esa-sceva/satcom-chunk-collection huggingface dataset:
# Parameter-Matching Multi-Objective Optimization for Diesel Engine Torsional Dampers
## 2 Research Methodology
### Dual Objective Optimization of Torsional Vibration Dampers  
The conventional dual-pendulum model, while historically valuable for torsional damper design, suffers from the oversimplification of multi-cylinder diesel engine dynamics, leading to inaccurate predictions of crankshaft vibration responses. Moreover, single-objective optimization approaches focusing solely on nodal angular displacements overlook the cumulative stress effects under variable operating conditions--a critical oversight that risks fatigue-induced crankshaft failures. To address these limitations, this study proposes a multi-objective optimization framework targeting the simultaneous minimization of both torsional angles and component stress amplitudes.  
Leveraging the pre-trained Bayesian hyperparameter-optimized neural network model, this study implements the NSGA-II algorithm to conduct multi-objective optimization of torsional vibration responses in a crankshaft system equipped with torsional  
Figure 5: Neural network regression effect diagram. (**a**) Training set testing regression effect diagram; (**b**) test set regression effect diagram.  
Figure 6: SHAP analysis of crankshaft system response to torsional damper parameters. (**a**) SHAP contribution analysis of twist angle; (**b**) SHAP contribution analysis of additional stress.  
vibration dampers. The optimization framework explicitly targets two critical performance metrics: the minimization of nodal angular displacements \(Y_{1}\) and the reduction in component stress concentrations \(Y_{2}\). By systematically varying three key damper parameters--the moment of inertia \(X_{1}\), torsional stiffness \(X_{2}\), and damping coefficient \(X_{3}\)--within the theoretically constrained ranges prescribed by dual-pendulum modeling, the algorithm generates Pareto-optimal solution sets that quantify inherent tradeoffs between vibration attenuation and structural integrity preservation. This methodology advances conventional practices by resolving the oversimplification inherent in single-objective optimization approaches while maintaining compatibility with established engineering design boundaries.  
The objective function is  
\[\begin{cases}Y_{1}=\text{Min}[Y(X_{1},X_{2},X_{3})]\\ Y_{2}=\text{Min}[Y(X_{1},X_{2},X_{3})]\end{cases} \tag{23}\]  
The constraint range is  
\[\text{s.t.}\begin{cases}\begin{array}{c}2.2\leq X_{1}\leq 6.6\\ 771,863\leq X_{2}\leq 2,025,310\\ 483\leq X_{3}\leq 2160\end{array}\end{cases} \tag{24}\]  
In the NSGA-II dual objective optimization algorithm model, the initial population size is set to 30, the crossover probability is 0.9, and the mutation probability is 0.3. After 1000 iterations of optimization, a Pareto front solution set was obtained, and the parameters of the torsional vibration damper with the best suppression effect were predicted.
## 5 Multi-Giga-Hertz Agile Radio Front-End Design  
Demand for agile radio is increasing from applications in both communications and radar aspects. A dream agile radio should be able to sense the frequency spectrum, make a best strategy and dynamically access to a desired frequency band. These serial actions must be done as quickly as possible, which poses design and implementation challenges.  
In addition to the requirement for quick response, wide frequency range ability is another essential requirement for an agile radio to take full advantage of wide range of spectral availability. A multi-giga-Hertz frequency coverage may sound aggressive, but it is technically achievable, considering recent advances in electronic devices and our experience in UWB radio.  
Figure 23: Philips WSD cognitive radio node.  
The key subsystem in a multi-giga-Hertz agile radio system is the front-end. The main challenges in designing and implementing such a front-end include: (1) wideband amplifier with low noise figure and large dynamic range, and (2) fast switching between subbands in both transmitter and receiver RF chains.
