esa-sceva/satcom-chunk-collection huggingface dataset:
## Materials and Methods
### Transesterification Process  
Prior to the transesterification reaction, the acid value has to be measured. Therefore, in this study, the content of free fatty acid (FFA) of CSO was determined using titration. Using the [PERSON] et al. [53] method, the acid value of CSO was calculated. The results showed that acid value of CSO was less than 1%. Therefore, due to the low value of the acid number, the acid-catalyzed esterification step was not necessary and the transesterification reaction was performed directly. To carry out the transesterification reaction, methanol and potassium hydroxide were mixed by magnetic stirrer for 4 min to produce methoxide. The produced methoxide mixture was mixed with CSO in a conical flask. The mixture of potassium methoxide-CSO was then transferred to a reaction chamber to be exposed to ultrasonic waves. Pulse, amplitude and reaction time were adjusted by the controller. In order to carry out the transesterification reaction, the temperature, amplitude and pulse were considered as 50 \({}^{\circ}\)C, 70% and 70, respectively. The effect of different variables including reaction time, molar ratio (methanol to oil), and catalyst concentration on the rate of Camelina \"Soheil\" oil (CSO) conversion to methyl ester was studied. After the reaction, a two-phase product was generated with the upper phase being biodiesel, and the lower being glycerol. After separating the glycerol, biodiesel was purified (In order to purify the biodiesel, the biodiesel was washed by water three times). Using gas chromatography (Clarus 580 GC), the fatty acid composition of biodiesel was determined and the biodiesel yield (rate of CSO conversion to methyl ester) was calculated.
## 2 Mathematical Model
### Dynamic Model of TB-TSS
#### 2.3.1 Equilibrium Points  
In this subsection, we examine the equilibrium configurations of TB-TSS. At the equilibrium point, the state variables with respect to time are all equal to zero. Considering the effect of the gravity gradient, the subsatellites are defined as collinear. To identify the equilibrium point equation of the system, variables \(\theta_{1}^{\prime}\), \(\theta_{2}^{\prime}\), \(\theta_{1}^{\prime\prime}\), \(\theta_{2}^{\prime\prime}\), \(\alpha_{1}^{\prime}\), \(\alpha_{1}^{\prime\prime}\), \(\alpha_{2}^{\prime}\), and \(\alpha_{2}^{\prime\prime}\) are set to zero and \(\theta_{1}=\theta_{2}=\theta_{e}\), \(\alpha_{1}=\alpha_{2}=\alpha_{e}\) are set in Equations (15)-(18). After some calculation, we obtain:  
\[-3\omega^{2}cos\ \theta_{1}sin\ \theta_{1e}=0 \tag{21}\]  
\[-\omega^{2}cos\ \alpha_{1e}sin\ \alpha_{1e}-3\omega^{2}cos^{2}\theta_{1e}sin\ \alpha_{1e}cos\ \alpha_{1e}=0 \tag{22}\]  
\[-3\omega^{2}cos\ \theta_{2e}sin\ \theta_{2e}=0 \tag{23}\]  
\[-\omega^{2}cos\ \alpha_{2e}sin\ \alpha_{2e}-3\omega^{2}cos^{2}\theta_{2e}sin\ \alpha_{2e}cos\ \alpha_{2e}=0 \tag{24}\]  
where \(\theta_{ie}\) and \(\alpha_{ie}\) denote the equilibrium solutions for \(\theta_{i}\) and \(\alpha_{i}\), respectively. The equilibrium points are solved by Equations (21)-(24) as:  
\[\theta_{ie}=\frac{k\pi}{2},\ \ k=0,1,2\ldots \tag{25}\]  
\[\alpha_{ie}=\frac{n\pi}{2},\ \ n=0,1,2\ldots \tag{26}\]  
Three kinds of equilibrium configurations can be acquired, and they are shown in Figure 3. The equilibrium configurations are parallel to the x-axis, y-axis, and z-axis of the LVLH frame. To study the stability of the equilibrium points, we can substitute the equilibrium points into the linearized Equations (15)-(18) to calculate the eigenvalues and determine the stability [29]. According to linear system theory, eigenvalues are stable if they are negative and unstable if they are regular. Considering small oscillations around the equilibrium points, it is easy to obtain that the configuration shown in Figure 3a is stable and those in Figure 3b,c are unstable.
