esa-sceva/satcom-chunk-collection huggingface dataset:
# Technology and Data Fusion Methods to Enhance Site-Specific Crop Monitoring
## 2 Sensor Types
### Hyperspectral Sensors  
Hyperspectral sensors are moderately adopted for simplifying crop monitoring methods [104]. In a hyperspectral system, the camera acquires data from more wavelengths than the three commonly measured wavelengths (400-1300; 400-2350; and 1400-2350 nm wavelengths) [105]. Due to decreasing technology prices, the method is now economical and has thus become accessible to users globally. New methods have been introduced that push the limits of the hyperspectral imaging system for broad-range analysis. Hyperspectral imaging technology has the capacity to identify small changes in plant growth and development. To accurately derive the crop parameter data, the basic problem of atmospheric correction must first be solved. Thus, spectral radiance estimation includes the radiance emitted by the source and thermal radiation produced by the surroundings that is reflected from the surface of the source. Thus far, hyperspectral estimation of crop monitoring has gained little attention. Thus, new hyperspectral systems are needed to address the limitations by using narrowband measures, which provide precise emissivity retrieval and, therefore, better parametric estimations compared to thermal cameras [106].  
Hyperspectral sensors with hundreds of bands can collect a wide variety of spectral responses and subtle variations of ground covers and their fluctuations over time. Thus, they can be used to address the challenges, such as spectral resolution and support accurate and timely estimation [107]. Studies have also shown a moderate performance of hyperspectral in monitoring crop vegetation, such as LAI [108], crop types [109] and biomass [110]. Hyperspectral sensors have been tested comparatively less in agricultural applications due to the high cost of the system and other applications [111]. Although terrestrial hyperspectral data can be reliably estimated with the help of a spectroradiometer which has already been used by many studies for observing canopy- and leaf-level spectral characteristics [112]. Such terrestrial estimations are limited to a few numbers of samples, and are not able to acquire spatial distribution in large tests.
## 4 Simulation Results
### Simulation Parameters  
The simulation scenario is a typical tunnel within an urban rail transit system, which begins from one station and ends at the next station. The trackside nodes are randomly distributed along the tunnel wall, and the nodes in the train are also distributed randomly. Source nodes on the trackside are changed as the train moves, and destination nodes are located at the stations. Therefore, we only consider the simulation from the station to the middle of the tunnel, due to the symmetry on both sides of the tunnel.  
The transmission power and the receiver sensitivity are set to10 dBm and \(-77\) dBm, respectively. Each transceiver antenna's gain is 5 dBi, and the frequency point is 2.4 GHz. The parameters above were obtained based on [23]. The parameters of the improved ACO algorithm are also given, which were obtained through a large number of experiments to confirm the best route. The parameters are summarized in Table 3.
# Training Autoencoders Using Relative Entropy Constraints
## 2 Background
### Solving for Feature Mapping Parameters  
Suppose the training set is \(\mathcal{D}=\{(\mathbf{x}_{m},\mathbf{o}_{m})|\mathbf{x}_{m}\in\mathbf{R}^{d}, \mathbf{o}_{m}\in\mathbf{R}^{k}\}_{m=1}^{M}\), where \(\mathbf{x}_{m}\) represents the input feature of the training sample, \(\mathbf{o}_{m}\) represents the label of the training sample, and \(M\) represents the number of training samples. Let \(\mathbf{X}=[\mathbf{x}_{1},\cdots,\mathbf{x}_{M}]\) denote the training sample feature matrix and \(\mathbf{O}=[\mathbf{o}_{1},\cdots,\mathbf{o}_{M}]\) denote the training sample label matrix. The input features and labels of the training samples are all represented by column vectors, and each column corresponds to one datum. The autoencoder takes the input data as the reconstruction target, that is, \(\mathbf{O}=\mathbf{X}\). In this case, the training set can be expressed as \(\mathcal{D}=\{\mathbf{x}_{m}|\mathbf{x}_{m}\in\mathbf{R}^{d}\}_{m=1}^{M}\). Let \(J\) denote the number of hidden nodes of the autoencoder. Let \(\mathbf{h}_{m}=[h_{m1},\cdots,h_{mJ}]^{T}\), \(h_{mj}\) denote the activation of the input data \(\mathbf{x}_{m}\) on the hidden layer node \(j\) through feature mapping:  
