esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Measure of Energy Reduction  
Evidently, the NV strategies and the PCM-spackle affect the local heat exchange in the zones under consideration and the energy requirement for mechanical cooling is thus a meaningful measure for comparison. To assess this, energy saving (Equation (1)) is calculated for the whole building during the entire simulation period, which reveals the energy saved for cooling in relation to the reference case.  
\[E_{saved}=\frac{E_{localcooling,ref}-E_{localcooling}}{E_{localcooling,ref}} \cdot 100\ [\%] \tag{1}\]  
To get a broader picture of how the NV strategies and the PCM-spackle affect the total energy consumption of the building, all energy posts that varies due to the strategies should be considered. Both the natural and the hybrid NV strategy affect the energy requirement by the air handling unit's (AHU) fans. Additionally, in comparison with the reference case, some extra energy benefits or deficits for the AHU are necessary to consider (Equation (2)). The AHU is equipped with a rotor wheel heat exchanger with an efficiency of 0.9.  
\[E_{saved}=\left(1-\frac{E_{localcooling}+E_{fans}+\Delta E_{AHU}}{E_{local cooling,ref}+E_{fans,ref}}\right)\cdot 100\ [\%] \tag{2}\]  
where \(\Delta E_{AHU}=(E_{AHUhating}-E_{AHUhating,ref})+(E_{AHUcooling}-E_{AHUcooling,ref})\).  
The description of the variables in Equations (1) and (2) are as follows: \(E_{saved}\) is the energy saved in relation to the reference case, \(E_{localcooling}\) is the energy used by local AC  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Mon–Fri**} & \multicolumn{3}{c}{**Sat–Sun**} \\ \hline
**System** & **07:00–08:00** & **08:00–18:00** & **18:00–21:00** & **21:00–07:00** & **07:00–21:00** & **21:00–07:00** \\ \hline Internal heat gain & & ✓ & & & \\ CAV low & ✓ & & ✓ & & ✓ \\ CAV high & & ✓ & & & ✓ \\ Night ventilation & & & ✓ & & ✓ \\ \hline \hline \end{tabular}
\end{table}
Table 6: Schedule of ventilation and internal heat gains.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Simulation Case** & **PCM Layer** & **Night Ventilation Air Flow Rate** \\ \hline Reference & No PCM & CAV low \\ Natural NV & 0, 2, 4 mm & \(0.5,1,2,3,4,5\)ACH \\ Hybrid NV & 0, 2, 4 mm & \(0.5,1,2,3,4,5\)ACH \\ \hline \hline \end{tabular}
\end{table}
Table 7: Simulation cases.  
Figure 6: Outdoor air temperature during the simulation period.  
units in the occupied zones, \(E_{fans}\) is the energy used by the AHU's fans and \(\Delta E_{AHU}\) is the energy used by the AHU other than the fans. The subscript \"_ref_\" indicates values belonging to the reference case, while terms without this subscript belongs to the particular simulation case.
# Variance-Triggered Two-Step GPS Acquisition
## 5 Experiments and Results  
In this section, we present the results for the proposed VTTS acquisition and four other approaches. One approach refers to the use of the same Two-Step Acquisition (TSA) method of the VTTS acquisition but using the Ratio Detection [14] instead of the proposed variance-triggered detection. This method is called TSA-RD hereafter. The other methods refer to the use of the conventional Serial Acquisition (SA), presented in Section 2, with Fixed Threshold Detection (SA-FTD), with the proposed Variance-Triggered Detection (SA-VTD), and with the Ratio Detection (SA-RD).  
The data used in the experiments are GPS L1 band signals with 10 seconds of duration at an intermediate frequency sampled at the rate of 16.368 MHz with 2-bit resolution. The signals were obtained using the commercial GNSS simulator Spectracom GSG-5 [17]. All results shown here come from repeating the acquisition 1000 times with a 1-millisecond signal extracted from different random starting points of the signal. We used signals with C/N0 varying between 34 and 50 dB-Hz, which represents a strong signal scenario. The analysis of weak signal scenarios (C/N0 \(<\) 34 dB-Hz) should be covered in future work taking into account longer integration times.  
The frequency range used in the coarse search was \(\pm\)10 kHz, which is typically sufficient to cover civil applications [15; 18]. The coarse search bin width used was 1 kHz and the fine search bin width was 500 Hz. A single fixed variance threshold was used for each of the search steps despite the different C/N0 levels. For the CS step, the threshold was 0.12 while for the FS step it was 0.09.  
