esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Application Scenario: Step-Down Converter
### Simulation-Based Comparison of Two Cooperativity-Enforcing Similarity Transformations  
Considering the parameters listed in the previous subsection, it can be shown that there does not exist a common, point-valued transformation matrix \(\boldsymbol{\Theta}\) according to Section 2.3 that simultaneously leads to a Metzler matrix representation for the transformed system dynamics stated in Equation (15) and at the same time preserves the asymptotic stability of the original system (46).  
Hence, a transformation of the system model into an interval-valued diagonal structure according to (34) and (36) has been performed first. After a subdivision of the parameter box  
\[[\mathbf{p}]=\begin{bmatrix}[R_{\mathrm{C}}]&[R_{\mathrm{S}}]\end{bmatrix} \tag{48}\]  
in a regular grid with \(500\times 200\) subintervals, the following eigenvalue bounds were obtained by using IntLab's verifyeig routine:  
\[[\sigma_{1}]=[-115.15\;;\;-100.1]\quad\text{and}\quad[\sigma_{2}]=[-2500\;;\;- 124.2]\enspace. \tag{49}\]  
Due to the fact that these eigenvalues are purely real, the transformation matrix from (37) becomes time-invariant according to  
\[\boldsymbol{\Theta}\in\begin{bmatrix}[0.0568\;;\;0.9948]&[0.0002\;;\;0.0352] \\ [0.1020\;;\;0.9984]&[0.9993\;;\;1.0000]\end{bmatrix}\;. \tag{50}\]  
Here, the entries are displayed after outward rounding to four decimals. The simulation routine makes use of \(\boldsymbol{\Theta}\) to convert the results \([\mathbf{z}](t)\) back into the original state variables \(\mathbf{x}(t)\). The inverse  
\[\boldsymbol{\Theta}^{-1}\in\begin{bmatrix}[1.0052\;;\;45.9135]&[-1.6126\;;\; -0.0002]\\ [-45.8675\;;\;-0.1025]&[1.0000\;;\;2.6116]\end{bmatrix} \tag{51}\]  
of this matrix is required to express the influence of the control action according to (16). The interval enclosures for the temporal evolution of the state variables are depicted in Figure 3. These bounds are valid both, see Remark 2, for time-invariant uncertain parameters and for parameters that are changing their values arbitrarily within the complete uncertainty intervals.  
In contrast, the novel solution procedure, with \(N=10\) and \(N=50\) in Algorithm 1 yields significantly tighter state enclosures as shown in Figure 4. Here, the solution is represented by the interval hull over all individual state enclosures, restricting the validity of the computed bounds to variations of the parameters within each of the boxes displayed in Figure 5. Please note that increasing the parameter \(N\) in the multi-sectioning strategy does not necessarily lead to tighter interval bounds. Hence, its systematic optimization in combination with a sensitivity-based selection of the splitting direction [41; 42] will be especially helpful to make the procedure applicable to systems with a larger number of uncertain parameters in future work.
# Each-Phase Metering with Gas-Liquid Stratified Flow Based on the Multi-Frequency Coriolis Principle
## 6 Conclusions  
The entrainment of gas will increase the errors of the existing non-separated multiphase metering. This paper proposes a method for each-phase metering based on the multi-frequency Coriolis principle to solve these problems. The simulation results with a single-frequency Coriolis flowmeter show that the maximum relative deviations of total density and total mass flowrate are \(-37.3\%\) and \(-9.3\%\), respectively. The vibrational responses characteristics corresponding to the first-order and third-order modes of the measuring tube were selected and analyzed. Based on the qualitative relationship between the change of the physical parameters of the fluid and the corresponding vibration response of different modes of the measuring tube, a correction model suitable for stratified flow based on the multi-frequency Coriolis principle was established. The compensation results show that the total density and mass flow deviations of the gas-containing fluid are within \(\pm 4.1\%\) and \(\pm 3.2\%\), respectively, and the flow deviations of gas-phase and liquid-phase are reduced to \(\pm 9.1\%\) and \(\pm 7.2\%\), respectively. The accuracy of the stratified flow metering method based on the multi-frequency Coriolis principle can meet the requirements of wellhead measurement.  