## 3 Experiments and Results
### Long Sequence Language Modeling
\begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline
**Model** & **1024** & **2048** & **3072** & **4096** & **5120** & **6144** & **7168** & **8192** & **9216** & **10,240** \\ \hline Original & **5.617** & **4.785** & 25.844 & 79.499 & 146.776 & 247.862 & 397.569 & 582.819 & 767.523 & 952.580 \\ PI & 6.731 & 5.682 & 5.155 & 5.007 & 5.081 & 4.944 & 4.719 & 4.647 & 5.761 & 10.304 \\ NTK & 6.070 & 5.148 & 4.691 & 4.554 & 4.629 & 4.687 & 9.119 & 18.220 & 30.109 & 44.844 \\ YaRN & 6.103 & 5.166 & 4.709 & 4.572 & 4.652 & 4.532 & **4.353** & **4.577** & **5.333** & 8.801 \\ SBA-RoPE & 6.047 & 5.148 & **4.493** & **4.455** & **4.538** & **4.418** & **4.424** & **4.506** & 5.381 & **8.037** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Perplexity comparison for the Pythia-2.8b model and its variants fine-tuned with a scaling factor of \(s=4\), extending the context window to 8 k and beyond. The evaluated perplexities range from 1 k to 10 k tokens, in increments of 1 k. Values are adjusted to highlight the lowest (in **bold**) and second-lowest (in underlined) perplexity at each token length. Conversely, our method maintains superior performance in the previously unseen 5 k to 10 k window range, consistently demonstrating the most favorable outcomes.
## 1 Introduction
### Contingencies
#### 1.2.2 Ranking  
Contingency classification in power systems is the process of assessing and prioritizing potential contingencies or disruptions that may occur in the power grid. The goal is to identify and solve the most important problems to ensure the reliable operation of the electricity system.  
Typically, power system engineers rely on their prior experience to assess system contingencies, which may not be appropriate for analyzing severe contingencies. Therefore, it is desirable to develop a contingency classification algorithm that classifies contingencies according to their relative severity. Contingencies can be classified according to their impact on transmission line load or bus voltages.  
Ranking methods rank contingencies by their relative severity. The sequence of contingencies is determined according to the performance index (PI) or the system stress index [18]. PI is explicitly expressed in terms of network variables, such as active power flows or voltages, and they are directly evaluated. Several PI-based methods have been proposed and tested for power system security analysis; for example, some of them are considered a measure of the degree of line overload [19].  
One of the disadvantages of ranking methods is the masking effect. There are cases where there are many heavy loads but no severe violations caused by them, but the PI value is similar to cases with one or more violations [20]. In addition, another important shortcoming is the incorrect ordering of contingencies, which appears mainly due to inaccuracies in the model used to calculate the PI and errors in the ordering of the relative severity of some contingencies.  
The purpose of the Contingency Selection and Ranking function is to select a small number (specified by the operator) of the most important contingencies from a large list of possible contingencies and rank them according to their severity. This approach is suitable for systems where some misclassification of contingencies can be tolerated. In addition, the method is used for the determination of the stress level of the studied system in post-emergency assessment.
# Graphene Nanoplatelets Impact on Concrete in Improving Freeze-Thaw Resistance
## 2 Experimental Program  
### Material and Specim Preparation  
In order to prepare cement composites, Ordinary Portland Cement of grade 42.5 was purchased from Weihai Shanshui Cement Co., Ltd. (Weihai, China). Table 1 display the chemical composition of cement. The maximum size of natural sand and coarse aggregate are 5 mm and 20 mm, respectively, and the fineness modulus of sand is 2.7. GNP was supplied from Graphene Suspension (20 wt %), and it had been provided by Weihai hi-tech carbon materials Co., Ltd. in China. The Graphene Suspension can be stably dispersed in ethanol aqueous solution, which consists of graphene layers with thickness of 20 nm and diameter of 10 micrometers. The scanning electron microscope (SEM) images can be shown in Figure 1.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Composition** & **CaO** & **SiO2** & **Al2O3** & **Fe2O3** & **SO3** & **MgO** & **Loss** & **Of Ignition** \\ \hline Content (\%) & 63.79 & 22.6 & 4.62 & 3.26 & 2.29 & 1.70 & 3.14 & \\ \hline \end{tabular}
\end{table}
Table 1: Chemical composition of P.O 42.5R cement.  
