esa-sceva/satcom-chunk-collection huggingface dataset:
# Enhancing Collaborative Filtering-Based Recommender System Using Sentiment Analysis
## 3 Materials and Methods
### Sentiment Analysis Approach  
Our methodology involves training and evaluating the BI-LSTM model on two datasets sourced from Amazon.  
The BiLSTM model incorporates bidirectional LSTM (Long Short-Term Memory) units. LSTM is an advanced type of Recurrent Neural Network that addresses the issues of gradient explosion and vanishing gradient commonly encountered in traditional RNNs. LSTM is capable of handling sequences of varying lengths and effectively processing sequential data, which helps mitigate the problem of information loss that occurs in recurrent neurons [38].  
The Long Short-Term Memory (LSTM) architecture consists of several key components, including the input word (\(x_{t}\)) at the current time step, the cell state (\(c_{t}\)), the temporary cell state (\(c_{t}\)), the hidden layer state (\(h_{t}\)), the forgetting gate (\(f_{t}\)), the memory gate (\(i_{t}\)), and the output gate (\(o_{t}\)). The LSTM model operates through three main stages: the forgetting stage, the selective memory stage, and the output stage.  
In the forgetting stage, the LSTM selectively forgets information stored in the cells, retaining important information. This process is controlled by the forgetting gate (\(f_{t}\)). Following the forgetting stage is the selective memory stage, where the LSTM selectively \"remembers\" information from the input cells. Important information is emphasized and retained, while unimportant information is discarded. The memory gate (\(i_{t}\)) plays a crucial role in controlling this stage. Finally, the output stage determines which information will be selected as the final output. The output gate (\(o_{t}\)) regulates this stage. The LSTM model operates as a sequential process, with each stage influencing and being influenced by the previous stage. The overall framework of the LSTM model, showcasing these stages and their respective components, is illustrated in Figure 2.  
As shown in Figure 3, the Bi-LSTM architecture consists of two interconnected components: the forward LSTM and the backward LSTM. The forward LSTM processes the input sequence in its original order, while the backward LSTM operates by reversing the input sequence and computes its output in a manner similar to the forward LSTM. By combining the outputs of both LSTMs, the Bi-LSTM model incorporates information from both preceding and subsequent elements in the sequence. Unlike the traditional LSTM, which only considers past context, the Bi-LSTM takes into account both preceding and following information due to the bidirectional processing. This allows the model to capture dependencies and patterns from both directions, enhancing its understanding of the overall sequence. By stacking the outputs of the forward and backward LSTMs, the final result of the BiLSTM model is obtained.
# Analysis of the Aroma Chemical Composition of Commonly Planted Kiwifruit Cultivars in China
## 3 Results and Discussion
### HS-SPME-GC-MS Analysis
#### 3.1.3 Qualitative and Quantitative Analysis of Volatile Components in Kiwifruit Samples
The lowest aromatic hydrocarbon and homolog contents were present in jinfu (8.97 \(\mu\)g/L, 0.29% of TVC). In addition to the above eight categories of volatiles, there were nine other volatiles, but their contents were low. Thus, they were not considered primary components influencing the aroma of kiwifruit. According to the research of [PERSON] et al. (2014) on _A. chinensis_, 72 volatiles were detected, approximately 70% of which were consistent with the results measured in our paper. Meanwhile, the study showed that the volatile profile of all six kiwifruit cultivars was mainly composed of C6 aldehydes and alcohols and the ester ethyl butanoate. This result is consistent with this study. The results in our paper indicate that esters, aldehydes, and alcohols are the main volatiles in kiwifruit. The esters provide fruit sweetness to kiwifruit, while the aldehydes and alcohols provide fresh grassy fragrance. Ethyl butanoate, (E)-2-hexen-1-ol, and (E)-2-hexenal are the core aroma components in kiwifruit. This conclusion is similar to previous research showing that ethyl butanoate, (E)-2-hexen-1-ol, and (E)-2-hexenal have a certain impact on aroma in many kiwifruit cultivars, such as Hayward, Hort16A ([PERSON] et al., 2013), and Hongyang (2019). In summary, it could be concluded that the volatile components of kiwifruit cultivars are mainly composed of C6 aldehydes, alcohols, and ethyl butanoate. Based on the series comparative analysis of the aroma chemical composition among different cultivars, ethyl butanoate, (E)-2-hexen-1-ol, and (E)-2-hexenal could be considered the core aroma components in kiwifruit, but still need further confirmation using Sensomics in the future.
