esa-sceva/satcom-chunk-collection huggingface dataset:
# Design of SBS-Modified Bitumen Stabilizer Powder Based on the Vulcanization Mechanism
###### Abstract  
In order to improve the problem of the bad thermal storage stability of Styrene-Butadiene-Styrene Block Copolymer Modified Bitumen (SBS-modified bitumen), the vulcanization reaction was selected as the basic mechanism for the research and development of the stabilizer. Sulfur, tetramethylthiuram disulfide (TMTD), zinc oxide, butylated hydroxytoluene (BHT), kaolin (Al2O3-2 SiO2:2H2O) and carbon-white (SiO2) were used as raw materials, and 32 kinds of components with different contents of raw materials were designed. The 48-h segregation softening point and other modified bitumen technical indicators, such as ductility, penetration, penetration index, viscosity, and so on, were tested and analyzed. The fluorescence microscope test was also conducted to explore the crosslinking situation based on the vulcanization reaction. The results show that the component with good performance of the SBS-modified bitumen stabilizer powder was S:TMTD:ZnO:BHT: Al2O3-2 SiO2:2H2O = 60:16:4:20:1250 or S:TMTD:ZnO:BHT:SiO2 = 60:16:4:20:625. The optimum content of stabilizer powder was 0.9% (S/modified bitumen). The stabilizer was dry powder and can be directly put into bitumen. It can cross-link SBS and especially has an effect on preventing SBS segregation. The technical performance of SBS-modified bitumen with stabilizer powder, such as hot temperature performance, low temperature performance, anti-aging performance and thermal storage stability, all meet the relevant specifications.  
SBS-modified bitumen; stabilizer; thermal storage stability; vulcanization reaction; 48-h segregation softening point difference +
Footnote †: journal: Applied
## 2 Modal Based Damage Detection: Development & Challenges
### Development of Modal-Based Damage Sensitive Features
#### 2.1.4 Modal Curvatures  
[PERSON] et al. [51] expanded mode shape monitoring theory to focus on modal curvatures, which are mode shapes' second derivative, in a technique known as the Modal Curvature Method (MCM). It utilises the relationship between curvature (\(\varphi^{\prime\prime}\)) and flexural stiffness (\(EI\)), as per Equation (2), with the premise that one can discern stiffness loss through increased modal curvature values. Equation (3) depicts how the MCM simply assesses the damaged curvature (\(\varphi_{d,j}^{\prime\prime}\)) and undamaged curvature (\(\varphi_{u,j}^{\prime\prime}\)) values to detect damage. This methodology demonstrated a high level of damage sensitivity and produced good results when tested [52]. [PERSON] and [PERSON] [53] advanced the MCMby developing the Modal Curvature Squared Method, which amplifies curvature variations so that abnormal changes are clearly separated and more easily discerned.  
The MCM also has some drawbacks; it requires many sensors to define higher modes and its performance is dependent on the number of modes considered [54]. Moreover, using vibration data to calculate curvatures inherently attains errors due to the application of the central difference approximation method to displacement mode shapes. This issue is further amplified by high-frequency noise that increases the variance of the extracted damage features [55], while using larger sampling signals to avoid noise can cause truncation error [56]. These calculation errors have been deemed unacceptable by some authors who compared the results with curvatures calculated from measured strain values [57]. Given such drawbacks, the MCM alone is not recommended for the purposes of damage identification, although its appropriate utilisation in conjunction with other sub-optimal modal parameters such as frequencies can enhance each other's credentials. This was demonstrated by [PERSON] et al. [58], who enhanced natural frequency's damage sensitivity on an arch bridge using a course sensor distribution by taking advantage of MCM's sensitivity to local variations to overcome natural frequency's indeterminacy of damage localisation in symmetric structures.  