## Appendix A Background  
### Gaussian Process  
A GP is a stochastic process formed by a collection of random variables that have a multivariate normal distribution. In this work, the random variables represent the value of a function \(f(x)\sim GP(m(x),k(x,x^{\prime}))\) that gives the probability of having PO at location \(x\). Such GP is characterised by the mean \(m(x)\) and the covariance \(k(x,x^{\prime},\theta)\) between any two given positions \(x\) and \(x^{\prime}\). The Squared-Exponential (SE) covariance kernel, defined as \(k(x,x^{\prime},\theta)=\sigma_{f}^{2}exp(-\frac{||x-x^{\prime}||^{2}}{2l^{2}})\), is utilised to describe the covariance value between two locations. In the SE covariance expression, the parameters are indicated as \(\theta=[\sigma_{f}^{2},l]\), where \(\sigma_{f}^{2}\) represents the maximum allowable covariance, and \(l\) is the kernel _length scale_ that defines the kernel smoothness [11]. The joint distribution of the observed training values \(y=\left\{y_{i}\right\}_{i=1,\ldots,N}\) and the test values \(f_{*}\) is given by [8]  
\[\begin{bmatrix}y\\ f_{*}\end{bmatrix}\sim\mathcal{N}\bigg{(}0,\begin{bmatrix}K(X,X)+\sigma_{n} ^{2}I&K(X,X_{*})\\ K(X_{*},X)&K(X_{*},X_{*})\end{bmatrix}\bigg{)} \tag{1}\]where \(\sigma_{n}^{2}\) is the variance of the noise fluctuations, \(X=\{x_{i}\mid i=1,\cdots,N\}\) are training locations, and \(X_{*}=\{x_{i}^{*}\mid i=1,\cdots,N_{*}\}\) represent testing locations. Applying standard rules for conditioning Gaussian functions, given the training locations, the predictive distribution over the test locations is given by \(f_{*}\sim\mathcal{N}(\bar{f}_{*},\mathbb{V}[f_{*}])\). The resulting expressions of mean and variance are  
\[\bar{f}_{*}=K(X_{*},X)[K(X,X)+\sigma_{n}^{2}I]^{-1}y \tag{2}\] \[\mathbb{V}[f_{*}]=K(X_{*},X_{*})-K(X_{*},X)[K(X,X)+\sigma_{n}^{2} I]^{-1}K(X,X_{*}) \tag{3}\]  
where \(\mathbb{V}[f_{*}]\) represents the prediction uncertainty in the query locations \(X_{*}\), and \(\bar{f}_{*}\) the mean probability of having PO in \(X_{*}\).
## Materials and Methods  
### Reagents and Chemicals  
The following reagents were utilized as metal sources for the preparation of HESO-samples: magnesium (II) acetate tetrahydrate, Mg(CH\({}_{3}\)COO)\({}_{2}\)\(\cdot\)4H\({}_{2}\)O (purity: 98%; CAS No. 16674-78-5, Sigma Aldrich, Burlington, MA, USA); titanium (IV) butoxide, Ti(C\({}_{4}\)H\({}_{9}\)O)\({}_{4}\) (purity: 97%; CAS No. 5593-70-4); chromium (III) acetate hydroxide, Cr\({}_{3}\)(OH)\({}_{2}\)(CH\({}_{3}\)COO)\({}_{7}\) (purity: 98%; CAS No. 39430-51-8, Sigma Aldrich); manganese (II) acetate tetrahydrate, Mn(CH\({}_{3}\)COO)\({}_{2}\)\(\cdot\)4H\({}_{2}\)O (purity: 99%; CAS No. 6156-78-1, Sigma Aldrich); iron (II) acetate, Fe(CH\({}_{3}\)COO)\({}_{2}\) (purity 95%; CAS No. 3094-87-9 Sigma Aldrich); cobalt (II) acetate tetrahydrate, Co(CH\({}_{3}\)COO)\({}_{2}\)\(\cdot\)4H\({}_{2}\)O (purity: 99%; CAS No. 6147-53-1, Sigma Aldrich); nickel (II) acetate tetrahydrate, Ni(CH\({}_{3}\)COO)\({}_{2}\)\(\cdot\)4H\({}_{2}\)O (purity: 98%; CAS No. 6018-89-9, Sigma Aldrich); copper (II) acetate, Cu(CH\({}_{3}\)COO)\({}_{2}\), (purity: 98%; CAS No. 142-71-2, Sigma Aldrich); zinc (II) acetate dihydrate, (CH\({}_{3}\)COO)\({}_{2}\)Zn\(\cdot\)2H\({}_{2}\)O (purity: 98%; CAS No. 5970-45-6, Fischer Scientific, Hampton, NH, USA). In addition, lithium acetate, Li(CH\({}_{3}\)COO) (purity: 99.95%; CAS No. 546-89-4, Sigma Aldrich) was used as a lithium source to prepare a lithiated HESO-sample.  
Citric acid monohydrate, HOC(COOH)(CH\({}_{2}\)COOH)\({}_{2}\)\(\cdot\)H\({}_{2}\)O (purity: 98%; CAS No. 5949-29-1, Sigma Aldrich), was used as a complexing agent for the preparation of HESO-samples via the sol-gel method.  
