esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### MFC Construction and Operation  
A two-compartment electrochemical reactor with an anode and cathode was used as a MFC system for bioelectricity generation. The working volume of each compartment was 25 mL. The redox-hydrogel-modified anode and platinum-gauze cathode (80-mesh, ALS Co., Ltd., Tokyo, Japan ) were connected to a potentiostat (660E; CHI Instruments, Inc., Austin, TX, USA) by titanium wire (0.5 mm-diameter; resistance, \(<\)1 \(\upmu\Omega\) cm\({}^{-2}\), Sigma-Aldrich). The anode and cathode reactor were separated by a proton-exchange membrane (Nifion117; thickness, 183 \(\upmu\)m). Ag/AgCl (saturated NaCl; BASi(r) Analytical Instruments, West Lafayette, IN, USA) was used as a reference electrode. In the MFC system, the anolyte contained a bacterial inoculum and the catholyte was a 0.1 M sodium phosphate buffer (pH 7.0) with air bubbling (2.5 mL min\({}^{-1}\)). During MFC operation, anaerobic conditions were maintained in the anode compartment by supply of 100% N\({}_{2}\) gas (0.22 \(\upmu\)m; sterilized, filtered) for 30 min prior to operation at flow rates of 2.5 mL min\({}^{-1}\) with magnetic stirrer (100 rpm). Finally, the anode chamber was sealed with butyl rubber stoppers and caps to prevent influx of oxygen. The cathode compartment was oxygenated by constant air bubbling and stirring at 100 rpm with magnetic stirrer. To determine the MFC power production the reactor was held at open circuit potential for one hour, and then the external resistance was decreased from 1000 to 50 \(\Omega\) every 20 min with the voltage recorded at each resistance.
# Nickel and Cobalt Price Volatility Forecasting Using a Self-Attention-Based Transformer Model
## 4 Empirical Results
### The Importance of the Variables  
Multi-horizon forecasting in practical applications typically involves utilizing a range of data sources, such as advance knowledge about future events (also known as the known inputs), external time series, and unchanging metadata, with no prior understanding of how they relate to each other.  
The TFT model takes in three types of inputs: unchanging metadata, also known as static covariates, past inputs that change over time, and future inputs that are already known. To select the most important features from these inputs, variable selection is employed to make careful and deliberate choices.  
In the given case, the encoder variables used in the prediction process contained features for which previous values were already known. These variables included the features that had been selected previously, along with an index indicating the relative time. As mentioned in Figure 6, percentage-wise variable importance is described. Also, Table 5 shows the weights of the variables included in the study according to the TFT model in both scenarios.  
In general, the time_idx (time index) variable always tends to have the highest weight in the TFT neural architecture, since it indicates the importance of the time dependence of the series in the construction of the TFT model; a similar argument is valid for the relative_time_idx (relative time index) variable, followed by volatility (the target variable). As for the explanatory variable of interest, the price_wti (WTI prices), this had greater relevance in the construction of Scenario 2, which reflects the short-term dependence at the level of history, with a weight of 24.2%. In the construction of Scenario 1, it had a weight of only 5.5%. The low weight shown by the variable in relative terms could partly explain the low adjustment capacity shown by the model in the test sets.  
Figure 5: Out-of-Sample Forecast: Scenario 2, Volatility: Nickel/ Confidence Intervals 95% (where Linf and Lsup are lower and upper confidence limits).
# Multi-Descriptor Random Sampling for Patch-Based Face Recognition
## 2 Overview of the Proposed Method  
As mentioned above, this paper proposes a random patch (RP) sampling method for face recognition under distortions targeting partial occlusion in particular. The use of multiple local descriptors helps capture smaller texture patterns. Therefore, they offer higher accuracy compared to holistic feature-based descriptors that tend to average over the given image. The local descriptors used are Local Binary Patterns (LBPs) and Histogram of Oriented Gradients (HoGs). These two descriptors provide different type of features, which are complimentary and therefore offer more discriminative power. For example, their combination would offer an advantage over using a single descriptor. For dimensionality reduction, KPCA, which is nonlinear extension of the conventional PCA, offers more refined features. For the matching process, the proposed approach uses Random Patch Sampling based on the employment of several Support Vector Machine (SVM) classifiers. It operates by considering all generated face patches equally to build multiple sub-classifiers to further improve the recognition performances.  
