esa-sceva/satcom-chunk-collection huggingface dataset:
# Signal Modulation Recognition Algorithm Based on Improved Spatiotemporal Multi-Channel Network
## 3 Improved Spatiotemporal Multi-Channel Network (IQGMCL)
### LSTM and Bi-LSTM
#### 3.2.2 Bi-LSTM  
Because RNN has problems of long-term dependence, gradient disappearance, or gradient explosion in processing time series, so researchers propose LSTM to solve the issues of RNN. However, LSTM can only process forward information inputted into the neural network to obtain the predicted results. Bi-LSTM obtains the prediction results by processing the forward and backward information inputted into the neural network. The prediction result of Bi-LSTM is often better than that of LSTM [27]. The Bi-LSTM structure is shown in Figure 3.  
For Bi-LSTM networks, the hidden layer state \(h_{t}\) of each level is superimposed by three parts [28]. The output state \(h_{t-1}\) of the hidden layer at the previous moment propagating in the forward direction along the time axis. The output state \(h_{t-1}\) of the hidden layer at the previous moment propagating in the reverse direction along the time axis, and the input amount \(x_{t}\) at the current moment. The combined process of each hidden layer state can be represented by Equations (7)-(9).  
\[h_{t}=\text{LSTM}(x_{t},h_{t-1}) \tag{7}\]  
\[h_{i}=\text{LSTM}(x_{t},h_{i-1}) \tag{8}\]  
\[\text{y}_{t}=a_{t}h_{t}+b_{t}h_{i}+c_{t} \tag{9}\]  
where LSTM refers to the operation process of a traditional LSTM network. \(h_{t}\) is the forward hidden layer output at the current moment. \(h_{t-1}\) is the forward hidden layer output at the previous moment. \(h_{t-1}\) is the backward hidden layer output at the previous moment. \(x_{t}\) is the input of the current time. Combined with Figure 3, it can be concluded that the LSTM layer is able to combine the current moment input \(x_{t}\) with the previous moment output \(h_{t-1}\), and the Bi-LSTM can combine the forward LSTM output \(h_{t}\) with the reverse LSTM output \(h_{i}\) to obtain \(y_{t}\), which can better gain the data information. \(a_{t}\) is the output weight of the hidden layer of the forward propagation unit. \(b_{t}\) is the output weight of the hidden layer of the backward propagation unit. \(c_{t}\) is the hidden layer bias optimization parameter at the current moment, and \(y_{t}\) is the total output at the current moment.  
Compared with traditional LSTM networks, the Bi-LSTM model is structurally a combination of forward and reverse propagation bidirectional cyclic structures. The data flow in the LSTM network is a one-way flow from the past to the future. On the basis of LSTM, the Bi-LSTM network increases the data flow from the future to the past, and there is no connection between the hidden layer used in the past and the hidden layer used  
Figure 3: Bi-LSTM structure.  
in the future. Therefore, Bi-LSTM can better explore the temporal characteristics of the data [29; 30; 31].
## 2 Presentation of Proposed FPO-Guided FMLT Method
### Bearing Fault Diagnosis Based on Extracted IF Ridges  
With the improved TFR, the bearing fault diagnosis cannot be finished yet because the fault type cannot be identified without knowing the relationships among IF ridges related to shaft speed and instantaneous fault characteristic frequency (IFCF). Therefore, multiple IF ridges are required to be extracted from the improved TFRs by iteratively using the FPO algorithm. To obtain multiple IF ridges associated with the IFCF as well as shaft IF, the frequency band of the signal is chopped to obtain a lower frequency band signal and a resonance frequency band signal. The lower frequency band signal can be obtained by low-pass filtering the signal. The cut-off frequency of the low-pass filter should be set to cover the shaft IF and several harmonics of the shaft IF. The resonant frequency band signal is gained by the popular spectral kurtosis (SK) method. The details of the SK method can be found in ref. [27].  
