esa-sceva/satcom-chunk-collection huggingface dataset:
## Materials and Methods
### Analysis of Volatile Organic Compounds (VOCs)  
Leaf samples were treated by infiltrating bacterial cultures as detailed above and incubated in airtight jars at 28 \({}^{\circ}\)C. Volatile compounds produced during 24 h were adsorbed onto a solid-phase microextraction (SPME) fiber divinylbenzene/carboxen/polydimethylsiloxane (DVB/CAR/PDMS) that was inserted through septum into each jar for the final 1 h. Every SPME then served to inject the adsorbed VOCs into the injection port kept on 250 \({}^{\circ}\)C, of a HP Agilent series 4890D gas chromatograph, equipped with a flame ionization detector (GC-FID), DB-5 MS column (30 m \(\times\) 0.32 mm, 0.5 \(\upmu\)m film thickness; J & W Scientific, Folsom, CA, USA) operated in split mode. For calla lily samples, initial temperature of the oven was held at 50 \({}^{\circ}\)C for 1 min, raised to 220 \({}^{\circ}\)C at a rate of 8 \({}^{\circ}\)C/min. For cabbage samples, the initial temperature of the oven was held at 50 \({}^{\circ}\)C for 1.5 min, and then increased to 200 \({}^{\circ}\)C at a rate of 5\({}^{\circ}\)C/min, and to 280 \({}^{\circ}\)C at a rate of 10\({}^{\circ}\)C/min. Helium served as carrier gas, injection port temperature was set to 250 \({}^{\circ}\)C and detector temperature to 300 \({}^{\circ}\)C. Gas chromatography-mass spectrometry (GC-MS) was carried out on Agilent GC 7890A and 5975C MSD, operated as above. Chromatographic separation was carried out on a Rxi-5 MS capillary column (30 m \(\times\) 0.25 mm, 0.25 \(\upmu\)m film thickness; Restek, Bellefonte, PA, USA). Mass spectra were acquired in positive electron-impact (EI) scan mode (m/z 20-350). Identification of the VOCs was performed by comparing their mass spectra and retention time with the NIST/EPA/NIH Mass Spectral Database (NIST 05, National Institute of Standards and Technology, Gaithersburg, MD, USA). Further, six commercially available compounds (methyl acetate, ethyl acetate, ethanol, isopentyl alcohol, isobutyl acetate, 4-methyl heptane) were purchased (Sigma-Aldrich, Rehovot, Israel) and chromatographed using GC-FID to support the identification, as described above.
## 2 Computational Details  
All calculations were carried out using the Vienna ab-initio simulation package (VASP) [22; 23], which is an implement of DFT. The projector augmented wave (PAW) method [24; 25] was used to describe the ion-electron interactions. The generalized gradient approximation (GGA) parameterized by [PERSON] (PBE) [26] was applied for the exchange-correlation function. The cutoff energy for the plane wave basis set was fixed at 500 eV. The Brillouin zone is sampled by a Monkhorst-Pack type k-point mesh with density of 2\(\pi\times\) 0.03 A\({}^{-1}\). Full relaxation of MgBi\({}_{2}\)O\({}_{6}\) unit cell was performed until the changes in total energy and force on each atom are less than 10\({}^{-5}\) eV and 10\({}^{-3}\) eV/A, respectively. In this work, Mg-3s, Mg-2p, Bi-6s, Bi-6p, O-2s, and O-2p states are taken as valence electrons. Based on the DFT calculation, a more accurate screened coulomb hybrid functional (HF) developed by [PERSON], [PERSON], and [PERSON] (PBE0) [27; 28] (containing 30% of the exact exchange, and 70% of the PBE exchange, and 100% of the PBE correlation energy in this work) was used to calculate the electronic structure. In the calculations of optical properties, a dense k-point density of 2\(\pi\times\) 0.015 A\({}^{-1}\) including the \(\Gamma\) point was used to ensure the accurate precision. Moreover, 88 extra empty bands are included in the calculations to hold the excited electronic states. Phonon calculations were performed within the framework of density functional perturbation theory to investigate the lattice dynamical properties of MgBi\({}_{2}\)O\({}_{6}\). The phonon dispersion curve and phonon density of states (PDOS) were calculated by the PHONOPY code [29] on the basis of force constants obtained from VASP code. In the phonon calculations, a 2 \(\times\) 2 \(\times\) 1 supercell was used.