# An Overview of Biopolymers for Drug Delivery Applications
## 2 Types of Biopolymers Used in Drug Delivery Systems
### Nucleic Acids  
Nucleic acids are supramolecular biopolymers that exist universally in living organisms. The best known are DNA and RNA that play fundamental roles in biological processes. DNA stores, encodes, and transfers genetic information to other components of cells, while RNA serves as an intermediate messenger for gene expression [110,111]. In addition to this, they participate in the catalysis of certain biochemical reactions and in the regulation of certain activities in the cells [112].  
Nucleic acids have utility in various biomedical applications. Unmodified nucleic acids cannot be used in drug delivery. However, multivalent nucleic acid nanostructures and nucleic acid aptamers have led to a rapid increase in the number of drug delivery systems with a potential for controlled release [113].
## 6 Conclusions  
Accurate rotor temperature prediction in PMSMs is critical for improving operational reliability and prolonging service life. This study proposes RIME-XGBoost, which synergizes metaheuristic optimization with ensemble learning to address the challenges of modeling rotor temperature under varying data conditions. By integrating dynamic parameters (e.g., motor speed, torque, d-q-axis current voltage) through a multi-source feature fusion strategy, the model achieves robust performance across medium-scale datasets with pronounced thermal fluctuations (Group A) and small-sample high-temperature regimes (Group B).  
The hybrid framework of RIME-XGBoost uniquely balances precision and computational efficiency. The key findings include the following:  
(1) RIME-XGBoost surpasses comparable hybrid models by delivering precise predictions on medium-sized datasets. Although its performance diminishes slightly on smaller datasets, it remains effective, whereas other models falter.  
(2) RIME-XGBoost employs a hybrid strategy to optimize critical parameters, balancing precision, stability, and computational efficiency. It also identifies intricate relationships between input data and rotor temperature.  
(3) SMA-RF, while swift due to its parallel architecture, struggles with limited data. SO-BiGRU suffers from slow training and sensitivity to hyperparameters. EO-SVR, though stable, is less accurate than RIME-XGBoost due to hyperparameter dependencies and optimization limitations.  
(4) In industrial applications, the developed model reduces dependence on physical sensors and maintenance expenses. It minimizes manual tuning requirements and establishes a foundation for proactive thermal management and extending motor lifespan.  
Future work will prioritize three directions: (1) Testing under diverse environmental conditions (e.g., extreme temperatures, variable loads) and motor architectures. (2) Developing adaptive feature selection and hyperparameter tuning pipelines to minimize manual intervention. (3) Deploying lightweight variants on embedded systems for real-time thermal monitoring.  
J.S.: methodology, formal analysis, validation, data curation, writing--original draft. Z.C.: methodology, conceptualization, software, writing--original draft. F.L.: supervision, resources, formal analysis, writing--review and editing. All authors have read and agreed to the published version of the manuscript.  
This research received no funding.  
Not applicable.  
All data used in this study are publicly available. The \"Electric Motor Temperature\" can be accessed through Kaggle at: [[https://www.kaggle.com/datasets/wikrgsn/electric-motor-temperature](https://www.kaggle.com/datasets/wikrgsn/electric-motor-temperature)]([https://www.kaggle.com/datasets/wikrgsn/electric-motor-temperature](https://www.kaggle.com/datasets/wikrgsn/electric-motor-temperature)) (accessed on 20 September 2024).  
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
###### Abstract  
This paper proposes a convex quadratic optimization commutation method to raise the equalization degree of power loss distribution of coil array for magnetic levitation planar motors. Starting with the modeling of electromagnetic forces/torques and commutation of coil array, the global power loss and the local power losses of coil array are analyzed, and the power loss equalizing degree is defined to evaluate the power loss distribution of coil array being commutated dynamically. Then, in consideration of the fact that the global power loss is the quadratic function of commutated coil currents, the convex quadratic function optimization with equality constraint and boundary constraints is applied to commutate the coil array, and the power loss equalizing degree is raised by decreasing the boundary constraints of optimization. Taking the magnetically levitated planar motor under investigation as examples and using quadprog routine in Matlab Optimization Toolbox, which is a dedicated quadratic optimization routine, it is verified that the power loss equalizing the degree of coil array is raised gradually and the power loss distribution of coil array becomes more uniform along with decrease of the boundary constraints. The convex quadratic optimization commutation is verified experimentally on a constructed multi-dimension force/torque measurement platform. Using the convex quadratic optimization commutation can not only improve the power loss distribution of coil array of magnetically levitated planar motors, but also make it possible to select lower capacity power amplifiers to produce the identical desired electromagnetic forces and torques.  