\[\varphi^{\prime\prime}=\frac{M}{EI} \tag{2}\]  
\[\Delta\varphi^{\prime\prime}=\sum_{j=1}^{m}\left(\begin{array}{cc}\varphi^{ \prime\prime}_{d,j}-&\varphi^{\prime\prime}_{u,j}\end{array}\right) \tag{3}\]
## 3 Results
### Sensitivity Analysis  
#### 3.2.1 Global Sensitivity Analysis of the FPAR  
A global sensitivity analysis was conducted using simulated canopy spectral data with the PROSAIL model and the EFAST algorithm to quantify the relative contributions of the leaf and canopy parameters to the variation in the FPAR (Figure 5). The results consistently indicated that the three parameters of LAI, SZA and ALA produced the dominant influences on the variability of the FPAR among the nine parameters of interest, accounting for over 95% of total variance. C\({}_{\text{ab}}\) and posoil ranked fourth and fifth, respectively, in terms of their contributions. Although there was a slight difference in the total-order indices between the general and specific datasets, LAI remained the greatest contributor to the variations in the FPAR. For the general dataset, the ALA and SZA jointly dominated the variability of the FPAR, making up about 10% of the variance, except for the LAI. Meanwhile, as the parameters distribution narrowed (specific dataset), the sensi
# A Multi-Fidelity Successive Response Surface Method for Crashworthiness Optimization Problems
## 5 Multi-Fidelity Successive Response Surface  
As mentioned in the introduction, it is quite clear that the SRSM method has some limitations that limit its application to specific real-world scenarios. In this section, we introduce our improved version of SRSM, tailored for multi-fidelity crashworthiness problems, which we will refer to as the Multi-Fidelity Successive Response Surface (MF-SRS). We will provide an overview of the main workflow and then discuss its key components, highlighting the improvements over the traditional approach.  
The general workflow of the MF-SRS approach is shown in Figure 2. The first step is to formulate the optimization problem as described in Section 2. Therefore, it is necessary to define the \(d\) design variables with their respective bounds, the objective function, and any \(n_{c}\) constraint functions with associated thresholds. Next, we can start the variable domain exploration phase using a hybrid sampling strategy. In this phase, we start with an Optimal Latin Hypercube Design (OLHD) to set the highest fidelity DoE, \(\mathcal{D}_{s}\). Samples of lower fidelity are distributed by sequential sampling to gather information about areas left unexplored by the OLHD.  
After generating the datasets for each fidelity level and computing their respective responses, the datasets \(\mathcal{D}_{1},\ldots,\mathcal{D}_{s}\) undergo preprocessing, which includes scaling of input variables and normalizing the response values. These pre-processed datasets are then fed into the NARGP model, creating a metamodel for each response function. The accuracy of these metamodels is assessed using a cross-validation-based error procedure. If the metamodels do not meet user-defined accuracy targets, infill algorithms can be optionally employed to add more samples. Within the original variable domain, an initial solution is extracted using Metamodel-Based Optimization with an evolutionary algorithm. The identified optimum is verified using Finite Element Analysis (FEA) simulations with the obtained optimal design variables to measure any deviations from the initial prediction. This result is incorporated into the respective DoEs and serves as the center of the RoI for the subsequent iteration. After adjusting the range of variables using the pan-and-zoom method, new exploratory samples are added to the RoI based on the reused samples. The metamodels are then updated with this augmented data. The process iterates until a convergence criterion is met or until a predefined maximum iteration limit is reached.  
The following subsections describe the main steps of the MF-SRS framework, starting with sampling strategies. It then explores multi-fidelity response surfaces and sample reuse, RoI adjustments, and appropriate optimization approaches, and concludes with a discussion of the convergence criteria.
## 4 Model Validation
### Validation of Load Model  
Different studies were conducted to determine the optimal number of states for load using an hourly annual load dataset for the RBTS. The original RBTS with no wind power integration has been considered since the study focused on selecting the number of states for the load. Multi-state load models were developed by varying the number of states, ranging from 1 to 4, and the system was evaluated for CSR using the developed models. Table 2 presents the EENS and EDLC results, which indicate that increasing the number of load states improves the accuracy. The three-state load model was selected, as there is no change in EDLC values and only a minimal difference in EENS values when moving from three states to four. Additionally, increasing from the three-state to the four-state load model adds 781 scenarios, with each scenario requiring the evaluation of (\(C_{1}^{20}+C_{2}^{20}+C_{3}^{20}\)) number of contingencies. The number of power flows, therefore, increases multiple times per added scenario, leading to a significant increase in the computation time. To validate the selected load model, the EDLC result for the generation adequacy assessment using the three-state load model was compared to that obtained using the complete set of hourly annual load data. A comparison of the results shown in Table 3 shows that the results are very close for all practical purposes. This validates the use of a three-state load model for practical application. Furthermore, a 1.9% change in energy under the load duration curve before and after applying the three-state modeling confirms the sufficiency of the number of states to represent the load.