\[h_{mj}=\sigma(\mathbf{w}_{pj}\mathbf{x}_{m}+b_{pj}), \tag{2}\]  
where \(\mathbf{w}_{pj}\) is a row vector consisting of the \(j\)th row of \(\mathbf{W}_{p}\), and \(b_{pj}\) is the bias consisting of the \(j\) item of vector \(\mathbf{b}_{p}\). The average activation value of the training set \(\mathcal{D}\) on the hidden node \(j\) through feature mapping is:  
\[\hat{\rho}_{j}=\frac{1}{M}\sum_{m=1}^{M}h_{mj}. \tag{3}\]  
REAE imposes the following constraint on the average activation of feature mapping on the hidden node \(j\):  
\[\hat{\rho}_{j}=\rho, \tag{4}\]  
where \(\rho\) is a dataset-dependent average activation parameter, which represents the average activation value required by the constraint. In addition, this paper assumes that the average activation constraints take the same value on all hidden nodes, that is, \(\hat{\rho}_{j}=\rho,j=1,\cdots,J\).  
REAE uses relative entropy to constrain the average activation of hidden nodes. According to the definition of relative entropy, the loss function of feature mapping can be defined as:  
\[\mathcal{L}(\mathcal{D};\mathbf{W}_{p},\mathbf{b}_{p})=\sum_{j=1}^{J}\left( \rho\log\frac{\rho}{\hat{\rho}_{j}}+(1-\rho)\log\frac{1-\rho}{1-\hat{\rho}_{ j}}\right). \tag{5}\]  
The loss function (5) has no analytical solution, so the feature mapping parameters \(\mathbf{W}_{p}\) and \(\mathbf{b}_{p}\) can be solved for by gradient descent.
# Smart Viniculture: Applying Artificial Intelligence for Improved Winemaking and Risk Management
## 4 Artificial Intelligence in Production, the Fermentation Process, and Bottling Quality Control
### Predictive Control of Fermentation  
#### 4.2.1 Predictive Modeling  
In addition to monitoring, AI systems are utilized for the purposes of predicting and controlling the fermentation process. Predictive models utilize historical data and real-time inputs to forecast the behavior of the fermentation process, thereby enabling preemptive adjustments that ensure consistency and quality. These models, which employ machine learning algorithms, can predict the optimal time to intervene in the fermentation process, such as adjusting temperatures or adding nutrients, in order to achieve the desired wine characteristics [86].  
For instance, neural networks can analyze fermentation data from past batches to identify patterns indicating successful fermentations. By recognizing these patterns in real-time data, the AI system can forecast potential issues and recommend adjustments before problems arise. This foresight helps maintain the consistency and quality of the wine. [PERSON] et al. highlight the use of advanced predictive models in winemaking, incorporating techniques like regression analysis and decision trees to predict fermentation outcomes accurately. Integrating multiple data sources, such as temperature, pH levels, and sugar content, allows for a comprehensive understanding of fermentation dynamics. The study also discusses Transfer Learning, enabling models to adapt to new datasets with minimal retraining, enhancing generalization across different fermentation batches. [86]. This study presents the development of a web-based software application to model, predict, and manage the white wine fermentation process using a neural network pre-trained with genetic algorithms. This research aimed to digitalize the fermentation process to optimize white wine production by using a multi-layer perceptron neural network to predict alcohol and substrate concentrations during fermentation, based on variables such as temperature, initial substrate concentration, and biomass concentration. By optimizing the neural network configuration with genetic algorithms, the prediction accuracy was significantly enhanced. This AI-driven approach minimized the need for manual measurements and extended the lifecycle of sensors, demonstrating the potential of integrating AI and real-time data to improve fermentation efficiency and product quality [86].  
