esa-sceva/satcom-chunk-collection huggingface dataset:
# Generation of High Resolution Vegetation Productivity from a Downscaling Method
###### Abstract  
Accurately estimating vegetation productivity is important in the research of terrestrial ecosystems, carbon cycles and climate change. Although several gross primary production (GPP) and net primary production (NPP) products have been generated and many algorithms developed, advances are still needed to exploit multi-scale data streams for producing GPP and NPP with higher spatial and temporal resolution. In this paper, a method to generate high spatial resolution (30 m) GPP and NPP products was developed based on multi-scale remote sensing data and a downscaling method. First, high resolution fraction photosynthetically active radiation (FPAR) and leaf area index (LAI) were obtained by using a regression tree approach and the spatial and temporal adaptive reflectance fusion model (STARFM). Second, the GPP and NPP were estimated from a multi-source data synergized quantitative algorithm. Finally, the vegetation productivity estimates were validated with the ground-based field data, and were compared with MODerate Resolution Imaging Spectroradiometer (MODIS) and estimated Global LAnd Surface Satellite (GLASS) products. Results of this paper indicated that downscaling methods have great potential in generating high resolution GPP and NPP.  
GPP; NPP; downscaling; STARFM +
Footnote †: journal: Remote Sens. 10, 1748; doi:10.3390/rs10111748
## 4 Materials and Methods
### Synthesis of PSt-l-PDMS and PSt-l-PDMS/DVB Conetworks  
The polystyrene-\(l\)-poly(dimethylsiloxane) (PSt-\(l\)-PDMS) conetworks were synthesized via free radical copolymerization of MA-PDMS-MA (\(M_{\rm{n}}\) = 4700 g/mol) macromolecular crosslinker and styrene (St). The polystyrene-\(l\)-poly(dimethylsiloxane)/divinylbenzene (PSt-\(l\)-PDMS/DVB) type conetworks with additional DVB crosslinkers were prepared under the same condition. In both types of these conetworks, the PDMS content was varied between 30 and 70 \(m/m\)% in the feed. In the case of the PSt-\(l\)-PDMS/DVB conetworks, the St/DVB weight ratios were constant, 36:1 or 5:1. The feed compositions for the conetwork syntheses are presented in Tables S1-S3 in the Supporting Information. During the conetwork forming reactions, the ratio of the total monomer concentration ([M]) and the square root of the AIBN initiator concentration ([I]\({}^{1/2}\)) that is, [M]/[I]\({}^{1/2}\), was 23.2 in all cases. The concentration of the reaction mixture was 0.4 g/mL. First, the appropriate amounts of starting materials and solvent (THF) were mixed and homogenized in a glovebox under nitrogen atmosphere. Then, the reaction mixtures were poured into disk-shaped Teflon molds. The molds were sealed, removed from the glovebox, and heated in an oven at 60 \({}^{\circ}\)C for 72 h. After the reaction, the molds were removed from the oven, allowed to cool to room temperature, and then opened to allow the THF to evaporate slowly.  
For sample identification, each conetwork sample is denoted with capital letters representing the components, that is, styrene (\"S\") and divinylbenzene (\"D\"). The number, following the letters, shows the St/DVB weight ratio. The middle number, separated by a dash (\"-\"), indicates the number average molecular weight of the MA-PDMS-MA in kg/mol. The last number specifies the PDMS content, expressed as \(m/m\)%.  
The synthesized PSt-\(l\)-PDMS and PSt-\(l\)-PDMS/DVB conetworks were gently dried under vacuum at 130 \({}^{\circ}\)C for one day. Subsequently, the samples were purified by solvent extraction using distilled THF, hexane and 1-nitropropane (1-PrNO\({}_{2}\)) to remove the unreacted components and oligomers. During the extraction, the solvent was changed daily for three days. In the case of hexane and 1-PrNO\({}_{2}\), the extraction was carried out separatelywith each solvent. The extraction residues of the samples were collected and analyzed by \({}^{1}\)H NMR. Finally, the conetworks were dried by gradual heating in a vacuum oven until a constant weight was achieved. Before structural analyses, all the samples were annealed under vacuum at 130 \({}^{\circ}\)C for 24 h and then cooled slowly to room temperature in order to avoid thermal history effects.
