esa-sceva/satcom-chunk-collection huggingface dataset:
# Neural Network Exploration for Keyword Spotting on Edge Devices
## 4 Evaluation and Deployment Results
### Evaluation Results
#### 4.1.5 Effect of Quantization on Inference Latency  
The inference latency of the neural network models with the four quantization schemes is shown in Figure 9.  
Figure 7: Inference accuracy of neural network models with different quantization schemes: (**a**) 16-bit floating point; (**b**) dynamic range; (**c**) integer-float; (**d**) full integer.  
Like the nonquantized versions, the FFCN architecture was the clear winner. Beyond FFCN, no architecture was unequivocally better than another. However, each architecture exhibited the same linear trend in every quantization method. The deviations from the linear trend also have the same hardware-related explanations (number of memory reads/writes, CPU clock rate, and CPU branch predictor hit/miss rate).  
The average inference latency of the 16-bit floating point, integer-float, and the full-integer quantization models was very close to the nonquantized models, presumably due to the lack of runtime quantization. However, inference latency increased significantly with dynamic range quantization compared with the other quantization methods. This increase in latency may be due to the additional operations required to quantize operations at runtime. This increase in inference time is the most significant in ResNet but comparatively less in CENet.
## 4 Functionalization of Electrospun Nanofiber Mats for Electromagnetic Shielding
### Morphology of Nanofiber Mats  
Besides the conductive and magnetic properties of electrospun nanofiber mats, their morphology also plays an important role for the shielding effectiveness, especially due to the multi-reflection inside the nanofibrous membranes (cf. Figure 4). Nevertheless, these properties are in most studies not varied, so that only few literature reports can be found quantifying these effects.  
[PERSON] et al., reported on designing the morphology of electrospun MnO-nanograv-decorated vanadium nitride/carbon nanofibers, in which the nitriding time could be used  
Figure 5: Schematic of the electromagnetic wave absorption in the Ni/SiC/C nanofibers. From [76], copyright (2021), with permission from Elsevier.  
to tailor the MnO nanograin shape covering the VN/C nanofibers [82]. They found a high reflection loss of 63 dB at 8.8 GHz and showed that, by adjusting the thickness, the nanofiber mats showed high absorption in the whole X-band and Ku-band.  
[PERSON] et al., investigated graphene-filled PVA nanofiber mats and found a variation in the nanofiber diameter distribution with the graphene content [83]. However, the graphene content also significantly influenced the surface resistivity, so that a certain variation in the shielding effectiveness with graphene content can not only be attributed to the nanofiber diameter.  
[PERSON] et al., compared SiCN and SiCN/PAN nanofibers, having average diameters of approx. 1790 and 570 nm, respectively; only the latter showed sufficient return loss due to large-enough open pores between the nanofibers where multi-reflections were possible [84]. They underlined this idea by a comparison with the literature regarding electrospun ceramic nanofibers from Si-based precursors, as depicted in Figure 6, which indicates a relation between small fiber diameters and large return losses [84]. This correlation between morphology and EMI-shielding effectiveness could be expected based on the previously discussed shielding effect of increased multi-reflections inside a nanofiber mat.  
After discussion of the different factors influencing the electromagnetic shielding effectiveness, the next sections will give an overview of the most recent research results in this area.