A pre-IF of shaft is first extracted from the STFT-resulting TFR of the lower frequency band signal using the FPO to construct the bases of FMLT. Subsequently, the proposed FMLT can be implemented and enhanced TFRs can be obtained. The shaft IF and its harmonics can then be iteratively extracted from the enhanced TFR of the lower frequency band signal, and similarly, the IFCF and its harmonics can be extracted from the enhanced TFR of the resonant frequency band signal. The involved iteration operation means that peaks associated with the extracted IF ridge are removed from the peak map and a new peak map is formed, i.e., the FPO algorithm is iteratively used to extract the next IF ridge from the newly generated peak map. The number of IF ridges is user-specified as long as that the fault diagnosis can be fulfilled. Generally, two IF ridges respectively extracted from lower and resonant frequency band signals can be enough for the bearing fault diagnosis under time-varying speed.  
With the extracted IF curves, the bearing fault type can be diagnosed in the light of the ratio of any two IF ridges respectively from the lower frequency band signal and resonant frequency band signal. The flow chart of the bearing condition monitoring under variable speed operations via the proposed FPO-guided FMLT method is presented in Figure 5.  
Figure 4: Time frequency analysis (TFA) for noisy multicomponent signal: (**a**) noisy signal, (**b**) real IF ridges of signal components, (**c**) TFR obtained using STFT, (**d**) TFR obtained using CT, and (**e**) TFR obtained using proposed FMLT.
## 4 Discussion
Since the study area is covered by grass-dominated vegetation, S2 images improved spectral configuration with four narrow bands in the RE region and two bands in the SWIR region could better detect the subtle differences in the cultivated pasture characteristics [26]. For multispectral remotely sensed images, the spectral bands are correlated with each other in the spectral domain for the same target of interest. Therefore, although UAV images do not have a SWIR band, the SWIR bands of S2 brought additional information to the RF-based imputation models. Thus, the sensed target plays a critical role in defining themost important predictor variables and choosing optical satellites to impute missing parts in UAV orthomosaics. Although the high spatial resolution of the PS images contributed to the good validation results of the RF-based imputation models built using PS data, the PS spectral characteristics used in this study were crucial for their worse performance than S2 images. The selected PS image covering the study area was captured by a PS satellite with only four spectral bands. However, Planet has announced the next generation of PS imagery powered by SuperDove, the latest Planet CubeSats design Planet [27]. The SuperDove satellites will introduce new spectral bands to PS imagery, resulting in eight spectral bands. Six of them are interoperable with S2 images (coastal blue, blue, green II, red, red-edge, and NIR), and two are unique spectral bands (green I and yellow) [27; 43]. These improvements to PS images will undoubtedly contribute to a better performance of the proposed framework using PS data to impute missing parts of UAV orthomosaics in future applications. The superior individual performance of the RF-based imputation models using S2 data and limited PS data access, since PS imagery is not open access, highlights the importance of using free operational satellite data to assess new approaches for imputing missing parts in UAV orthomosaics. Moreover, whereas our analysis was restricted to PS and S2 images, the proposed framework could be readily adapted to use a wide variety of remotely sensed images as long as all remotely sensed imagery are entirely co-registered to each other. In addition, although we had applied the proposed spatial imputation approach in a study area covered by grass-dominated vegetation, the concepts that were the basis of the development of this approach are expected to be consistent regardless of the land use and land cover (LULC) classes on the region of interest, as long as all LULC classes are present in the non-missing parts of the images. Additionally, although the imputed UAV images may be inaccurate for PA applications requiring very high spatial resolution (<1 m), the proposed framework is appropriate for applications where imaging large agricultural areas using UAV systems are still a challenge. For such tasks and considering the limitations of UAV systems, the proposed framework may be used to optimize UAV imaging surveys in time- or resource-constrained situations, planning intentional missing parts for further imputation using satellite data.
# Data Sampling Methods to Deal With the Big Data Multi-Class Imbalance Problem
## 4 Experimental Set-Up  
### Database Description  
The dataset of images used in this work was obtained from GIC (Grupo de Inteligencia Computacional, by the acronym in Spanish) ([[http://www.ehu.eus/ccwintco/index.php/Hyperspectral_Remote_Sensing_Scenes](http://www.ehu.eus/ccwintco/index.php/Hyperspectral_Remote_Sensing_Scenes)]([http://www.ehu.eus/ccwintco/index.php/Hyperspectral_Remote_Sensing_Scenes](http://www.ehu.eus/ccwintco/index.php/Hyperspectral_Remote_Sensing_Scenes))). The dataset corresponds to six hyper-spectral images with multiple bands of light ranging from the ultraviolet to the infrared spectrum; the number of bandsdepends on the sensors used to capture the images. In Table 1, the number of bands corresponds to the number of features. Every dataset was previously tagged in order to generate a distribution of the pixels, in accordance with the class that was tagged, the number of the classes and the index of the imbalance.  