In this experiment, to observe the influences of GNP on durability-related behavior, at first, five groups of concrete samples were designed, we defined them as the first batch, which including a control mix and four groups of mixes with 0.1, 0.2, 0.3 and 0.4 wt% GNP, labeled as GC0, GC0.1, GC0.2, GC0.3, and GO0.4, respectively. According to the test results, a dosage of GNP between 0.01% and 0.1% could derive better performance; then, we added two group's specimens and defined as the second batch, 0.02% and 0.05%wt GNP, labeled as GC0.02 and GC0.05. The mass percent of water to cement ratio was kept 0.6, a concrete mix of proportions, by weight of cement, sand and aggregate in this experiment's compound is 1: 2.06: 3.36. Firstly, Graphene Suspension was dissolved in water and manually stirred 1 min; then, cement and graphene-water, which account for about 35% of cement weight, were added to the mixer to stir for 20 s. Afterwards all coarse aggregate are added to stir for 20 s, all sand is added to stir for 20 s, and finally, the remaining graphene-water mixture is added to stir for 30 s. After being blended, the concrete was casted into the cubic mold (100 mm \(\times\) 100 mm \(\times\) 100 mm) and then compacted on a vibration table. Each group contains three specimens. Finally, these samples were cured in the laboratory at a temperature 20 \(\pm\) 1 \({}^{\circ}\)C; after 24 \(\pm\) 2 hours, all samples were demoulded and cured in a concrete standard curing box at a temperature of 20 \(\pm\) 1 \({}^{\circ}\)C and a relative humidity of 95\(\pm\) 1% for 28 days according to the Chinese standard GB/T 50081-2002 [22].
# Improved Gradient Descent Iterations for Solving Systems of Nonlinear Equations
## 1 Introduction, Preliminaries, and Motivation  
Our intention is to solve a system of nonlinear equations (SNE) of the general form  
\[F(\mathbf{x})=0,\ \ \mathbf{x}\in\mathbb{R}^{n}, \tag{1}\]  
where \(\mathbb{R}\) is the set of real numbers, \(\mathbb{R}^{n}\) denotes the set of \(n\)-dimensional vectors from \(\mathbb{R}\), and \(F:\mathbb{R}^{n}\mapsto\mathbb{R}^{n}\), \(F(\mathbf{x})=(F_{1}(\mathbf{x}),\ldots,F_{n}(\mathbf{x}))^{\mathsf{T}}\), and \(F_{i}:\mathbb{R}^{n}\mapsto\mathbb{R}\) is the \(i\)th component of \(F\). It is assumed that \(F\) is a continuously differentiable mapping. The nonlinear optimization problem (1) is equivalent to the subsequent minimization of the following goal function \(f\):  
\[\min_{\mathbf{x}\in\mathbb{R}^{n}}f(\mathbf{x}),\quad f(\mathbf{x})=\frac{1}{ 2}\|F(\mathbf{x})\|^{2}=\frac{1}{2}\sum_{i=1}^{n}(F_{i}(\mathbf{x}))^{2}. \tag{2}\]  
The equivalence of (1) and (2) is widely used in science and practical applications. In such problems, the solution to SNE (1) comes down to solving a related least-squaresproblem (2). In addition to that, the application of the adequate nonlinear optimization method in solving (1) is a common and efficient technique. Some well-known schemes for solving (1) are based on successive linearization, where the search direction \(\mathbf{d}_{k}\) is obtained by solving the equation  
\[F(\mathbf{x}_{k})+F^{\prime}(\mathbf{x}_{k})\mathbf{d}_{k}=0, \tag{3}\]  
where \(F^{\prime}(\mathbf{x}_{k})\equiv J_{F}(\mathbf{x}_{k})\), and \(J_{F}(\mathbf{x})=\left[\frac{\partial F_{1}(\mathbf{x})}{\partial\mathbf{x}_{ j}}\right]\) is the Jacobian matrix of \(F(\mathbf{x})\). Therefore, the Newton iterative scheme for solving (1) is defined as  
\[\mathbf{x}_{k+1}=\mathbf{x}_{k}+t_{k}\mathbf{d}_{k}=\mathbf{x}_{k}-t_{k}\big{(} F^{\prime}(\mathbf{x}_{k})\big{)}^{-1}F(\mathbf{x}_{k}), \tag{4}\]  
where \(t_{k}\) is a positive parameter that stands for the steplength value.
