esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Shearer Positioning System Based on SINS
### Experimental Data Processing Methods  
As can be seen from Equation (4), the shearer speed and position at each sampling time are derived from the speed and position at the last sampling time. If there is an error in the acceleration data measured at a certain time, the error will always exist in the later calculation, so the error of SINS positioning will gradually accumulate. Consequently, the positioning accuracy of SINS will decrease with time. To solve the problem of error accumulation over time, a Kalman filter algorithm is introduced to eliminate part of the positioning error [43; 44].  
The Kalman filter equations for shearer positioning are as follows. State transition equation:  
\[X_{k+1}=\Phi_{k+1|k}X_{k}+\omega_{k}. \tag{5}\]  
Observation equation:  
\[Z_{k+1}=H_{k+1}X_{k+1}+\
u_{k+1}. \tag{6}\]  
In Equation (5), \(X_{  
where \(\hat{X}_{k+1|k}\) is the predicted value of \(\hat{X}_{k}\) and \(\hat{X}_{k}\) is the optimal estimated state vector of \(X_{k}\). Also, the predicted value of \(P_{k}\) is  
\[P_{k+1|k}=\Phi_{k+1|k}P_{k}\Phi_{k+1|k}^{T}+Q_{k} \tag{8}\]  
where \(P_{k}\), \(P_{k+1|k}\), \(Q_{k}\), and \(O_{k+1}\) are the covariance matrices of \(X_{k}\), \(X_{k+1}\), \(\omega_{k}\) and \(\
u_{k}\), respectively, and \(P_{k}\) is the covariance matrix of the state vector.  
The filter gain matrix is  
\[K_{k+1}=P_{k+1|k}H_{k+1}^{T}\Big{[}H_{k+1|k}P_{k+1|k}H_{k+1|k}^{T}+O_{k+1} \Big{]}^{-1} \tag{9}\]  
The optimal estimated state vector of \(X_{k+1}\) is  
\[\hat{X}_{k+1}=\hat{X}_{k+1|k}+K_{k+1}\big{(}Z_{k+1}-H_{k+1}\hat{X}_{k+1|k}\big{)} \tag{10}\]  
and  
\[P_{k+1}=P_{k+1|k}(I-K_{k+1}H_{k+1}) \tag{11}\]  
where \(I\) is the identity matrix.  
In this experiment, the data obtained after compensation are decomposed along the X- and Y-axis directions, and the distribution of the estimated points is determined according to the dead reckoning principle and formula fitting.
# Detecting and Responding to Concept Drift in Business Processes
## 4 Methodology
### Bottom Layer of the Hierarchical MM  
The bottom layer of the hierarchical MM is an SMP with the duration estimated by ga-mm. As discussed in Section 3.2, \(\mathbf{S_{S}}\) is all the unique activities in a business process and \(\mathbf{P_{S}}\) covers the transition probabilities between each state transition. For example, in the hospital billing scenario, \(\mathbf{S_{S}}=\{New,FIN,ChangeDiagnosis,CodeOK,\cdots,Billed\}\). The time spent between each state transition from \(i\) to \(j\), \(i,j\in\mathbf{S_{S}}\), is fitted by a gamma mixture model with the number of mixing components optimized by the Bayesian information criterion (BIC) [47]. For example, we increase the number of mixing components from 1 to 10, and the ga-mm with the lowest BIC is selected as the best model. The Nelder-Mead algorithm [48] is used to compute the maximum likelihood estimator. The transition matrix (\(\mathbf{P_{S}}\)) and gamma mixture models are the low-level representation of the customer behaviour behind the data. We then generate a matrix \(\mathbf{G_{S}}=g_{S}(i,j),i,j\in\mathbf{S_{S}}\), consisting of the average duration for all state transitions, which is used for obtaining the top layer representation. \(g_{S}(i,j)\) refers to the average transition time between state \(i\) and \(j\) and can be calculated using Equation (5).  
\[g_{S}(i,j)=\sum_{v=1}^{m}\frac{\alpha_{v}\times z_{v}}{\lambda_{v}}. \tag{5}\]  
\(m\) is the number of mixing components. \(\alpha_{v}\), \(z_{v}\) and \(\lambda_{v}\) are the mixing proportion, shape and rate parameter of the \(v\)th gamma component.