## 1 Introduction
These knowledge gaps are limiting our ability to understand the effects of increased anthropogenic pressure. Increased water column turbidity often occurs concomitantly with increased nutrient inputs. While it is well known that increased nutrient availability can lead to phytoplankton blooms, increases in filamentous benthic macroalgae and ultimately coastal eutrophication [28; 29], the response of MPB production is less understood. This may be partially attributed to the majority of research focusing on the individual influences of elevated nutrients during one phase of a tidal cycle. However, nutrient responses by MPB are likely to be tightly coupled to light availability [30] and differ depending on tidal state. For example, an increase in nutrient availability has been shown to stimulate MPB productivity in a nutrient-limited system with high light availability during tidal submergence [31]. However, in a highly turbid but nutrient-rich estuary, light limitation was sufficient to prohibit MPB production and thus nitrogen uptake [32]. Conversely, during tidal emergence in a high nutrient system, MPB were not observed to be nutrient-limited [33; 34], while in a low-nutrient system, the response of MPB is still largely unknown. Considering the multifaceted response of MPB within intertidal habitats to nutrient enrichment, it is vital for investigations to include a comparison of responses across tidal cycles, in combination with other pressures (such as water column turbidity) and which encompass timeframes that allow the incorporation of longer-term responses (e.g., microbial and macrofaunal community responses). The objective of this study was to investigate the interactive effects of sediment nutrient enrichment and water column turbidity on both submerged and emerged MPB primary production. This was carried out by experimentally enriching the sediment at three levels for up to 20 months along a gradient of water column turbidity. Within the main objective, we postulate two questions; (1) how does water column turbidity influence submerged and emerged MPB primary production, and is this modified by sediment nutrient enrichment? In addition, (2) are these responses temporally variable? This research aims to fill a critical gap in our understanding of how two pervasive stressors may interact within natural environments, with a focus on MPB production, an often forgotten but integral component of coastal ecosystems [35].
# Perturbations During Gait on a Split-Belt Treadmill: A Scoping Review
## 3 Results
### Perturbations in the Mediolateral (ML) Direction
#### 3.2.2 Measurement Tools  
As in Table 1, Table 2 demonstrated that different tools were used to capture data during perturbations. The motion capture system was employed in fourteen studies, predominantly using a Vicon system operating on a full-body model and recording kinematic data at 60 Hz [46], 100 Hz [9; 55], 120 Hz [51; 53; 56], 200 Hz [47], or 250 Hz [54]. Two studies [48; 52] did not specify the data recording frequency.  
EMG data were recorded in three studies [47; 48; 51], measuring muscle activation bilaterally in response to ML platform translation. [PERSON], et al. [47] registered gluteus medius muscle activity, [PERSON], et al. [48] analyzed tibialis anterior and vastus lateralis muscles, and [PERSON], et al. [51] had signals from medial gastrocnemius, soleus, tibialis anterior, and gluteus medius.  
As in the case of slip- and trip-like perturbation, seven types of treadmills were used to induce perturbations in the studies listed in Table 2. The Caren Mottek treadmill was the most frequently used (appearing in five studies [9; 46; 48; 56; 59]), followed by the Grail Motek in three papers [47; 52; 55]. The Senly [54; 57] and M-gait Motek [49; 50] treadmills were each used in two studies. Single studies used the Woodway [58], an unspecified Motek [51], and a custom-made AMTI treadmill [53]. In the first group, which investigated perturbations solely in the ML direction, the Caren Motek and M-gait Motek treadmills were used most commonly, and each of them appeared in two studies. In the group studying perturbations in both ML and AP directions, the Grail and Caren Mottek treadmills were used most frequently, each in two studies. The last group exhibited diversity, including Woodway, Senly, and Caren Motek treadmills.
## 3 Materials and Methods
### Optimization of a Set of Independent Variables by Feature Selection Techniques  
A large number of inputs or predictors can make the ML model very complex. It can also complicate its interpretability, requires increased memory space in the system and increases the chances of overfitting to training data. However, the problem of poor accuracy of prediction and classification is often solved precisely by including additional parameters or variables. It means that achieving a compromise (optimum) between simplicity and accuracy is one of the most important goals when creating an ML model [23; 24].  