Integrating IoT devices with predictive modeling further refines the process, with real-time data from sensors informing dynamic adjustments to maintain optimal fermentation conditions. Concurrently, the incorporating feedback loops, where outcomes of previous adjustments refine future predictions, ensure continuous improvement in model precision and effectiveness. By leveraging predictive modeling, winemakers achieve greater control, reducing variability and improving wine quality [86].
# Research on the Compactness of Lunar Soil Simulant Based on Static Cone Penetration Test
## 4 Discussion
In the tests for the remaining simulants using the same cone tip angles, it is considered that the particle size has a certain impact on the test results. Table 7 shows the median particle sizes of five types of lunar soil simulant. Among them, the CUG-1A simulant has the smallest median particle size, and the GRC-1 simulant has the largest. When the normalized cone tip resistance is less than 750, the lunar soil simulant is relatively loose. Under the same normalized cone tip resistance, CUG-1A has the highest compactness, while GRC-1 has the lowest compactness. In this case, the particle size has a significant impact on the normalized cone tip resistance. The larger the particle size, the looser the lunar soil simulant is at the same normalized cone tip resistance. When the normalized cone tip resistance is greater than 750, it is more difficult to penetrate the particles of CUG-1A and CUMT-1 due to their smaller sizes compared to JSC-1 and GRC-1. That is, under the same normalized penetration resistance, the compactness of the smaller sized lunar soil is lower than that of the larger sized lunar soil. The uniformity also has a significant impact on the normalized cone tip resistance.
## 2 PTOS Control Scheme
### The Stability Region of PTSTP Control Scheme  
According to the work in [28], the system stability analysis of PTSTP control scheme using Appendix A is as follows.  
**Theorem 1**.: _The PTSTP control scheme is globally asymptotically stable if the following conditions hold._  
\(\begin{array}{l}\left(1\right)\,k_{2}>0.\\ \left(2\right)\,k_{1}>\frac{1}{a}.\\ \left(3\right)\,f\left(0\right)=0.\\ \left(4\right)\,f\left(z\right)z>0,\forall z\
eq 0.\\ \left(5\right)\,f^{\prime}\left(y\right)\,\overset{\Delta}{\Delta}\,df\left( y\right)/dy\,exist,\forall y.\\ \left(6\right)\,-a+\frac{1}{k_{2}}f^{\prime}\left(-y\right)<-f^{\prime}\left( -y\right)f\left(-y\right)<a-\frac{1}{k_{2}}f^{\prime}\left(-y\right),\forall y.\end{array}\)__  
_The \(f\left(\cdot\right)\) is associated with Equation (5)._  
Proof.: When the PTSTP control scheme is tracking SRSs, the time-invariant system in Figure 2 and Equation (12) can be described as  
\[\dot{x}=\left[\begin{array}{cc}0&1\\ -ak_{1}&-ak_{2}\end{array}\right]x+\left[\begin{array}{cc}0\\ ak_{1}\end{array}\right]r_{tp} \tag{27}\]  
with  
\[x=\left[\begin{array}{c}y\\ v\end{array}\right],\dot{x}\left(0\right)=0.\]  
Select the positive scalar function \(V\left(x\right)\) as follows.  
\[V\left(x\right)={x_{1}}^{2}+{x_{2}}^{2} \tag{28}\]  
As the input signal does not change the stability of the system and the derivative of \(V\left(x  
Moreover, the PTSTP control scheme should meet conditions (3-6) in Theorem 1 when tracking WRSs. The proof of this is rather long and is found in [12].  
Thus, Theorem 1 is proved.