## 1 Introduction
While this approach effectively addresses the obstacle impact, it requires extensive path planning computation. As the quantity of target points rises, the computational complexity also escalates. In the context of the BE-LRP, it is essential to address the influence of obstacles on the distribution distance during the initial phase. Subsequently, in the realm of task planning, it is imperative to identify a set of delivery destinations, which are contingent upon the distribution center's location, capacity limitations, time constraints, and other pertinent factors. The primary contribution of this paper lies in proposing a method to address the BE-LRP encountered in cabin construction within the context of large cruise ship manufacturing. The paper introduces a blocked distance estimation method that incorporates a block coefficient using Gaussian process regression to quantify the influence of the blocking environment on distribution distances. Subsequently, an HO-NSGAII algorithm with mixed obstacle blocking processing is suggested to tackle the BE-LRP, which is formulated  
Figure 1: Illustration of LRP and BE-LRP. (**a**) LRP. (**b**) BE-LRP. as an optimization model that accounts for the effects of obstacles blocking. This algorithm can not only solve the problem of cruise ship construction but can also be applied in other similar scenarios. The remainder of this paper is structured as follows: Section 2 describes the research problem and establishes the mathematical model of the BE-LRP. Section 3 presents the implementation process of the blocked distance estimation method by incorporating a block coefficient through Gaussian process regression. Section 4 introduces the designed HO-NSGAII algorithm. A case study is conducted in Section 5, and the paper is summarized in Section 6.
# Evolving Dispatching Rules for Dynamic Vehicle Routing with Genetic Programming
## 3 Hyper-Heuristic Approach for VRP
### Generating Heuristics for VRP
The definition of \"best value\" of the priority function is quite arbitrary, but in this case we use the lowest returned value as the best one (other options include the maximum value, minimum absolute value etc.). For instance, if the priority function equals simply the distance to the customer, then the same priority function will be evaluated for every possible customer, and the customer with the lowest priority value (the smallest distance) will be selected as the next customer for this vehicle. Another meta-algorithm we propose is a parallel one that builds the schedule concurrently for every vehicle. This variant is defined as Algorithm 2. The parallel algorithm requires one important input, which is the current number of vehicles. If the given number of vehicles is insufficient to construct a valid schedule, then this value can be increasedand the algorithm rerun. However, in our simulations this is not needed for the following reasons. While the serial meta-algorithm can be used in static conditions, where the schedule is constructed beforehand, it cannot be used during the actual execution of the system, for instance after an interruption has happened (such as a vehicle failure). In the case of dynamic conditions, we can use the parallel meta-algorithm to construct the remainder of the schedule, and in that case, the current number of vehicles is always known to the algorithm. The presented approach with the parallel meta-algorithm can also be used for stochastic VRP, where the actual parameter values are not known with certainty until the related event occurs. While we do not model a stochastic VRP in our experiments, the parallel algorithm can be readily applied in those conditions, since it builds the remainder of the schedule starting from any given moment in time with an arbitrary system state. Furthermore, for environments such as CVRP, the required minimum number of vehicles can always be determined beforehand based on given demand, vehicle capacity and other possible constraints. **Input** number of vehicles to use  
```
1:while there are customers to service and available vehicles do
2: wait until at least one vehicle is available (advance time);
3:if no valid customers then
4: end route by returning the vehicle to the depot;
5:endif
6:nextCustomer \(\leftarrow\) customer with the best value of the priority function;
7: add nextCustomer to current route;
8:endwhile
```  
**Algorithm 2** Parallel VRP meta-algorithm  
The time complexity of priority scheduling depends on the meta-algorithm, but it is in most cases negligible compared to search-based techniques, which allows using this method for on-line scheduling [49] and in dynamic conditions. It should be noted that the priority function must be previously evolved. The evolution process itself may take several hours on average, but this can always be performed offline, before the actual scheduling occurs.