However, this paper only uses the simulation results to verify the application effect of the wellhead measurement method based on the multi-frequency Coriolis principle, and further field experiments will be caught out to optimize further the modified model combined with the field test results.  
All authors analyzed the data and were involved in writing the manuscript. [PERSON] conceived the idea of the study, analyzed the data and revised the manuscript. [PERSON] designed the experiments, analyzed the data and wrote the initial draft of the manuscript. [PERSON] performed the experiments and wrote the initial draft of the manuscript. [PERSON] and [PERSON] analyzed the data and revised the manuscript. All authors have read and agree to the published version of the manuscript.  
This research was funded by the Science Foundation of Shandong Province (CN) [grant number ZR2016 EEM46].  
All authors declare no conflict of interest.  
Figure 11: Comparison of each-phase measurement deviation correction results of stratified flow between the uniform bubble flow correction model and the proposed correction model. (**a**) Gas-phase content. (**b**) Comparison of relative deviations of gas-phase mass flow. (**c**) Comparison of relative deviations of liquid-phase mass flow.
# Design and Investigation of Capsule Networks for Sentence Classification
## 3 Proposed Model  
The CapsNet architecture proposed in this paper is given in Figure 1. CapsNet introduced the concept of capsules which are a group of neurons whose activity vector represents different parameters of a specific entity [3]. The magnitude of a specific vector in a capsule represents the probability of detecting a feature and its orientation represents its instantiation parameters. In CapsNet, the output of a given capsule is sent to an appropriate parent in the layer above using an algorithm called dynamic routing by agreement [3]. In addition to having the capability to maintain all spatial information, capsules also show far more resistance to white box adversarial attacks than standard convolutional neural network. The basic CapsNet given in [3] contains a convolution layer, another convolution layer named primary capsules layer, and the final layer called digital capsules layer.  
We tested several architectures including CapsNet with only a single-layer convolution, two-layer convolution, and three-layer convolution, all with and without the parallel convolution operations,  
Figure 1: Proposed CapsNet architecture for sentence classification.  
one example among which is shown in Figure 2. The architecture which produced the best results (as much as 3-4% increase in accuracy compared to others) is the one given in Figure 1. Another attempt was made to use parallel primary capsule layers after every parallel convolution block. The results of this were compared to the network given in Figure 1 and there was no noticeable difference and hence we decided to use the network with the least complexity given in Figure 1.  
As it can be seen from Figure 1, the first few stages contain an array of convolution layers each with different kernel sizes to extract varying features from the input dataset. Prior to connecting the input to the proposed network, the input data have to pass through a number of preprocessing stages. These preprocessing stages are summarized below.
## 2 Materials and Methods
### HS-SPME Method Optimization  
A few parameters were determined based on published literature and experience. The 2 cm tri-phase polydimethylsiloxane/carboxen/divinylbenzene (PDMS/CAR/DVB)\(d_{f}\,50/30\) mm fiber (Supelco, Bellefonte, PA, USA) was selected for the experiment. The incubation time was set as 10 min. The thermal desorption temperature and time were set as 270 \({}^{\circ}\)C and 5 min to maximize the desorption efficiency and avoid carry-over issues. The agitation speed was set at 250 rpm, as per the previous literature [48].  
3.1 Optimization of Vial Size, Dilution Factor, Extraction Time, Extraction Temperature, and Salt Concentration  
The optimum vial size was selected by analyzing the undiluted homogenized samples at 43 \({}^{\circ}\)C for 30 min using the HS-SPME conditions reported in the HS-SPME optimization paper from our group [48]. Three headspace vial sizes were tested: 20 mL, 10 mL, and 2 mL vials. Due to their height limitation, the 1 cm fiber was employed in the 2 mL vials, while the 10 mL and 20 mL vials utilized a 2 cm fiber. Three replicates were analyzed for each vial size.  
