esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Spatiotemporal Dynamics of GPPVPM and Grain Production from NASS Dataset from 2008-2018
When including only those counties with differences less than 20% between the planted area from CDL and the harvested area from NASS, the relationship between GPP\({}_{\text{VPM}}\) and grain production was much stronger (R\({}^{2}\) > 0.8, Table 4). Figure 8: (**a**) The relationships between county-level winter wheat GPP\({}_{\text{VPM}}\) and grain production in the CONUS from 2008 to 2018, labeled by year, and the black solid line is the linear regression results for all the county-year data. (**b**) Linear regression between GPP\({}_{\text{VPM}}\) and NASS grain production from 2008 to2018, labeled by the relative difference between CDL-derived planted area (plt_CDL) and NASS harvested area (harv_NASS). (**c**) Density plot of the relationship between HI\({}_{\text{GPP}}\) and the difference between plt_CDL and harv_NASS. (**d**) Histogram of HI\({}_{\text{GPP}}\) for all the county-years with a difference of plt_CDL and harv_NASS less than <20%. HI\({}_{\text{GPP}}\) values had a moderate range of variation across most of the county-years (Figure 8c), which was not affected much by the difference between the two cropping areas (Figure 8c). For those counties with small differences (<20%) between the two cropping areas, HI\({}_{\text{GPP}}\) values ranged from 0.2-0.3 (Figure 8d). Geographically, high HI\({}_{\text{GPP}}\) values occurred in the northern part of the CONUS, and the winter wheat belt region in the Southern Great Plains generally had a lower HI\({}_{\text{GPP}}\), except for part of western Kansas (Figure 9). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Relative Difference** & **Slope** & **R\({}^{2}\)** & **Bias (10\({}^{3}\) ton)** & **RMSE (10\({}^{3}\) ton)** & **Number of Counties** \\ \hline
[0,10] & 0.27 & 0.87 & 0.42 & 113.62 & 3715 \\
[10,20] & 0.22 & 0.83 & 3.07 & 71.80 & 2501 \\
[20,30] & 0.17 & 0.79 & 4.48 & 48.47 & 1609 \\
[30,40] & 0.14 & 0.80 & 4.28 & 40.65 & 1076 \\
[40,50] & 0.11 & 0.81 & 3.47 & 34.56 & 691 \\
[50,60] & 0.10 & 0.83 & 3.48 & 38.22 & 519 \\ >60 & 0.07 & 0.69 & 0.44 & 26.57 & 2127 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Statistics of linear regression results between county-level GPP\({}_{\text{VPM}}\) and winter wheat grain production from NASS over CONUS from 2008–2018 by considering the relative differences between CDL-derived planted area and NASS harvested area. Figure 9: Spatial distribution of harvest index derived from GPP\({}_{\text{VPM}}\) and NASS grain production (HI\({}_{\text{GPP}}\)) in 2010 for (**a**) all the counties and (**b**) counties with small differences (<20%) between CDL-derived planted area and NASS harvested area.
## 3 Results and Discussion  
### Influence of Shear Rate  
#### 3.1.1 Fluorescence Microscopy Test  
Samples prepared with shear rates of 600, 4000, 5000, and 6000 r/min were examined by fluorescence microscopy to investigate the morphological characteristics of the asphalt phase and SBS phase. The results are shown in Figure 3.  
The SBS phase morphology changed from droplet (600 r/min without network structure) to the filament (4000 r/min with network structure) with an increasing shear rate. The distribution pattern became more uniform and the area ratio gradually increased.  
The higher the shear rate, the smaller the SBS particle was sheared and the more evenly distributed the SBS was. It is easier for smaller SBS particles to absorb more light components to achieve a greater degree of swelling, resulting in a stronger network structure, which is beneficial to the overall performance of SBS-modified asphalt.
## 2 Study Area and Experimental Data
### Data
#### 2.2.2 Satellite Data  
MOD17A2H (Collection 6) is a standard satellite product with a spatial resolution of 500 m and a temporal resolution of 8 days. It is calculated based on the light use efficiency (LUE) approach by [PERSON] [31]:  
\[\text{GPP}\ =\varepsilon\times\text{fPAR}\ \times\ \text{PAR} \tag{1}\]  
where \(\varepsilon\), fPAR, and PAR denote the radiation use efficiency coefficient (RUE), the fraction of incident PAR absorbed by the surface, and photosynthetically active radiation, respectively [32].  