Keywords:commutation; power loss equalizing degree; convex quadratic optimization; boundary constraint; magnetic levitation; planar motor +
Footnote †: journal: Applied
## 2 Materials and Methods
### Cyclotron Targetry and Irradiation  
Irradiations were performed using TR19 and TR24 variable energy (H\({}^{-}\): 13-19 and 16.2-24 MeV, respectively) cyclotrons (ACSI, Richmond, BC, Canada) while using a straight 90\({}^{\circ}\) target station (ACSI). \({}^{89}\)Zr was produced via the \({}^{89}\)Y(p,n)\({}^{89}\)Zr transmutation reaction while using a solid \({}^{89}\)Y-foil or pressed target mounted on custom-made aluminum and niobium coin target supports (Figure 1). Irradiation times (T\({}_{\rm irr}\)) were 1-5 h with a beam current of 10-40 \(\mu\)A for both cyclotrons. The beam size in all irradiation experiments was \(\sim\)10 mm in diameter. The incident proton-beam energies were selected based on the degrader materials used and varied between 16.2-18 MeV. The proton-beam energies deposited on the target materials were 11.3-15 MeV for all the productions and they were calculated by Monte Carlo simulation while using SRIM [19]. The ACSI small solid target irradiation station provided efficient target cooling through a helium gas jet directed on the front side of the target while the backside was cooled by chilled water (16.5-18.5 \({}^{\circ}\)C).  
Figure 1: Photographs of the CIMS’ (Sherbrooke Molecular Imaging Centre) custom-made degraders and holder for the \({}^{89}\)Y(p,n)\({}^{89}\)Zr reaction. (**A**) Al degrader with a thickness of 1.0 mm and a hole diameter of 21 mm (Al-21-1). (**B**) Nb degrader with a thickness of 0.35 mm and a hole diameter of 13 mm (Nb-13-0.35). (**C**) Nb degrader with a thickness of 0.35 mm and a hole diameter of 6 mm (Nb-6-0.35). (**D**) Al open window target holder with an inner diameter of 13 mm (Al-13).
# Design and Experimental Study of Potato-Soil Separation Device for Sticky Soils Condition
## 4 Separation Device Performance Test
### Assessment Index  
The machine performance standards (good or bad) were defined by complying with the separation effect during the harvest process. Moreover, the more minor the potato tuber damage, the better it would be. Three indicators (i.e., percentage of small soil \(\lambda\), the percentage of soil adhering to potato tuber \(\gamma\), and damage rate of potato tuber \(K\)) were developed to assess the separate effect of potato-soil separation device while in indoor testing. Two of them (\(\lambda\) and \(\gamma\)) were used to assess the effect of potato-soil separation, and the remaining one (\(K\)) was adopted to assess the degree of potato damage. The specific role and calculation method are defined below.  
Percentage of small soil \(\lambda\) was primarily used to assess the crushing degree of potato-soil mixture. The soil whose diameter was smaller than the spacing between chain rods fell from between the rods of the lifting chain to separate potato-soil mixture when the potato-soil separation device operated. The soil whose diameter was smaller than the spacing between chain rods (\(L_{6}=35\) mm) in each direction was defined as small soil after separation. The percentage of small soil \(\lambda\) was expressed as:  
\[\lambda=\frac{m_{5}}{M_{S}}\times 100\% \tag{16}\]  
where \(\lambda\) denotes the percentage of small soil, %; \(m_{5}\) represents the mass of small soil after separation, kg; \(M_{S}\) expresses the total soil mass in the potato-soil mixture before separation, kg.  