The effect of the dilution factor was evaluated by comparing non-diluted samples with the samples diluted (1:2% \(v/v\)) using a PBS solution. The extraction was performed at 43 \({}^{\circ}\)C for 30 min, using a 10 mL headspace vial. Three replicates were analyzed for each dilution factor.  
A two-variable (k = 2, \(\alpha\) = 1.414, block = 2) inscribed rotatable central composite experimental design (CCD) was used to optimize the extraction time and temperature. The extraction time was tested between 20 and 50 min. The extraction temperature was tested within a range of 37 \({}^{\circ}\)C to 50 \({}^{\circ}\)C. The experiment order was produced by Minitab version 21.1.1 (Minitab, LLC), including nine different sampling conditions (Table 1). Six replicates of the center point run were performed and used to evaluate the precision of the method.  
The salt addition effect was assessed at three levels: 0%, 20%, and 40% (% \(w/v\)) NaCl. All the other extraction steps were performed at the optimum condition determined in the previous steps. Three replicates were analyzed for each salt concentration level.
###### Abstract  
Soil loss can be reduced by enhancing the slip resistance of soil. Therefore, the slip resistance of borrowed soil of rock slopes is key to ecological restoration. An effective way to enhance the soil slip resistance is to improve the permeability and shear strength of the soil by using polymeric materials. Most current projects have confirmed the amount and effect of additions via repeated tests because of the lack of a quantitative characterization model. This leads to high implementation costs and inhibition of the method's popularization and application. Therefore, based on different dosages and application processes of aqua-dispersing nano-binder (ADNB), the present study aims to examine the change rules of the particles, permeability, and shear strength after soil improvement through particle analysis, permeability, and direct shear experiments. It was found that ADNB could increase the average particle diameter, permeability coefficient, and shear strength of soil, and significantly enhanced the slip resistance of rock slope soil. On this basis, this paper establishes quantitative characterization models of the slip resistance of rock slopes, reveals the clay improvement mechanism, and proposes the optimal addition range. The study also verified the reliability of the models through an engineering application. It is proved that the appropriate amount of ADNB based on the model can enhance the slip resistance of borrowed soil as well as the ecological restoration effect of rock slopes. The model in question solves the problem of repeated tests during implementation due to the lack of a quantitative characterization model and provides theoretical guidance for the ecological restoration of rock slopes.  
Keywords: aqua-dispersing nano-binder; rock slope; borrowed soil; slip resistance; quantitative characterization +
Footnote †: journal: Applied Soft. **2019**, 9, 3618; doi:10.3390/appr9173618
## 2 Materials and Methods
### Coronary Artery Disease Dataset
#### 2.1.2 Image Data Preprocessing  
In the domain of medical image analysis, image preprocessing holds immense significance as it serves to enhance the quality of DICOM images before embarking on further analysis [25]. Among the essential transformations applied, the utilization of color maps stands out as a key technique to convert grayscale images into visually informative colored representations. This enables the visualization of distinct structures within the image, such as bones and soft tissue, by assigning different colors to various anatomical elements. In the realm of medical imaging, where accurate identification and differentiation of structures are paramount, color maps prove to be an invaluable aid [7].  
To execute the application of color maps, widely used libraries like Matplotlib offer a diverse selection of color maps to suit specific requirements. In the context of this study, each patient's data yield four polar maps, each corresponding to different conditions, capturing information with and without attenuation correction during rest and stress states. These four polar maps are then thoughtfully consolidated into a single Joint PhotographicExperts Group (JPEG) image format, preparing them for input into the model, enabling more in-depth analysis and interpretation. The transformation of the image via color maps serves an additional purpose, converting the visual representation into a numerical array format. This numerical representation unlocks the potential for conducting various numerical operations, such as filtering, segmentation, and registration, on the image data. It facilitates advanced analytical techniques and computational methodologies to extract meaningful insights from medical images [7].  
In light of DL models' prevalence in modern medical image analysis, proper normalization of the image data becomes paramount. This step ensures that the image data are brought within a consistent range, avoiding issues arising from variations in scales and intensity. Given that DL models are highly sensitive to the input data's scale, normalization becomes an indispensable preprocessing step [18]. Two commonly employed normalization techniques include dividing the image values by their maximum value or performing subtraction of the mean and division by the standard deviation [2].