_applied_  
_sciences_  
_Article_  
**Breast Cancer Prediction Based on Differential Privacy and Logistic Regression Optimization Model**  
**[PERSON] 1,*, [PERSON] 1, [PERSON] 1,2, [PERSON] 1, [PERSON] 1 and [PERSON] 1**  
1 School of Science, Hubei University of Technology, Wuhan 430068, China; [EMAIL_ADDRESS] (N.W.); [EMAIL_ADDRESS] (Y.Z.); [EMAIL_ADDRESS] (K.M.); [EMAIL_ADDRESS] (M.T.); [EMAIL_ADDRESS] (G.C.)
2 School of Computer Science and Technology, Wuhan University of Bioengineering, Wuhan 430415, China
* Correspondence: [EMAIL_ADDRESS]  
**Abstract:** In order to improve the classification effect of the logistic regression (LR) model for breast cancer prediction, a new hybrid feature selection method is proposed to process the data, using the Pearson correlation test and the iterative random forest algorithm based on out-of-bag estimation (RF-OOB) to screen the optimal 17 features as inputs to the model. Secondly, the LR is optimized using the batch gradient descent (BGD-LR) algorithm to train the loss function of the model to minimize the loss. In order to protect the privacy of breast cancer patients, a differential privacy protection technology is added to the BGD-LR model, and an LR optimization model based on differential privacy with batch gradient descent (BDP-LR) is constructed. Finally, experiments are carried out on the Wisconsin Diagnostic Breast Cancer (WDBC) dataset. Meanwhile, accuracy, precision, recall, and F1-score are selected as the four main evaluation indicators. Moreover, the hyperparameters of each model are determined by the grid search method and the cross-validation method. The experimental results show that after hybrid feature selection, the optimal results of the four main evaluation indicators of the BGD-LR model are 0.9912, 1, 0.9886, and 0.9943, in which the accuracy, recall, and F1-scores are increased by 2.63%, 3.41%, and 1.76%, respectively. For the BDP-LR model, when the privacy budget \(\varepsilon\) is taken as 0.8, the classification performance and privacy protection effect of the model reach an effective balance. At the same time, the four main evaluation indicators of the model are 0.9721, 0.9975, 0.9664, and 0.9816, which are improved by 1.58%, 0.26%, 1.81%, and 1.07%, respectively. Comparative analysis shows that the models of BGD-LR and BDP-LR constructed in this paper perform better than other classification models.  
**Keywords:** breast cancer; feature selection; batch gradient descent; differential privacy; logistic regression
# Go Wider: An Efficient Neural Network for Point Cloud Analysis via Group Convolutions
## 1 Introduction
convolutions over irregular point clouds, which can leverage the advantages of MLP convolutions but also reduce the redundancies by making use of sparse MLP convolutions. In the area of point cloud analysis, there are also other methods proposed to decrease model complexity. For instance, the models proposed in [14; 15] attempt to develop efficient CNN-like kernels to reduce the use of MLP-like operations. Conversely, _PointNet++_[11] leverages the use of Farthest Point Sampling (FPS) to downsample the points in advance before applying the PointNet architecture to the features. However, only few researchers studied the impact of channel extension and wider neural networks. Inspired by group convolutions as introduced by _AlexNet_ and _ShuffleNet_[20; 22], we primarily focus on building a deep-wide neural network to reduce redundancy and also achieve high performance. The main contributions of this paper are summarized as follows:  
* We propose an efficient deep-wide neural network, named _ShufflePointNet_, aiming at significantly reducing redundancies in depth and exploiting more features information in width for better point cloud understanding. * We split features into groups and shuffle them to allow features between groups to exchange respective information. * We evaluate our model on both small point cloud dataset (e.g., ModelNet40) and real world large-scale point cloud datasets (e.g., S3 DIS and KITTI), and we compare both accuracy and latency performances to various state-of-the-art models. * To the best of our knowledge, ShufflePointNet is the first deep-wide model that employs the channel shuffling operation and group convolution to efficiently capture local representations for point cloud.
