esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 System Architecture of the IoT AP
### IoT AP System Architecture  
The proposed IoT AP is integrated with the applied technologies in IoT smart home applications. As shown in Figure 2, the IoT AP provides three types of communications within the heterogeneous network, such as ZigBee, Wi-Fi, and Ethernet. Through this IoT AP, a home network can be established to control home devices by using the low-power ZigBee protocol. Afterward, through the ZigBee agent embedded in the IoT AP, the remote and local users are able to obtain the access right of controlling the ZigBee-based home devices.  
As shown in Figure 2, this paper presents the development of an IoT AP in which we had embedded the ZigBee agent into the existing Wi-Fi AP. Through the developed ZigBee agent, the remote users are able to control the ZigBee devices, which connect to the Internet. In the ZigBee agent, the _UPnP Device Management Module_ is installed in the IoT AP. This module allows the information of ZigBee devices to be broadcasted onto the network and hence these devices can connect with each other wirelessly. In addition, the remote handheld devices are able to discover the ZigBee devices by using UPnP protocol. The ZigBee agent mainly consists of five modules. Consider that a user wants to control some ZigBee devices, which are at home, by sending commands to the IoT AP via Ethernet or Wi-Fi. The _Serial Port Control Module_ provides reading and writing functions to ZigBee USB dongles. Upon the IoT AP receiving the command from a user, the _Command Process Module_ transfers it to the _Serial Port Control Module_.  
Figure 2: The system architecture of the proposed IoT AP.  
_Module._ The _Command Process Module_ uses the functions provided by _Serial Port Control Module_ to exchange and subsequently execute the command from ZigBee transceiver. After that, _Device Management Module_ sends the command to the _Command Process Module_ for grabbing the information of ZigBee devices and transferring it to the _UPnP Device Management Module_ and _HTTP Command Process Module_. If the user command were sent by using HTTP, the _HTTP Command Process Module_ executes the command first and returns the results to grab the ZigBee devices information. As shown in Figure 2, the _Channel Allocation Module_ implements the negotiation function of the channel usage between Wi-Fi and ZigBee networks. In addition, the _Emergency Process Module_ changes the ZigBee network from power saving mode to the emergency mode, increasing the opportunities of sensors to report their data readings.
# System Design and Reliability Improvement of Wireless Sensor Network in Plant Factory Scenario
## 2 Materials and Methods  
### Experimental Site Description  
The experiment was carried out in a plant factory in Shanghai, China, an 8064 m\({}^{2}\) Verlo-type plastic greenhouse equipped with internal and external shading nets. The nutrient film technique (NFT) system was applied for lettuce cultivation. Cultivation beds were densely arranged in a uniform distribution, with a spacing of 0.5 m and a height of 0.7 m, as shown in Figure 1. Experimental data were collected between 22 July and 28 July 2024.
## 3 Materials and Methods
### Principal Component Analysis (PCA)  
After applying SHAP and LIME to each of the 49 features of the dataset and analyzing their impact on the model, we employed principal component analysis (PCA) as a dimensionality reduction technique to validate our findings. PCA not only mitigates the curse of dimensionality but also unveils the underlying structure of the data by transforming the original features into a set of orthogonal components. With PCA, we created new features by grouping fixations, blinks, saccades, and micro-saccades. As a result of this, we had 15 features named: Number of Micro-Saccade, Number of Blink, Number of Saccade, Number of Fixations, Blink Duration, Fixation Duration, Saccade Duration, Saccade Direction, Saccade Amplitude, Saccade Length, Micro-Saccade Amplitude, Micro Saccade Direction, Micro Saccade Peak Velocity, Micro Saccade Vertical Amplitude, and Micro Saccade Horizontal Amplitude.  
We applied both SHAP and LIME, respectively, to these newly derived features for interpretability following ET and thoroughly analyzed the results. The findings corroborated our previous results obtained using the original 49 features, reinforcing the validity and consistency of our initial interpretations.
Sensors_**2007**, \(7\), 3209-3241  
_Seinsoys_  
**ISSN 1424-8220**  
(c 2007 by MDPI  
www.mdpi.org/sensors  
_Review_  
**Remote Sensing Sensors and Applications in Environmental Resources Mapping and Modelling**  
**[PERSON] 1,*, [PERSON] 2, [PERSON] 3 and [PERSON] 4**  
1 Department of Environmental Studies, Florida International University  
2 Center for Urban and Environmental Change, Department of Geography, Indiana State University  
3 International Water Management Institute, Sri Lanka  
4 SAIC, contractor to U.S. Geological Survey (USGS) Center for Earth Resources Observation and Science (EROS), Sioux Falls, SD, USA.  