## 3 Reliability Models of WSNs
### Tooling
#### 3.6.2 Evaluating the Reliability  
As shown in Figure 17, to evaluate the reliability of a particular WSN, an user initially needs to implement an application (in nesC) and configures the network parameters using the _editor_ (Step 1): the reliability of links, applications, operating system, radio, hardware and middleware; and regions and routing protocol used. Next, the _translator_ is used to create the power consumption models (Step 2) and the _evaluator_ calculates the power consumption (Step 3). These two steps were showed in details in Figure 2. When the evaluation finishes, the results are stored and the network topology is used to create the region model. This topology has information provided by the user (e.g., link reliability, regions and routing protocol) and _evaluator_ (e.g., battery level and if radio is on or off of all nodes), which collected these data after the evaluation of the power consumption. Next, the _translator_ generates the region model from the WSN topology (Step 4). This step is divided into three sequential tasks: the _translator_ converts links and nodes into basic blocks (using data defined by the user and _evaluator_), executes the routing algorithm (which was implemented inside of the _translator_) used by the WSN to create Path models, and combines them to create the Region models. In addition, for example, if a topology has 10 regions, the _translator_ creates 10 Region models. It is necessary to evaluate each region individually (as mentioned in Section 3.4). Finally, in Step 5, the Region Models are evaluated (by generating reliability results to each Region model created) and the results are presented (reliability of the regions and power consumption of the application and network) to the user.  
Finally, this environment is a Web application developed in Java, has been deployed at Amazon EC2 [29] and its source code is available in the GitHub [30].
###### Abstract  
This article describes the screening, ranking and characterization of ultramafic and mafic rocks in southern Portugal for mineral carbonation as an alternative to conventional CO2 storage in sedimentary rocks. A set of criteria including mineralogy, structure, surface area, distance to CO2 sources, expected volume, and socioeconomic conditions was applied to screen ultramafic and mafic rock massits in the Alentejo region, southern Portugal. Ranking of the massits indicated that the plutonic massits of Sines and of Torrao-Odivelas were the most promising. A characterization was made of the Sines massif, a subvolcanic massif composed mostly of gabbros and diorites, located immediately adjacent to the CO2 sources and outcropping along 300 km2 onshore and offshore. These studies confirmed that these rock samples exhibited the appropriate mineralogical and geochemical features, but also indicated that the secondary porosity provided by the fracture patterns was very small.  
CO2 capture; utilization and storage; mafic plutonic rocks; mineral carbonation; screening and ranking; Sines massif; Portugal +
Footnote †: journal: Applied Sciences
# Recent Progress in On-Chip Erbium-Based Light Sources
## 3 Gain Medium Based on Different Host Materials
### Er-Doped Al\({}_{2}\)O\({}_{3}\)
Meanwhile, the mode transfer between the two layers is achieved by a vertically tapered adiabatic coupler and the optical mode is efficiently coupled from the Si\({}_{3}\)N\({}_{4}\) waveguide to the Al\({}_{2}\)O\({}_{3}\):Er waveguide. Finally, up to 18.1 \(\pm\) 0.9 dB net gain was achieved at 1532 nm with an over 70 nm broadband gain covering S, C, and L bands.
## 5 Conclusions  
This paper focuses on enhancing the effectiveness of recommendation algorithms for heterogeneous graphs. We propose the multi-feature interaction meta-path aggregation heterogeneous graph neural network (MIMA) approach, which leverages feature interaction and meta-path aggregation to improve recommendation capabilities. In order to better learn the impact of attribute information on the MIMA, heterogeneous graphs consisting of user nodes, item nodes, and their attribute nodes are constructed. Firstly, a multi-head attention mechanism is employed to fuse the attribute features of users and items into the feature interaction layer to explore the latent preferences of users and items towards the embedding vector representation of multi-feature fusion. And then, a graph convolution operation is applied to aggregate the semantic features of the constructed meta-path information, which consists of attribute information, and in subsequent steps, we achieve high-order feature fusion to capture new recommendation rules between users and items by using attribute information. Moreover, the weights of the MIMA training model and the optimal embedding vectors of users and items are generated for recommendations after multiple iterations. Finally, the experimental results on three public datasets show that the  
Figure 10: Interpretability analysis of MIMA model on KuaiRec dataset.  
performance of the MIMA algorithm proposed in this article is significantly improved in terms of NDCG, precision, and recall evaluation indicators compared to the baselines.  