The Indian and Salinas datasets were captured by the AVIRIS sensor in north-western Indiana and Salinas Valley, California, respectively; both image datasets have 17 classes that correspond to crops, grass, stone, buildings and other types. In the north of Italy, the sensor ROSES acquired the images Pavia and PaviaU with 102 and 103 spectral bands belonging to visible water, trees and shadows, among others classes. The Botswana dataset was gather in 2004 by the sensor EO-1 in the region of over the Okavango Delta, Botswana, where 15 classes were identified representing the types of swamps and woodlands. Finally, the KSC (Kennedy Space Center in Florida) has 14 classes representing the various land types. The hold-out method [68] was used to randomly split each dataset: training (**X**) 70% and testing (**TS**) 30%.  
Table 1 summarizes the main characteristics and details of the benchmarking datasets, where \"Major\" and \"Minor\" correspond to number of samples of the bigger and smaller majority and minority classes, respectively; \"IR\" is the class imbalance ratio between both classes (Minor/Major); and \"Distribution\" shows the number of samples in each class.
## Materials and Methods
### 2.8.1.  
Operating Conditions for the Filtration Experiments  
First, the membranes were soaked in deionized water (DI) for about 30 min. Then, they were placed into the cartridge filtration unit (Pall Co., New York, NY, USA) and then compacted by filtering of DI for another 30 min. Subsequently, HAOPs were deposited onto the surface of the membranes by injecting the desired dose of HAOPs into the cartridge filter with a syringe [19; 20; 21; 22; 23; 26; 30]. Right after depositing of HAOPs, DI was passed through the deposited layer and both the pressure and the flux were monitored. No significant changes to either of them were recorded. Additionally, the permeate turbidity was monitored in order to ensure that HAOPs would not leach into it. Turbidity check is a standard operational procedure in this technology. After filtrating the DI for 30 min, the feed water was introduced. The layer of HAOPs created this way has the resistance across the layer between 1.4 and 2.4 \(\times\) 10\({}^{11}\) m\({}^{-1}\)[21] and the HAOPs thickness of 5-10 \(\upmu\)m [27].  
For the filtration test, the water from the Olesnica river was delivered to the unit (Figure 1) which was operated in a dead-end mode using a flat sheet PES membrane fitted into the filter cartridge at operating conditions of 150 Liters per square meter per hour (LMH) and ambient temperature (20 \(\pm\) 5 \({}^{\circ}\)C).  
The water flux \(J\) was then calculated from Equation (5):  
\[J=\frac{V}{A\cdot t} \tag{5}\]  
where \(V\) (L) is the cumulative volume of permeate obtained during time \(t\) (h) and the surface area \(A\) (m\({}^{2}\)) of the membrane.  
All cycles had similarly long durations except for the test carried out on a pristine membrane (the control run).  
In the first experiment, the feed water was applied directly to the cartridge filter with no exposure to any pre-treatment. The second experiment involved depositing 20 g/m\({}^{2}\) HAOPs as the effective surface load of Al (mg Al per liter of water treated). The third experiment was identical to the second, but the dose of HAOPs was reduced by half (10 g/m\({}^{2}\)). In the last experiment, 17 g/m\({}^{2}\) of HAOPs was applied. After each HAOPs deposition, in all filtration experiments, the feed water was fed to the filter. Usually, the permeate was collected for analysis as a composite from the entire filtration cycle, and, in several tests, the parameters discussed were measured each hour.
