esa-sceva/satcom-chunk-collection huggingface dataset:
# Principal Subspace of Dynamic Functional Connectivity for Diagnosis of Autism Spectrum Disorder
## 3 Material and Method
### rs-fMRI Data Preparation  
First, preprocessed ABIDE rs-fMRI data are downloaded from the Preprocessed Connectomes Project (PCP) database, and the Data Processing Assistant for rs-fMRI (DPARSF) is selected [28]. The ABIDE data include 1035 subjects collected from different universities and institutions, of which 505 are ASD subjects and 530 are NC subjects. Details about the dataset, its scanning parameters and the number of subjects are listed in Table 1.  
For extraction of BOLD signals, Automated Anatomical Labeling (AAL) is chosen as the default brain atlas; it essentially divides the brain into 90 nodes [29]. For each subject, the BOLD signal is extracted at each brain node, and this forms a matrix \(B\in\mathbb{R}^{TP_{l}\times\text{node}}\), where each column contains the BOLD signal of length \(TP_{l}\) of a specific node \(=[1,2,...,90]\)  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Site** & **Country** & **Manufacturer** & **Time Points (TP\({}_{l}\))** & **\# of ASD** & **\# of NC** & **Total** \\ \hline SDSU & US & GE & 175 & 14 & 22 & 36 \\ STANFORD & US & GE & 235 & 19 & 20 & 39 \\ UM & US & GE & 295 & 66 & 74 & 140 \\ KKI & US & Philips & 151 & 20 & 28 & 48 \\ LEUVEN-1 & Belgium & Philips & 245 & 14 & 15 & 29 \\ LEUVEN-2 & Belgium & Philips & 245 & 15 & 19 & 34 \\ SBL & Netherlands & Philips & 195 & 15 & 15 & 30 \\ TRNITY & Ireland & Philips & 145 & 22 & 25 & 47 \\ CALTECH & US & Siemens & 145 & 19 & 18 & 37 \\ CMU & US & Siemens & 315 & 14 & 13 & 27 \\ MAX & Germany & Siemens & 115 & 24 & 28 & 52 \\ NYU & US & Siemens & 175 & 75 & 100 & 175 \\ OHSU & US & Siemens & 77 & 12 & 14 & 26 \\ OLIN & US & Siemens & 215 & 19 & 15 & 34 \\ PITT & US & Siemens & 195 & 29 & 27 & 56 \\ UCLA & US & Siemens & 115 & 54 & 44 & 98 \\ USM & US & Siemens & 235 & 46 & 25 & 71 \\ YALE & US & Siemens & 195 & 28 & 28 & 56 \\ \hline Overall & - & - & 505 & 530 & 1035 \\ \hline \hline \end{tabular}
\begin{tabular}{c c} \hline \hline  & Legend: SDSU, San Diego State University; STANFORD, Stanford University; UM, University of Michigan; KKI, Kennedy Krieger Institute; LEUVEN, University of Leuven; SBL, Social Brain Lab; Trinity, Trinity College Institute of Neuroscience; CALTECH, California Institute of Technology; CMU, Carnegie Mellon University; MAX, Ludwig Maximilian University of Munich; NYU, New York University; OHSU, Oregon Health and Science University; OLIN, Olin Center; Institute of Living at Hartford Hospital; PITT, University of Pittsburgh School of Medicine; UCLA, University of California; Los Angeles; USM, University of Utah School of Medicine; Yale, Yale School of Medicine.  
\end{table}
Table 1: Details of rs-fMRI ABIDE database for ASD and NC subjects acquired using 3T MRI scanner.  
Figure 1: Method of investigation in the development of automatic ASD diagnosis using PWC of resting-state fMRI BOLD signals and convolutional neural networks.