## 3 Results
### Footprint Climatology and Hyperspectral Data Integration  
The convolution of the estimated \(P_{\text{fp}}\) on the hyperspectral images provided not only weighted representative spectra of vegetation contributing to half-hourly fluxes; but also the vegetation classes' relative contribution or weight within the EC footprint (\(\alpha_{GPP}\))--as observed from a remote sensor. An example is shown in Figure 4a,b, where the values of \(NVDI_{\text{fp}}\) (Figure 4c,d) and \(PRI_{\text{fp}}\) (Figure 4e,f) of each vegetation class separated and combined are presented. Data shown correspond to the 31 days selected around the flight over the CTRL tower in two very different phenological stages: April 2015 (left column) and July 2015 (right column). \(\alpha_{GPP}\) (Figure 4a,b) is similar for the different dates, remaining stable in average for each of the 31 days of the study period. \(\alpha_{GPP}\) temporal variability is governed by footprint size and direction. Grass \(NVDI_{\text{fp}}\) shows large differences between the two dates; though much lower for trees. On the contrary, \"trees\" \(PRI_{\text{fp}}\) varies more between dates than \"grass\" \(PRI_{\text{fp}}\).  
As shown in Figure 4, \(NVDVI\) footprint-induced variability is much lower than changes induced by phenology. For each overpass, the standard deviation of each SVI is calculated within the 31 days study period. The averaged standard deviation for all the overpasses is lager for \"grass\" (subindex \"g\") (\(\overline{\sigma}_{NDVI,g}\) = 1.52) than for \"trees\" (subindex \"t\") (\(\overline{\sigma}_{NDVI,t}\) = 0.61). However, when \(NDVI_{\text{fp}}\) of both categories are mixed as a function of \(\alpha_{GPP}\) (subindex \"veg\"), variability increases (\(\overline{\sigma}_{NDVI,veg}\) = 1.72). In the case of \(PRI\), \"grass\" and \"trees\" variability is equal (\(\overline{\sigma}_{PRI,g}\) \(\overline{\sigma}_{PRI,t}\) = 0.17) and reduces when mixed (\(\overline{\sigma}_{PRI,veg}\) = 0.15). Footprint-induced variability was also related to vegetation status in some cases. Considering the 11 overpasses, \"grass\" \(NDVI_{\text{fp}}\) was positively related to standard deviation \(\sigma_{NDVI,g}\) (\(r\) = 0.77, \(p\) < 0.05), whereas \"trees\" \(NDVI_{\text{fp}}\) was negatively related to \(\sigma_{NDVI,t}\) (\(r\) = -0.55, \(p\) < 0.10).  
Figure 4: Footprint induced variability in optical signals acquired over the control (CTRL) tower on: 23 April 2015 (**a,c,e**); and 3 July 2015 (**b,d,f**), on the relative area of the different vegetation covers contributing to: \(GPP_{\text{hh}}\) (**a,b**); the Normalized Vegetation Index (\(NDVI_{\text{fp}}\)) (**c,d**); and the Photochemical Reflectance Index (\(PRI_{\text{fp}}\)) (**e,f**).  
\"Grass\" \(PRI_{\text{fp}}\) was related to \(\sigma_{PRI,\text{g}}\) (\(r=0.71\), \(p<0.05\)), whereas in \"trees\" the relationship was not significant. For \"vegetation\" pixels, \(PRI_{\text{fp}}\) was significantly related to \(\sigma_{PRI,\text{veg}}\) (\(r=0.60\)\(p<0.10\)).