The solution for the synthesis of electrospun samples was prepared by utilizing polyacrylonitrile, (C\({}_{3}\)H\({}_{3}\)N)n (average molecular weight: 150,000 g mol\({}^{-1}\); purity: 99.9%; CAS No. 25014-41-9, Sigma Aldrich), or polyvinylpyrrolidone, (C\({}_{6}\)H\({}_{9}\)NO)\({}_{n}\) (average molecular weight: 1,300,000 g mol\({}^{-1}\); purity: 98%; CAS No. 9003-39-8, Sigma Aldrich), as a polymer. In the former case, N,N-dimethylformamide, HCON(CH\({}_{3}\))\({}_{2}\) (anhydrotus: 99.8%; CAS No. 68-12-2, Sigma Aldrich), acted as a solvent. In the latter, a mixture of N,N-dimethylformamide and ethanol, CH\({}_{3}\)CH\({}_{2}\)OH (purity: 99.5%; CAS No. 64-17-5, Sigma Aldrich), was used.  
All chemicals were used without any further purification.
# An Effective Wide-Bandwidth Channel Access in Next-Generation WLAN-Based V2X Communications
## 3 Proposed Scheme and Its Variants
### Proposed Channel Access Scheme for 20-MHz Transmission
#### 3.1.1 Device Architecture  
In order to provide maximum performance, the device architecture of the proposed 20-MHz channel access scheme is designed to include two preamble detectors to perform PD sensing in both 10-MHz channels. The proposed station architecture to support the proposed scheme is shown in Figure 4. The proposed station architecture has an additional simplified receiver module with PHY layer receiver and simple MAC layer. In the proposed station structure, one preamble detector is a normal part of IEEE 802.11 MAC and PHY architecture [19], and the other preamble detector is part of the additional simplified receiver module. The additional simplified receiver module has a simplified MAC layer and PHY layer receiver for a secondary channel to perform PD sensing on that secondary channel. The added PHY receiver only includes the receiver part of a normal PHY layer [19]. The added simplified PHY and MAC layers for a secondary 10-MHz channel only includes the functionality of PD sensing: the decoding of the LENGTH field in the preamble's L-SIG and Duration field in the MAC header of the received frames. The simplified receiver module detects the channel state of the secondary channel according to the decoded LENGTH field and Duration field. Since the simple MAC layer is only for the detection of channel usage time in the secondary channel, the decoded frames are not transferred to the upper layer.
# A Preemptive-Resume Priority MAC Protocol for Efficient BSM Transmission in UAV-Assisted VANETs
## 4 Problem Formulation and Performance Metrics
### Minimization of BSM Transmission Interval Problem  
In the limited case of UAV, we aim to maximize the frequency of BSM transmissions as much as possible. This approach improves safety. Therefore, we need to discuss the problem of minimizing the BSM transmission interval. Based on Equation (39), minimizing the BSM transmission interval can be understood as minimizing the overall delay for each priority level. An optimization problem with an objective function and constraints can be formulated as follows:  
\[\underset{\underset{T_{i}}{\text{min}}}{\text{min}}\ \ E(T_{BSM\;i}). \tag{40}\]  
As derived from Equation (39), the above minimization problem can be obtained from the following minimization problem:  
\[\underset{i}{\text{min}}\ \ T_{i} \tag{41}\] \[v_{min\;i}\geq 17\ (m/s)\ v_{max\;i}\leq 34\ (m/s)\] \[\lambda_{i}\leq 0.2,\]  
where \(i\) is the set priority level.  
Algorithm 2 provides the process of minimizing the BSM transmission interval.  
```
Initialize: set the number of priority levels r Input : speed thresholds for each priority level \(\mathrm{v}_{\text{maxi}}\ \mathrm{v}_{\text{mini}}\)  vehicle arrival rate \(\lambda_{i}\) Output : optimal value of transmit interval of BSM \(E(T_{BSM\;i})\) Begin : For i = 1 to r do \(N_{i}\) = calculate vehicle count i (\(\lambda_{i}\)) For k = 1 to \(\mathrm{N}_{i}\) do  Speed k = get speed(k)  If \(\mathrm{v}_{\text{mini}}\leq\mathrm{v}_{\text{f}}\leq\mathrm{v}_{\text{ maxi}}\)  Priority k = calculate priority (Speed k) End if End for \(E(T_{BSM\;i})\ =\ \text{calculate transmit interval i (i)}\) End for End End
```  
**Algorithm 2**Proposed algorithm for Transmit Interval of BSM minimization
## 3 Data
### Data Collection  
#### 3.2.1 Questions and Answers  
We collected the questions and their associated answer candidates from the National Medical Board Examination in the USA ([[https://www.usmle.org/](https://www.usmle.org/)]([https://www.usmle.org/](https://www.usmle.org/)), Accessed on 10 March 2021), Mainland China ([[http://www.nmec.org.cn](http://www.nmec.org.cn)]([http://www.nmec.org.cn](http://www.nmec.org.cn)), Accessed on 5 April 2021), and Taiwan ([[https://wwwq.moex.gov.tw/exam/wFrmExam-QandASearch.aspx](https://wwwq.moex.gov.tw/exam/wFrmExam-QandASearch.aspx)]([https://wwwq.moex.gov.tw/exam/wFrmExam-QandASearch.aspx](https://wwwq.moex.gov.tw/exam/wFrmExam-QandASearch.aspx)), Accessed on 23 March 2021). For convenience, we denote the datasets from these three sources as USMLE, MCMLE, and TWMLE, respectively. These tests assess a physician's ability to apply knowledge, concepts, and principles, and the ability to demonstrate fundamental patient-centered skills. We include problems from both real exams and mock tests; all are freely accessible online for public usage. Details about the sources that we collected data from are described in the Appendix A.  