The following subsections present three different performance aspects of the methods under comparison: the success rate; the rate of false positives; and the computation time necessary to acquire the signal.
# Enabling Intelligent 6G Communications: A Scalable Deep Learning Framework for MIMO Detection
## 4 Problem Formulation and Data Preparation
### Data Generation and Preprocessing  
The data preparation phase plays a crucial role in determining the performance and generalization of any ML-based model. In our simulation framework, we begin by generating a set of transmitted symbols based on a predefined modulation scheme, such as QAM or quadrature phase shift keying (QPSK), with a specified constellation size (e.g., 4-QAM, 16-QAM). To create the training and testing datasets, two distinct sets of random indices are generated to form the transmitted symbol vectors, denoted by \(\mathbf{s_{train}}\) and \(\mathbf{s_{test}}\), respectively. The sizes of these vectors are defined by the parameters \(n_{train}\) and \(n_{test}\). Each transmitted symbol vector is paired with a corresponding MIMO channel matrix \(\mathbf{G}\), which is generated using a Rayleigh fading distribution. The Rayleigh model is suitable for representing environments with multiple non-line-of-sight signal propagation paths and is widely adopted for modeling wireless channels. The elements of \(\mathbf{G}\) are normalized such that their magnitudes follow a Rayleigh distribution with a unit scaling parameter, ensuring realistic channel behavior. The received signal vector \(\mathbf{r}\) is computed based on the canonical MIMO system equation, where \(\mathbf{w}\) is the additive white Gaussian noise vector, and the SNR is varied across a predefined range to evaluate the robustness of the model under different noise conditions. For each SNR point, a large number of samples are generated to ensure sufficient diversity in the training and testing processes. Feature extraction is then performed by separating the real and imaginary parts of the received signal \(\mathbf{r}\) and the channel matrix \(\mathbf{G}\). This results in a concatenated feature vector containing \(\mathrm{Re}\{\mathbf{r}\}\), \(\mathrm{Im}\{\mathbf{r}\}\), \(\mathrm{Re}\{\mathbf{G}\}\), and \(\mathrm{Im}\{\mathbf{G}\}\), which is used as input to the neural network. The ground truth labels corresponding to the transmitted symbols \(\mathbf{s}\) are also stored and later used for supervised learning during the training phase. The complete data pipeline ensures that the model is exposed to a wide variety of transmission scenarios, enabling it to learn robust feature representations and improve its generalization capability. This diverse training data servesas the foundation for developing a neural network-based MIMO detector that can adapt to complex channel conditions and outperform traditional detection algorithms.
## 10 Results and Discussion
### Attack-Detection Time  
In order to enable security experts to promptly take suitable measures in reaction to potential threats, it is desirable to have a low attack-detection time. Figure 12 shows how the number of nodes changed over time for the GBG-RPL and BCPS-RPL methods.  
Figure 11: Attack-detection rate.  
Figure 12: Attack-detection time.  
The potential of an approach to detect the existence of a node exhibiting abnormal behavior within the CPS network determines how long it takes to detect an attack. The GBG-RPL has a lower attack-detection time throughout the simulation time compared to BCPS-RPL, which can be attributed to three factors. Firstly, it is due to the careful selection of the trust parameters and configuration of the trickle timer. Secondly, the GBG-RPL performs all trust-related calculations in the fog server, which is a powerful computing device and reduces the processing time of trust calculations. Thirdly, the utilization of the Gini index improved the accuracy of the detection technique, as it provides granular-level details of defining a threshold to categorize behavior as normal or malicious. In contrast, in the case of the BCPS-RPL, numerous tasks, including trust-related processing, are being performed by the resource-constrained nodes, which take more time to identify a node behaving as abnormal. Furthermore, using packet drop as the only metric reduces the accuracy of the technique to detect abnormal behavior. Therefore, the overall attack-detection time of the GBG-RPL is better, which makes the attack-detection rate higher and enhances the reliability and efficiency of the CPS network.