Conceptualization, Y.L. and S.Y.; methodology, Y.L. and F.Z.; software, S.Y. and F.Z.; validation, Y.L., S.Y. and F.Z.; formal analysis, Y.J.; investigation, Y.J., S.C. and L.W.; resources, Y.L.; data curation, F.Z.; Writing--original draft preparation, Y.L. and S.Y.; Writing--review and editing, Y.L., F.Z., L.W. and L.M.; visualization, S.Y.; supervision, L.M.; Project administration, L.M.; Funding acquisition, Y.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Beijing Natural Science Foundation (4234083), the R&D Program of the Beijing Municipal Education Commission (KM202410009003), and the National Key R&D Program of China (2023 YFC3107804).  
No datasets were generated during the current study.  
We thank the anonymous reviewers for their comments.
## 3 Results
### Effects of Phase Information  
Phase-domain information is utilized to enhance the proposed MDCCSP method. Thus it is supposed to validate the effects of phase information in engagement recognition. To extract phase information, the Hilbert transform [38] is employed following Equation (17). And therefore, the analytic signal is obtained in Equation (18). It should be noted that AP features are extracted from \(x(t)\) and \(\Theta(t)\) in the experiments. The optimal filter bank with 13 filters in Table 2 is adopted. And the S-CC is used in the proposed MDCCSP method. The regularization parameter in the MDCCSP is set to \(\alpha=0.001\). The effects of extracting only amplitude information, extracting only phase information, and extracting both amplitude and phase information are compared. The results are shown in Figure 4 and Table 4, from which it can be observed that for all the subjects, the MDCCSP extracting only phase information achieves an average accuracy of \(76.57\pm 6.94\%\), which is much higher than the chance level 50% (\(p<0.001\)). These results prove that only phase information still matters in engagement recognition. Moreover, the MDCCSP extracting both pieces of AP information achieves the best performance \(87.74\pm 5.98\%\) (\(p<0.001\)), which demonstrates that the phase information can enhance the effectiveness of the proposed engagement recognition method.
## 3 Methodology
### Execution Cycle  
The optimization algorithm and its execution considers the daily sun period. Thus, the horizon of each execution is end of the day. This can be observed in Figure 3, where the execution cycle during the day \(d\) is depicted.  
EMS period \(T\) execution: At period \(T\in\{1,\ldots,nT_{\text{EMS}}\}\), the \(P_{T,p}^{bat^{*}}\) and \(P_{T,p}^{PV_{max}^{*}}\)\(\forall\)\(p\in\{1,\ldots,nT_{\text{intra}}\}\) are sent to their respective converters. These values are calculated in previous EMS executions (see Figure 3). Then, the SOC at the beginning of the EMS period \(T+1\) is estimated using the current SOC and the battery setpoints for the current execution period.  
Using the estimated SOC at the EMS period \(T+1\) and the forecast for the rest of the day \(d\), the optimization problem is solved, and \(P_{t,p}^{bat^{*}}\) and \(P_{t,p}^{PV_{max}^{*}}\)\(\forall\)\(p\in\{1,\ldots,nT_{\text{intra}}\}\), \(\forall\)\(t\in\{T+1,\ldots,nT_{\text{EMS}}\}\), and \(D_{t}^{con^{*}}\)\(\forall\)\(t\)\(\in\{T+1,\ldots,nT_{\text{EMS}}\}\) are calculated.  
The solution must be reached before the beginning of the EMS period \(T+1\). Otherwise, the setpoints calculated for the EMS period \(T+1\) by the EMS execution at the period \(T-1\) are sent to the respective converters.