We remove duplicate problems and randomly split the data based on questions, with 80% training, 10% development, and 10% test. The overall statistics of the dataset are summarized in Table 3. To comply with fair use of law ([[https://www.copyright.gov/fair-use/more-info.html](https://www.copyright.gov/fair-use/more-info.html)]([https://www.copyright.gov/fair-use/more-info.html](https://www.copyright.gov/fair-use/more-info.html)), Accessed on 23 March 2021), we shuffle the order of answer options and randomly delete one of the wrong options for each question for USMLE and MCMLE datasets, which results in four options with one right option and three wrong options. Percentages of each option as the correct answer for the development set are summarized in the Appendix A.  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**Datasets** & **LiveQA** & **Medication QA** & **BioASQ** & **emQA** & **MEDIQA** & **MedQuAD** & **MedQA** \\ \hline
**Question Source** & consumer health & consumer health & biomedical & EMR & consumer health & mechanical\&clinical \\
**Answer Format** & retrieval & retrieval & span based\&binary & ranking & retrieval & multiple choice \\
**Annotation Method** & manual & manual & manual & automatic & manual & automatic & manual \\
**Dataset Size** & 660 & 674 & 3743 & 455,837 & 383 & 47,457 & 61,097 \\
**OpenQA?** & No & No & No & No & No & Yes \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of our dataset with existing medical QA datasets. In terms of the answer format, “retrieval” means that the answer is a snippet of retrieval result by searching relevant websites; “ranking” means that the answer is a list of candidates and the task is to rank them with higher ranking ones being better answers. Automatic annotation is obtained via an algorithm.
# Regression Equations for Estimating Landslide-Triggering Factors Using Soil Characteristics
## 3 Results
### Landslide Pattern  
Factors such as terrain complexity and geometry are closely related to the occurrence and spatial distribution of various types of landslides [59; 60; 61]. Key direct factors of the landslide are topography, geological characteristics, and rainfall. The study areas located in South Korea are composed of two different major geological types. One is Yangju, Gyeonggi-do, a genesis region, while the other is in Sangju, Gyeongsangbuk-do, a granite region. Most of the landslides in these areas occurred during the heavy rains in early August, affecting 175 sites, including 76 for genesis and 99 for granite.  
Figure 3 shows the locations of landslides in the areas of study as well as their geological distribution. In Yangju, Gyeonggi-do, the destructive patterns of landslides in gneiss regions started with translational slides that changed into flows as the landslide materials moved to lower slopes. Thus, it was found that a translational type plane destruction occurred on a relatively flat slope, and the destroyed materials moved downward, mixing with surrounding materials, and transformed into a debris flow going further down the V-shaped valley. Moreover, a shallow solum that consisted of residual and colluvial soils was found to have collapsed along with the interface of the bedrock. In Sangju, Gyeongsangbuk-do, granite was widely distributed, while sedimentary rocks were partly distributed. Landslides occurred widely in the totality of the landscape but were more frequent in granite areas. Furthermore, landslides in this area were mostly found to be of translational-type flow, most of which were relatively shallow at depths of less than 1 m, while the upper soil layers, which consist of residual and colluvial soils, collapsed along the boundary of the bedrock. It was also shown that landslide materials moved to the bottom of the slope and became a flow. Overall, landslide patterns in the granite region were found to be similar to gneiss areas.
# Review Rating Prediction Based on User Context and Product Context
## 5 Conclusions  
In this paper, we present a new RRP method that is based on the user context and product context. More specifically, in order to solve problem of existing RRP methods based on review content, considering the user personalized information and product context information, which are useful to predict rating of review, we propose a RRP method based user context and product context. Experimental results on four datasets show that our proposed methods have more performance than the state-of-the-art baselines in RRP.  