## 2 Bi-Objective Model for APP with Production in Advance and Backordering  
On-time delivery (OTD) refers to the delivery of products that meet the quantity and quality requirements to customers in the period of the promised delivery date, which is a key metric to measure delivery performance [23]. OTD can ensure the reliability of delivery and, most importantly, customer loyalty as well as improve the reputation of enterprises and customer satisfaction. In reality, due to the decline of actual production capacity caused by the change of workers, equipment maintenance or damage, holidays and sudden epidemic situations, or due to the shortage of production materials, production enterprises may produce in advance or be backordered. Production in advance will increase the inventory cost, while backorders will affect customer satisfaction. Generally speaking, for customers, the longer the delayed delivery time and the greater the quantity, the lower the customer satisfaction. If enterprises cannot meet their customers' expectations, then they may find a supplier who can. This leads to a loss of sales, which greatly increases the lost sales cost (LSC). For the multi-product and multi-stage production mode, production in advance or backorder often occurs, which will directly or indirectly lead to an increase in enterprise production cost and can irreparably damage the customer relationship. So, it is necessary to properly arrange the production plan to improve the production management level of the enterprise. Therefore, a bi-objective optimization model is established in this section for the APP problem considering the impacts of production in advance and backorder. These two objective functions are formulated in Section 2.3, and the constraints are analyzed in Section 2.4 based on the mathematical notations defined in Section 2.1.
###### Abstract
The comparative analysis of larger deletions (>5 Mb) and smaller deletions (\(\leq\)0.5 Mb) involving the SHANK3 gene has demonstrated notable differences in facial morphology. Facial features were observed in approximately 35-80% of individuals [30]. Conversely, individuals with SHANK3 gene variants tend to exhibit fewer dysmorphic features compared to those with microdeletions [30]. Crani features of PMS are often subtle and heterogeneous [11; 31]. Cranial morphology is often characterized by an elongated or dolichocephalic shape. Facial features commonly include a broad forehead, deep-set eyes, puffy eyelids, a bulbous nose, and long, thick eyelashes. The midface may appear flattened, accompanied by a broad nasal bridge and full cheeks [11; 31]. The chin is frequently pointed and may become more prominent with age [11; 31]. The ears are typically large and may be appear prominent or malformed. Less frequently observed features include ptosis, epicanthal folds, strabismus, an elongated philtrum, and a high-arched palate [11; 31]. The subtlety of the craniofacial features and the variability in manifestations, likely influenced by the size of the deletion, make it challenging to establish a diagnosis of this syndrome based exclusively on craniofacial characteristics [11]. Dental overcrowding and malocclusion are among the most commonly observed dental issues. These conditions are frequently linked to contributing factors, such as reduced muscle tone, habitual chewing, tongue thrusting, and bruxism. Malocclusion is often associated with difficulties in swallowing and salivation, which may further complicate speech impairments [32; 33]. Facial lymphedema is a rare condition commonly associated with prior surgery in the head and neck region, infection, radiation therapy, or local tumor growth. Lymphedema increases the risk of infection due to lymphatic system impairment. The most common infection is cellulitis, which can affect the edematous area [34]. Only a few cases of isolated facial lymphhedema have been reported in the literature, and none of them are associated with PMS. However, lymphhedema, beyond facial locations, is associated with PMS in 10 to 25% of cases, becoming a relatively common manifestation in PMS [2; 35]. Furthermore, lymphhedema in PMS appears to be attributable to deletions of the 22q13 gene without alterations in SHANK3, as reported in Table 1 [14]. CELSR1 has recently been identified as a likely cause of lymphhedema observed in PMS [13; 24].