* Author to whom correspondence should be addressed. E-mail: [EMAIL_ADDRESS]  
_Received: 8 November 2007 / Accepted: 26 November 2007 / Published: 11 December 2007_  
**Abstract:** The history of remote sensing and development of different sensors for environmental and natural resources mapping and data acquisition is reviewed and reported. Application examples in urban studies, hydrological modeling such as land-cover and floodplain mapping, fractional vegetation cover and impervious surface area mapping, surface energy flux and micro-topography correlation studies is discussed. The review also discusses the use of remotely sensed-based rainfall and potential evapotranspiration for estimating crop water requirement satisfaction index and hence provides early warning information for growers. The review is not an exhaustive application of the remote sensing techniques rather a summary of some important applications in environmental studies and modeling.  
**Keywords:** remote sensing, sensors, environment, urban, hydrology, early warning  
**1. Overview of remote sensing in environmental studies/modeling**  
The use of remotely-sensed data in natural resources mapping and as source of input data for environmental processes modeling has been popular in recent years. With the availability of remotely-sensed data from different sensors of various platforms with a wide range of spatiotemporal,radiometric and spectral resolutions has made remote sensing as, perhaps, the best source of data for large scale applications and study. In this review, we summarize some of the most commonly used applications of the technique in environmental resources mapping and modeling. Applications of remote sensing in hydrological modeling, watershed mapping, energy and water flux estimation, fractional vegetation cover, impervious surface area mapping, urban modeling and drought predictions based on soil water index derived from remotely-sensed data is reported. The review also summarizes the different eras of sensors development and remote sensing and future directions of the remote sensing applications.
# A Parameter-Driven Methodology of Wheel Flat Modeling for Wheel-Rail Impact Dynamics
###### Abstract  
A wheel flat is a typical wheel defect that significantly impacts the wheel-rail system, posing substantial challenges to vehicle operation safety. In the existing literature, the wheel flat plane model does not account for the contribution of the width direction to the impact response and thus cannot accurately reveal the wheel-rail contact state with a flat. This paper systematically proposes a three-dimensional analytical model that considers multiple worn stages and constructs a spatial complex surface reconstruction model for flats based on NURBS technology. A vehicle-track coupled dynamics model, considering the geometry of the flat, is established to investigate the effects of flat geometry on the wheel-rail impact response and contact relationship in detail. The results show that in the subcritical regime, the wear degree of the flat predominantly affects the impact force, while in the transcritical regime, both the wear degree and velocity together determine the magnitude of the wheel-rail impact force. As the wear degree increases, the moment of wheel lateral jump occurs earlier. The spatial modeling method for flats proposed in this paper offers a novel technical approach for accurately simulating the dynamic behavior of wheel-rail contact when a flat is present.  
three-dimensional wheel flat model; NURBS; vehicle-track coupled dynamics model; wheel-rail impact force; wheel-rail contact geometry
# Rapid Deployment Method for Multi-Scene UAV Base Stations for Disaster Emergency Communications
## 1 Introduction
For large-scale disaster scenarios, we proposed a large area UAV deployment elitist strategy genetic algorithm (LAESGA) to solve the UAV two-dimensional deployment problem. By improving the selection, crossover, and mutation operations, the premature convergence of genetic algorithm is avoided. Simulation results show the convergence of the algorithm. The remaining sections of this paper are as follows: Section 2 presents the system model and problem-solving approach for the rapid deployment of disaster emergency communication UAVs. Section 3 proposes deployment methods for unmanned UAVs applicable to different scales of disaster scenarios, along with the validation of the proposed algorithms' effectiveness in problem solving. Section 4 provides simulation results and analysis. Finally, Section 5 concludes the paper.