## 1 Introduction
Furthermore, a satisfactory solution for indoor interior reconstruction has not been developed due to the complexity of the indoor environment and unfiltered data noise. In this study, a novel method is developed that combines line structures and 3D surface geometry to automatically build a 3D indoor model with detailed structural and semantic information using MLS point clouds. A fully automatic room segmentation was performed on unstructured point clouds via multi-label graph cuts, which can solve the over-segmentation in long corridors. Point cloud slices of individual rooms are transformed as a binary image, from which extraction, and regularization of line elements, aimed at improving the computational efficiency and structural accuracy. 3D structured models were reconstructed using multi-label graph cuts, and room segmentation and lines were used as semantic and structural constraints of 2D floorplan, with surfaces providing 3D geometric information. Finally, an innovative approach was employed using 5G signal simulation based on the reconstructed model, where the basic structural elements (e.g., windows, doors, pillars, walls, ceilings, and floors) have a critical effect on the signal transmission. This study offers two major contributions. First, room segmentation was employed to semantically label the unstructured point clouds using multi-label graph cuts with semantic constraints of openings that can overcome the over-segmentation in corridors. Point cloud slices of individual rooms are transformed into images, which are then extracted and regularized into the floorplan lines. This innovation contributes to improving the efficiency and precision of extracting structural elements. Second, 3D structured models were reconstructed using multi-label graph cuts with room segmentation and 2D floorplan lines, and with 3D surfaces used as constraints. The resulting structured models provided room adjacency relationship, geometric characteristics, and semantic information, which are then applied to the signal simulation to provide the optimal location of the 5G small base station in indoor environment for future.
## 3 Analysis and Discussion
### Modeling
#### 3.3.2 Numerical Modeling  
Numerical modeling is crucial for estimating SM, providing robust frameworks to simulate SM dynamics under varying climatic and soil conditions [98]. Prominent models include the Soil and Water Assessment Tool (SWAT), the Community Land Model (CLM),the Variable Infiltration Capacity (VIC) model, and Hydrus-1D model. The SWAT model predicts the impact of land management on water, sediment, and chemical yields in large watersheds [99]. CLM offers accurate SM simulations by integrating biogeophysical and biogeochemical processes [100]. VIC is suitable for large-scale SM estimation, balancing water and energy while emphasizing spatial heterogeneity [101]. Hydrus-1D effectively profiles soil moisture by simulating water, heat, and solute movement in variably saturated media [102].  
These models utilize key equations such as Richard's Equation, [PERSON]'s Equation, Darcy's Law, and Brooks-Corey's Equation. Advantages include the ability to integrate diverse data sources, flexibility in simulating different scenarios, and robustness in handling complex soil, vegetation, and climate interactions. However, these models often require extensive calibration, are computationally intensive, and may lack flexibility in adapting to new data without significant reconfiguration.  
Figure 6 shows a framework for SM measurement and monitoring, including the integration of instruments, environmental data, numerical model, and machine learning model.
## 5 Experiments and Results
### Quality Assessment Metrics
#### 5.2.4 Mean Absolute Error (MAE)  
The MAE quantifies the average absolute difference between actual and predicted values:  
\[\text{MAE}=\frac{1}{N}\sum_{i=1}^{N}|Y_{i}-\hat{Y}_{i}|. \tag{13}\]  
Unlike MSE and RMSE, MAE is less sensitive to outliers, thereby providing a robust measure of central tendency in prediction errors.  
These metrics collectively offer a well-rounded evaluation of the predictive accuracy and reliability of the MagEMNet model across different scenarios, supporting the model's validation in both laboratory and field applications.
## Materials and Methods
### Total RNA Extraction, cDNA Library Construction, and High Throughput Sequencing  
Total RNA was extracted from the fruits collected at 60, 75, 90, 105 and 135 DAF, respectively, using a RN38 EASYspin plus Plant RNA Kit (Aidlab Biotechnologies Co., Ltd., Beijing, China). The yield and quality of RNA samples was determined using a NanoDrop2000 spectrophotometer (Thermo, Wilmington, DE, USA). Approximately 6 \(\upmu\)g of total RNA was used for cDNA library construction. The cDNA library was constructed by using a SuperScriptIII 1 st Strand cDNA Synthesis Kits (Invitrogen, Carlsbad, CA, USA). Briefly, after mRNA was isolated and purified using Oligo (dT) magnetic beads, it was fragmented using fragmentation buffer. The first-strand cDNA was synthesized using a random hexamers with mRNA as the template. Then the second-strand cDNA was synthesized by adding buffer, dNTPs, RNase H and DNA polymerase I. Double-stranded cDNA was purified using a MinElute PCR Purification Kit (Qiagen, Dusseldorf, Germany), and then repaired by adding EB buffer. The A-tails were ligated to the 3' ends, and the sequenced joints were added. Agarose gel electrophoresis was used for fragment size selection, and a cDNA library was constructed using PCR amplification. The quality of library was assessed using the Agilent 2100 Bioanalyzer (RNA Nano Chip, Agilent, Cambridge, MA, USA). Finally, the library was constructed and sequenced on the Illumina HiSeq(tm) 2000 sequencing system.