## 3 Proposed Method
### Model Architecture
#### 3.2.2 Intermediate Data Processing  
The main goal of intermediate data processing is to take the spatial features extracted from the previous part as supplementary information regarding students' original exercise features, and then input them into the next part to extract the temporal features of students' learning process. Specifically, firstly, the spatial learning features \(\mathbf{F}_{KS}\) are activated by the sigmoid function. Then, the processed spatial features are suitable for the next part after classification and one-hot coding. Finally, the spatial features are combined with the student learning record sequences processed by one-hot coding to form joint features as the input of BiLSTM. The process is shown by the formula as follows:  
\[\mathbf{F}^{\prime}_{KS}=\sigma(\mathbf{F}_{KS}) \tag{8}\]  
\[\mathbf{F}^{\prime\prime}_{KS}(t)=\begin{cases}1,&\text{if }\mathbf{F}^{\prime}_{KS}>0.5 \\ 0,&\text{if }\mathbf{F}^{\prime}_{KS}\leq 0.5\end{cases} \tag{9}\]  
\[\mathbf{AP}_{1}(t)=\mathbf{F}^{\prime\prime}_{KS}(t)\cdot M+s_{t} \tag{10}\]  
\[\mathbf{F}_{ILF}=OneHot(\mathbf{AP}_{1},2M) \tag{11}\]  
\[\mathbf{AP}_{2}(t)=(1-r_{t})\cdot M+s_{t} \tag{12}\]  
\[\mathbf{F}_{LRS}=OneHot(\mathbf{AP}_{2},2M) \tag{13}\]  
\[\mathbf{F}_{LJF}(t)=\mathbf{F}_{ILS}(t)\oplus\mathbf{F}_{LRS}(t) \tag{14}\]  
where \(\mathbf{AP}_{1}\) and \(\mathbf{AP}_{2}\) are used to calculate the position of 1, which is used for the one-hot coding transformation of the spatial features and learning record sequence. \(\mathbf{F}_{ILF}\) represents the spatial features encoded by one-hot, and \(\mathbf{F}_{LRS}\) represents the learning record sequences encoded by one-hot. The two are connected and combined to form the joint feature of students' learning \(\mathbf{F}_{LJF}\). \(\mathbf{F}_{LJF}\) is a \(k\times 4M\) matrix.
## 2 Topology Optimization of the Mathematical Model Based on SIMP
### Objective Function and Constraints  
This paper aims to minimize compliance with the volume as a constraint and density as a design variable. The main goal is to find the material density distribution that minimizes the structural deformation under predefined boundary conditions and to provide a general deformation measure called compliance, as presented in the following formula:  
\[c(\widetilde{\mathbf{x}})=\mathbf{F}^{\mathbf{T}}\mathbf{U}(\widetilde{ \mathbf{x}}) \tag{5}\]where \(c(\widetilde{\mathbf{x}})\) denotes the compliance vector, \(\mathbf{F}\) represents the nodal force vector, and \(\mathbf{U}(\widetilde{\mathbf{x}})\) signifies the nodal displacement vector. Combined with the volume constraint, the minimum compliance problem can be mathematically defined as  
\[\begin{split}\text{find}\ \mathbf{x}&=[x_{1},x_{2}, \ldots,x_{e},\ldots,x_{n}]^{\mathsf{T}}\\ \text{minimize}\ c(\widetilde{\mathbf{x}})&=\mathbf{ F}^{\mathsf{T}}\mathbf{U}(\widetilde{\mathbf{x}})\\ \text{subject to}\ v(\widetilde{\mathbf{x}})&= \widetilde{\mathbf{x}}^{\mathsf{T}}\mathbf{v}-\overline{v}\leq 0\\ &\mathbf{x}\in\chi,\ \chi=\left\{\mathbf{x}\in\Re^{n}:0<x_{\min} \leq\mathbf{x}\leq 1\right\}\end{split} \tag{6}\]  
where density \(\widetilde{\mathbf{x}}\) is given in Equation (2), \(n\) is the number of units after discretization, \(\mathbf{v}=[v_{1},\ldots,v_{n}]^{\mathsf{T}}\) represents the unit volume vector, and \(\overline{v}\) denotes the volume constraint for the predefined design region (i.e., volume fraction). The vector \(\mathbf{F}\) depends on the design variables and the nodal displacement vector, where \(\mathbf{F}=\mathbf{K}(\widetilde{\mathbf{x}})\mathbf{U}(\widetilde{\mathbf{x}})\). To avoid singularity in the global stiffness matrix \(\mathbf{K}(\widetilde{\mathbf{x}})\), \(x_{\min}\) is set equal to 0.001. The computational formula is given in the following form:  
\[\mathbf{K}(\widetilde{\mathbf{x}})=\frac{E}{1-\mu^{2}}\mathbf{k}(\widetilde{ \mathbf{x}}) \tag{7}\]  
where \(\mathbf{k}(\widetilde{\mathbf{x}})\) represents the unit stiffness matrix.
## 4 Results and Discussions
### Validation of FEM Model Using Field Measurements
#### 4.3.1 Comparison of Magnitude of Dynamic Responses Based on Same Settings
Therefore, the feasibility and effectiveness of the FEM models were verified, which could well simulate dynamic responses for asphalt pavement in dry and saturated conditions.