## 2 Materials and Methods
### Circular Dichroism Measurement (CD)  
CD measurements were utilized to evaluate the fraction of the triple helical structure present in electrospun collagen. The untreated collagen blended with PEO with the weight ratio at 4:1, electrospun collagen membrane without crosslinking was dissolved in 0.1 M acetic acid at the concentration of 1 mg mL\({}^{-1}\). Thermally denatured collagen was obtained by heating a separated solution prepared from untreated collagen in an oven at 95 \({}^{\circ}\)C for 30 min. Subsequently, CD spectra were recorded on a Jasco Model J-815 CD spectrometer (Jasco, Great Dunmow, UK) at a scan speed of 100 nm/min from 190 to 240 nm. The path length of the cuvette was 1 mm. The spectrum of PEO dissolved in 0.1 M acetic acid at the concentration of 0.2 mg/mL was recorded and used as the baseline. The CD spectra of samples were scanned three times and obtained by averaging. According to the standard method, the fraction of triple helical collagen in the electrospun samples was calculated as follows [31,32]:  
\[f_{TH}=\frac{[\theta]_{EC}-[\theta]_{DC}}{[\theta]_{UC}-[\theta]_{DC}}\times 100 \tag{1}\]  
where \(f_{TH}\) (%) is the fraction of triple helical structure in electrospun collagen. Respectively, \([\theta]_{EC}\), \([\theta]_{UC}\) and \([\theta]_{DC}\) (deg cm\({}^{2}\) dmol\({}^{-1}\)) are the ellipticity of electrospun collagen, untreated collagen foam and denatured collagen at wavelength \(\lambda\).
# ForecastNet Wind Power Prediction Based on Spatio-Temporal Distribution
## 1 Introduction
Comprehensively analyzing existing studies, this paper analyzes the spatial distribution and dynamic contextual information of wind turbines. In order to facilitate the study of the spatial characteristics of wind power data and better analyze the location information, this paper predicts wind power from the perspective of wind turbines rather than wind farms. The dataset used in this paper includes data such as the angle of the wind received by each turbine, the environmental temperatures around the different wind turbines, the internal temperatures of the turbine nacelles, and the orientation of each turbine nacelle. These data are used to model the spatial correlation between the wind turbines. Predictions for each wind turbine are obtained considering the spatial and temporal distribution of the turbines, and then the predictions for the different turbines are summed to obtain the final prediction. Specifically, this paper makes the following contributions:  
* We detect outliers based on the isolated forest algorithm and repair them using the Lagrange interpolation method. * We extract the information about the neighboring nodes of individual turbines in a wind farm based on the graph attention mechanism and construct the input feature matrix. * We use a time-variant deep feed-forward neural network (ForecastNet) model to obtain the wind power probability density prediction results based on three different hidden layer variants. The rest of the paper is structured as follows: Section 2 presents the theoretical knowledge of the method proposed in this paper. Section 3 presents the experimentsusing the method proposed in this paper and analyzes the experimental results. Section 4 provides the conclusions of this paper.
## 2 Model of the PMSM  
### Machine Equations  
Since the parameters of the motor model are not always constant, some researchers have compensated for the nonlinear effects by adding the phase currents and voltages as well as the DC link currents [35]. However, the work focuses on the performance evaluation of the MPTC, and the parameters are reasonably constant. An electrical model of the PMSM is established in the stationary coordinate system with stator current \(\mathrm{i_{s}}\) and stator magnetic flux linkage \(\psi_{\mathrm{s}}\) as state variables (Equation (1)):  
\[\dot{x}=Ax+Bu \tag{1}\]  
where \(x=\left[\mathrm{i_{s}},\psi_{\mathrm{s}}\right]^{\mathrm{T}}\) is the state variable; \(\mathrm{u}=\mathrm{u_{s}}\) is the shaft voltage of stator \(\mathrm{d}\) and \(\mathrm{q}\); and  
\[A=\begin{bmatrix}j\omega_{r}-\frac{R_{s}}{L_{s}}&-\frac{i\omega_{r}}{L_{s}}\\ -R_{s}&0\end{bmatrix}B=\begin{bmatrix}\frac{1}{L_{s}}\\ 0\end{bmatrix} \tag{2}\]  
where \(R_{s}\), \(L_{s}\), and \(\omega_{r}\) represent stator resistance, stator inductance, and rotor speed, respectively.  
Electromagnetic torque in the static coordinate system is expressed as  
\[T_{e}=\frac{3}{2}N_{p}\psi_{\mathrm{s}}\otimes i_{s}. \tag{3}\]  
where \(N_{p}\) is the number of pole pairs, and \(\otimes\) represents the cross-product.  