The potato tuber remained to bond with portions of the soil after potato-soil separation, so the percentage of soil adhering to potato tuber (\(\gamma\)) was employed to assess the cleaning degree of potato tuber, as expressed below:  
\[\gamma=\frac{m_{6}}{M_{S}}\times 100\% \tag{17}\]  
Figure 8: Samples of potato-soil mixture.  
Figure 9: Test prototype of potato-soil separation device.  
where \(\gamma\) denotes the percentage of small soil, \(\%\); \(m_{6}\) represents the percentage of soil adhering to potato tuber, kg.  
The damage rate of potato tuber (\(K\)) was largely adopted to assess the degree of potato damage. The formulations are presented in Equation (22).  
\[K=\frac{m_{7}}{M_{P}}\times 100\% \tag{18}\]  
where \(K\) is the damage rate of potato tuber, \(\%\); \(m_{7}\) is the mass of potato tuber with damage after separation, kg. \(M_{P}\) is the total potato mass in the potato-soil mixture before separation, kg.
## 3 Results and Discussion
### Optimization  
In this section, optimization algorithms connect to predictive models, and search for optimal conditions for FOS. Since each optimization model requires a function in order to obtain the optimal conditions, the regression model of the previous stage that was developed is used here. Due to this fact, four models were developed in the previous step for FOS simulation. Among the developed models, two models had better conditions for evaluating this issue. The RBF model can act as a better option than the polynomial model  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Density** & **C (kpa)** & **\(\boldsymbol{\phi}\) (degree)** & **\(\boldsymbol{\beta}\) (degree)** & **H (m)** & **ru** & **FOS** \\ \hline
25.578 & 14.62 & 42.16 & 46 & 495 & 0 & 1.16 \\ \hline
22.834 & 8.35 & 40.21 & 44 & 420 & 0 & 1.18 \\ \hline
22.148 & 3.2 & 36.88 & 40 & 40 & 0 & 2.59 \\ \hline
23.814 & 6.96 & 37.44 & 40 & 80 & 0 & 2.19 \\ \hline
25.088 & 8.26 & 37.94 & 42 & 100 & 0 & 1.86 \\ \hline
25.872 & 22.67 & 41.21 & 50 & 307 & 0 & 1.19 \\ \hline
23.422 & 2.48 & 35.11 & 40 & 80 & 0 because it was more accurate and could offer greater flexibility for evaluation. Therefore, in this part, the RBF model is used as the base model.  
The RBF model is connected to two optimization algorithms, WOA and HHO, so that different parameters can be optimized. The two WOA and HHO models were implemented in MATLAB software. To examine their performance in solving various problems, four sample problems were used to find optimal locations. Figure 14 shows the comparison results between these models. As can be seen, the models have the ability to find the optimal locations. The goal is to find the optimal and sensitive conditions for FOS. Since each optimization algorithm has its own conditions, its parameters must also be determined for the optimal state. Therefore, numerical analysis was performed on the two optimization algorithms WOA and HHO to find their optimal coefficients. After determining the optimal coefficients, the algorithms use the basic RBF model to obtain the optimal conditions. Table 7 presents the optimal parameters determined based on the two WOA and HHO algorithms. As can be seen, the optimization algorithms can each offer different optimal conditions that can be used depending on the different conditions. The solution of the algorithms shows that each goes to the optimal state and ultimately leads to the desired results. In general, it can be inferred from the results that using optimization methodology, more efficient designs can be done for engineering and sensitive projects.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Optimal Values** & **Parameters** \\ \hline
**HHO** & **WOA** & \\ \hline
28.0186 & 30.548 & \(\gamma\) \\
159.1915 & 199.118 & \(c\) \\
27.209 & 39.8714 & \(\phi\) \\
44.5905 & 29.8562 & \(\beta\) \\
0.3128 & 0.1915 & \(nu\) \\
563.3418 & 397.9759 & \(H\) \\
2.4411 & 2.4301 & \(FOS\) \\ \hline \hline \end{tabular}
\end{table}
Table 7: Optimal parameters of FOS based on WOA and HHO.  
Figure 14: Cont.