The proposed algorithm works as follows: First, each image is partitioned into several 50% overlapping regions/blocks. Then, the LBP and HOG descriptors are used to individually extract features from the generated image patches. Since the previous step generates high dimensionality descriptors, potentially including redundancies, the KPCA method is used in order to extract the most significant feature patterns of the descriptors. Next, the reduced descriptors of the image patches are normalised and fed to the classification module. Finally, a number of patches are randomly sub-sampled within each image training set in order to build multiple SVM classifiers from each subset. The validation of the proposed algorithm was performed through extensive experiments using a single sample per person as per real world conditions. A combination of the final results of the performances generated from all the sub-classifiers is performed with a union rule. Figure 1 depicts the process.
## 2 Results  
### Construction of Plasmid pCDFDuet-AtaPT-CstDXS  
Engineering _E. coli_ as the in vivo prenylation system requires at least two steps: (1) overexpression of CstDXS as the rate-limiting step enzyme in the DXP pathway to increase the available pool of prenyl-donors; and (2) transferring the prenyl-donors into the core aromatic acceptor by the catalytic function of AtaPT. Therefore, the truncated DXS (CstDXS: GenBank accession no. AB354578.1) was used for enhancing the flux of isoprenoid precursors in _E. coli_. The gene encoding for AtaPT (GenBank accession no. KP893683) was chosen for incorporating the prenyl donors into the 4 HC core structure. Construction of pCDFDuet-AtaPT-CstDXS was achieved via stepwise incorporation of AtaPT, followed by CstDXS into the multiple cloning site 1 (MCS-1) and MCS-2 of pCDFDuet-1 vector, respectively. DNA sequencing confirmed that both genes were inserted into the correct regions of the pCDFDuet-1 vector with no frame-shifted insertion. Each gene was independently controlled by their T7 promotor, and gene expression was driven by adding IPTG into the culture (Figures S1 and S2). The 5' end of AtaPT in the MCS-1 (_BamHI_/_Not_I) was joined with His-tag, and the 3' end of CstDXS was fused with the S-tag. The map created using GenScript ([[https://www.genscript.com/gensmart-design/#](https://www.genscript.com/gensmart-design/#)]([https://www.genscript.com/gensmart-design/#](https://www.genscript.com/gensmart-design/#)) (accessed on 26 September 2021)) is depicted in Figure 2.  
Figure 2: Map of recombinant plasmid pCDFDuet-AtaPT-CstDXS.
### Literature Review
The data acquisition time with the iPad Pro was approximately 7.51 min per sample plot (radius equal to 7 m), trees were mapped with a 97.3% detection rate for trees with a DBH less than 10 cm, and the RMSE of the best DBH measurement was 0.0313 m.  
[PERSON] et al. [13] analysed the performance of the iPhone 12 Pro in geoscience-related applications to replace conventional geological instruments. The authors tested the device's GNSS, IMU, magnetometer, cameras and LiDAR sensor. Regarding the LiDAR sensor, the authors concluded that it was mostly useful for \"soft\" applications, such as geoheritage documentation and the production of educational materials.
## 2 Theoretical Model
### Initial and Boundary Conditions  
#### 2.6.1 Initial Conditions  
The bubble is initially assumed to be a hemisphere with diameter equal to the real surface/bubble contact surface diameter \(d_{w}\) (which may be equal to \(d_{OR}\), depending on the membrane wettability) in static equilibrium with the surrounding liquid. The reality is somewhat more complex because the liquid flow field is affected by the preceding bubble wake. As these effects are difficult to determine, the actual initial conditions are generally unknown, and the initial bubble shape chosen represents a compromise. The velocities and accelerations of all elements are initially taken as zero. The initialpressure in the gas chamber is taken as the sum of the hydrostatic pressure at the defect, the surface tension pressure, and the elastic pressure as follows:  
\[t=0:P_{g|0}=P_{atm}+\rho_{l}g\bigg{(}H-\frac{d_{W}}{2}\bigg{)}+\frac{4\sigma}{d _{W}}+P_{e}=P_{b|0},\ V_{b}=\frac{\pi}{12D_{0}^{3}},\ u_{0}=0 \tag{5}\]  
where \(H\) is the water depth on the membrane, \(\rho_{l}\) is the water density, and \(P_{e}\) is the elastic pressure that should be determined experimentally for each flexible membrane according to Reference [14].