# Application of Machine Learning for Bulbous Bow Optimization Design and Ship Resistance Prediction
## 5 Conclusions  
This study developed a surrogate model based on error classification and used the whale algorithm to analyze ship resistance with varying bulbous bow parameters. The method overcame the time-consuming and inefficient issues of traditional computational fluid dynamics (CFD). The main conclusions are as follows:  
* The free-form deformation method converts hull surface deformation into the displacement of NURBS control point coordinates, offering a dependable approach for arbitrary ship alteration and delineating the essential parameters for generating the surrogate model.
* The CBR model can allocate the most appropriate prediction model for various sample points of the type of ship, resulting in superior overall performance in resistance prediction for the type of ship DTMB5415 compared to traditional neural networks BP and RBF.
* The improved whale algorithm demonstrates the fastest convergence among algorithms for identifying the optimal low-resistance ship type. This is due to the whale's bubble net attack, which focuses on high-quality solution areas within the search space, leading to superior results in less time.
* The bulbous bow exhibiting the least resistance features an elongated forward section relative to the original design, while simultaneously being elevated. It was expanded by a factor of 1.5771 in width, transforming the overall configuration from a U-shape to a V-shape, thereby achieving a 4.95% decrease in the hull's total resistance. The alteration in shape is predicated on a design velocity of 30 knots, where \(F_{r}\) is equal to 0.4137.  
Overall, the surrogate model proposed in this study provides a new approach for ship resistance prediction and lays the foundation for applying machine learning in bulbous bow design.  
Methodology, Y.S. and L.C.; data curation, Q.J. and B.J.; validation, Y.S. and B.J.; project administration, S.Y., Y.Z. and L.Q.; visualization, Q.J. and L.C.; writing--original draft preparation, Y.S.; writing--review and editing, S.Y., Y.Z. and L.Q. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by (1) Research on the Mechanism of Hydrogen-Oxygen Flame Line Heating and Forming of Outer Hull Plate and Intelligent Operation Decision Support System (National Natural Science Foundation of China) grant number 51875270; (2) V-DT-driven and Al-enabled Comprehensive Protection Technology for Complex Equipment of Large Ships at Sea Research grant number JC2024021.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The raw data supporting the conclusions of this article will be made available by the authors upon request.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
## 1 Introduction
Therefore, it is difficult to learn users' location preferences and location characteristics from too few interaction behaviors, let alone to obtain high-accuracy location recommendation results. In order to solve the data sparsity problem, a common and useful practice is introducing external sources, in which user interests or location features can be filled up [18; 19; 20]. For example, we can extract the auxiliary information such as users' age, gender, social relationship, POIs around locations [21; 22; 23] to solve data sparsity problem. However, the acquisition of these additional sources is very difficult to collect for the privacy preservation of users. Although these research works made contributions on enhancing representations [24; 25] or tackling data sparsity [26; 27], they did not solve the two problems simultaneously. In this paper, we propose a graph-based representation model that not only increases recommendation accuracy but also tackles the data sparsity problem. To be specific, we introduce app usage records into the graph-based model, which can obtain high-order associations of user-location-app, i.e., user-location, user-app, location-app etc., contributing a lot to recommendation quality even under severe data sparsity. The rest of this paper is organized as follows. Section 2 reviews the related work about location recommendations and the data sparsity problem. Section 3 introduces the datasets and analyses the feasibility of location recommendations with app usage data. Section 4 describes our recommender framework in detail. Section 5 evaluates its performances and discusses the experimental results, followed by the conclusion in Section 6.