## 5 Experiments and Results  
In this section, extensive experiments were conducted to evaluate the performance of the proposed optimized data storage and access method and hardware data controller. The evaluation experiments were divided into two parts. First, the hardware data controller based on the optimized dual-channel data mapping method proposed in Section 4 is implemented in FPGA. Then, the processing performance of the SAR data matrix with the same granularity as the spaceborne conditions is tested. The experimental settings and detailed experimental results are described in the following subsections.
## 2 Materials and Methods
### Data
#### 2.2.1 Atmospheric Forcing Data  
SURFEX was run in offline mode, this means that the surface fluxes have no effect on the atmospheric fields, but the model needs meteorological data (air temperature, humidity, wind speed, precipitation, long- and short-wave radiation). This information was obtained from the ALADIN (Aire Limitee Adaptation dynamique Developpement InterNational) numerical weather prediction model [30], except for the radiation, which is derived from LandSAF (Land Surface Analysis Satellite Applications Facility) observation. The reason for this choice is that radiation influences to a great extent the photosynthesis and the output fields of NWP are not as accurate as of the satellite observations. ALADIN is a numerical weather prediction model used operationally at HMS to produce daily weather forecasts [31]. It is a hydrostatic, spectral limited area model which describes the time evolution of the processes in the atmosphere. The model has its data assimilation system: incremental 3D-Var and an OI technique for the upper-air and surface analysis respectively [32]. The model is applied for a domain over Europe with 8 km x 8 km horizontal resolution and 49 vertical layers. The lateral boundary conditions are provided by ECMWF/IFS model at 3-hourly time intervals.  
The forcing dataset covered the period 2007-2015 and included meteorological information at 3-hourly time intervals.
## 2 Problem Formulation
We divide the operation modes of each path segment \(\Gamma_{i}\) between \(p_{i}\) and \(p_{i+1}\) into three modes according to the specific environment the trip involves. In each mode, we set weighted edge \(\xi_{i}\), to describe the energy cost per meter during the mission. Our goal is to minimize the cost of the whole path and then generate a smooth, safe trajectory that conforms to velocity and acceleration constraints in both static and dynamic environments. Let the obstacles in the environment be \(OBS\); let \(F_{t}^{x,y,z}\) be the output trajectory. Let \(D_{i}\) be the distance of the path segment between target spot \(p_{i}\) and \(p_{i+1}\). The problem can be generalized as in Equation (1):  
\[ \begin{split}\textbf{minimize:}&\sum_{i=1}^{n-1} \xi_{i}D_{i}\\ \textbf{s.t.:}& v_{i}\leq v_{max}^{i}\\ & a_{i}\leq a_{max}^{i}\\ & F_{j}^{x,y,z}\cap OBS=\varnothing\\ & F_{j}^{x,y,z}\cap TE=\varnothing\end{split}  \tag{1}\]  
Figure 1 gives an example of a path segment within a whole path:  
Along each path segment \(\Gamma_{i}\), the HAUV should move with the corresponding maximum velocity and acceleration. Additionally, the final output trajectory \(F_{t}^{x,y,z}\) should be free from obstacle spots, which contain the unknown obstacles \(OBS\) and terrain barriers \(TE\).