# A SAR-Based Index for Landscape Changes in African Savannas
## 5 Conclusions  
Our study showed that landscape changes can be analyzed by radar imagery in a transparent and adaptive way. It is not limited to certain landscape types or specific sensors, but requires a minimum number of consecutive images from the same time of the year. The proposed approach substantially contributes to field of post-classification change analysis as it overcomes limitations regarding the number of investigated scenes, the variation within the investigated time, and the occasionally criticized complexity of high-level approaches which cannot be adapted by users with limited technical or scientific background. It therefore serves as an ideal intermediate between innovative analyses and user-friendly, adaptable frameworks. These will gain further importance with the growing availability of archived and newly acquired SAR data for scientific, organizational, and commercial operational use.  
The integration of user expertise about the relevance of land-use and land cover classes hinders the automation of the process but clearly improves the validity of the results. Attaching weights to the selected classes allows for the generation of an index which refines the results according to the knowledge of the user and the information he requires.  
Although it did not reveal large scale trends in our study area, the proposed index is a good approach for a semi-automated evaluation of changes over long time spans and within greater regions. The use of radar data additionally reduces the dependency from atmospherically undisturbed conditions. All parts of the study were performed on free and open source tools (QGIS, ESA SNAP, OTB, Python) which allows for a transfer of the method onto other case areas. We encourage readers to implement and further improve the proposed approach in order to increase the quality of time-series analyses of radar data. This work is a first step towards new standards in change detection applications which are comprehensible and still specified to the users' needs.  
This study was funded by the Austrian Research Promotion Agency (FFG) under the Austrian Space Applications Programme (ASAP 12, 854041). ALOS data was provided by Japan Aerospace Exploration Agency (JAXA). Landsat and SRTM data was provided by the US Geological Survey (USGS). The authors would like to thank [PERSON] for proofreading the manuscripts and the anonymous reviewers for their valuable comments and suggestions to improve the manuscript. We acknowledge support by Deutsche Forschungsgemeinschaft and Open Access Publishing Fund of University of Tubingen. [PERSON] and [PERSON] designed the study and selected the data; [PERSON] processed and analyzed the data; Results were interpreted and discussed by [PERSON] and [PERSON] The manuscript was submitted, edited and revised by [PERSON]  
The authors declare no conflict of interest.
## 3 Proposed Model
### ROI Extraction Module  
We propose an architecture for ROI extraction based on the one discussed in Section 3.1 by replacing the last fully connected layer, FC8, with a four neuron-layer unit (depicted in Figure 3). Since Chatfield's architecture expects a fixed pixel size, a pre-processing procedure is required for input images. In brief, REM consists of two steps: (i) pre-processing and (ii) palmprint localization. In the following, each of these parts is discussed in detail.
## 2 Materials and Methods
### Digital Image Correlation  
A pair of high-definition cameras were installed at the base of the testing frame. The cameras were positioned along a stabilizer beam to ensure that no movement occurs during testing. The DIC area of interest (AOI) was limited to a quartile of the panel, assuming a symmetric response along the length and width of the panel. Optical lenses of 21 mm focal length were mounted on the cameras. A stereo-scopic calibration of the system was performed before testing. Pairs of synchronized images were captured and stored using the VIC-Snap software. The images were post-processed using the VIC-3D software provided by Correlated Solutions. Further details on the DIC setup are listed in Table 3.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Transducer** & **R15** & **AE Device** & **PAC micro-8** \\ \hline Sampling rate & 2 MHz & Pre-amplification & 40 dB \\ Sampling frequency range & 10 kHz–2 MHz & Wave velocity (m/s) & 3000 \\ Threshold & 35 dB & Event lock-out value (mm) & 2000 \\ \hline \end{tabular}
\end{table}
Table 2: AE setup details.  
A measurement was taken after the load placement at each loading cycle. The reference image was considered the one taken at the intact state before loading started. In the Results section of this paper, the principal strain E1 was selected to be discussed, since it provides a clear mapping of cracks considering the strain distribution in both X and Y directions. Additionally, the out-of-plane displacement (W) in the Z direction was plotted to track the interfacial debonding of the TRC skin. It was noted that the panel rigid body motion was removed (post-testing correlation function of the VIC 3D software) to ensure that only the deformation of the TRC skin was measured.