# A Review of the Synthesis and Applications of Polymer-Nanoclay Composites
## 4 Conclusions and Prospects  
The development of innovative polymer-nanoclay composites has resulted in novel materials with specialized properties, which are mainly dependent on the type of modified nanoclay and synthesis approach. In general, melt blending is considered to be an industrially viable, as well as an ecofriendly synthesis approach. The in-situ polymerization technique delivers more control of the synthesis process in terms of the grafted amounts of organics, the clay interlayer spacing and the dispersion of nanoclays in the polymer matrix. Three different synthesis methods and their impact on the structure and properties of polymer-nanoclay composites were discussed in this review. Applications of polymer-nanoclay composites have gained momentum and these composites show promise in a wide range of innovative applications. This paper presents recent developments in novel polymer-nanoclay composites with potential applications in various industries, such as petroleum, food packaging, biomedical, and wastewater treatment. Some composites exhibit excellent reinforcement characteristics for physicochemical properties of materials. The process of selecting combinations of polymer and nanoclay to design and synthesize composites appears to be without a well-established scientific principle. Thus, the theory and modeling related to the design of polymer-nanoclay composites may require additional focus to provide a better understanding of high-performance composites. The Freedonia Group [150] estimates that by 2020, the demand for novel composite materials is likely to increase to about 3.2 million tons and at a cost of US$ 15 billion per year. Major areas of application for future novel polymer-nanoclay composites are likely to include health (biomedical applications), safety (food packaging) and the environment (biodegradable materials).  
Writing-original draft preparation, F.G.; writing-review & editing, S.A. and Y.H.; visualization; YJ.  
This work is in part supported by the National Key R&D Plan (No. 2017 YFC0210202-1) and the Fundamental Research Funds for the Central Universities of China (No. 2015 ZZD3).  
S.A. thanks [PERSON] at the University of Wyoming Carbon Management Institute for our lively discussions.  
The authors declare no conflict of interest.
## 2 Experimental Procedure  
### Materials and Specim Preparation  
In this test, the mixing proportions (determined by the strength grade of C35 in Chinese standard) of concrete specimens are shown in Table 1. The ordinary Portland cement (strength grade of 42.5), the well graded coarse aggregate (grain diameter was 5-16 mm), the fine aggregate (continuously graded medium size sand with a fineness modulus of 2.42) and tap water was used. The performance parameters of basalt fiber are shown in Table 2. The volume fraction of basalt fibers are 0.1%, 0.2%, 0.3% and 0.4% respectively. The properties of fly ash are as follows: density 2.4 g/cm\({}^{3}\), fineness 43 \(\upmu\)m, moisture content about 5%. Considering that ready-mixed concrete with water reducer is often used in practical engineering, the naphthalene-based water reducer with 7-9 pH values and 20-35% water reduction rate is used in this test.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Item** & **Water** & **Cement** & **Fine Aggregate** & **Coarse Aggregate** & **Fly Ash** & **Water Reducer** \\ \hline Proportion & 0.56 & 1 & 2.03 & 3.80 & 0.40 & 0.02 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Mixing proportions of concrete specimens.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Diameter** & **Length** & **Tensile Strength** & **Elasticity Modulus** & **Fracture** & **Density** \\
**(\(\upmu\)m)** & **(mm)** & **(MPa)** & **(GPa)** & **Elongation (\%)** & **(kg/m\({}^{3}\))** \\ \hline
17.4 & 12 & \(\geq\)2000 & \(\geq\)85 & 2.5 & 2699 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Basalt fibers performance parameters.  