According to [PERSON] et al. [32], the GPP values of MOD17A2H refer to the sum of the GPP during an 8-day period. In this study, we averaged the total GPP to generate the 8-day averaged GPP.
## 2 Bottleneck Network  
In the past several years, the latent features extracted from the bottleneck layer have been widely used in many tasks such as speech recognition [15; 16; 17], audio classification [18; 19], speech synthesis [20] and speaker recognition [21]. The latent features are generated from a multi-layer perceptron (MLP) or DNN with a middle bottleneck layer having a small number of hidden units compared to the other hidden layers. This special hidden layer creates a constriction in the network to compress the task-related information into a low dimensional representation. Therefore, the latent features can be considered as nonlinear transformation and dimensionality reduction of the input features.  
One may wonder how to derive the latent features in both the unsupervised and supervised method. In the unsupervised manner, an autoencoder with several hidden layers is trained to reconstruct the inputs as shown in Figure 1, and does not need explicit tables to train on. In this figure, the autoencoder has three layers: input, output and hidden layer. The input vector \(\mathbf{x}\) is encoded to the hidden vector \(\mathbf{h}\) of the autoencoder by a nonlinear activation function \(\sigma\), with the learned weight matrix \(\mathbf{W}^{(1)}\) and the bias vector \(\mathbf{b}^{(1)}\). Then, the input is decoded from the hidden vector to produce a reconstructed vector \(\widetilde{\mathbf{x}}\) using the learned weight matrix \(\mathbf{W}^{(2)}\) and bias vector \(\mathbf{b}^{(2)}\). The autoencoder parameter \(\theta=(\mathbf{W}^{(1)},\mathbf{b}^{(1)}),(\mathbf{W}^{(2)},\mathbf{b}^{(2)})\) is learned using the back-propagation algorithm by minimizing the mean square error (MSE) loss.  
Further, a stacked autoencoder can be used to extract latent features, which are progressively encoded using successive hidden layers. Then, fine-tuning on the entire stack of hidden layers is performed using the back-propagation algorithm. This allows each hidden layer to exhibit different levels of representation for the input feature.  
In the supervised approach, latent features are created by the MLP trained to predict the class label (e.g., phoneme states) as shown in Figure 2, where the MLP is the feed-forward neural network made of an input layer, output layer, and at least one hidden layer. Usually, for the classification task, the softmax function is employed to convert the values of arbitrary ranges into a probabilistic representation. The learning process is accomplished to minimize the prediction error with respect to the parameter \(\theta=(\mathbf{W}^{(1)},\mathbf{b}^{(1)}),(\mathbf{W}^{(2)},\mathbf{b}^{(2) }),\cdots,(\mathbf{W}^{(L)},\mathbf{b}^{(L)})\), and a cross entropy error function [22] is generally selected as the loss function in the MLP. While the supervised method carries out the subsequent classification task, the classification information is compressed into the excitation of the latent units as much as possible via the continuous training. Hence, the latent features contain the expressive power to show more invariant and discriminative capability.  
Figure 1: Structure of the autoencoder model.
# Nanomaterials-Based Electrochemical Sensors for
## 2 In Vitro Detection of Typical Neurotransmitters
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Neuro-Transmitters** & **EC Method** & **Nanomaterials** & **EW vs. RRD** & **Interference** & **Linear Range** & **LOD (\#MD)** & **Ref** \\ \hline  & DPV & MWCNT-PAN-TOO, & 0.021 & 5-HT \(\Delta\)A & 49-75 & 0.160 & [10] \\  & DPV & Hyper-permeable methods/Cold nanoparticles & 0.914 & Glomom-Difference, citric acid, UA, AA, DA & 5-700 & 0.258 & [10] \\  & CV & Cryptocontrol & 0.654 & AA and UA & 1-1000 & 0.24 & [10] \\  & DPV & MWCNT & 0.782 & AA & 7-5.48 & 6.4 & [10] \\  & CV & TX 10-modified carbon paste electrode & 0.810 & AA and FA & 10-50 & 1.0 & [10] \\ \hline  & DPV & 22-beagonal boron nitride & 0.862 & AA and UA & 3-75 & 0.65 & [42] \\  & DPV & Carbon dots & 0.885 & AA and UA & 0.01-30 & 0.01 & [10] \\  & AP & PxN minoulays & 0.922 & Glomce, AA, and UA & 0.5-250 & 0.01 & [10] \\  & AP & Multilayer graphene nanobels & 0.81 & Hydraulic, UA, AA, lactic acid, glucose & 2-20 & 0.58 & [59] \\  & CV & Multilayer based hollow sphere & 0.922 & None & 0.5-25 & 0.5 & [59] \\ \hline  & DPV & Carbon nanotubes & 0.910 & 0.948 & AA, EP, DA, UA, FL, Glucose & 0.01-1000 & 0.001 & [10] \\  & DPV & MWCNT/ZnO & 0  