## 1 Introduction
The neural network's output layer weights--corresponding to proportional, integral, and derivative gains--were tuned using Particle Swarm Optimization (PSO). In [28], the authors developed a computational intelligence-based controller for CSTR concentration control, employing a radial basis function neural network (RBFNN) optimized via hybrid PSO variants. These advanced control schemes have offered distinct advantages but also have limitations, leaving room for improvement [29; 30]. Building on the existing literature, this study introduces an adaptive NPID control scheme that enhances the robustness and reliability of CSTR control. The strengths of NPID control (customizable nonlinear functions and parameters) are synergistically combined with the function approximation and online learning capabilities of RBFNNs, retaining the benefits of the conventional PID controller, including its simple structure and three tuning parameters. To prevent excessive integral action, the error input to the integral action of the NPID controller is scaled by a nonlinear function--specifically, the product of the error and a nonlinear gain. The proposed method's effectiveness is rigorously evaluated through simulations encompassing tracking performance, disturbance rejection, and parameter variations. The key contributions of this study are as follows:  
* To propose a nonlinear adaptive PID control technique combining the existing nonlinear PID controller and an RBFNN;
* To refine the NPID controller's gain function to enhance its performance. This paper is organized as follows: Section 1 contains the introduction. Section 2 presents the modeling of a CSTR and its identification using the RBFNN. In Section 3, an adaptive control strategy is provided based on the identified model and an NPID controller. In Section 4, a set of simulations are conducted to observe the performance of the proposed method. Finally, the conclusion is presented in Section 5.
###### Abstract
Both static and dynamic parameters affect the accuracy of building settlement prediction models. The hyperparameters of the model, such as batch size, need to be parameter-optimized [36]. The paper proposes a data-driven model for predicting the settlement of five buildings around a foundation pit. There have been a number of studies using machine learning. Machine learning methods are used to develop empirical susceptibility prediction models [37; 38]. Calibrated code-based seismic vulnerability indices were predicted by developing several regression and classification models on a dataset including nearly 300 buildings [39]. Different machine learning methods were used to predict the maximum surface settlement caused by tunnel excavation [40]. In addition, machine learning has been widely applied to other tunnel-related predictions [41; 42]. Therefore, this paper intends to take a deep foundation pit project of a metro station in Shanghai as the research background, and based on the LSTM artificial neural network, to artificially determine the reference values of the main hyperparameters of the model (batch size, training set ratio), and then to establish the prediction model of the settlement of neighboring buildings in the construction of deep foundation pits. We then compare the accuracy of single-point settlement prediction and cooperative prediction of multi-point settlement of buildings, study the influence of construction parameters (construction method, construction conditions), engineering geological parameters (unit weight, cohesion, internal friction angle, Poisson's ratio, and void's ratio), and spatial parameters (vertical and horizontal) on the accuracy of the settlement prediction model of the building, and validate the LSTM neural network through the actual monitoring data, the network prediction results, and the evaluation indexes. The accuracy of the building settlement prediction model is verified by actual monitoring data, network prediction results, and evaluation indexes. The research results can provide a certain reference for the safe construction and normal operation of the foundation pit project.
## 1 Introduction
However, the computation of large datasets of imagery, preprocessing, and downloading can be time-consuming and limited for traditional processing methods. Platforms like Google Earth Engine (GEE) provide free access to high-performance computing resources allowing to produce a vast amount of computation analysis to unprecedented time and extent scales [26]. Regarding quality in terms of spatial and temporal resolution, Landsat collection since the early 1970s [27] became freely available which widely expanded its use among researchers [28]. In May 2018 in a new effort, the United States Geological Survey (USGS)reorganized the archive into a tiered collection launching the Landsat level 1 product. This product provides a consistent quality of Landsat images that are comparable across Landsat sensors 4, 5, 7 and 8, suitable for time-series analysis and therefore ideal for monitoring [29]. In this study, we used Landsat level 1 surface reflectance data to evaluate the spatiotemporal patterns of IC, EVI, and NDVI over a tropical forest in Costa Rica in irregular terrain. We tested GEE capacity to calculate IC over a large number of images between the years 1984-2017. The specific objectives of this study were: (1) evaluate the patterns of IC change over time in Landsat and the factors behind these patterns, (2) explore the relationships between IC, EVI, and NDVI in changing conditions of topography and time with improved resolution, (3) assess the importance of IC when monitoring forested areas in irregular terrain. Our study supports previous evidence about the importance of using IC when monitoring forests and gives additional insights about the effect of the topography in Landsat sensors.