## 2 Modeling Methods  
### The Hybrid Modeling Approach  
The present modeling strategy used a hybrid numerical-analytical approach. This was developed to simulate entire geared transmissions with a reduced computational effort. The mechanical components of the system, such as gears, bearings (including the rolling elements), shafts, housing, etc., could be modeled without the need of geometrical simplifications, ensuring a much higher accuracy of the results. The approach exploited a traditional finite element (FE) solver to model the macroscopic deformations of the components and the Hertzian theory to predict the pressures in the contacts (i.e., gear teeth, rolling elements, races in bearings, etc.) for which a traditional FE method requires an immense mesh refinement.  
The points at which the individual contacting surfaces were closest to each other before the application of the load were computed first [13]. After that, the surface's normals were identified, and the size of the contact zone was estimated. A computational grid was laid out around each principal contact point (Figure 1) and projected on both body surfaces. The computed contact pressures were not very sensitive to the size of the grid.  
The displacement \(\mathbf{u}(\mathbf{r}_{ij};\mathbf{r})\) of a field point \(\mathbf{r}\) due to a load at the surface grid point \(\mathbf{r}_{ij}\) can be expressed as:  
\[u\big{(}\mathbf{r}_{ij};\mathbf{r})\ =\ \big{[}u\big{(}\mathbf{r}_{ij};\mathbf{r} \big{)}\ -\ u\big{(}\mathbf{r}_{ij};\mathbf{q}\big{)}\big{]}\ +\ u\big{(}\mathbf{r}_{ij};\mathbf{q}\big{)} \tag{1}\]  
Figure 1: The contacting surface and the computational grid.  
where \(\mathbf{q}\) is a point inside the solid body, sufficiently far from the surface (Figure 2). The term [u(\(\mathbf{r}_{\mathrm{ij}}\); \(\mathbf{r}\)) and u(\(\mathbf{r}_{\mathrm{ij}}\); q)] was evaluated using the surface integral approach, and u(\(\mathbf{r}_{\mathrm{ij}}\); \(\mathbf{q}\)), was obtained from FE.  
The term between square brackets represents the deflection of \(\mathbf{r}\) with respect to the point \(\mathbf{q}\). This relative component can be better estimated using the Boussinesq half space solution ([PERSON] et al. 2011) rather than using the FE results. The deformation of the body will, in fact, not significantly affect this term. On the other hand, if \(\mathbf{q}\) is far enough from the surface, the term u(\(\mathbf{r}_{\mathrm{ij}}\); \(\mathbf{q}\)) is not significantly affected by local stresses at the surface. u(\(\mathbf{r}_{\mathrm{ij}}\); \(\mathbf{q}\)) is better estimated from the FE model of the solid body. The location of \(\mathbf{q}\) is called the \"matching\" point. In order to match the surface integral and FE solution, a set of points \(\Gamma\) can be used instead of a single point q (Figure 2) ([PERSON] et al. 2011). Additional details are given in ([PERSON] et al. 2000).
## 4 Examples of Critical Constructability Features in Facade Panels of the Orchard Commons Project
### Feature 6: Composition of Facade Panels  
**Constructability Constraints and Experts' Knowledge:** In repetitive patterns, the composition of the components conveys the context of the design, thereby translating the design's impression. From a constructability perspective, the position of panels dictates the installation order. As explained earlier, the vertical order of panels in the OC project is determined by the constructability constraints of \"minimum panel types\" and \"inclined straight edges\". However, in this project, like in many projects, panel installation proceeded floor by floor (row by row) rather than bay by bay (Figure 8). Based on experts' input, minimizing panel variation for each floor enhances the flow of the installation process, as productivity improves when similar components are installed. Therefore, the horizontal order of the panels and, consequently, the order of the panel bays were significant in minimizing horizontal panel variation, and so it was essential to optimize the overall panel positioning to achieve such minimization. As a result, the decision was made to use only 9 different panel types on each floor, even though a total of 34 panels were required to enclose both sides of the building on a single floor.  
**Constructability Feature and Computational Implementation:** The described constructability constraint above corresponds to the overall panel positioning. Developing suitable features requires considering all components and their composition within the overall design. Consequently, a corresponding constructability feature was integrated into the computational design of the OC project to minimize panel variation on each floor.  
These types of constructability constraints are primarily associated with the installation activities and serve to optimize the installation process. Composition features can be defined in computational design by specifying the global positioning of the components, denoted by their global location points (\(p_{(x,y,z)}\)).  
Figure 8: The composition of facade panel types constrained by having the minimum number of types per floor.