# Prophet-CEEMDAN-ARBiLSTM-Based Model for Short-Term Load Forecasting
## 4 Conclusions  
The paper proposes a novel load forecasting model, Prophet-CEMDAN-ARBiLSTM, for predicting the load of the next day. The ARBiLSTM block enhances forecasting accuracy and mitigates the gradient vanishing issue. Additionally, this paper tests the generalization ability of different models on the New England dataset.  
The results indicate that Prophet-CEEMDAN-ARBiLSTM combines the strengths of Prophet, CEEMDAN, and BiLSTM. Moreover, it outperforms existing models in forecasting performance across different time periods. However, this paper only considers a single forecasting factor, limiting the model's generalization ability. Future research should incorporate additional information such as temperature, humidity, and calendar information to enhance forecasting ability.  
Conceptualization, J.Y.; Data curation, X.Z.; Funding acquisition, J.Y.; Investigation, J.Y.; Resources, F.R.; Software, X.Z.; Writing--original draft preparation, W.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Electric Power Science Research Institute, Yunnan Power Grid Co., Ltd., grant number 0562002023030301 PD00004.  
**Data Availability Statement**: Public datasets were applied in the paper. These data can be found here: [[https://www.iso-ne.com/isoexpress/web/reports/load-and-demand/](https://www.iso-ne.com/isoexpress/web/reports/load-and-demand/)]([https://www.iso-ne.com/isoexpress/web/reports/load-and-demand/](https://www.iso-ne.com/isoexpress/web/reports/load-and-demand/)) (accessed on 5 November 2023).  
**Conflicts of Interest**: [PERSON] and [PERSON] are hired by the Yunnan Power Grid Co., Ltd. The authors declare that no conflicts of interest exist in the paper.
# Development of an Artificial Intelligence-Based System for Predicting Weld Bead Geometry
## 2 Materials and Methods
### SMAW, MIG, and TIG Welding Processes  
The mechanism of the shielded metal arc welding (SMAW) process is shown in Figure 2. The SMAW process uses a flux-coated consumable electrode with a metal rod at the core. The arc between the electrode and the base metal plate creates the required heat for generating the weld bead [22].  
Figure 3 shows the mechanism of the tungsten inert gas (TIG) welding process. The TIG process uses a power source, a shielding gas, and a tungsten inert gas torch. The TIG welding process is used to produce weld joints with a non-consumable tungsten electrode. In some cases, the filler rod is used to have the added material to fill up the gap between two joints. In the TIG welding process, the arc is formed between a pointed tungsten electrode and the base material plate in an inert gas such as argon (Ar), helium (He), or an Ar-He mixture [23].  
Figure 3: Mechanism of TIG welding process.  
Figure 2: Mechanism of SMAW welding process.  
Figure 4 shows the mechanism of the MIG welding process. The MIG process uses a power source, a shielding gas, and a consumable wire electrode. In the MIG welding process, the arc melts the electrode wire to add the material to the base metal plates. The shielding gas keeps the weld pool free from atmospheric contamination.
# Improved SPGD Algorithm for Optical Phased Array Phase Calibration
## 2 OPA Characteristics and System Construction  
In this work, a 512-channel OPA was used. By adjusting the wavelength and phase delay of the laser source, the beam could be controlled vertically and horizontally. The structure of our 512-channel OPA is like the 128-channel OPA described in reference [7]. The 128-channel OPA includes four parts: input coupler, optical beam splitter, thermos-optic phase shifter and optical antenna. The grating antenna is constructed on a wide silicon slab. The grating period is 470 nm, the duty cycle is 50%, and the regular 70 nm shallow etching. A large two-dimensional FOV of 104\({}^{\circ}\)\(\times\) 17.6\({}^{\circ}\) can be obtained. We also extend it to a 512-channel OPA, and it is tested by our phase calibration algorithm. More comprehensive experimental results of the 512-channel OPA will be reported in the future.  
In order to evaluate the performance of our calibration algorithm, a system is built, as shown in Figure 1. The system consists of two parts: an electric circuit and an optical circuit. The electric circuit is used to transmit signals, feed algorithms, and control the phase difference between waveguides. The optical circuit is used to supply a laser source for OPA chips. Grating is sensitive to the polarization of light, so a polarization controller is installed in this system. The algorithm fitness function is the intensity of light emitted by the OPA chip from a reflection screen.  