## Materials and Methods  
### Requirement Change-Definition and Demarcation  
Before developing a method to assess risk of requirement change systematically, it is necessary to develop a concept of what is defined as a requirement change and what factors need to be considered for risk assessment. In literature, there is no common definition of a requirement change, although this is essential to define the boundary of support. Some research sees requirement changes as part of Engineering Change (EC) (for instance [6; 29]). Following the predominant understanding of EC, it is focused on the solution space solely [21]. Within the contribution at hand, EC is separated from requirement change and defined as follows:  
\"Engineering changes are changes and/or modification in fits, functions, materials, dimensions, etc. of a product and constituent components after the design is released\" ([39]; S.481)  
In contrast to EC, requirement change focuses on the problem space and in particular requirement artifacts. Those can be of any specification level [40], but need to be formalized. Formalized is understood as somehow processable and includes natural language description. Within the contribution at hand, the following definition is used:\"Requirement changes are defined as any change made to formal requirements.\" (adapted from [6; 29])  
This definition already includes another demarcation: In literature, requirement change is often used as a general term for all changes in the requirement set. Following the understanding of [PERSON] (Figure 1), changes in the requirement set come from new requirements, changed requirements and deleted requirements. The ongoing refinement of a requirement by a set of subordinated requirements is included in new requirements. According to the definition of requirement changes, the contribution at hand focuses on changed requirements. Accordingly, new or deleted requirements are not primarily addressed.
# Review of GIS-Based Applications for Mining: Planning, Operation, and Environmental Management
## 6 Discussion
### Future Direction of GIS in Mining  
In recent years, new technologies, such as sensor networks, mobile devices, wireless communication, and UAVs, have been introduced into mining sites in accordance with the fourth industrial revolution. In particular, information-communication-technology-based mine safety management systems have been actively utilized in open-pit and underground mines to track equipment and worker locations and monitor the mine operation environment. To transmit location data to the web server, a wireless communication network is installed in the open-pit and underground mines. Several studies have been conducted to simulate the truck haulage system using data derived from the mine safety management system. For instance, [PERSON] and [PERSON] [72] presented a knowledge-based simulation methodology for truck haulage systems in underground mines by considering the truck travel time, which was extracted from big data of a mine safety management system. Moreover, [PERSON] and [PERSON] [73] developed a deep neural network (DNN) model, which was trained using a large set of truck haulage system operation conditions and truck cycle times to predict the ore production and crusher utilization of a truck haulage system in an underground mine.  
However, the aforementioned studies manually generated input data, which were required for the truck haulage system simulation and DNN model training of big data from a mine safety management system without GIS-based spatial and attribute database generation for the system. Therefore, in the future, current research needs to be expanded as follows: (a) the introduction of a GIS-based spatial and attribute database for a mine safety management system with consideration of the packet data frame (e.g., equipment detection time, ID of detected equipment); (b) the development of algorithms to automatically measure input parameters (e.g., haulage operation time, number of dispatched trucks, truck travel time) of a truck haulage system simulation and DNN model training from the spatial and attribute database and generate input data in a matrix format; and (c) the development of database query functions to extract specific input data according to the haulage operation time, date, and type of equipment.