## 1 Introduction
The time-series MODIS VI data products are, therefore, composited at 16-day intervals to minimize the degree of cloud cover by substituting a cloud covered pixel with a later uncontaminated pixel within a 16-day period. Unlike MODIS-NDVI, the MODIS-EVI accounts for the influences of dense vegetation covers, atmospheric aerosol scattering, and variable soil background reflectance [21]. The multi-temporal signatures of the time-series MODIS EVI and NDVI data were shown to capture essential phenological metrics of various natural land cover types (e.g., forest, grassland, and shrubland) and to respond differently to land cover types, canopy structures, and climate regimes [16, 22, 23]. Based on the 37-year mean climate data (1968 to 2004) from 269 meteorological stations, Dynamic Ecosystem Classification and Productivity (DECP) model developed by [PERSON] _et al_. [24] determined six biogeoclimate zones and five potential natural land cover types of Turkey according to the combination of the schemes by [PERSON] [25], [PERSON] [26, 27], and the IGBP (International Geosphere-Biosphere Programme) classification system [28]. The distribution patterns of the resultant 16 natural terrestrial ecosystems (NTEs) (land use excluded) were quantified by coupling the multiple linear regression (MLR)-based interpolations of biotemperature (BT) and mean monthly temperature of the three coldest months (MMT\({}_{\rm coldest}\)) as a function of digital elevation model (DEM), latitude, longitude, distance-to-sea, and aspect, and the inverse distance weighting (IDW)-based interpolation of growing season precipitation (GSP) [24]. The objective of this study was to carry out the spatio-temporal analyses of seasonal and interannual variability of vegetation dynamics for the NTEs of Turkey for the period from 2000 to 2007, based on the 16-day MODIS VI (NDVI/EVI) data. In this study, spatial disaggregating instead of the entire country was used in the form of the 16 NTE classes with different fractional tree cover for analyses of VI time series in order to differentiate among local-to-regional vegetation dynamics and to model their spatio-temporal variations for each of the NTEs.
# NC-OFDM Satellite Communication Based on Compressed Spectrum Sensing
## 4 Subcarrier Power Allocation Algorithm
### Analysis of Algorithm Performance  
In order to analyze the performance of optimal algorithms for power allocation, 2000 Monte Carlo simulations were conducted for each algorithm. Assuming that the  
Figure 5: Optimization process of the HGAPSO algorithm.  
simulation scenario contains 10 cognitive users and 1 authorized user, the signal-to-noise ratio (SNR) of the channel is 0 dB. In order to ensure that each iteration operation time of the three algorithms is similar, the parameters adopted in the simulation are shown in Table 4.  
Figure 6 shows the system throughput changes generated by iterative operation simulation using three algorithms. As can be seen from the comparison of curves in Figure 6, the HGAPSO algorithm achieves the highest system throughput under the same number of evolution, followed by GA, and the PSO algorithm has the worst performance. After hundreds of iterations, the three algorithms converge on their best performances, among which, the peak throughput of the HGAPSO algorithm is 5.325 Mbps, and that of the GA and PSO are 5.077 and 4.496, respectively. This result shows that under the same evolutionary algebra and complexity, the HGAPSO power allocation algorithm achieves the maximum cognitive user throughput, which is improved by 4.88% and 18.44%, respectively, compared with the GA and the PSO algorithm.
## 3 Experiments  
### Experimental Setup  
#### 3.1.1 Datasets  
This paper evaluates our model on two Chinese medical datasets. Their statistics are listed in Table 2.  
**CEMR (Chinese Electronic Medical Record) dataset**: a corpus constructed by ourselves in order to facilitate the study of Chinese EMR entity and relation extraction tasks and future work on related topics. In this dataset, the relations between medical entities are divided into fifteen categories. The normalization of the labeling process refers to a large number of annotation guidelines including the medical relation annotation specifications of 2010 i2b2/VA Challenge [28]. All EMRs are from Third-Class A-Level hospitals in Gansu Province, China, and are real electronic medical records. These data contain 4000 EMR across 14 medical departments.  
**CMeIE (Chinese Medical Information Extraction) dataset**. The CMEIE dataset [29] includes pediatric training corpus and one hundred common diseases training corpus. The pediatric training corpus is derived from 518 pediatric diseases, and the one hundred common diseases training corpus is derived from 109 common diseases. We re-adjust the ratio of train dataset, development dataset, and test dataset in the original dataset.
# Real-Time Communication Aid System for Korean Dysarthrie Speech
## 2 Related Works
### Korean Dysarthric Speech Recognition  
In [27], an ASR model developed for Korean dysarthric speech recognition is provided along with the dataset. The AI-Hub mentioned in [27] is a public data platform operated by the Korean government, offering highly reliable data with detailed descriptions. The collection of the data and the development of the dysarthric ASR model were carried out by Prof. [PERSON] at Hallym University. The model structure is based on a hybrid of connectionist temporal classification (CTC) and attention (CTC/attention) [28], utilizing a transformer encoder and decoder. The development and training of the model were conducted using ESPnet [29], referencing the transformer ASR model architectures from [30,31].  
