esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Distributed Drive Vehicles Based on MCSRCKF
### Square-Root Cubature Kalman Filter  
For a better solution of nonlinear vehicle state estimation, a square root filtering idea is introduced in CKF; the QR decomposition is used instead of the Cholesky decomposition step from CKF, while the square root is directly passed in the iterative process to avoid the square root step, so it can cope with filtering divergence caused by the loss of positivity in the error covariance matrix, which improves the accuracy of the algorithm and lowers the calculated complexity. The excellent performance of SCKF has good application prospects in the engineering field.  
First, the system state equation obtained by discretization of Equation (9) using the first-order Euler method follows:  
\[x_{k+1}=f(x_{k},u_{k})+q_{k}=\begin{bmatrix}v_{x,k}\\ v_{y,k}\\ r_{k}\\ \omega_{fl,k}\\ \omega_{fr,k}\\ \omega_{rl,k}\\ \omega_{rr,k}\end{bmatrix}\]\[+\left[\begin{array}{c}v_{y,k-1}\dot{r}+a_{x,k-1}\\ -v_{x,k-1}\dot{r}+a_{y,k-1}\\ \frac{1}{I_{x}}\begin{pmatrix}a\left(F_{yff}cos\delta_{fl}+F_{yff}cos\delta_{ fr}\right)+a\left(F_{xff}sin\delta_{fl}+F_{xff}sin\delta_{fr}\right)-b\left(F_{yff}+F_{ yrr}\right)\\ -\frac{B_{f}}{2}\left(F_{yff}sin\delta_{fl}+F_{yff}sin\delta_{fr}\right)+\frac{B_{f}}{2} \left(F_{xff}cos\delta_{fl}+F_{xff}cos\delta_{fr}\right)+\frac{B_{f}}{2}\left( F_{xvl}+F_{xrr}\right)\\ \frac{1}{L_{w}}\left(T_{dff}-T_{bff}-R_{0}F_{xff}\right)\\ \frac{1}{L_{w}}\left(T_{dff}-T_{bfr}-R_{0}F_{xff}\right)\\ \frac{1}{L_{w}}\left(T_{dff}-T_{brr}-R_{0}F_{xrt}\right)\\ \frac{1}{L_{w}}\left(T_{dff}-T_{brr}-R_{0}F_{xrt}\right)\\ \frac{1}{L_{w}}\left(T_{drr}-T_{brr}-R_{0}F_{xrr}\right)\\ \end{array}\right].T+q_{k} \tag{19}\]  
Similarly, the system measurement equation is as follows:  
\[y_{k+1}=h(x_{k},u_{k})+r_{k}=\begin{bmatrix}0\\ 0\\ r_{k}\\ \omega_{f,k}\\ \omega_{f,k}\\ \omega_{r,k}\\ \omega_{r,k}\\ \end{bmatrix}+\begin{bmatrix}\frac{1}{m}\left(F_{xff}cos\delta_{fl}+F_{xff}cos \delta_{fr}-F_{yff}sin\delta_{fl}-F_{yff}sin\delta_{fr}\right)\\ +F_{xrt}+F_{xrr}\\ \frac{1}{m}\left(F_{xff}sin\delta_{fl}+F_{xff}sin\delta_{fr}+F_{yff}cos\delta _{fl}+F_{yff}cos\delta_{fr}\right)\\ +F_{yrt}+F_{yrr}\\ 0\\ 0\\ 0\\ 0\\ \end{bmatrix}.T+r_{k} \tag{20}\]  
According to the system equation above, the SCKF algorithm for estimating driving state based on the 7-DOF vehicle model can be derived as follows:
## 2 Methods and Performance Figures
### System Description  
As an application example of the methodology explained previously, a simple traditional solar thermal system for domestic hot water (SDHW) was considered. A Trnsys model of such system was developed to understand the effect of different control strategies. The design and components sizing was made starting from the thermal storage because the numerical model of this component [70] was validated using monitoring data from a SolarCombi+ installation [71] following the procedure reported in Reference [72].  
The thermal storage consists of a 1 m\({}^{3}\) water container with two internal heat exchangers, one for the solar primary loop and one for the domestic water loop, as shown in Figure 5. A stratifying storage was employed to take advantage of the increased heat exchange between the stored water and the DHW heat exchanger due to the stratification of the temperature inside the storage. It was assumed that a mixture of water and propylene glycol (30% in volume) was present in the primary loop to avoid freezing during winter seasons. A collectors area of 12 m\({}^{2}\) was adopted using standard design rules of thumbs of solar thermal systems for DHW [20]. The DHW request profile was computed using DHWcalc [73; 74] considering a multi-family house with a daily consumption of 50 L/gg per person and three families composed of four persons each. An electrical backup system was considered to fulfil the energy demand reaching the DHW set temperature of 40 \({}^{\circ}\)C when not enough solar energy was harvested and stored in the tank. The configuration with two heat exchangers was used also to avoid problems with bacteria from the _Legionella_ genus [20].  