## 2 Methods
### Calculations of Sediment Volume  
In Figure 3, video footage in the front of the slope was used to identify the coordinates of eroded soil for transport estimations. A basic grid network and coordinate system were set up. Under time domains, approximately every seven centimeters, we drew a point along the edge of the soil sediment. Every peak in the sediment must be identified in this way. In the \(Y\)-direction (positive and negative), two cameras permitted estimation of the height of the sediment (peak value). Erosion characteristics from the video images were extracted in the form of coordinates after video grid analysis. Calculation of sediment is as shown in Figure 4. Specifically, considering the two different images (current and original digital elevation model (DEM), for example, Figure 5a,b), we used seven time steps to extract a 3D point cloud for every minute. Then, based on this, we developed different digital elevation models by the Kriging gridding interpolation using Golden Surfer 9.0 (Golden Software Inc., Golden, CO., US, 2009), which is a full-function 3D visualization, contouring, and surface-modeling package. The software includes an extensive variogram modeling subsystem, and we chose the linear variogram model [15]. Finally, as the difference between original and current DEM (Figure 5c), based on these models, we estimated the volume of sediment deposition. Extraction of coordinates owes some variances to the real value (-4%) and lens distortions, and the distance between camera and ground affects the accuracy of the picture area (-4.2%) [12].  
Figure 4: The principle of sediment volume calculation. The red area is the initial slope part and the blue area is the sediment volume.  
Figure 3: Sediment volume under 25 mm/h at (**a**) 0 min, (**b**) 1 min, (**c**) 2 min, (**d**) 3 min, (**e**) 4 min, (**f**) 5 min, (**g**) 6 min.
## 2 Materials and Methods
### Transmission Phase
#### 2.3.4 Data Communication and Energy Consumption at Main Cluster Head to Base Station  
The MCH collects data from its part nodes and aggregates them with data received from all CHs. Equations (8) and (9) calculate the energy utilized in this process determined by the energy consumption model.  
\[E_{MCH_{Tx}}=\odot\times l_{ength}\times\left(e_{t}+e_{fs}\times d^{2}\right) \tag{8}\]  
\[E_{MCH_{Rx}}=\odot\times l_{ength}\times\left(e_{r}+E_{agg}\right) \tag{9}\]  
Here, \(\odot\) is the total number of CHs in the network.  
The MCH forwards the compacted data to the BS using the minimum energy that is calculated as given in (10).  
\[E_{MCH-BS_{Tx}}=\mathbb{N}\times l_{ength}\times\left(e_{t}+\epsilon_{fs}\times d ^{2}\right) \tag{10}\]  
Here, \(\mathbb{N}\) shows the sum of sensors in the deployed field.
# A Real Case of Implementation of the Future 5G City
## 1 Introduction
### Field Trial Details  
As stated before the 3 GPP completed (at the end of 2017), the first version of the 5G standard ready for deployment that requires fallback to Long Term Evolution (LTE) networks for partial operation. This 5G solution is called non-standalone 5G NR (NSA 5G NR). Release 15 (June 2018) has defined the first draft of the Stand Alone (SA) 5G network architecture.  
The project 5G has been organized in phases in order to follow the standardization process:  
1. _Set-up phase_ * _Enhanced studies_ --5G new technologies are studied and evaluated, in order to define possible algorithms and strategies to be implemented in the successive field tests; * _Lab tests_--5G new technologies and devices are tested in the ZTE Research and Innovation laboratories, with particular reference to 5G NR.
2. _Roll-out phase_ * _NSA tests_--all network elements and basic network functions are tested on the field with the deployment of several NR base stations (BSs), while the core network (CN) is based on existing LTE-CN following the NSA network architecture.  
* _SA tests_--definition and deployment of SA network architecture on the field with the deployment of the 5G-CN.
* _Service phase_
* _Service tests_--test of innovative smart systems and services provided on the 5G network infrastructure and validation of KPIs.  
The project timeline related to 3 GPP standardization process is represented in Figure 1. It includes the currently existing assets (green) as well as upcoming assets (gray).  
In particular, while current field deployments refer to the latest defined standard version (Release 15), in the successive phase, new features of Release 16 will be included during the next year. Moreover, some enhanced studies are carried out on advanced features of Releases 15 and 16, even if these will not be deployed during the field trials.  