The BFRC was cast in the shape of a 150 mm \(\times\) 150 mm \(\times\) 150 mm cube. After 1 day curing at the room, the cubes were removed from molds and kept in water at 20 \(\pm\) 2 \({}^{\circ}\)C for 28 days. Before the test, 50 mm \(\times\) 25 mm cylinder specimens were made from cubes through coring, cutting process. To ensure that the non-parallelism and non-perpendicularity were less than 0.02 mm, the two end faces of the specimens were polished
# Evaluating the Efficiency of Physical and Cryptographic Security Solutions for Quantum Immune IoT
## 5 Evaluation Approach for Quantum Immune Security
### Implementations for IoT Key Exchange Scenario
#### 5.2.2 Modeled Cryptoalgorithms and Testbed  
We studied the costs of key establishment in ARM platforms. To enable simulations, we created workload models for the NewHope and Frodo algorithms. The models were created using the workload model generators of the ABSOLUT toolset and the source codes from the Open Quantum Safe (OQS) project [59; 60]. OQS is an open source effort to incorporate and adapt quantum-immune cryptographic software into a single library. Furthermore, the project integrates the key exchange algorithms to OpenSSL, which is an implementation of the Transmission Layer Security (TLS) protocol. The project has developed a C-language library (oqslib) of implementations for different key exchange algorithms. The supported key exchange algorithms include NewHope and Frodo (with recommended parameters). The library can be executed on the Intel and 64 bit ARM platform, but (14 November 2017 version) does not work e.g., on Raspberry Pi 3 devices with 32-bit Raspbian operating systems. However, the simulation models can be executed on a PC to evaluate the energy efficiency of the target system with a 64-bit OS.  
The OQS library includes a test harness and a benchmarking functionality to evaluate and compare the performance of the implementations. Essentially, the test runs [PERSON]'s and [PERSON]'s algorithms for key exchange in a single machine where communication delays are not relevant, and measures the time and computation cycles used. We utilized the source codes from oqslib and test harness to generate NewHope and Frodo models for our simulator.
## 3 Results  
### Search Results  
The database search for studies published in English since 2000 yielded a total of 1166 papers from Engineering Village, and 129 papers from Scopus. After removing 458 duplicates, the titles and abstracts of papers were screened, which resulted in 79 papers for full-text assessment. These papers were then reviewed to determine whether they met the inclusion or exclusion criteria. Finally, a total of 36 papers met the inclusion criteria and thus were included in the data extraction and analysis. Figure 1 illustrates the flow of the study selection process.  
Figure 1: Search Strategy Flow Chart.
# Interpretable Machine Learning for Legume Yield Prediction Using Satellite Remote Sensing Data
## 2 Materials and Methods
### Machine Learning Used for Yield Prediction  
#### 2.3.1 Tested Machine Learning Algorithms  
In order to predict crop yield based on spectral bands and VIs, six ML algorithms were employed, demonstrating different modeling techniques: (a) tree-based (Decision Tree (DT), Extreme Gradient Boosting (XGBoost), Random Forest (RF)); (b) distance-based (K-Nearest Neighbors (KNN)); (c) kernel-based (Support Vector Regressor (SVR)); and (d) neural network-based (Multi-Layer Perceptron Regressor (MLPR)) models.  
In short, tree-based models were particularly useful, because of their inherent feature importance estimation, while KNN offered a simple, efficient distance-based approach for localized yield prediction. SVR, leveraging the kernel-based optimization, had the ability to model high-dimensional nonlinear interactions, while MLPR explored DL capabilities in learning complex patterns from VIs and spectral inputs. The diversity of these algorithms facilitated a thorough assessment of the models' predictive accuracy and their ability to generalize effectively to unseen crop yield data.
## 5 Architecture, Optimization, and Technical Challenges in LLM4 Rec
### Performance Evaluation and Benchmarking of LLM4 Rec
#### 5.4.2 Evaluation Metrics and Performance Indicators  
Performance evaluation of LLM4 Rec involves both traditional metrics and emerging indicators tailored to the specific challenges of large-scale models. Key metrics include  
* Precision@K and Recall@K evaluate the accuracy of the top-K recommendations.
- NDCG@K (Normalized Discounted Cumulative Gain) assesses ranking quality by accounting for the positions of relevant items in the recommendation list [106].
* Intra-List Diversity (ILD) measures the variety of items in recommendation lists.
- Coverage evaluates the system's ability to recommend items across the entire catalog [92; 107].
* Click-Through Rate (CTR) measures the likelihood of a user clicking on recommended items.
- Dwell Time indicates user satisfaction by tracking how long users interact with recommended content [97].
* Temporal Adaptability evaluates how well recommendations evolve with changing user preferences.
- Sentiment Sensitivity measures the model's ability to align recommendations with user sentiments, especially in domains like health and wellness [104].  
LLM4 Rec systems excel in improving ranking metrics such as NDCG and Recall while also addressing diversity and relevance challenges.