Figure 4: Output signal of the FC under two different types of membership function deformations.  
(a) (H)-FC family; (b) (M)-FC family.  
Figure 5: Layout of the solar domestic hot water (SDHW) system.
## 2 Related Work
The work in [21] jointly considers VEC server selection, offloading strategy, computation resource allocation, and load balancing among VEC servers for a multi-user multi-server VEC system. Due to the fact that the task processing delay is short, moving vehicles may have high satisfaction, and the utility function as a satisfaction function should monotonically decrease with the delay of TaV. Moreover, because of the limitation of computation resources, offloading can be less efficient and result in overload if all vehicles select the same VEC server to offload their task. The logarithmic utility is used here to satisfy the delay and load balancing requirements. Work in [22] considers a fixed number of time periods and minimizes the average offloading delay below the following three modes: (1) V2V offloading: TaVs directly offload tasks to their SeVs with surplus computing resources in a distributed manner; (2) V2I2V offloading: When there are no SeVs, tasks are first offloaded to an RSU and then assigned to other vehicles in a centralized manner; (3) V2I offloading: Tasks are offloaded to RSUs for direct processing. A comparison of the characteristics and the pros and cons in the recent research is presented in Table 1.
## 6 Experiments
### Mode Discovery and Mode Coverage  
In this experiment, we would like to study how our approach behaves for the mode collapse problem. To achieve this, we train three GAN models: DCGAN [2], WGAN-GP [14] and our SoftGAN. We use the Geometry Score [13] to assess the mode discovery and mode coverage abilities of different models. This approach allows us to compare the topology of the underlying manifolds for point clouds in a stochastic manner, providing us with a visual way to detect the mode collapse and a score, which allows us to compare the quality of various trained models. The lower the score is, the more modes are covered by the generated images. If the value of the Geometry Score does not decrease or even rises as the training progresses, the model may suffer from the mode collapse problem. Following the parameter settings of the original works, we train each model for 100,000 iterations and generate 1000 samples every 100 iterations to evaluate the Geometry Score using the parameter \(\gamma=\frac{1}{1000}\). Since the Geometry Score is calculated with only 1000 samples, the results are fluctuating. So, we smooth the result using the tool in Tensorboard (a suite of visualization tools to make it easier to understand, debug and optimize the machine learning workflow) with the smoothing parameter equal to 0.6 and then report the obtained results in Figure 8. It can be seen from the figure that the SoftGAN shows an advantage in mode coverage at the beginning of training. Compared with those of WGAN-GP and DCGAN, the Geometry Score of our method rapidly decreases to a lower value and is almost at a relatively lower level compared to the other two.  
Figure 7: The change in the Earth Mover’s Distance (EMD) during the training process with corresponding generated samples (LSUN bedroom dataset [31]). The horizontal axis represents the number of iterations of the training, and the vertical axis represents the Earth Mover’s Distance calculated using a batch of samples. The images below are the images generated from the same noise for every 10,000 th iteration. The pictures are best viewed magnified on screen.