The mechanical equation is  
\[\begin{array}{c}\frac{d\omega_{r}}{dt}=-\frac{B}{T}\omega_{r}+\frac{N_{p}}{f}(T_ {e}-T_{l})\\ \frac{d\theta}{dt}=\omega_{r}\end{array}. \tag{4}\]  
where \(\omega_{r}\) is used as the parameter. Motors have two time constants. Usually, the electromechanical time constant is much larger than the electrical time constant, and the approximation remains valid in such instances.  
The stator current and stator flux at the next sampling moment can be predicted by (1) to ensure the accuracy of prediction. Improved Euler discretization is adopted to discretize (1), which can be expressed as  
\[\left\{\begin{array}{l}x_{p}(k+1)=x(k)+T_{sc}(Ax(k)+Bu_{s}(k))\\ x(k+1)=x(k)+\frac{T_{sc}}{2}A\big{(}x_{p}(k+1)+x(k)\big{)}.\end{array}\right. \tag{5}\]  
where \(T_{sc}\) is the control time step; predicted value \(x_{p}(k+1)\) is a preliminary approximation obtained by [PERSON]'s formula; and approximation \(x(k+1)\) is obtained by correcting with the ladder formula.  
Improved Euler discretization is used to preliminarily discretize the stator current as  
\[i_{s}(k+1)=\left(1+T_{sc}j\omega_{r}-\frac{T_{sc}R_{s}}{L_{s}}\right)i_{s}(k) -\frac{T_{sc}j\omega_{r}}{L_{s}}\psi_{s}(k)+\frac{T_{sc}}{L_{s}}u_{s}(k). \tag{6}\]  
Based on the analysis of electromagnetic torque ripple, electromagnetic torque can be predicted as  
\[T_{e}(k+1)=\frac{3}{2}N_{p}\psi_{s}(k+1)\otimes i_{s}(k+1). \tag{7}\]  
The prediction of stator flux is  
\[\psi_{s}(k+1)=(u_{s}(k)-i_{s}(k)R_{s})\cdot T_{sc}+\psi_{s}(k). \tag{8}\]
# A Lightweight Model for 3D Point Cloud Object Detection
## 2 Related Work
### Three-Dimensional Object Detection  
The development of deep learning has promoted the research of 3D point cloud object detection and the construction of deeper and larger convolutional neural networks has become the mainstream trend. Representative methods are: PointNet [27], which uses a multilayer perceptron to learn the spatial features of points and maximum pooling to aggregate global features; VoxelNet [28], which divides the point cloud into multiple voxels, characterizes the point cloud data by voxel feature encoding, and extracts features by convolutional middle layer; PointPillars [29], which, unlike VoxelNet, divides the point cloud into different vertical cylinders and projects the point cloud in each cylinder onto a 2D feature map; PointRCNN [30], which uses a deformation-based convolution for feature extraction, retaining local structure information; GLENet [31], which constructs probabilistic detectors by generating uncertainty labels and proposes an uncertainty-aware quality estimator architecture to guide the training of IoU branches with predictive localization uncertainty.  
However, these advanced 3D detection models have high complexity and are unable to be deployed in real-time applications such as autonomous driving. Exploring an efficient 3D object detection model has become a research hotspot. Among them, SECOND [32] uses 3D sparse convolution instead of 3D standard convolution. A structured knowledge distillation framework is proposed in PointDistiller [33] to obtain a lightweight 3D point cloud object detection model. Ref. [34] proposes to simplify KNN search and graph shuffling to improve the efficiency of convolution. Ref. [35] combines the features of the point-based branch and the voxel-based branch, which not only perform effective feature extraction but also reduce memory occupancy. Ref. [36] uses sparse point-voxel convolution instead of point-voxel convolution to reduce the complexity of the model. For the indoor 3D target detection task, [PERSON]. [37] proposes a generative sparse detection network, where the key component of the model is a generative sparse tensor decoder that uses a series of transposed convolution and pruning layers to extend the support of sparse tensors while discarding unlikely object centers to maintain minimal runtime and memory footprint. [PERSON]. [38] proposes an anchor-free method to achieve 3D target detection using a purely data-driven approach. A novel oriented bounding box parameterization is also introduced that reduces the number of hyperparameters.