In many cases, more inputs to the model does not mean better model performance. Feature selection represents one of the techniques for reducing the dimensionality of a data set (Dimensionality Reduction) by filtering certain predictors that are redundant or not relevant in the ML model. By excluding such independent variables, the prediction  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Mean** & **StDev** & **Var** & **Min** & **Median** & **Max** & **Skewness** & **Kurtosis** \\ \hline
4.1503 & 2.6520 & 7.0329 & 1.8081 & 3.2516 & 25.8282 & 2.81 & 10.17 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Descriptive statistics for the dependent variable D\({}_{\text{EtoE}}\).  
Figure 5: Histogram of the dependent variable D\({}_{\text{EtoE}}\).  
accuracy or classification performance of the model can be significantly improved [25]. For this purpose, three basic variants of the Feature selection technique are available:  
* Filter technique--This is based on measuring the importance of variables considering features such as variance and relevance to the output variable. Predictors are selected according to the desired level of importance or relevance, after which an ML model is created using the selected set of inputs [26].
* Wrapper technique--Model training is performed using a selected subset or the entire set of independent variables, and then individual predictors are added or removed based on a certain criterion that measures the change in model performance. Model training and testing are repeated until predefined stopping criteria are met [26].
* Embedded technique--Assessing the importance of the predictor is, in this case, an integral part of a model training process.
###### Abstract  
Existing green wave control methods for passenger cars and buses mainly focus on maximizing bandwidths at the arterial level. There is little research on green wave control for both at the closed road network level, which makes it difficult to improve the efficiency of the entire area. To address this, a green wave control model that considers both passenger cars and buses in closed road networks is presented in this paper. The objective function of the model is to maximize the sum of the weighted bandwidths of passenger cars and buses on each segment of the road network. The relationships between car green bands, bus green bands, offsets, phase sequences, red time, green time, etc. are analyzed on the level of arterials and road networks, respectively, using time-space diagrams. Based on these analyses, the key constraints of the model are constructed accordingly. In addition, \(0/1\) variables and a sufficiently large positive number \(M\) are introduced to relax some of the constraints to ensure that the presented model has feasible solutions. The results of the numerical example demonstrate that compared with the fixed phase sequence schemes 1, 2, and 3, the total weighted bandwidth generated by the presented model increased by 9.5%, 16.4%, and 17%, respectively. Compared with the model without constraint relaxation, the presented model can still find a global, optimal solution when the common cycle time is fixed, while the model without constraint relaxation has no feasible solution.  
green wave control; closed road network; passenger car; bus; time-space diagram Article
## 5 IoT Service Provisioning via NDN in the Smart Campus
### Service Provisioning  
Service provisioning is performed on demand when service interest packets are sent by consumer applications. The latter are hosted in end-devices (e.g., users' smartphones or laptops, monitoring utilities, etc.) connected to the leaf nodes of the campus network. When receiving the request, each NDN-fog node applies a processing routine to discover if the result is already cached locally or if itself can be a candidate service executor. The service interest processing includes a bidding procedure that aims at executing the service at the edge in the best available NDN-fog node, which is the one offering the _lowest service provisioning time_. The latter is computed as the sum of the estimated processing time and the IoT data collection time.  
The processing time is estimated according to the currently available node's resources, as provided by a standard CPU monitoring utility available in the node, once the CPU requirements of the service are known. The IoT data collection is estimated by reading the routing cost from the FIB entry per the requested content name.  
The estimated service processing time is included in a new header field of the service interest that we call SPT. The consumer sets the SPT field in the interest to an upper bound value, which represents the time of executing the service in the remote cloud. The parameter is overwritten by an NDN-fog node offering a shorter delay.  
If no cached result is located at the edge and no NDN-fog node is available to satisfy the request, e.g., because there are not enough computing resources, then the interest is forwarded to the cloud. As a result, two different forwarding steps are foreseen.  
First, the service interest is forwarded in the campus network towards the IoT producer and the discovery is performed by on-path NDN-fog nodes. If the on-path search fails, then the second step starts and the service interest is forwarded towards the cloud. During this stage, newly traversed NDN-fog nodes still attempt to resolve the request locally. If no cached result or executor are located, finally the root node will forward the request to the cloud.