Existing review rating prediction methods only use a single model to capture the sentiment of review texts. Our method respectively considers affection of user and product on reviews content in capturing the sentiment of review texts. However, our method does not regarding common affection of user and product on review texts in capturing the sentiment of review texts. Regardless of existing methods or our method, only the review content information is modelled in RRP. In the future, in order to address these issues, we will firstly model together affection of user context and product context on reviews texts, incorporating the user-product-specific model into our method to further improve the performance of RRP. At the same time, we will use user and product context information as the attention mechanism of the neural network model to adjust the weighting coefficient of the global model, user-specific model, product-specific model, and user-product-specific model in our proposed future method.  
Conceptualization, B.W.; Data curation, Y.H.; Formal analysis, B.W.; Investigation, B.W.; Methodology, B.W.; Project administration, Y.H.; Supervision, X.L.; Writing--original draft, B.W.; Writing--review & editing, X.L.  
This research was funded by Foundation of He'nan Educational Committee (19A520032) and Ph.D. Start-up Foundation of Pingdingshan University (PXY-BSQD-2018007) and National Natural Science Foundation of China (U1536201, U1405254, and 61271392).  
Thanks to [PERSON] for valuable discussions on the research and the manuscript.  
The authors declare no conflict of interest.  
Figure 4: Impacts of User Context and Product Context on RMSE of Our Methods in the Four Datasets.
## Selection and Reproduction
### Architecture of ANFIS  
The Sugeno fuzzy model is represented by the Adaptive Network-Based Fuzzy Inference System (ANFIS) architecture. The antecedent and consequent parameters of Sugeno fuzzy rules can be determined by using a neural network learning algorithm. In other words, the Sugeno fuzzy model can be provided with a self-learning ability to adjust the optimum fuzzy rules by itself [15].
## 3 Results
### ANN-LM Results  
The results showed that the RBFNN-predicted models outperformed the GRNN models in terms of protection distance and elevation angle. The prediction model based on RBFNN showed 100% accuracy. Thus, the curve closer to the desired INR of \(-12.2\) dB is more effective in blocking the interference. The ANN-LM considered elevation angles of \(15^{\circ}\), \(20^{\circ}\), and \(30^{\circ}\) for protection distances of 10, 20, and 35 km, respectively. Therefore, the ANN-LMs (RBFNN and GRNN) can be used to accurately predict the interference at various protection distances, as shown in Figures 22-24.  
Figure 21: Attenuation CDF for the INR of adjacent interference is based on the out-of-band emission mask of 5G-BS.  
Figure 23: Predicted interference probability using ANN-LM RBFNN and GRNN for protection distance of 20 km.  
Figure 24: Predicted interference probability using ANN-LM RBFNN and GRNN for protection distance of 35 km.  
Figure 22: Predicted interference probability using ANN-LM RBFNN and GRNN for protection distance of 10 km.
## 9 Conclusions  
These conclusions are intended to set the stage for the implementation of the ASI based on the twin pillars of Areas of Vulnerability and the Predictive Module. From these basic concepts, the structure and operation of the ASI becomes evident as the mechanism for predicting the severity of stresses and the possibility of system collapse.  
1. Identifying areas of vulnerability is a critical step in anticipating the process of system collapse since it marks the beginning of the migration of the system states.
2. The advent of uncertainty in power system operation as a result of wind generation will require a probabilistic approach to identify areas of vulnerability and the subsequent deployment of the ASI's based on their prescribed design characteristics, namely, autonomous, selective, intelligent and accurate.
3. The transmission system plays a significant role in this environment of uncertainty as it governs the successful operation of the power system under such stresses.
4. The concept of a Predictive Module has not been found in the current literature.
5. The idea that the system parameters can be stored and compared with those of the simulation exercise has not been found in the current literature.
6. What is significant is that the assessment of the degree of severity can be evaluated and forwarded to the System Operator for remedial action as required.
7. The Predictive Module is the key to the successful operation of the autonomous stress indicator provided these ASIs are deployed correctly.  
* 8. The design of the Predictive Module has involved two features not commonly associated with power system prediction, namely data mining and pattern recognition. Yet both of these concepts are essential to the design of the Predictive Module.
* 9. As a first application of the autonomous stress indicator, it is suggested that the system contingencies be restricted to the Alert State (the Degraded State) so as to estimate the correctness of this device.
* 10. In conclusion, the autonomous stress indicator could be identified as a stress-alert device since its main function is to alert the System Operator of the possibility of system collapse
* 11. There are no financial incentives for the authors to publish this paper and there are no relationships with a company which could market such a device.  
Figure 3: 10-machine, 39-Bus New England Test System.