For in vitro analysis, various nanomaterials-based electrochemical sensors and biosensors have been reported for the measurement of EP, DP, NE, ACh, l-Glu, NO, and ATP, as listed in Table 1. The developed electrochemical sensors and biosensors showed efficacy in detecting the neurochemicals either individually or in the presence of multiple neurotransmitters. In general, multiple neurotransmitters were discriminated by enzyme-modified nanomaterials-based electrodes. In addition, nanoengineering of sensing platforms with separation techniques, such as micro dialysis, have been demonstrated for the selective detection of neurotransmitters. A difficult challenge to overcome is the biofouling of the electrode surface, and it significantly decreases the sensitivity and stability of the sensors. Future research may focus on the functionalization of nanomaterials to retard the accumulation of byproducts on the electrode. The selective detection of structurally related neurochemicals is challenging, and further development is required in nanomaterials-based electrochemical sensors. The toxicity analysis of nanomaterials and neurochemical oxidation byproducts are required for the use of the nanomaterials-based electrochemical sensors in direct clinical applications.
# Robust Downlink Mechanism for Industrial Internet of Things Using LoRaWAN Networks
###### Abstract
The article first introduces previous related work and summarizes the key aspects and issues with the technology. Subsequently, it proposes a mechanism called DROB (downlink rate optimization for class B), which is an implementation of a modified ADR for downlink. Next, the behaviour of DROB, considering not only the Packet Delivery Ratio (PDR) but other key indicators as well, is derived from a scheduled MAC (such as discarded events in devices' buffers and energy consumption) and analysed. To reproduce these scenarios as faithfully as possible, the simulator was enhanced with the inclusion of external and localized interference sources, which also served to highlight the response of the proposed mechanism to these situations in real life.
## 4 Intravalley Relaxation and Fermi Degeneracy Formation
### Effective Electron Temperature Approximation  
As mentioned in Section 4.3, in the final stage of the relaxation, the electron temperature \(T_{\rm e}\) is well defined. In this case, we can include an effect of the elastic e-e scattering in terms of the electron temperature approximation (ETA). By using the second order perturbation theory, the time dependence of \(T_{\rm e}\) is given by:  
\[\frac{\partial T_{\rm e}}{\partial t}=-\frac{\Gamma(T_{\rm e})}{C(T_{\rm e})}. \tag{10}\]  
where:  
\[\Gamma(T_{\rm e})=\frac{\pi S^{2}}{N}\sum_{q,k,\sigma}\omega_{q}(1-\langle n_{ k-q,\sigma}\rangle)\langle n_{k,\sigma}\rangle\delta(\omega_{q}+\varepsilon_{k-q}- \varepsilon_{k}). \tag{11}\]  
In the derivation of Equation (10), the energy conservation between the electronic system and phonon one has been considered [11]. It is well-known that the electronic heat capacity \(C(T_{\rm e})\propto T_{\rm e}\) at low temperature [27]. Electron-hole pair numbers around the Fermi energy and the acoustic phonon energy are also proportional to \(T_{\rm e}\), while the phonon mode density is proportional to \(T_{\rm e}^{2}\). Thus, the \(\Gamma(T_{\rm e})\propto T_{\rm e}^{4}\), and hence, we obtain \(T_{\rm e}\propto t^{-1/2}\). This leads to the shift of the mean energy of electronic system as:  
\[\Delta E(t)=C(T_{\rm e})|\Delta T_{\rm e}|\propto t^{-1}. \tag{12}\]  
Then, by taking the elastic e-e scattering into account in ETA, the relaxation speed in the final stage further slows down more than that only by the e-ph scattering, and this result is consistent with the experiment [7].