## 3 Proposed Framework
### Data Pre-Processing
#### 3.2.4 Machine Learning Classifiers  
After transforming data into numerical vectors, in order to examine our approach in classification perspective, we employed k-fold cross validation on training data. We randomly divided the data into five-fold CV cross validation; we applied different machine learning classifiers to classify the information into seven categories, which we defined in detail previously in Section 3.2.3. The major advantage of k-fold cross validation is that every observation of data has a chance of appearing in the training and testing sets. The machine learning classifiers used were: SVM (Support vector machine) with the linear kernel, with the radial basis kernel and with the sigmoid kernel; RF (Random forest); multinomial naive Bayes; KNN (k-nearest neighbor) and the Logistic regression classifier. Typically, classification algorithms have accuracy, F1 measure, precision and recall measures to measure the performance of classifiers. Accuracy is a measure used to identify all correctly classified categories; we determine the classification accuracy of each fold on our datasets, and evaluate the average classification accuracy at the end. Precision is a measure used to identify the positive class from all predicted positive classes, while recall is a measure to correctly identify positive class from actual positive classes, and F1 is a harmonic mean of precision and recall [53], ([[https://medium.com/analytics-vidhya/accuracy-vs-f1-score-6258237](https://medium.com/analytics-vidhya/accuracy-vs-f1-score-6258237) becca2]([https://medium.com/analytics-vidhya/accuracy-vs-f1-score-6258237](https://medium.com/analytics-vidhya/accuracy-vs-f1-score-6258237) becca2) (accessed on 10 July 2020)). The average accuracy, F1 score, precision, and recall score of each classifier are given in Table 2. The results indicate that SVM with the radial basis kernel setting performs best among all. In the different situations on textual data, different classification algorithms give different results, while in the context of our data, in which we use it to label the data by using classifiers, among all
## 2 Materials and Methods
### Statistical Analysis and Visualization  
Statistical differences in the SCFA and \(\alpha\)-diversity indices were estimated using analysis of variance (ANOVA). Microbial community differences were estimated using analysis of molecular variance (AMOVA). The differential abundance of microbes between the two groups was evaluated using linear discriminant analysis effect size (LEfSe) and linear discriminant analysis (LDA) in which scores higher than 3.0 and \(p\)-values of less than 0.05 were considered significant [18]. Microbial community types for each fecal donor were identified based on the Dirichlet multinomial mixtures as identified by the get.communitytype mothur subroutine using default parameters [19]. STAMP [20] was used to perform the Wilcoxon t-test to evaluate significant differences in metabolic activities with adjusted \(p\)-values by Bonferroni multiple testing correction. A heatmap was drawn using R packages heatmap.2 and pheatmap.
## 4 Traffic Filtering-Based Techniques
### Filtering HSR Traffic (FHT)  
#### 4.5.1 FHT Concepts  
FHT [16] is an HSR traffic reduction technique that solves all HSR issues mentioned in Section 3. The FHT approach prunes unicast traffic for both unused DANH and QuadBox rings, and removes duplicated and circulated traffic from all active rings in HSR networks.  
The FHT define two types of QuadBoxes: access QuadBoxes and trunk QuadBoxes. Access QuadBoxes are QuadBoxes that connect to at least one DANH ring, and trunk QuadBoxes are  
Figure 9: Filtering unicast traffic under the EPL approach.  
QuadBoxes that connect two QuadBox rings. To filter unicast traffic for unused DANH rings, each access QuadBox learns MAC addresses of DANH nodes that connect to its DANH ring and store these MAC addresses in a MAC1 table. To prune unicast traffic for unused QuadBox rings, each trunk QuadBox builds a MAC2 table that is a collection of MAC1 tables of access QuadBoxes that connect to its QuadBox rings.  
To implement the functionality of filtering unicast traffic, the FHT approach defines two traffic filtering rules:  
* Filtering rule 1: Filtering inbound traffic for DANH rings. Access QuadBoxes are used to filter inbound traffic for DANH rings based on their MAC1 table. An access QuadBox node forwards a unicast frame into its DANH ring if and only if its MAC1 table contains the destination MAC address of the frame.
* Filtering rule 2: Filtering outbound traffic for QuadBox rings. Trunk QuadBox nodes are used to filter outbound traffic for QuadBox rings based on their MAC2 table. A trunk QuadBox node forwards a unicast frame from its first QuadBox ring to its second QuadBox ring if and only if the first QuadBox ring's MAC2 table does not contain the destination MAC address.  
Figure 9(a) shows the process of filtering inbound unicast traffic for a DANH ring at an access QuadBox node, and Figure 9(b) shows the process of filtering outbound unicast traffic for a QuadBox ring at a trunk QuadBox node.  
Additionally, the FHT approach removes circulated traffic from active rings by defining a new traffic forwarding rule.  
* Forwarding rule: QuadBoxes forward a unicast frame once, at most. When a QuadBox receives a unicast frame, it checks if the frame has previously been received and forwarded. If not, it forwards the frame. If so, it discards the frame. This new forwarding rule allows FHT to remove circulated traffic in rings.
## 5 Results and Discussion
### Review Rating Prediction
#### 5.2.4 Social Media App Review Dataset  
Table 8 represents a comparative analysis of the Review Rating Prediction model for several performance metrics through altering training data. The analysis of TD-Spider TaylorChoA-GRU for the precision, recall and F-measure is listed below. In 90% of training data, the precision of the developed TD-Spider TaylorChoA-GRU is 0.841. The recall of TD-Spider TaylorChoA-GRU is 0.865 for 90% of training data and, considering 90% of training data, the F-measure of the developed model is 0.853.  
Figure 11 represents the accuracy and loss for the Review Rating Prediction model. Figure 11a represents the accuracy curve, which indicates both the testing accuracy and training accuracy. Figure 11b represents the loss curve, which indicates both the testing loss and training loss.  