The paper is organized as follows. The 5G network infrastructure is presented in Section 2 with reference to the different deployment phases providing details on implemented technologies. Successively, the use cases of the service-tests phase are briefly described with relative service KPIs in Section 3. More details are provided for the smart mobility use case.
# Optimal Routing for Time-Driven EH-WSN under Regular Energy Sources
## 2 Energy Consumption Model
### Energy Consumption Model for the LPL mechanism in TinyOS Sensor Nodes
Specifically, the two expectations in Equation (1) can be formulated as follows:  
\[\varepsilon[E_{T}(X)]=(\varepsilon[k]-1)\cdot E_{c}(X)+E_{c}^{\prime}(X)+E_{l}^{ DAR}, \tag{2}\]  
\[\varepsilon[E_{R}(X)]=\varepsilon\Big{[}E_{fd}\Big{]}+E_{rx}^{pkt}+E_{tx}^{ack}. \tag{3}\]  
In these equations, \(\varepsilon[k]\) and \(\varepsilon\Big{[}E_{fd}\Big{]}\) are, respectively, the expected number of tries and the expected duration of a fragment of duty period, \(E_{c}(X)\) is the energy wasted in non-successful transmission cycles, whose duration is \(T_{c}\), and \(E_{c}^{\prime}(X)\) is the energy wasted in a successful transmission cycle, the duration of which is \(T_{c}^{\prime}\) (Figure 2). Additionally, \(E_{l}^{DAR}\), \(E_{rx}^{pkt}\) and \(E_{tx}^{ack}\) are, respectively, the energy consumed in a DELAY_AFTER_RECEIVE period, the energy wasted to receive a packet and the energy wasted to transmit an acknowledgment. Note that, implicitly, it has been assumed that power control is disabled, because the energy wasted to transmit an acknowledgment, which is part of the energy wasted to receive a packet, does not exhibit any dependence on the specific node to which the acknowledgment is sent. This is reflected in the fact that the traffic load generated by all descendants of node \(X\) has been grouped into a single term \(\sigma(X)\) in Equation (1). Whereas this contributes to simplifying the analysis it does not cause any detriment on the generality of subsequent results.
# Advances in the Metabolic Engineering of _Escherichia coli_ for the Manufacture of Monoterpenes
## 5 Controlling the Flux Distribution of Essential Intermediates
### Spatial Organization of Heterologous Enzymes  
A series of enzymes driving the engineered metabolic pathway could be spatially colocalized in a specific area of host cells by a programmable scaffold manner. This intriguing strategy has a significant scalability for enzyme population and can balance the overall pathway fluxes and alleviate metabolic burdens to optimize yields of target products [67]. The reason is that highly ordered enzymes reduce intermediate transmission time, protect them from diffusing or competing the bypass route, and circumvent undesirable equilibria and kinetics resulting from bulk-phase metabolite concentrations [68; 69; 70]. The synthetic scaffold strategy was initially inspired from the natural synthase complex exhibiting substrate channeling and the programmable nucleotide-protein interaction. [PERSON] et al. constructed synthetic protein scaffolds that spatially recruit three MEV biosynthetic enzymes to improve the mevalonate titers to 77-fold (~5 mM) with relatively low enzyme expression [71]. Another work arranged individual enzymes of the metabolic pathway at the proper stoichiometry via fusing enzymes to zinc-finger domains that specifically bind to corresponding DNA sequences. The titers of metabolites resveratrol, 1,2-propanediol, and mevalonate increased up to about 5-, 4.5-, and 2.5-fold compared with controls, respectively [72]. Our group demonstrated that fusing the acetoacetyl-CoA synthase (AtoB), 3-hydroxy3-methylglutaryl-CoA synthase (HMGS), and 3-hydroxy-3 methylglutaryl-CoA reductase (HMGR) genes with rationally designed transcription activator-like effectors (TALEs) increases the mevalonate production by 3.7-fold [73]. RNA aptamers were also introduced to the scaffold systems for the designable colocalization of sequential metabolic enzymes [74; 75]. The modularity and programmability of all these scaffold systems enable them to organize pathway enzymes in specific orientation and optimal stoichiometry. These assembled synthetic complexes could be conductive to the formation of a concentrated metabolic pool and benefit the manufacture of various multi-enzyme metabolic pathways.