# Transfer Learning Approach for Human Activity Recognition Based on Continuous Wavelet Transform
## 2 Related Works
However, despite not requiring large labelled datasets, this approach is not applicable to small HAR datasets because it still requires an extensive quantity of recorded sensor signals. The continuous wavelet transform (CWT) is a promising technique for improving the accuracy of CNN for signal classification. The notable feature of CWT is that it allows the conversion of the 1D signal recognition problem into the image classification problem, which has undergone significant development in recent years [45]. In biomedical signal processing, CWT is commonly used in EEG signal classification [46; 47; 48; 49] and is regarded as a proven tool for improving the accuracy of CNN models. Considering the wearable sensor-based HAR domain, the CWT was used in [50; 51; 52], which resulted in a promising performance increase. Even though CWT is not a new tool in signal processing, and the combination of CWT and CNN is widely used for EEG signal classification, no studies have yet investigated the effect of transform parameters on neural network performance. Moreover, there are currently no studies in the wearable sensor HAR domain comparing the performance of different CNN architectures when classifying CWT-generated scalograms. Therefore, the focus of this study is on analyzing the effect of CWT configurations on the performance of well-known network architectures. On the basis of the results of the performed analysis, we propose a DL model pre-trained on CWT-generated scalograms, which will make it possible to train a deep CNN on relatively small HAR datasets by transferring knowledge from a larger and more general source dataset. The proposed model was then evaluated on a target dataset with varying numbers of frozen layers to determine its performance on new data.
# Spatial Signal Analysis Based on Wave-Spectral Fractal Scaling: A Case of Urban Street Networks
## 3 Empirical Analysis
### Results and Analysis
(**a**) Beijing; (**b**) Shanghai. Note: The spatial signals can be turned into density curves of urban traffic networks by using Equation (14). If the density data could be fitted to an inverse power law, we should estimate fractal dimension directly by means of Equation (1). Due to the curves depart from power function, we had to employ the wave-spectrum scaling analysis to estimate fractal dimension. The fractal dimension estimated by wave-spectrum scaling can be used to replace the radial dimension. The radial dimension has three meanings for spatial analyses: firstly, the degree of space filling: the denser the traffic network of a city, the higher the fractal dimension value; secondly, the degree of spatial homogeneity: the slower the attenuation of traffic network density from a city center to the periphery, the higher the fractal dimension value; thirdly, the degree of spatial correlation: the stronger the accessibility from one place to another, the higher the fractal dimension value. Beijing's road network has higher degrees of space filling, spatial uniformity, and spatial correlation, its radial dimension is high. Tianjin's network density is lower than Beijing's network density, indicating lower spatial filling extent, and its fractal dimension is significantly lower than Beijing's. Shanghai's road network has lower spatial homogeneity, that is, the spatial decay rate of the urban network density is higher. Therefore, the radial dimension of Shanghai's road network is not so high. The distribution of road network in Nanjing is similar, to some extent, to that of Shanghai. In Nanjing, a higher network density attenuation rate leads to relatively low fractal dimension. The traffic network filling degree of Hangzhou is not as high as that of Shanghai, but the network attenuation rate is lower than that of Shanghai. Wuhan is the hub of land and water transportation in Central China. Probably due to the Yangtze River, the road network filling degree of Wuhan is not particularly high. The fractal dimension of the Guangzhou road network is relatively high. The distribution of traffic network in Guangzhou is similar, to a degree, to that of Beijing. Shenzhen's road network bears lower space filling extent, but has higher spatial homogeneity. In other words, the spatial decay of Shenzhen's network density is not significant. Therefore, the radial dimension of Shenzhen's road network is very high. In southwest China, Chengdu is located in the plain area, so its road network bears higher fractal dimension. In contrast, Chongqing is located in the mountainous area, and its road network possesses relatively low fractal dimension. Mountains and rivers reduce the fractal dimension values of urban traffic networks because they affect the space-filling degree of streets and roads in urban regions.