Figure 10: Performance analysis based on (**a**) accuracy and (**b**) loss for Review Rating Prediction using Yelp 2014 dataset.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Metrics** & **Data (\%)** & **DNN** & **CNN+LSTM** & **Bi-GRU** & **CNN** & **[**34**]** & **Proposed** \\ \hline \multirow{4}{*}{Precision} & 60\% & 0.659 & 0.678 & 0.704 & 0.754 & 0.778 & 0.807 \\  & 70\% & 0.678 & 0.685 & 0.725 & 0.778 & 0.799 & 0.814 \\  & 80\% & 0.699 & 0.714 & 0.741 & 0.789 & 0.814 & 0.829 \\  & 90\% & 0.701 & 0.733 & 0.754 & 0.799 & 0.825 & 0.841 \\ \hline \multirow{4}{*}{Recall} & 60\% & 0.678 & 0.690 & 0.715 & 0.769 & 0.785 & 0.814 \\  & 70\% & 0.687 & 0.699 & 0.735 & 0.778 & 0.799 & 0.824 \\  & 80\% & 0.699 & 0.704 & 0.745 & 0.787 & 0.814 & 0.848 \\  & 90\% & 0.704 & 0.715 & 0.755 & 0.807 & 0.825 & 0.865 \\ \hline \multirow{4}{*}{F-measure} & 60\% & 0.668 & 0.684 & 0.709 & 0.761 & 0.782 & 0.811 \\  & 70\% & 0.683 & 0.692 & 0.730 & 0.778 & 0.799 & 0.819 \\ \cline{1-1}  & 80\% & 0.699 & 0.709 & 0.743 & 0.788 & 0.814 & 0.838 \\ \cline{1-1}  & 90\% & 0.703 & 0.723 & 0.754 & 0.803 & 0.825 & 0.853 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Comparative analysis of Review Rating Prediction for social media app review dataset in terms of precision, recall and F-measure.
# Multimodal Magnetic-Plasmonic Nanoparticles for Biomedical Applications
## 2 Synthetic Methods
### Thermal Decomposition Techniques
#### 2.2.2 Thermal Decomposition for Synthesis of Magnetic-Plasmonic Core/Silica/Shell Nanoparticles  
Similar to the approaches used in coprecipitated nanoparticles, often an intermediate layer is introduced onto the either the magnetic or plasmonic moiety in order to facilitate easier adhesion of the two components, and silica remains a popular choice. Flower-like Au@Fe\({}_{3}\)O\({}_{4}\) nanoparticles were prepared by [PERSON] et al. using thermal decomposition followed by a seeded growth approach in which the Fe\({}_{3}\)O\({}_{4}\) nanoparticles are grown onto the gold nanoparticles to form heteranocrystals [41]. Two iron precursors, Fe(acac)\({}_{3}\) and Fe(CO)\({}_{5}\) were used in the presence of pre-formed gold nanoparticles to investigate the effect that different reaction parameters have on the morphology of the hetero-nanocrystals. A silica coating is then applied to illustrate the ease with which the resultant nanoparticles can be transferred from the organic to the aqueous phase, and hence their applicability for biomedical applications. Silica is also utilised in work carried out by [PERSON] et al., where the Fe\({}_{3}\)O\({}_{4}\) nanoparticles are coated with silica and then further functionalised with APTES to obtain surface amino groups [42]. Gold nanoseeds are prepared in situ with the pre-formed Fe\({}_{3}\)O\({}_{4}\)@SiO\({}_{2}\) and NaBH\({}_{4}\) as a reducing agent; the Au seeds adhere to the silica coating electrostatically. Nanortatles are then achieved through etching of the silica using potassium carbonate.  
Acetylacetonates can be used in the thermal decomposition method to prepare magnetic alloys, as is demonstrated by [PERSON] et al. through their synthesis of multimodal hybrid FePt@SiO\({}_{2}\)@Au nanoparticles [43]. Fe(acac)\({}_{3}\) and Pt(acac)\({}_{3}\) were added to a mixture of benzyl ether, oleic acid and oleylamine, and heated to 200 \({}^{\circ}\)C for 30 min, and then heated to 280 \({}^{\circ}\)C for a further 30 min. The FePt magnetic-plasmonic alloy was used as it has a much higher saturation magnetisation than iron oxides; it also is thought to be a better contrast agent. A silica shell was added using TEOS and further functionalised with APTES. The gold nanoparticles were added using a two-step seeding approach. This was realised through adding HAuCl\({}_{4}\)\(\cdot\)3H\({}_{2}\)O and NaOH to the FePt@SiO\({}_{2}\) mixture and stirring at 70 \({}^{\circ}\)C for 5 min, allowing the seeding of Au(OH)\({}_{3}\) onto the silica surface. Ice-cold NaBH\({}_{4}\) was then used to reduce the gold onto the surface of the nanoparticles.