## 2 Materials and Methods
### Validation
#### 2.6.3 Visual Interpretation of High Resolution Imagery  
High resolution imagery was available from a Leica ADS40 airborne digital camera (0.5 m) for most of the state, from the NSW government's standard capture program. The ADS40 sensor is equipped with a SH52 sensor head with a field of view (swath angle) of 64\({}^{\circ}\) and multiple linear CCD's of 12000 pixels each, enabling the capture of four spectral bands of information [68]. The spectral ranges are blue (0.428-0.492 \(\upmu\)m); green (0.533-0.587 \(\upmu\)m); red (0.608-0.662 \(\upmu\)m); near infrared (NIR) (0.833-0.887 \(\upmu\)m). The sensor is flown in north-south direction and the resulting swaths are mosaicked together and colour balanced. As ADS40 imagery was not available in western NSW, alternative 2.5 m SPOT5 imagery from 2011 was used.  
Validation points (6648 pixels) were selected across NSW using a stratified random approach, ensuring that tree and non-tree pixels (determined by the previous Landsat derived map) from the 13 major catchments were sampled (Figure 3E). Each point was visually assessed against the high resolution imagery and classified as either tree or not-tree. The proportion of each stratum, determined from the Landsat map, was used when calculating the error matrix statistics, including the standard errors [67].
# Phase-Coded and Noise-Based Brillouin Optical Correlation-Domain Analysis
## 3 Brillouin Optical Correlation-Domain Analysis Based on Amplified Spontaneous Emission Noise
### Distributed Brillouin Analysis over Planar Photonic Waveguides  
The demonstration of ASE B-OCDA over fiber serves as a useful proof of concept. However, the resolution achieved thus far does not exceed that of frequency modulation B-OCDA, the measurement SNR is inherently worse, and the scanning of position is more difficult. The poor SNR keeps ASE B-OCDA over fiber from reaching the ultra-high resolution that could be provided by the short coherence length of the source. Such demonstrations had to wait for a different, more suitable platform.  
SBS was first demonstrated in a planar optical waveguide in As\({}_{2}\)S\({}_{3}\) chalcogenide glass in 2011 [51]. The Brillouin gain coefficient in this material is 2 orders of magnitude larger than that of silica [51]. In addition, the waveguide design provides effective guiding of both optical and acoustic modes in a tight cross-section and with large spatial overlap [52]. Due to these properties, the Brillouin gain per unit length in chalcogenide glass waveguides is far larger than that of standard fiber: signal power amplification by as much as 52 dB has been achieved on-chip [53; 54]. Large SBS gain at the device level provides opportunities for many all-optical and microwave-photonic signal processing applications [55; 56]. Among those, distributed Brillouin analysis may now be preformed with unprecedented, ultra-high spatial resolution.  
A first striking demonstration of ASE B-OCDA on-chip was provided earlier this year by [PERSON] et al. [33]. Figure 12 shows an SBS gain map taken with a spatial resolution of 800 \(\upmu\)m [33]. The results represent a new record for Brillouin sensing resolution. The ASE source used was filtered to a bandwidth of almost 90 GHz (note that the group velocity of light in the waveguide is slower than in silica fibers). Similar bandwidths are difficult to achieve with modulation of laser light. This work conquers new ground for Brillouin sensing, and it is likely to be the first of many analyses of photonic integrated circuits.  
Figure 11: Amplified spontaneous emission (ASE)-based B-OCDA. (**a**) Map of normalized Brillouin gain as a function of correlation peak position and frequency offset between pump and signal waves. Red dots indicate the estimated local Brillouin frequency shifts in each measurement location. (**b**) Top view image of the fiber under test alongside a scale bar (in cm). Three local hot-spots are introduced through the attachment of the fiber to heated aluminum bars. The red trace shows the measured local Brillouin frequency shifts (same as panel (**a**)) as a function of position on the same scale. The left and central hot-spots, with widths of 3 mm and 2 mm, respectively, are properly identified in the measurements.