###### Abstract  
Global enhanced human activities have deeply influenced grassland ecosystems. Quantifying the impact of human activities on grasslands is crucial to understanding the grassland dynamic change mechanism, such as grassland degradation, and to establishing ecosystem protection measures. In this study, potential net primary productivity (PNPP), actual NPP (ANPP), and the forage harvest NPP (HNPP) were employed to establish the human activities index (HAI) to reveal the spatiotemporal changes of the effects of human activities on grassland ecosystems in eastern Inner Mongolia from 2000 to 2017, and to further explore the relationship between human activities and grassland degradation. The results showed that the total average PNPP, ANPP, and HNPP of grasslands in eastern Inner Mongolia were 187.2 Tg C yr\({}^{-1}\), 152.3 Tg C yr\({}^{-1}\), and 8.9 Tg C yr\({}^{-1}\), respectively, during the period of 2000 to 2017. The HAI exhibited a clear decreasing trend during the study period, with annual mean values ranging from 0.75 to 0.47, which indicates that the NPP loss induced by human activities is weakening, and this trend is dominated by the difference between potential NPP and actual NPP. About 42.4% of the study area was non-degraded grassland, and the declining grassland degradation index (GDI) indicated that the degradation grade in eastern Inner Mongolia improved from moderate to light degradation. A positive relationship was found between HAI and GDI. This relationship was more significant in Xilingol League, which is a typical ecologically fragile area, than that in Xing'an League and Hulunbuir City. human activities; net primary productivity; grassland degradation; climate change; forage harvest
# Mobile Robot Path Planning Using a Laser Range Finder for Environments with Transparent Obstacles
## 2 Related Works  
### Environment Map  
The first step to path planning for automatic mobile robots is to configure the environment map. Many different methods of generating environment maps according to different methods of expression exist [13; 14]. For example, there are grid maps, feature-based maps, etc [15; 16].  
A grid map divides the map into cell units, and the occupancy state of each cell is denoted between 0 and 1 to represent probability with the inaccuracy of the cell being measured taken into consideration [17] (Figure 2). The benefit of a grid map is that it is easy to generate, applies quickly to the changing environment that surrounds an object, and provides good accessibility. However, the grids result in inaccuracies in the real environment, and the complexity of grid maps is high because all cells must hold an occupancy state [14; 18]. Recently, there has been the paper on path planning using a grid-based potential field [19].  
Figure 1: The phenomenon when measuring transparent object using a laser range finder (LRF).  
Figure 2: An example of an occupy grid map.  
Feature-based maps (Figure 3) can better express the real environment compared to grid maps because they indicate obstacles with points and lines. However, when it comes to map generation, the accuracy of an environment map is increased only when sensors with high accuracy are used because feature-based maps are highly affected by the performance of the sensor [20].
# Ionospheric Polarization Techniques for Robust NVIS Remote Sensing Platforms
## 5 Results
### BER CDF According to Power Transmission  
As we have seen, the power transmission of 50 W is not enough to obtain good results for higher modulation orders. The modulations of order 16 denotes a high decrease of the probabilities to achieve low BERs. Despite the results, we have seen that selection combining brings us the best results, especially for PSK modulation. However, a power transmission of 50 W is high, so taking into account these conclusions, we analyze the behavior of the PSK modulation orders 4 and 8 for a power transmission between 6 W and 50 W. As we can see in Figure 11, the modulation 4 PSK brings the best results in comparison to the 8 PSK, regardless the power transmission. Even so, the bitrate of the 8 PSK is higher. As the graphic shows, 4 PSK with a power transmission of 50 W, 25 W, 12 W, and 6 W have probabilities of 96%, 94%, 92%, and 71%, respectively, to achieve a BER lower than \(10^{-4}\). The 8 PSK with a power transmission of 50 W, 25 W, 12 W, and 6 W have probabilities of 69%, 50%, 32%, and 30%, respectively, to achieve a BER lower than \(10^{-4}\).  
Figure 11: Selection combining (SC) BER CDF according to power transmission.  
Figure 10: BER CDF order 16 modulations at 50 W.
## 2 Materials and Methods
### EEG Preprocessing and Graph Construction  
Signal preprocessing: EEG signals are filtered to remove noise, artifacts, and baseline drifts. Functional connectivity is calculated using metrics such as Pearson correlation or mean phase-locking values (mPLV).  
Graph representation: The functional connectivity matrices are thresholded to construct adjacency matrices, representing EEG channels as graph nodes and connectivity strengths as edges.
## 3 Methodology
### Correlation-Based Feature Selection  
The authors of [15] reported that the selection of only the most relevant features improves the quality of landslide identification and classification. Working with large numbers of features causes numerous problems. As reported in [43] and [14], some of these problems include the slow run time of algorithms due to the consideration of numerous resources, low accuracy when the number of features exceed the number of observation features, and overfitting when irrelevant features are used as inputs. Therefore, the most significant features should be selected to enhance the accuracy of feature extraction. In this study, relevant features were extracted using the CFS algorithm with Weka 3.7 software. Furthermore, the CFS algorithm was applied to all LiDAR-derived data, visible bands, and textural features, and was used to determine the feature subsets required to develop models for landslide identification. The CFS algorithm comprises two basic steps: the ranking of initial features and the elimination of the least important features through an iterative process.