## 1 Introduction
The bandpass filter was constructed by interposing an ultrathin dielectric substrate between two lossless FSS layers. The FSS layers were realized with a meandered I-shaped unit-cell that was placed vertically at the top layer and horizontally at the bottom layer, respectively, guaranteeing the structure polarization insensitivity. The main contributions of this study are threefold. First, the miniaturization of the FSS element was achieved through meandering the I-shaped unit cell, which allowed for an extremely low-profile unit cell with electrical dimensions equal to 0.16\(\lambda_{L}\)\(\times\) 0.16\(\lambda_{L}\)\(\times\) 0.003\(\lambda_{L}\), where \(\lambda_{L}\) is the free space wavelength at the lowest frequency of the operational band. Second, the proposed filter demonstrated a \(-\)3 dB transmission band from 2 to 6.76 GHz (a fractional bandwidth of 108.7%) under normal incidence, exhibiting remarkable wideband performance with a flat bandpass. Finally, the filter was proven to have stable responses across various polarizations and significant angular insensitivity up to 40\({}^{\circ}\) for the incidence wave. The rest of the paper is organized as follows. In Section 2, a description of the structure under analysis is presented, along with the equivalent circuit model development. In Section 3, the numerical results obtained through accurate full-wave simulations are reported and confirmed by experimental validations. Finally, the results are discussed in Section 4, and concluding remarks are drawn in Section 5.
## 3 Results
### Single Cell
#### 3.1.2 Effect of External Heating  
Figure 4a shows the battery temperature profiles from both the theoretical solution and numerical simulation under external heating conditions \(q_{heating}\) = 1.0 \(\times\) 10\({}^{5}\), 2.0 \(\times\) 10\({}^{5}\), 3.0 \(\times\) 10\({}^{5}\), and 4.0 \(\times\) 10\({}^{5}\) (W/m\({}^{3}\)). As the heating power increases, the timing of the rapid temperature rise occurs earlier in both cases. Similar to the baseline case described in Section 3.1.1, a slight delay is observed in the theoretical solutions, with the delay being more pronounced for lower heating powers. However, under conditions where thermal runaway does not occur (i.e., \(q_{heating}\) = 1.0 \(\times\) 10\({}^{5}\) (W/m\({}^{3}\))), the two solutions agree exactly. Figure 4b shows the time evolution of the temperature rate of change, _dT/dt_. In the theoretical solution, the timing of the rapid heat generation is delayed compared to the numerical simulation, with this delay becoming more noticeable for lower heating powers. Figure 4c presents the time evolution of the unreacted fraction \(\alpha\) from the numerical simulation. The timing of the sharp decrease in \(\alpha\) (i.e., the rapid progression of the reaction) coincides with the spike in heat generation observed in Figure 4b.  
Figure 3: (**a**) Battery cell temperature profiles under baseline conditions in both analytical and numerical approaches. (**b**) Comparison of time derivative of cell temperatures. (**c**) Time evolution of unreacted fraction \(\alpha\).
###### Abstract  
Inertial measurement unit (IMU) technology has gained popularity in human activity recognition (HAR) due to its ability to identify human activity by measuring acceleration, angular velocity, and magnetic flux in key body areas like the wrist and knee. It has propelled the extensive application of HAR across various domains. In the healthcare sector, HAR finds utility in monitoring and assessing movements during rehabilitation processes, while in the sports science field, it contributes to enhancing training outcomes and preventing exercise-related injuries. However, traditional sensor fusion algorithms often require intricate mathematical and statistical processing, resulting in higher algorithmic complexity. Additionally, in dynamic environments, sensor states may undergo changes, posing challenges for real-time adjustments within conventional fusion algorithms to cater to the requirements of prolonged observations. To address these limitations, we propose a novel hybrid human pose recognition method based on IMU sensors. The proposed method initially calculates Euler angles and subsequently refines them using magnetometer and gyroscope data to obtain the accurate attitude angle. Furthermore, the application of FFT (Fast Fourier Transform) feature extraction facilitates the transition of the signal from its time-based representation to its frequency-based representation, enhancing the practical significance of the data. To optimize feature fusion and information exchange, a group attention module is introduced, leveraging the capabilities of a Multi-Layer Perceptron which is called the Feature Fusion Enrichment Multi-Layer Perceptron (GAM-MLP) to effectively combine features and generate precise classification results. Experimental results demonstrated the superior performance of the proposed method, achieving an impressive accuracy rate of 96.13% across 19 different human pose recognition tasks. The proposed hybrid human pose recognition method is capable of meeting the demands of real-world motion monitoring and health assessment.  
2023