# Lossless Compression of Large Aperture Static Imaging Spectrometer Data +
## 4 Experimental Results  
In order to validate the effectiveness of the proposed scheme, we conducted a series of experiments on 30 real data sets, including various scenes from Xi'an City and Yantai City, China, acquired by the LASIS equipment. The two-dimensional CCD detector is composed of 256 rows in the along-track direction that correspond to 256 OPD bands, as well as 500 columns in the cross-track direction, representing different spatial locations.  
This section is divided into three parts to examine the influence of the three functional stages of the proposed compression approach on the overall compression performance. In each stage, several experiments are conducted for different related parameters, and one or more interrelated parameters are evaluated while the other parameters remain constant in each experiment. The compression performance is evaluated using the compression ratio, which is defined as the input image size divided by the compressed image size. A higher compression ratio implies better compression performance. The subsequent results display the average value obtained from all the test images.
## 1 Introduction
### Model Approaches  
STLF models have been developed through two major approaches, namely, statistical approaches [8; 9; 10; 16; 17; 18; 19] and Artificial Intelligence or data-driven approaches [20; 21; 22; 23; 24; 25].
## 1 Introduction  
The autonomous grounded robot (AGR) is a kind of mobile robot that can be used in any scenario with prominent applications such as assembly lines, storehouse management, production transportation, or as a part of pick-and-place systems. It appears not only in industry but also in logistics or international trade. The principal motivation of current research is to improve the low rate of AGR applications in reality. This autonomous hardware is expected to be more flexible, modular, and intelligent while it requires less cost for initial installation and uses resources to operate and develop. To solve these drawbacks, numerous developers have studied in a large scale of industrial fields.  
In [1; 2], a low-cost, efficient vehicle-guiding method using a consumer-grade camera and separated markers was investigated to drive for indoor environments. The symbols of capital letters or triangles which are easy to produce and maintain indicate the routing guide to the target location. In the same directional research, authors [3] applied a multiple-input multiple-output antenna radar to achieve high location precision. The results compared with the traditional global positioning system denoted excellent outcomes for autonomous robots in the port. In the new trend of artificial intelligence, more research related to fuzzy scheme or neural network control has been investigated. To recognize the operating environment and avoid an obstacle intelligently, a fuzzy controller which is constructed by rough sets and an adaptive neuro-fuzzy inference system was presented in [4]. Duration, maximum tracking error, and mean tracking error are computed to clarify the performance. The achievements in saving time and lessening maximum tracking error and mean tracking error have been proven as superior results. In other contexts, due to the complexity of surrounding space and the uncertainty of sensor data, a motion planning system based on hybrid deep learning was addressed [5]. Firstly, the convolutional neural networks to construct an auto-encoder model and reduce the dimension of the input image were initiated. Later, a path-tracking model based on recurrent neural networks was utilized to build the working map. Consequently, a control model and an evaluation model were set. In road environments, it promised highly robust visual navigation and an advanced ability of self-learning for autonomous vehicles.
# Modeling Evacuees' Intended Responses to a Phased Hurricane Evacuation Order
###### Abstract  
Phased evacuation is an under-studied strategy, and relatively little is known about compliance with the phased process. This study modelled households' responses to a phased evacuation order based on a household behavioral intention survey. About 66% of the evacuees reported that they would comply with a phased evacuation order. A latent class logit model sorted evacuees into two classes (\"evacuation reluctant\" and \"evacuation keen\") by their stakeholder perceptions (i.e., whether government agencies have responsibility for the safety of individuals) and evacuation perceptions (i.e., whether evacuation is an effective protective action), while risk perception becomes non-significant in interpreting their compliance behavior to a phased evacuation order. Those that evacuate to the home of friends/relatives and/or bring more vehicles during evacuation are less likely to follow phased evacuation orders. \"Evacuation reluctant\" individuals with a longer housing tenure are more likely to follow phased evacuation orders. \"Evacuation keen\" individuals with a longer travel delay expectation are more likely to comply with phased evacuation orders. This study not only unveiled the impacts of incorporating three psychological perceptions (i.e., risk, stakeholder, and evacuation perceptions) in modeling compliance behavior (e.g., parameter sign/significance shift) but also provides insights of evacuees' compliance behavior to phased evacuation orders.  
hurricane evacuation; phased evacuation; warning responses; behavioral intention; compliance