###### Abstract  
Edge computing and the Internet of Things (IOT) provide the technological basis for the development of intelligent manufacturing nowadays. In order to support the intelligent interconnection and application of all kinds of equipment in the industrial field, edge computing should be equipped close to or embedded in all kinds of equipment nodes in the industrial wireless network. Therefore, it is meaningful to investigate the wireless network design of the Industrial Internet of Things. Low power wireless sensor devices are widely used in the Industrial Internet of Things (IIoT), which are sensitive to electromagnetic noise. The electromagnetic noises in industrial scenarios are significantly different from the conventional assumed white noise. In this paper, the measurement results of electromagnetic noises at three different test positions are given in an automobile factory. The spectrum occupancy of the factory wireless environment in the 300 MHz-3 GHz band was obtained by frequency domain measurement. In the time domain measurement, four statistical parameters of the three bands of 315 MHz, 433 MHz, and 916 MHz were measured, and the electromagnetic noise distributions in different plant areas and different frequency bands were analyzed. According to the measurement results, the time-varying characteristics of electromagnetic noise can be characterized by continuous hidden Markov models (CHMM). These results are informative to the design and optimization for the edge computing networks for IIoT.  
Keywords:edge computing; Industrial Internet of Things; electromagnetic noise; measurement; continuous hidden Markov model +
Footnote †: journal: Sensors
# Selection of Features Based on Electric Power Quantities for Non-Intrusive Load Monitoring
## 2 Feature Definition  
Nowadays, due to the large spread of power electronic-based and nonlinear devices connected to the network, the time signals of both voltage and current, respectively \(v(t)\) and \(i(t)\), are typically non-sinusoidal. In particular, for steady state conditions, these signals can be modelled by considering two components:  
* the fundamental component, typically denoted with the subscript 1, that is a sinusoidal signal at system frequency \(f_{1}\): \[v_{1}(t)=\sqrt{2}V_{1}\sin(\omega t-\alpha_{1})\] (1) \[i_{1}(t)=\sqrt{2}I_{1}\sin(\omega t-\beta_{1})\] (2) where \(\omega=2\pi f_{1}\), while \(V_{1}\), \(\alpha_{1}\) and \(I_{1}\), \(\beta_{1}\), are the root mean square (rms) and the initial phase angle for voltage and current, respectively
* the remaining term, typically denoted with the subscript \(H\), which is obtained by summing up the sinusoids of all the harmonics and the possible dc component. \[v_{H}(t)=V_{0}+\sqrt{2}\sum_{h\
eq 1}V_{h}\sin(h\omega t-\alpha_{h})\] (3) \[i_{H}(t)=I_{0}+\sqrt{2}\sum_{h\
eq 1}I_{h}\sin(h\omega t-\beta_{h})\] (4) where \(V_{h}\), \(\alpha_{h}\), \(I_{h}\), and \(\beta_{h}\) are the rms and the initial phase angle of the voltage and current \(h\)-th order harmonic component.  
Consequently, the time signals of both voltage and currents can be obtained as follows:  
\[v(t)=v_{1}(t)+v_{H}(t) \tag{5}\] \[i(t)=i_{1}(t)+i_{H}(t) \tag{6}\]With reference to the analysis of a single-phase system, and according to the standard IEEE 1459 [25], it is possible to combine the two components of both voltage and current signals to define a number of different rms and power quantities. Among all the quantities proposed in the standard [25], the focus of this work will be on the 4 power-based quantities shown in Table 1, where \(\theta_{h}\) is the phase displacement between the \(h\)-th harmonic components of voltage and current. Parameters related to voltage were excluded because voltage variations are minimal while connecting and disconnecting loads mainly affect the current draws and power consumptions.  
According to their definitions, the power terms shown in Table 1 include information on the fundamental components (namely active, fundamental active and fundamental reactive power) and on the harmonic components (namely harmonic active power). These quantities will be considered as possible features in the proposed NILM approach and will be referred to as _parameters_ in the following, for the sake of brevity. In the following, the variation of all the parameters due to an event will be used as possible source of information to determine which load caused the event.