# Enhanced Multiple Speakers' Separation and Identification for VOIP Applications Using Deep Learning
## 3 Model Description
### Voice Separation for Multiple Speakers
#### 3.2.1 Speaker Separation Model  
Estimating C separate input sources \(s_{i}\in R^{T}\), where \(i\in[1,\dots,\mathrm{C}]\) is the goal of the single-channel source separation problem, given a mixture \(x(t)=\sum\limits_{i=1}^{\mathrm{C}}s_{i}(t)\). Since the durations of the input utterances might vary, T, the input length, is not a constant value.  
Figure 3: Proposed model integrated with VoIP system.  
Figure 2: Proposed model integrated with speaker separation and identification for real-time application.  
This paper focuses on the supervised setting, where the training set is \(S=\{x_{i},(s_{i,1}\ldots s_{i,c})\}_{i=1}^{n}\), and the goal is to learn the model to output C estimated channels \(\hat{\mathbf{s}}=(\hat{\mathbf{s}}_{1},\ldots,\hat{\mathbf{s}}_{c})\) in which the scale invariant source-to-noise ratio (SI-SNR) between the estimated and the target utterances is maximized where an unseen mixture x is given. This model was presented in [27] and had the advantage of a lower minimum latency and smaller model size, making it a good choice for both offline and real-time speech separation applications.
# Wireless Communication Technologies for Smart Grid Distribution Networks +
## 2 Available Wireless Technologies for Smart Grid Communications
### Bluetooth  
The Bluetooth specification was designed for personal area networks (PANs) and is, therefore, suitable for HANs. The specification supports functions such as mesh networking. Furthermore, the specification ensures less latency compared to the two previously mentioned standards through the use of a time division multiple access (TDMA) scheme. Similar to ZigBee, it uses CSMA, which can result in large latency if many devices are in operation. It has a very short range of approximately 10 m and a low data rate of 1.5 Mbps. It operates in the 2.4 GHz band.
# An Algorithm to Retrieve Precipitable Water Vapor from Sentinel-2 Data
## Materials and Methods
### Retrieval Algorithm Validation
Statistical metrics used to validate the precision of S2-L2A PWV included the correlation coefficient (\(R\)), root mean square error (\(RMSE\)), and overall bias (\(Bias\)), which were respectively calculated by:  
\[R=\frac{\sum_{i=1}^{n}\bigl{(}PWV_{S_{i}}-\overline{PWV_{S}}\bigr{)}\bigl{(}PWV _{R_{i}}-\overline{PWV_{R}}\bigr{)}}{\sqrt{\sum_{i=1}^{n}\bigl{(}PWV_{S_{i}}- \overline{PWV_{S}}\bigr{)}^{2}\bigl{(}PWV_{R_{i}}-\overline{PWV_{R}}\bigr{)}^ {2}}} \tag{7}\]  
\[RMSE=\sqrt{\frac{\sum_{i=1}^{n}\bigl{(}PWV_{S_{i}}-PWV_{R_{i}}\bigr{)}^{2}}{n}} \tag{8}\]  
\[Bias=\frac{\sum_{i=1}^{n}\bigl{(}PWV_{S_{i}}-PWV_{R_{i}}\bigr{)}}{n} \tag{9}\]  
where \(PWV_{S_{i}}\) is the estimated \(PWV\), \(\overline{PWV_{S}}\) is the mean value of estimated \(PWV\), \(PWV_{R_{i}}\) is the reference \(PWV\), and \(\overline{PWV_{R}}\) is the mean value of reference \(PWV\).
# A Geospatial Approach to Improving Fish Species Detection in Maumee Bay, Lake Erie
## 2 Methods
### Species Accumulation Curve  
To determine if the addition of targeted sampling in hot spot areas in 2017 increased species richness with equal sampling effort, species accumulation curves were constructed using the iNEXT package [22] in RStudio (version 1.0.1530) [23], along with sampling efficiency percentages. Sample size-based interpolation and extrapolation of species diversity data were performed using incidence-frequency data. Ten of the traditional sites were randomly chosen using the sample function with no replacement in RStudio for all three gear types, separately. Five hot spot sites were then substituted for the five traditional sites that were removed to equal 15 sites total for 2017, allowing for the comparison of the traditional and hybrid sampling approaches. Species richness estimates were determined by 50 bootstrap iterations performed at 40 knots to generate 95% confidence intervals. Sampling efficiency is described as the observed species richness divided by the estimated species richness and reported as a percentage. These results were then compared to running the same analysis using the 15 traditional sites. The catch was also examined for the presence of rare species to learn whether hot spot areas held more rare species relative to randomly selected sites. Unique and duplicate species (i.e., species that only occur in one and two samples, respectively; [24]) from 2013-2016 that were caught in 2017 were noted and used as proxies for rare species.