## 2 Related Work
### The Way of Deep Learning  
Based on the permutation and rotation invariance of point clouds, [PERSON] et al. [11] proposed Pointnet, which learns the features of points by sharing a multilayer perceptron (MLP) and then uses a symmetric pooling function to obtain global features. Based on the Pointnet [11], a series of networks were proposed: Pointnet++ [14] groups points by hierarchy and progressively learns from more significant regions. PointCNN [17] proposes an X-transformation to first process the point clouds data so that it \"regularizes\" the data, and then using convolution operations, the point clouds is processed. Pointconv [15] proposes a method that can efficiently perform convolution operations on non-uniformly sampled 3D point clouds data. GACNet [28] creates a graph structure of each point and its surrounding points by introducing attention mechanism calculating the edge weights of the centroid and each neighboring point, thus enabling the network to achieve better results. PATs [29] mainly improved the part of FPS in PointNet++ [14] by adding a self-attention mechanism to select the down-sampled points at locations with high attention weights. [PERSON] et al. [30] proposed a capsule-based deep learning framework for extracting and classifying road markers from a large number of disordered MLS point clouds. [PERSON] et al. proposed a deep learning model based on a dense feature pyramid network (DFPN) by combining the specificity and complexity of road markers [31]. [PERSON] et al. proposed a deep learning model using point clouds aggregated into a 2D image space and then transformed 2D projection using affine transformation for use in 3D LiDAR point clouds in rural accurate segmentation and extraction of road targets [32]. [PERSON] et al. used multi-scale dynamic point-by-point convolution operations to provide an end-to-end feature extraction framework for 3D point clouds segmentation [33]. However, the existing deep learning network cannot accurately segment the sparse and non-uniform large-scale road field scenic spot cloud data collected by the high-speed mobile LiDAR system. Therefore, our research is based on enhancing the characteristics of sampling points in MLS point clouds data, specifically through adding an adaptive sampling radius module, enhancing the high-dimensional features of sampling points, and improving the segmentation accuracy of inhomogeneous sparse point clouds by deep learning network.
## 2 Materials and Methods
### LSTM Neural Network  
LSTM was first proposed by [PERSON] and [PERSON] [24] and its structure is shown in Figure 1. LSTM is a special form of recurrent neural network (RNN). Unlike the traditional RNN, LSTM adds memory cell blocks to each neuron of the hidden layer, and these determine the degree of memory for forgetting of information through input gates, forget gates, and output gates. LSTM has a long-term memory function and can be effectively used with respect to regression problems for time series. The main formulas are as follows:  
\[f_{t}=\sigma\Big{(}W_{f}\cdot[h_{t-1},x_{t}]+b_{f}\Big{)} \tag{1}\]  
\[i_{t}=\sigma(W_{i}\cdot[h_{t-1},x_{t}]+b_{i}) \tag{2}\]  
\[\widetilde{C}_{t}=\tan h(W_{c}\cdot[h_{t-1},x_{t}]+b_{c}) \tag{3}\]  
\[C_{t}=f_{t}\cdot C_{t-1}+i_{t}\cdot\widetilde{C}_{t} \tag{4}\]  
\[o_{t}=\sigma(W_{o}\cdot[h_{t-1},x_{t}]+b_{o}) \tag{5}\]  
\[h_{t}=o_{t}\cdot\tan h(C_{t}) \tag{6}\]  
where \(t\) is the time step; \(f_{t}\) is the forget gate; \(i_{t}\) is the input gate; \(o_{t}\) is the output gate; \(C_{t}\) is the final cell output; \(h_{t}\) is the final state; \(x_{t}\) is the input; \(W_{f}\), \(W_{i}\), \(W_{o}\) and \(W_{C}\) are the weights; \(b_{f}\), \(b_{i}\), \(b_{o}\) and \(b_{C}\) are the biases; and \(\sigma\) is the sigmoid function, which increases the nonlinearity of the neural network algorithms.  
The LSTM network used in this study included three layers. The input layer contains the original HF radar currents, surface winds, and tides. The output layer is the \"true\" currents, which were set to the u- and v-component of the model-simulated currents. The LSTM layer comprises 128 neurons. Through the training process, the LSTM network built a nonlinear regression between the input factors and the model currents (outputs) by minimizing the radar-model errors. In other words, the LSTM training process rectifies the radar data deviations against the model currents by means of wind and tidal information. The data between 1-25 March were taken as the training set, and the data between 26-30 March were used as the validation set. Note that in this study, we treat the model currents as the true values, considering that no current in situ observations are available in this area. The results of this study will be useful for designing observation strategies for future field work.