# Correction of Directional Effects in VEGETATION NDVI Time-Series
## 3 Methodology
### BRDF Correction  
The land surface reflectance varies with the observation and illumination geometry (defined by the sensor and Sun positions) and wavelength. The BRDF quantifies the reflectance anisotropy, which can be approximated by a semi-empirical BRDF linear model with three free parameters, commonly known as BRDF parameters or descriptors [24; 25; 26; 27]. BRDF parameters are optimally retrieved based on an inversion method (see [28] for a review) over a period of time during which the surface properties are assumed not to undergo significant changes. The spectral BRDF models are then used to estimate normalized (to a standard Sun-sensor geometry) RED and NIR reflectances. These normalized reflectances are used to compute the BRDF-adjusted NDVI. The processing mentioned above, which is pixel-based, is summarized in a flowchart (see Figure 2).  
Figure 2: Diagram flow of the BRDF processor. Inputs and outputs are TOC reflectances and NDVI, respectively. The BRDF correction is applied independently to each spectral band and is pixel-based.  
The processing of BRDF-adjusted NDVI correction module runs in three main stages (blue blocks in Figure 2) which can be resumed as follows: (i) First step consists of collecting a set of clear observations (output of the atmospheric correction module) which is achieved within the assembling dataset stage; (ii) Second step is achieved during the BRDF correction stage when the sets of observed RED and NIR TOC reflectances are fitted independently using a semi-empirical linear BRDF model [24] to obtain normalized reflectances; (iii) In a last step, the NDVI is computed. Each module of the BRDF correction is exemplified as blue blocks in Figure 2, which comprises individual processes (listed in Figure 2) that are described in the following subsections.
## 2 Integration of Ordinal Optimization with Ant Lion Optimizer
### Multivariate Adaptive Regression Splines  
There have been many uses of approximation models in various applications, such as the radial basis function (RBF) [30], support vector regression (SVR) [31], kriging [32], artificial neural network (ANN) [33], and multivariate adaptive regression splines (MARS) [28; 29]. Among them, MARS approximates the relationship between outputs and inputs as well as interprets the relationship between the various parameters. MARS has been applied to many real-world problems, such as function approximation, curve fitting, time series forecasting, prediction and classification [29]. The main advantages of MARS include working well with a large amounts of predictor variables, automatically detecting interactions between variables, and robust to outliers. Thus, an approximation model based on the MARS is utilized to evaluate the fitness in this work.  
The MARS creates flexible nonlinear regression models by using separate regression slopes in distinct intervals of the independent variables. The end points of the intervals for each variable and the used variables are obtained by a very intensive searching process. The framework of MARS is demonstrated in Figure 1. The typical model of MARS is formulated as follows.  
\[F(\mathbf{x})=\omega_{0}+\sum_{i=1}^{K}\omega_{i}\times\Phi_{i}(\mathbf{x}) \tag{4}\]  
where \(\mathbf{x}\) represents an input variable, \(\omega_{0}\) is an intercept coefficient, \(\omega_{i}\) denotes the weight of basis functions, K indicates the amount of knots, and \(\Phi_{i}(\cdot)\) denotes the basis functions.  
The training data patterns are \((\mathbf{x}_{i},f_{a}(\mathbf{x}_{i}))\), where \(\mathbf{x}_{i}\) and \(f_{a}(\mathbf{x}_{i})\) denote a design and their objective value obtained by precise estimation, respectively. The purpose of MARS is to make the target output \(F(\mathbf{x})\) closer to the actual output \(f_{a}(\mathbf{x})\). The optimal MARS approximation model can be obtained by two-stage process: a forward stepwise selection  
Figure 1: Framework of MARS.  
and a backward prune. In the selection process, MARS selects basis functions which are added to the approximation model using a fast search scheme and builds a likely large model which is overfitting the training data. The selection process terminates when the model exceeds the maximum number of basis functions. In the prune process, the overfitting model is pruned for decreasing the complexity while maintaining the overall performance in order to fit to the training data. In this process, the basis functions without appreciably increasing the residual sum of squares are deleted from the overfitting model.  
MARS is trained off-line, which can be further simplified to reduce significantly the computing time for on-line. After training the MARS, the target output \(F(\mathbf{x})\) can be calculated using simple arithmetic operations for any \(\mathbf{x}\).