## 2 Existing Leak Detection and Localization Methods
### Methods Based on Knowledge
#### 2.3.3 Expert System  
A leak diagnosis expert system based on leak mechanism is a type of complex nonlinear distributed parameter control system [33, 34]. The whole reasoning process is how to synthetically utilize experience knowledge, leak pattern, leak mechanism model and physics laws. The system first completes the breadth search for the most serious abnormal phenomenon of the pipeline. Then it carries out the depth-first search, accomplishing effective qualitative diagnosis based on experience knowledge to determine whether a leak happens.
## 3 Case Study
### Implemented Methods
#### 3.2.2 Implementation of Adoptive Filters  
A user can easily obtain a data visualization of the whole dataset for a particular parameter, but sometimes some additional filters have to be applied. For this purpose query filters are implemented.  
Data filter is organized with this sections (options for selections):  
Figure 7: Simplified schema of server—client interactions and web application loading process.  
* Year;
* Project / Monitoring;
* Institution;
* Cruise;
* Parameter group;
* Parameter.  
In the first version, all fields were populated with all unique values from the entire dataset (all years, all institutions, etc.). In practice, users often selected combinations for which no data were available at all (e.g., some institutions did not submit data for a particular year). For this reason, adoptive filters were implemented. At each _onChange_ event of each of the listed fields, a database query was executed and each field was populated with possible combinations.  
To improve efficiency, the last used filter values (combinations) were stored as a cookie in the browser. On the next query, the user can start with the previous combination of filters, which saves some time (the user does not have to set all fields from the beginning). If for some reason the user wants to start with a new \"empty\" filter, there is a \"Reset filters\" button for this purpose.  
In addition to the main query filter, a filter for various validation factors or access restrictions was implemented. By default, validated or non-validated data were displayed in the chart. The user can also include suspicious or bad values by clicking the appropriate checkbox. The numbers within each checkbox for each validation factor indicate how many values were included in each category.
## 4 Multi-Objective Optimization Model under Uncertain Conditions
### Model Construction
#### 4.2.4 Economic Cost  
The economic cost involved in the multi-cycle disposal logistics network of infectious medical waste mainly includes the fixed operating cost \(Cfo_{t}\), the collection cost \(Cc_{t}\), the treatment cost \(Cp_{t}\), the landfill cost \(Cl_{t}\), the transportation cost \(Ct_{t}\), the technology upgrade cost \(Ctu_{t}\), the technical transformation cost \(Ct_{t}\) and the temporary storage cost \(Cts_{t}\):  
\[\min\ C_{t}=\min(Cfo_{t}+Cc_{t}+Cp_{t}+Cl_{t}+Ct_{t}+Ct_{t}+Cts_{t}) \tag{3}\]  
where fixed operating cost \(Cfo_{t}\) is expressed as:  
\[Cfo_{t}=\sum_{j}\sum_{t}Tfo_{t}+\sum_{m}\sum_{t}Nfo_{m_{t}}\cdot\tilde{nit}_{ mt}+\sum_{p}\sum_{t}Dfo_{pt}+\sum_{k}\sum_{t}Hfo_{k} \tag{4}\]  
where collection cost \(Cc_{t}\) is expressed as:  