# IoT and UAV Integration in 5G Hybrid Terrestrial-Satellite Networks
## 1 Introduction
Section 3 introduces use cases and related works about the UAV employment in the IoT, followed by the state-of-the-art about satellite employment in the IoT reported in Section 4. Section 5 lists the different possible solutions to integrate IoT, UAV, and satellites in the 5G paradigm, focusing on the solution we envision as the first step of the integration of the IoT in 5G hybrid terrestrial-satellite networks. Future challenges regarding this integration are stated in Section 6. Conclusions and future works are drawn in Section 7.
# Using the \(\beta\)/\(\alpha\) Ratio to Enhance Odor-Induced EEG Emotion Recognition
## 2 Materials and Methods
### Model Selection and Feature Construction  
We employed three classic models for emotion recognition, namely discriminant analysis (DA), support vector machine (SVM) [37], and random forest (RF). In the label assignment, EEG data from SEO inhalation were labeled as 0 (negative class), and EEG data from BEO inhalation were labeled as 1 (positive class). Given the potential of the \(\beta/\alpha\) ratio as an arousal indicator, we introduced it during feature construction. The mean PSD of traditional EEG frequency bands (\(\delta\), \(\alpha\)1, \(\alpha\)2, \(\beta\)1, and \(\beta\)2) and the \(\beta/\alpha\) ratio at each electrode in all ROIs were used as initial features. Each ROI thus contributed 7 initial features, resulting in a total of 35 features (see the \"Dataset\" module in Figure 1B). To assess the impact of incorporating the \(\beta/\alpha\) ratio on model performance, we constructed two sets of models. The first set (DA-1, SVM-1, RF-1) used traditional EEG frequency bands (\(\delta\), \(\theta\), \(\alpha\)1, \(\alpha\)2, \(\beta\)1, and \(\beta\)2) for feature construction, while the second set (DA-2, SVM-2, and RF-2) incorporated the \(\beta/\alpha\) ratio, with all other conditions kept constant.  
To enhance numerical stability and expedite model convergence, we applied standardization and normalization to the dataset, which consists of a 100 by 35 array. We performed both processes column-wise. Standardization adjusted the mean of each feature to zero and the standard deviation to one. Normalization scaled the feature data to the [\(-\)1, 1] interval, thereby mitigating the influence of outliers on the model. Given that the DA and RF models are insensitive to the range of feature data, we normalized the datasets only for these models. Conversely, the SVM model, which is sensitive to feature data range, underwent both standardization and normalization. To eliminate redundant information and improve model performance, we applied the partial least squares (PLS) regression algorithm for dimensionality reduction and selected the first m components, where the cumulative explained variance ratio exceeded 95%. The final dataset used for model input had a size of 100 by m (samples: 100, features: m).
## 6 Feature Selection Using Genetic Algorithm
The effectiveness of the methods significantly differs if the respective mean ranks differ by at least a value of critical difference. To evaluate the difference, the Nemenyi post-hoc test was applied after rejecting the null hypothesis of the Friedman test. The Nemenyi post-hoc test is intended to detect different groups of data. The point of the Nemenyi test is to do pair-wise performance tests. The results are presented as a Demsar significance diagram (Figure 2). These diagrams help to visualize significant differences for each pair of used methods. In the case of the difference of mean ranks between two methods being smaller than the automatically calculated critical difference value, their performance difference not significant and on the figure presented as a horizontal line. According to the diagram, reducing the training set to 50-100 informative features had a negative impact on the accuracy of the literary texts' author identification. The same result was achieved using all feature space (i.e., SVM without GA). The sets of 200-400 features allowed achieving comparable classification accuracy. For short texts, the best option was to use the SVM trained on 400 features obtained by GA, the worst--to limit the number of features to 50.