The calibration of phase difference between antennas mainly includes several steps. Firstly, the algorithm software works and sends signals to drive the circuit through the communication program. Then, the drive circuit converts the digital signals into analog ones and sends analog signals to phase shifters. After changing the voltage of phase shifters,  
Figure 1: The phase calibration system of an OPA.  
the light intensity of the beam far-field will change accordingly, and CCD receives the light intensity integral information, which will be fed back to the control system. Then, the algorithm process will be executed in specific logic, and the system will send the subsequent group signals to control the phase shifters. This cycle continues until the stop condition is met.
# An Evaluation of Orbital Angular Momentum Multiplexing Technology
## 5 Conclusions  
In this paper, we describe how we investigated wireless orbital angular momentum (OAM) multiplexing performance from theory to experimental perspectives. First, we conducted basic theoretical studies on wireless OAM multiplexing performance with respect to various aspects including modulation, demodulation, multiplexing, and demultiplexing. Then, we expanded our interest to experimental evaluation including a dielectric lens and end-to-end wireless transmission at 28 GHz frequency bands. We confirmed that using a dielectric lens can effectively reduce the beam divergence effect and correspondingly increase the transmission distance. In end-to-end experiments, we achieved 45 Gbps throughput using five OAM modes. In addition, we experimentally demonstrated the effectiveness of our proposed OAM-MIMO multiplexing method using a total of 11 OAM modes. In the experiments, we reached a new milestone in point-to-point transmission rates by achieving 100 Gbps at a 10 m transmission distance.  
[PERSON] and [PERSON] contributed to conceptualization and methodology, [PERSON] and [PERSON] conducted validation, [PERSON], [PERSON], [PERSON] and [PERSON] conducted data curation, [PERSON] contributed to writing original draft preparation and editing, [PERSON] supervised overall work.  
This research received no external funding.  
The authors declare no conflict of interest.
# Modeling the Health Benefits of Superblocks across the City of Los Angeles
## 3 Results
### Slow Streets and Al Fresco Dining Programs  
There were 173 slow street programs located in the City of LA that were approved by the LA Department of Transportation (LADOT), from which 116 streets (~67.1%) were included in the 597 Superblocks of the LA Superblocks Model and 112 streets (~64.7%) were included in the 530 Superblocks analyzed in the five scenarios. Ten of the 1019 slow street programs were in the Superblocks involved in Scenario 1, five streets were added to the Superblocks additionally involved in Scenario 2, 21 streets were added to the Superblocks additionally involved in Scenario 3, 38 streets were added to the Superblocks additionally involved in Scenario 4 and 38 streets were added to the Superblocks additionally involved in Scenario 5.  
There were 2273 al fresco dining locations in the City of LA approved by LADOT, from which 1087 locations (~47.8%) were in the 597 Superblocks included in the LA Superblocks Model and 1019 locations (~44.8%) were in the 530 Superblocks analyzed in the five scenarios. One hundred and seventy five of the 1019 al fresco dining locations were included in the Superblocks in Scenario 1, 138 locations were added the Superblocks additionally involved in Scenario 2, 147 locations were added to the Superblocks additionally involved in Scenario 3, 132 locations were added to the Superblocks additionally involved in Scenario 4 and 427 locations were added to the Superblocks additionally involved in Scenario 5.
# A Usable Encryption Solution for File-Based Geospatial Data within a Database File System
## 5 Usability Analysis and Implementation of the Security Solution  
This section focuses on the usability evaluation of the security solution. In doing so, we also demonstrate its use in a collaborative enterprise GIS environment. How the encryption solution can be implemented for protecting spatial files within a database-centric Web Portal [75] and geodatabases within a web atlas [76] are part of Supplementary Materials.  
The design objective of the encryption-decryption solution was to ensure its usability. In this regard, _usable security_ has received little attention from the developer community, for whom _usability_ is considered entirely independent and less important than security [54]. Usability evaluations of GI web applications and portals have focused on evaluating efficiency, effectiveness, and user satisfaction. [PERSON] et al. [77] carried out a spatial metadata usability evaluation of data directories and portals and reported several issues related to the aforementioned three aspects of metadata. [PERSON] et al. [78] investigated the same three metrics when evaluating the usability of a geoportal for complying with industry specifications, reporting several weaknesses in its design. Therefore, we also focus on these three aspects for investigating our security solution's usability.