\[Cc=\sum_{i}\sum_{t}w_{it}\ \cdot\ uncc \tag{5}\]  
where treatment cost \(Cp_{t}\) is expressed as:  
\[C_{P_{t}}=\sum_{j}\sum_{t}Tunpc_{j}\cdot(pw_{it}+\tilde{et}t_{jt}\cdot\tilde{ p}tw_{jt})+\sum_{m}\sum_{t}Nunpc_{m}\cdot\tilde{nit}_{mt}\cdot\tilde{n}pw_{mt} \tag{6}\]  
where landfill cost \(Cl_{t}\) is expressed as:  
\[Cl_{t}=\sum_{i}w_{i}\cdot(\eta\cdot\sum_{p}Dulc_{p}+\theta\cdot\sum_{k}Hulc_{ k}) \tag{7}\]  
where transportation cost \(Ct_{t}\) is expressed as:  
\[\begin{array}{ll}Ct_{t}=&\sum_{j}\sum_{t}\left(pw_{it}+\tilde{et}t_{jt} \cdot\tilde{p}tw_{i}\right)\cdot\left(\sum_{i}unct\cdot td_{jit}+\eta\cdot \sum_{p}sunct\cdot td_{jit}+\theta\cdot\sum_{k}fautc\cdot td_{jit}\right)+\\ &\sum_{m}\sum_{t}\tilde{nit}_{mt}\cdot\tilde{n}pw_{m}\cdot\left(\sum_{i}unct \cdot td_{imt}+\eta\cdot\sum_{p}sunct\cdot td_{mpt}+\theta\cdot\sum_{k}fautc \cdot td_{mkt}\right)\end{array} \tag{8}\]where technology upgrade cost \(Ctu_{t}\) is expressed as:  
\[Ctu_{t}=\sum_{j}\sum_{i}\widetilde{e}tt_{jt}\cdot etc_{j}^{\prime} \tag{9}\]  
where technical transformation cost \(Ctt_{t}\) is expressed as:  
\[Ctt_{t}=\sum_{m}\sum_{t}\widetilde{n}tt_{mt}\cdot ntc_{m}^{\prime} \tag{10}\]  
where temporary storage cost \(Cts_{t}\) is expressed as:  
\[\begin{array}{rl}Cts_{t}=&\left(\sum\limits_{j}\sum\limits_{t}Tunsc_{j} \cdot\hat{p}cc_{jt}\cdot\left(pw_{jt}+\widetilde{e}tt_{jt}\cdot\widetilde{p}tw_ {jt}-\bar{T}pc_{j}-\widetilde{e}tt_{jt}\cdot\widetilde{t}jpc_{j}\right)\right) \\ &+\left(\sum\limits_{m}\sum\limits_{t}Nunsc_{m}\cdot\hat{p}cc_{mt}\cdot \widetilde{n}tt_{mt}\cdot\left(\widetilde{n}pw_{wt}-\dot{N}pc_{m}\right)\right) \end{array} \tag{11}\]
# Spatiotemporal Variation in Absorption Aerosol Optical Depth over China
## 2 Data and Methodology
### MODIS-Aqua Data  
The present study utilized level 2 AOD at 550 nm retrieved from MODIS (Moderate resolution imaging spectroradiometry) Terra at a spatial resolution of 1\({}^{\circ}\)\(\times\) 1\({}^{\circ}\) for a period of 14 years (January 2005 to December 2018) to study the regional distribution of AOD over China. These satellite data products were obtained based on Giovanni maps from the NASA website [46]. Detailed information concerning the sensor, data products, retrieval algorithms, calibration, and uncertainties can be found elsewhere [47; 48; 49].
# Correlations between SSQ Scores and ECG Data during Virtual Reality Walking by Display Type
## 2 Research Methods
### Data Measurement and Analysis
Second, we utilized independent-sample \(t\)-tests to examine SSQ scores, HR, RMSSD, and LF/HF ratio differences between the two groups within each condition. Lastly, to determine whether objective indicators such as HR, RMSSD, and LF/HF ratio differences could also be used to distinguish between the motion-sickness and no-motion-sickness groups, in addition to the subjective indicator of SSQ scores, we conducted linear regression analysis. This analysis aimed to clarify whether higher SSQ scores correspond to higher or lower values of HR, RMSSD, or LF/HF ratio. Using SSQ scores as the dependent variable and the remaining variables (HR, RMSSD, LF/HF ratio differences) as independent variables, we sought to identify which variable best represents SSQ scores and could therefore serve as an objective indicator for distinguishing between the motion-sickness and no-motion-sickness groups.