# Analysis of Road Sign-Related Factors Affecting Driving Safety with Respect to City Size
## 5 Discussion
### Analysis of Participants' Responses  
The primary results of the analysis and their implications are summarized below. First, the variables of daytime road sign recognition (odds ratio: 1.547) and comprehensibility of guide information (1.222) exhibited a high degree of influence on driving safety. Therefore, road sign designs should be improved by considering the intuitiveness of guide information, avoiding excessive information, and appropriately selecting the guide information required by users to enhance driving safety. Additional road sign improvements that were deemed to be necessary include supplementing signs depicting the actual geometry of roads (e.g., complex intersections), implementing highly intuitive and legible designs such that drivers can comprehend the entire sign rather than individual elements, and providing guide information from the perspective of user experience rather than that of public management. Guide information sufficiency (1.449) and situation-specific real-time guide information (1.148) were observed to be statistically significant, motivating the development of variable signs capable of displaying information required by road users in real time. Although real-time traffic information is currently provided via VMSs, they impart fixed road traffic information. It is essential to provide guide information that satisfies the needs of several drivers over a short time period, such as information on routes to destinations with temporary events, e.g., local festivals or construction sites. Given the limitations of road signs, which are fixed infrastructure, new types of signs incorporating VMSs should be developed, and effective display methods, such as displays built into vehicles, should be explored. Further, the variable of customized guidance corresponding to various forms of mobility (0.112) was observed to be statistically significant. Given the rapid increase in the number of modes of transportation sharing the roads (personal mobility, bicycles, etc.), a new mobility road guidance system should be designed to prevent road accidents. In particular, future forms of mobility, such as autonomous cars and unmanned aerial vehicles, should be considered. Further, the variables of sign location/frequency adequacy (1.128), facility safety management (1.234), and integrated installation of road facilities (1.116) were observed to be statistically significant. These variables share a common feature--the excessive installation of signs (including traffic safety signs) and sporadic construction of other road facilities (traffic lights, street lights, CCTV, etc.) degrade the legibility of guide information and road esthetics and even threaten the safety of facilities during abnormal weather conditions, such as typhoons. Addressing such problems is challenging because road facilities are managed by different institutions. Nevertheless, different types of facilities should be linked and integrated based on policy proposals from higher-level institutions and close consultation between management institutions.
# Uncertainty of the 2D Resistivity Survey on the Subsurface Cavities
## 2 Methodology
### Forward-Inverse Modeling
We used a checkerboard cell size of 2 m in the vertical direction and 4 m in the  
Figure 1: Resistivity synthetic models that represent a target cavity set in limestone unit at six different depths: (**a**) Target cavity T\({}_{1}\) at 2.2 m, (**b**) target cavity T\({}_{2}\) at 4.2 m, (**c**) target cavity T\({}_{3}\) at 6.2 m, (**d**) target cavity T\({}_{4}\) at 8.2 m, (**e**) target cavity T\({}_{5}\) at 10.2 m, (**f**) target cavity T\({}_{6}\) at 12.2 m depth. The depth indicates the mid-point of the cavity. For better illustration, we presented only 40 m horizontal section of the models. horizontal direction with background resistivity of 1000 \(\Omega\)\(\cdot\)m, which was perturbed by \(\pm\)20%, 40%, 60%, and 80% anomalies. The conceptual models were simulated to generate synthetic resistivity data. We implemented the AGI EarthImagerTM 2D software [49] for the model simulations. The finite-element modeling calculates the resistivity data using a partial differential equation [50]. Throughout the entire lateral and vertical mesh formulations, 1.0 m thickness and depth increment factor were used. Finer meshes of 0.5 \(\times\) 0.5 m were applied to increase the forward modeling accuracy. The simulation was perturbed using Gaussian noise (a random value generated with zero mean and 3% standard deviation) in order to integrate the field data-acquisition noise effects. We applied a robust inversion scheme to recover the 2D geoelectric model. It optimizes [51] the least-square method [52] by integrating the weighting matrices, giving approximately equal weight for data misfit and model roughness vector in the inversion processes. Robust inversion also provides improved inversion results for sharper resistivity models. It obtains more compacted anomalies, and it is a sound method to reduce data outliers [53]. Consistently, in our modeling, the robust inversion provided minor inversion artefacts, especially for large resistivity contrasts over a short profile distance. The use of a larger grid size makes the inversion process more efficient; however, it leads to low model resolutions. Further discretization of the model parameters, on the contrary, does not show substantial changes in the model structure rather than smoothening the transition zones [53]. Hence, we applied an optimal grid of 1 \(\times\) 1 m for the inversion of apparent resistivity data. The damping and smoothing factors were set to 10 initially, and these factors were reduced iteratively.