. This is a measure of the network availability for those packets that have not overflowed the node's frame buffer. It is calculated as the total number of correctly delivered packets divided by the amount of queued packets;
* **End-to-end Packet Delivery Probability (EPDP)**. This measure is analogous to the latter, but it also takes into account the discarded packets due to excess of traffic on each user node. Therefore, EPDP should be strictly lower than QPDP since it is calculated as the total correctly received frames divided by the amount of generated frames;
* **Node active time**. This metric indicates the amount of time that a node has been correctly associated to the network. It is calculated as the difference between the timestamps related to periods in which the user node is correctly associated to the network;
* **Delivery time**. It is the time that the communication system spends to successfully deliver a frame. This measure is calculated as the difference between the timestamps of the first delivery attempt of a message and its correct reception.  
These metrics would depend on the simulation parameters such as the deployment radius and the amount of traffic as expected. Furthermore, the impact of the ceiling material was assessed through a parametric hypothesis test. This hypothesis has not been previously analyzed (up to the authors' knowledge), and would serve to validate the proposed comprehensive simulation scheme. Concretely, [PERSON]'s T-Test was performed to compare the average values of both scenarios according to Equation (12). The null hypothesis to be rejected in these cases was that the material had no impact. Hence, a bilateral test was carried out:  
\[T=N\frac{\bar{X}_{w}-\bar{X}_{p}}{\sqrt{\sigma_{w}^{2}+\sigma_{p}^{2}}}. \tag{12}\]  
\(N\) is the number of simulations, \(\bar{X}_{w}\) and \(\bar{X}_{p}\) are the average of metric \(X\) (throughput, probabilities, etc.) for wooden and plaster ceilings correspondingly, and \(\sigma^{2}\) is the quasi-variance. Moreover, no equality of variances is assumed. Hence, the degrees of freedom (dof) of the null hypothesis model (Student-T distribution) were estimated using [PERSON]--Satterthwaite's approximation (Equation (13)):  
\[dof\approx(N-1)\frac{(\sigma_{w}^{2}+\sigma_{p}^{2})^{2}}{\sigma_{w}^{4}+ \sigma_{p}^{4}} \tag{13}\]  
Both equations have been simplified considering that both populations are comprised the same number of elements (\(N\)). The contrast statistic \(T\) was ultimately used to obtain an estimation of the \(p\)-value (or probability of failing after rejecting the null hypothesis).