# Atmospheric Charge Separation Mechanism Due to Gas Release from the Crust before an Earthquake
## 3 Simulation Results  
Experimental evidence has revealed that the mobility of negative ions is approximately 1.3-1.4 times higher than that of positive ions. This disparity in mobility can likely be attributed to the asymmetry in the energy needed to remove ions of opposite charges from oxygen atoms in water molecules. Under the combined action of the electric field force, gravity, air resistance and thermal driving force, the upward movement speed of negative ions is higher than that of positive ions, and the two form an upward electrostatic field. When the charge further increases, the background field is reversed; notably, the so-called negative anomaly of the atmospheric electrostatic field occurs under fair weather conditions.  
As shown in Figure 5, when subjected to the natural atmospheric electric field (E), positive ions experience a downward movement toward the Earth's surface, where they eventually recombine. However, due to their relatively low mobility, a spatial layer of positive ions is formed near the surface over time. In contrast, negative ions exhibit vertical upward movement (neglecting the electrons within the scope of this model due to their low concentration at the Earth's surface). Consequently, a near-ground electrode layer is established, characterized by a local electric field (EI), which causes the natural atmospheric electric field to become negative. The primary consequence of the electrode effect is the development of an uncompensated electric charge across the ground surface. Turbulent movements and regular winds can transport this charge within the near-ground atmosphere, leading to the creation of an anomalous electrode layer over extensive areas. However, in reality, such situations typically persist for a short period since further turbulent movements tend to disrupt the electrode layer, mixing all ions and restoring electrical equilibrium. This physical process confirms the preseismic atmospheric electric field hourly scale anomalous signal shown in Figure 2.
###### Abstract  
In the era of the Internet of Things, innovative business model initiatives continue to deepen, and the trend of search domains continues to expand. This paper aims to scientifically analyze research trends of the Internet of Things in relation to Business Model Innovation through bibliometric studies. The data were collected using the Clarivate Web of Science (WoS) Core Collection (SSCI and SCI indexed) from 2005 to 2022 (November). However, the publications for the research domains started in 2015. The results show that scientific publications on the Internet of Things in relation to Business Model Innovation have increased gradually since 2019. The WoS database is utilized for analyses because it contains journals and conference proceedings deemed more relevant by the academic domain and highly reputable sources for bibliometric studies. The VOS viewer, R Language, and Microsoft Excel were also used to analyze and complete the study. Bibliometric and scientometric analyses were conducted to identify publication patterns, text analysis, most important keywords (co-word, word cloud, and co-occurrence), trends for the topicality, and content clustering for the publication periods. The visualization of the research trends of the Internet of Things in relation to Business Model Innovation resulted in four co-occurrence clusters leading to some of the topic areas mentioned as follows: (1) The Internet of Things, (2) Business model innovation, (3) Technology infrastructure, and (4) Digital transformation and capabilities. The results of this study will assist academics in identifying worldwide research trends related to the Internet of Things and Business Model Innovation as well as recommending future research areas.  
internet of things; IoT; business model innovation; bibliometric; co-word analysis; text mining/analysis; content analysis; scientometric; Web of Science
# Research Progress on the Influence of Thermo-Chemical Effects on the Swelling Pressure of Bentonite
###### Abstract  
The swelling pressure of bentonite changes dramatically due to diffused nuclear radiation heat and underground osmosis, causing the failure of the buffer isolation layer in deep geological repositories for the disposal of high-level radioactive waste. A detailed overview of the relevant research results on the swelling pressure variation of bentonite under thermo-chemical effects is presented in this paper. The results showed that the values of the swelling pressure obtained by different test methods are dissimilar. The swelling pressure of bentonite decreased with the increasing pore solution concentration; nevertheless, the effect of temperature on the swelling pressure is still controversial. At the micro-level, crystal layer swelling and double- layer swelling are generally considered to be the main factors affecting the swelling pressure; the pore structure and water distribution of bentonite will change owing to thermo-chemical effects. At the macro-level, involving intergranular stress, a mechanical parameter was proposed to explain the mechanism of the changes in the swelling pressure of bentonite. Finally, future research directions for the study of the evolution of bentonite swelling properties under thermo-chemical effects are proposed, based on the current research results.  
thermo-chemical; swelling pressure; test methods; microscopic properties; intergranular stress +
Footnote †: journal: Applied Sciences