## 2 Uncertainty Quantification Methods in Radar-Based Precipitation Estimation  
### Identification of Uncertainty Propagation  
Research on uncertainty in precipitation estimation based on hydrological and weather radars started in the 1970s [1; 2; 3]. Recently, to improve the accuracy of radar-based precipitation estimates, the main topics of studies on uncertainty have concerned the uncertainty due to the Z-R relation, parameter estimation of quantitative precipitation estimation models, and post-processing method such as bias correction methods. In the hydrological field, the studies have concentrated on analysis of the uncertainty of radar-based precipitation estimates as input data of hydrological models [13; 14; 22; 23]. However, research on uncertainty in the comprehensive process that consists of a serial combination of several models or methods has been rarely conducted in the field of weather radar application. This study defined the basic concept of an uncertainty propagation in radar-based precipitation estimation using the concept of an uncertainty cascade presented by [24], which served as the basis for the Third Assessment Report of the Intergovernmental Panel on Climate Change [25].  
In a radar-based precipitation estimation process, all stages must be considered consecutively and assessed in a comprehensive manner because the results of a previous stage inevitably affect the initial conditions of subsequent stages. Consequently, the final outcome in assessment and quantification of uncertainty should be presented as shown in Figure 1a. In contrast, uncertainty will occur largely in the radar measurement stage and this uncertainty is expected to be reduced during signal processing, quality control, precipitation estimation, and post-processing, as in Figure 1b. Therefore, this study proposed the uncertainty propagation in radar-based precipitation estimation shown in Figure 1, and analyzed the total uncertainty and its propagation.
## 2 Emerging Cloud Technologies
### Cloud-Related Architectures
#### 2.1.1 Hybrid Cloud-Fog Computing  
As already mentioned, the physical distance between the DCs of a cloud service provider and end users (devices) is considered to be a limitation for centralized cloud computing (i.e., the FC architecture). Because of the long distance, centralized cloud computing may not be compatible with the requirements of many latency-sensitive future applications, such as public protection and disaster relief (PPDR) [13], real-time tele-surgery [14] and autonomous driving applications.  
Fog systems can help cloud computing to provide lower latency by extending the architecture to the edge of the network and allocating some parts of the computations to the edge. We should consider that fog is tightly linked to the existence of a cloud, and it cannot work in a standalone mode [15; 16]. While fog resources help in providing the desired quality of service (QoS) for services, resource-limited fog devices cannot be a replacement for the the resource-rich cloud [17]. In fact, fog computing can fill the gap between the cloud and end devices (e.g., IoT nodes) by moving some resources (i.e., computing, storage, networking) and data management to a closer location (i.e., network nodes) to IoT devices. In this case, resource allocation and data management decisions are not only made in the cloud, but can also be made along the IoT-to-cloud path as data traverses to the cloud (preferred to be close to the IoT devices) [18]. It is worth mentioning that bringing the resources closer to the edge can also increase the battery lifetime for edge devices and reduce network traffic in the core of the network [15].  
A three-layer general architecture was introduced in several studies (e.g., [17; 18; 19; 20]) for fog computing: IoT devices (or edge) layer, fog layer and cloud layer.  
The edge layer consists of terminal nodes, embedded systems, sensors and actuators with very limited computation, energy and bandwidth. The fog layer includes intermediate networking devices or nodes (e.g., routers, gateways, switches, and access points) that work on various protocols. These devices are supported with computational and storage capabilities. Finally, cloud DCs that have very rich virtual capabilities in terms of storage and computing are considered to be the last layer [17]. IoT devices (end-users) can communicate with the fog (i.e., fog nodes) through the local area network (LAN). However, the communication between the IoT devices (end-users) and the Cloud is done over the wide area network (WAN), through the fog or otherwise [16].  
In these extended hybrid cloud-fog systems, the network scale, topology and configuration are highly heterogeneous, dynamic, and mission-oriented (e.g., temporary hospitals built during the COVID-19 pandemic).