## 5 Implementation and Testing Environment
### Evaluation Metrics  
Here, we list down the evaluation metrics used to access the system performance:  
(1) Accuracy: We calculated accuracy of the model by using following formula as shown in Equation (12):  
\[Accuracy=1-\frac{\|Y_{r}-,\hat{Y}\|F}{\|Y\|\,F} \tag{12}\]  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Experimental Data**} & \multicolumn{3}{c}{**Clicks Sequence Length (Min to Max)**} \\ \cline{2-4}  & **3 to 15** & **3 to 25** & **3 to 40** \\ \hline Total rows & 294894 & 294894 & 294894 \\ Discarded rows (len \textless{} Min) & 1440 & 2596 & 2321 \\ Useful Rows & 293454 & 292298 & 292573 \\ Expansion rows (\textgreater{}Max) & 3013 & 3013 & 3013 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Data characteristics based on click sequence length.  
\begin{table}
\begin{tabular}{c c} \hline \hline \multicolumn{1}{c}{**Experimental Data**} & **Details** \\ \hline Total no. of Records & 294894 \\ Data Duration & (1 year) 2019-2020 \\ Total No. of Unique Users & 51386 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Characteristics of data used for experiments.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Item Status Type** & **Clicked Frequency** \\ \hline Clicked Items & 244533 \\ Visited description & 42630 \\ Purchased & 7701 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Item status type vs click frequency.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**1**, source, **9** & **users, 696** & **users, 796** & **users, 896** & **users, 967** & **users, 976** & **users, 977** \\
23 1000-3456-6301 & 1010  
(2) Root Mean Square Error (RMSE): We calculated RMSE by using following Equation (13):  
\[\text{RMSE}=\sqrt{\frac{1}{MN}\sum_{j=1}^{M}\sum_{i=1}^{N}\ \left(y^{ij}-\hat{y }^{i}\right)^{2}} \tag{13}\]  
where \(y^{ij}\)_and_\(\hat{y}^{i}\) are subsets of \(Y\) & \(\hat{Y}\) and represent the actual user click data and predicted click of the jth time sample in the ith session respectively. M is total time samples, and \(N\) is number of products. RMSE is specifically used to estimate the prediction error. The smaller the value of RMSE is the better is prediction rate or score according to Equation (14):  
\[\text{{Prediction rate}}\ \propto\ \frac{1}{\text{RMSE}} \tag{14}\]  
while accuracy is used to detect the precision of predictions and it has an opposite effect than RMSE on prediction rate as shown in Equation (15). The higher the value of accuracy is, the better is prediction rate:  
\[\text{{Prediction rate}}\ \propto\ \text{Accuracy} \tag{15}\]  
(3) Mean Reciprocal Rank (MRR@10): MRR is defined as an average of reciprocal ranks of the correctly recommended products. In our case we used top 10 products i.e., the reciprocal rank becomes 0 when the actual rank surpasses 10. The purpose of using MRR is to capture the order of recommendation ranking. Therefore, a correct recommendation in the top of the ranking list if it has a larger value of MRR.
# A Massively Parallel SMC Sampler for Decision Trees +
## 4 Bayesian Decision Trees  
MC methods are widely used to estimate the state of a statistical model, which is defined by a posterior distribution (or simply the posterior). The posterior represents the probability density function of the state of the model given the observed data. The main idea is to generate a set of random samples that approximate the posterior, enabling the computation of various estimates. However, sampling directly from posterior distributions is often challenging. To address this, MCMC and SMC are two of the most commonly used Bayesian frameworks for the generation of samples when direct sampling is infeasible.  
For Bayesian approaches to DTs, the goal is to estimate the true tree, \(\mathbf{T}\), along with its associated set of features, \(\theta\), based on given data, \(\mathbf{Y}\), and its features, \(\mathbf{x}\), in a classification task. Specifically, we aim to sample from the posterior distribution \(\pi(\mathbf{T},\theta|\mathbf{Y},\mathbf{x})\), which describes the probability of \(\mathbf{T}\) and \(\theta\) conditioned on \(\mathbf{Y}\) and \(\mathbf{x}\).  
This section outlines how MCMC and SMC can be applied to generate a population of DTs in this Bayesian context. For further details, refer to [11; 12; 27].