# Multi-Means Comprehensive Forecasting Technology for Water Inrush in Long Tunnels
## 3 Comprehensive Geophysical Exploration Techniques
### Seismic Reflection Technique  
The seismic reflection technique involves controlled generation of seismic energy waves through strategically positioned micro-explosives or vibratory sources. Following operational protocols, 24 boreholes are typically arranged along the tunnel sidewalls to deploy regulated micro-explosive sources. These sources generate seismic waves that propagate through geological media and undergo reflection at impedance boundaries, including stratigraphic interfaces, joint surfaces, fracture zones, fault planes, and critical geohazard interfaces (e.g., karst cavities, underground conduits, and weak structural zones).  
Reflected wave energy is captured by triaxial receivers positioned in the tunnel, which record and store three-component waveform data containing amplitude and temporal information. The two-way travel time of reflected signals exhibits direct proportionality to the distance from the detection plane to geological discontinuities, while signal amplitude correlates with interface properties and spatial orientation.  
The seismic reflection methodology is fundamentally governed by three-dimensional wave equations describing P-wave and S-wave propagation:  
\[\frac{\partial^{2}\theta}{\partial t^{2}}=\frac{\lambda+2\mu}{\rho}(\frac{ \partial^{2}\theta}{\partial x^{2}}+\frac{\partial^{2}\theta}{\partial y^{2}}+ \frac{\partial^{2}\theta}{\partial z^{2}})=v_{g}^{2}(\frac{\partial^{2}\theta} {\partial x^{2}}+\frac{\partial^{2}\theta}{\partial y^{2}}+\frac{\partial^{2} \theta}{\partial z^{2}}) \tag{7}\]  
\[\frac{\partial^{2}\phi}{\partial t^{2}}=\frac{\mu}{\rho}(\frac{\partial^{2} \phi}{\partial x^{2}}+\frac{\partial^{2}\phi}{\partial y^{2}}+\frac{\partial^ {2}\phi}{\partial z^{2}})=v_{s}^{2}(\frac{\partial^{2}\phi}{\partial x^{2}}+ \frac{\partial^{2}\phi}{\partial y^{2}}+\frac{\partial^{2}\phi}{\partial z^{2 }}) \tag{8}\]  
where \(\theta\) is the volume strain, \(\lambda\) is the Lammel number, \(\mu\) is the shear modulus, \(\rho\) is the medium density, \(\phi\) is the shear strain, \(v_{p}\) is the longitudinal wave velocity, and \(v_{s}\) is the shear wave velocity.
###### Abstract  
This paper proposes an efficient technique to solve the electromagnetic scattering problem, in the near zone of scatterers illuminated by external fields. The technique is based on a differential formulation of the Helmholtz equation discretized in terms of a finite element method (FEM). In order to numerically solve the problem, it is necessary to truncate the unbounded solution domain to obtain a bounded computational domain. This is usually done by defining fictitious boundaries where absorbing conditions are imposed, for example by applying the perfect matching layer (PML) approach. In this paper, these boundary conditions are expressed in an analytical form by using the Dirichlet-to-Neumann (DtN) operator. Compared to classical solutions such as PML, the proposed approach based on the DtN: (i) avoids the errors related to approximated boundary conditions; (ii) allows placing the boundary in close proximity to the scatterers, thus, reducing the solution domain to be meshed and the related computational cost; (iii) allows dealing with objects of arbitrary shapes and materials, since the shape of the boundary independent from those of the scatterers. Case-studies on problems related to the scattering from cable bundles demonstrate the accuracy and the computational advantage of the proposed technique, compared to existing ones.  
bundled cables; Dirichlet-to-Neumann map; electromagnetic compatibility; electromagnetic scattering; near-field analysis Article