# Quantitative Retrieval of Soil Salinity Using Landsat 8 OLI Imagery
## 2 Materials and Methods
### Inversion of Soil Relative Humidity Based on TVDI from Landsat-8 OLI Image  
The simplified temperature vegetation dryness index (_TVDI_) proposed by [PERSON] et al. was used for the inversion of soil relative humidity in the study area based on the Landsat-8 OLI image [27]. The _TVDI_ is calculated as follows:  
\[TVDI=\frac{LST-LST_{\mathrm{min}}}{LST_{\mathrm{max}}-LST_{\mathrm{min}}} \tag{2}\]  
\[LST_{\mathrm{max}}=a+b\times\mathrm{NDVI} \tag{3}\]  
\[LST_{\mathrm{min}}=c+d\times\mathrm{NDVI} \tag{4}\]  
where \(LST\) is the land surface temperature of a pixel; NDVI is the normalized difference vegetation index. \(LST_{\mathrm{max}}\) and \(LST_{\mathrm{min}}\) are the dry edge (the upper fitted line in Figure 2) and wet edge (lower fitted line in Figure 2), respectively. The data were obtained by least-squares linear regression of the LST and NDVI values with a small NDVI interval (0.05). The Equations (3) and (4) were used to calculate the \(LST_{\mathrm{min}}\) and the \(LST_{\mathrm{max}}\) where \(a\) and \(c\) are their intercept and \(b\) and \(d\) are their slope, respectively. Subsequently, curve fitting was performed between the TVDI data obtained from Equation (2) and the field-measured soil moisture to obtain the spatial distribution of soil relative humidity. Compared with other soil moisture inversion methods, the advantage of this method is that the relationship between the relative changes of TVDI and soil moisture is more stable under different climatic conditions and surface cover conditions [28].  
Figure 2: Linear regression between LST and NDVI to obtain the dry edge and wet edge fitted line to estimate the temperature vegetation dryness index (TVDI).
# An Enhanced Precoder for Multi User Multiple-Input Multiple-Output Downlink Systems
## 4 MGBD Scheme and Adaptive MGBD Scheme
### Modified GBD Precoder
#### 4.1.2 Case 2  
When \(r\) is lower than rank of \(\mathbf{H}\)\((L>Kr_{i})\), the MGBD precoder uses \(L-r\) rank to obtain extra degrees of freedom. In this case, some ranks of \(\mathbf{H}\) are consumed to increase diversity gain. On the other hand, because the relationship between data rate and diversity gain has trade-off relation, the data rate for the system decreases. The throughput performance closely correlates with data rate and diversity gain. Therefore, the throughput performance can be optimized by adjusting suitably \(r_{i}\). In this concept, this paper additionally proposes the adaptive MGBD precoder.
# Revolution or Evolution? Technical Requirements and Considerations towards 6G Mobile Communications
## 8 Future Directions in 6G Technology Development
### Sustainable Networks  
The need of the hour in this fast-moving world is to adopt sustainable practices in communication systems on a large scale. [PERSON] et al. [141] put forward the idea of supporting 6G communication through the employment of hybrid whale spotted hyena optimization; this practice allows better cluster heads selection. Moreover, the offloading technique is also being used with the help of combined and transfer learning to delegate part of centralized services to edge computing devices in the vicinity. This was preceded by the introduction of the scheduling concept in 5G and 6G network BSs in Release 15 [142] and later enhanced in Release 16 [33]. This practice allows proper planning of BSs' sleep and wake schedule to optimize the energy requirement. [PERSON] et al. [143] suggested that the wake-up schedule for BSs can be planned with the help of hybrid energy-powered cellular networks, leading to improved energy efficiency and modified network resources. Network load switching, balancing, and route strategies can be selected in the same way. Transmissions energy is also optimized by integrating AI optimization techniques into 6G networks. [PERSON] et al. [144] proposed duty cycles scheduling performance enhancement using hybrid ML models with the aim to achieve higher QoS performance for massive and 3D MTCs.