# Survey on Q-Learning-Based Position-Aware Routing Protocols in Flying Ad Hoc Networks
## 6 Lessons Learned, Open Issues, and Research Challenges
### Open Issues and Research Challenges
#### 6.2.8 Routing for Space-Air-Ground Integrated Network (SAGIN)  
Recently, fifth- and sixth-generation (6G) technologies have gained interest in both academia and industry owing to their large spectrum resources, high data rates, and long-range transmission requirements. The space-air-ground integrated network (SAGIN) is introduced in 6G technologies to deliver global coverage and ubiquitous services for remote end-users in depopulated areas (desert and ocean), ships, LAP UAV swarms, and HAPs, which cannot be served by general ground BSs for geographical reasons. Owing to the periodic high mobility of LEO satellites, the mobility of LAP UAV swarms and quasi-stationary HAPs in determining the optimal routing in SAGIN becomes very complex. The concept of a store carry and forward [32] routing mechanism is required because the routing path is entirely time dependent owing to the periodic movement of LEO satellites. Here, an intelligent routing protocol is required that can route data to the remote control center by determining an optimal path in terms of delay and energy cost under the threshold cache memory of the LAP UAV swarm and HAPs. In addition, an accurate channel model and a delay model are required to define the link SINR, link delay, and data transmission rate [42; 70]. This area is still new, and to achieve the above objective, more focus is required to design the SAGIN network in the simulation environment. A resourceful guideline for implementing this concept is provided in [42; 70].
## 4 Discussion
### GPP with VIs and SIF
#### 4.3.2 Capture during Droughts  
The original hypothesis that SIF is superior to VIs was also denied in the capture of GPP changes by VIs and SIF during drought events. In sparse vegetation and grasslands, SIFs were significantly less able to capture GPP changes than VIs. This is similar to the study by [PERSON] et al. (2022) [PERSON] et al. (2022) in the Western U.S. ecological sub-regions, where NIRv consistently outperforms SIF in capturing seasonal changes in GPP at low-productivity sites such as sparse herbaceous and sparse shrubby sites. In sparse vegetation and grasslands, we also found that SIF decreased very much during drought events, which could be attributed to low productivity land classes in the first place. Another reason is that GPP increases faster than SIF from spring to summer due to the increase in temperature [PERSON] et al. (2022). This, plus the drought, makes the value of SIF even smaller.  
In the capture of GPP anomalies by VIs and SIF during drought events, we find that kNDVI performs best, NIRv and NDVI followed, and SIF worst. This is at variance with [PERSON] et al.'s (2023) [PERSON] et al. (2023) study of crop productivity in the Midwestern U.S., where they found that GOSIF could better capture yield anomalies of corn and soybean during drought compared with EVI and NIRv. The reason may be that the vegetation cover in the Loess Plateau is relatively sparse, and SIF is more affected by the noise signal in the low vegetation cover area. Moreover, this drought event occurred during the peak season of vegetation growth, and the escape ratio of the SIF signal in tall shrubs was low, which ultimately led toSIF's weaker ability to capture anomalies than VIs. We also found that the anomalies correlation showed a significant decrease in the land use types, which were forest-shrub-grass vegetation mosaic belt, deciduous forest, and evergreen forest. This may be due to the relatively large spatial variation in the production of GPP by shrubby vegetation types. kNDVI was initially designed to solve the nonlinearity problem between NDVI and GPP. In anomaly capture, the spatial heterogeneity is strong, and the nonlinearity problem is prominent.  
All in all, SIF outperforms VIs most of the time and in most regions, probably because it contains information related to photosynthesis. However, there are still times and regions that SIF does not solve well. Until SIF is able to solve these problems well, it is still necessary to combine VIs to solve multiple problems. SIF should especially pay attention to the integration with NIRv and kNDVI. NIRv performs better in the low vegetation cover and low productivity land classes, and kNDVI solves the problem of spatial heterogeneity better. This is very important for improving satellite monitoring of GPP changes, which will be very helpful for future agricultural production, vegetation greening, and carbon neutralization.