The overall construction and training process of the model is outlined in Figure 1. The model first extracts features from the input data through a Visual Geometry Group (VGG) with multiple CNN layers. The extracted feature vectors are then used to perform sequence modelling using a transformer. Through this process, the visual patterns of the speech data used as input can be reflected in the learning. However, this structure has the disadvantage that each process is performed independently, resulting in low computational efficiency and difficulty in handling long sequence inputs. It is also classified as a rather classical model because it consists of separate modules, which can lead to a lack of integration of local patterns obtained from the VGG into global patterns, and scaling problems often occur in the connection between the two modules.
# Model Updating Using Measurements from Sensors Installed in Arbitrary Positions and Directions
## 3 Numerical Example  
The simple numerical example shown in Figure 3 was adopted to explain numerically the method described in Section 2. The structure was a simply supported beam of 15 m with a height of 1 m and width of 0.5 m. The beam was modeled by two-dimensional Euler-Bernoulli beam elements. The elastic modulus was 25 GPa, the Poisson's ratio was 0.2, and the density was 2300 kg/m\({}^{3}\). Thestructural damping ratio for the 1 st and 2 nd modes was 5%. The strain gauges were installed at positions \(S1(2.5,\ -0.05)\), \(S2(7.5,\ -0.3)\), and \(S3(12.5,\ -0.05)\). Assuming that the strain gauges were installed on the tendon for the introduction of the pre-stress force in a concrete structure, this layout allows not only the section strain but also the pre-stress force to be measured [16; 17]. Accelerometers were installed vertically at the top of the center of each element.
## 2 Materials and Methods
### Study Selection and Data Extraction  
The titles and abstracts of the selected papers were independently screened by two reviewers ([PERSON] and [PERSON]) to assess their eligibility. In cases of disagreement, consensus was reached through discussion with a third investigator ([PERSON]). Following this, full-text screening of the eligible studies was performed independently by both reviewers before making final inclusion decisions. The inter-reviewer reliability was evaluated by computing [PERSON]'s Kappa coefficient. Data extraction from the included studies was performed independently based on the PICOS framework, collecting general details (author, publication year, and country), participant information (sample size, age, and gender), intervention or comparison (expander type), and measured outcomes (premolar inclination change, molar inclination change, and buccal bone thickness change).
## 1 Introduction
It considers convection and radiation as the main heat transfer mechanisms, taking into account the heat lost by natural convection, the effect of the flame tilt caused by wind or slope, the influence of fuel moisture content and fuel type, and certain random effects such as fire-spotting. The resulting Partial Differential Equations (PDEs) are solved using efficient numerical and computational tools to obtain a software with levels of efficiency comparable to empirical models, including its own finite element toolbox and the use of parallel computing techniques. Furthermore, PhyFire can be coupled with HDWind [14; 15], which is a high-resolution wind field that adjusts a set of specific wind measurements to local factors within the simulation domain, such as the slope, roughness of the terrain, and surface temperature gradients. This wind model is based on an asymptotic approximation of the Navier-Stokes equations, deriving a mass-consistent vertical diffusion model capable of providing a 3D wind field by solving only 2D linear equations. This model was developed at the same time as the PhyFire model, by the same authors, with the initial aim of improving wind data for the fire spread model, but it was soon realized that the wind model posed very interesting mathematical and computational challenges and provided new applications [16]. PhyFire and HDWind have now been integrated into a Geographical Information System (GIS) [17], which is available for use in Spain through the URL: [[http://sinumcc.usal.es](http://sinumcc.usal.es)]([http://sinumcc.usal.es](http://sinumcc.usal.es)) (accessed on 30 January 2023) [18]. This paper covers the PhyFire design process from its inception to the current GIS-integrated version, emphasizing its mathematical and numerical aspects and explaining why the modeling of such complex questions is a major source of interesting mathematical and numerical problems. Some of the results included here are unpublished, such as the flame temperature sub-model, the new predictor-corrector numerical scheme, and the use of new pre- and post-processing functionalities. The paper is organized as follows. Section 2 describes the initial fire spread models in chronological order, through their main developments, focusing on mathematical and numerical aspects. Section 3 summarizes the current PhyFire model and describes in detail the numerical method used, as well as some original ways of improving the model efficiency. Section 4 discusses certain details of the work performed to integrate the model into a GIS. Section 5 covers a real wildfire simulated with the PhyFire-HDWind system. We end with the conclusions and suggestions for future work.
## 2 Materials and Methods
### Analysis Method  
Previous studies using model simulations and satellite observations have demonstrated that NDVI and GPP have a linear correlation at the canopy scale [46]. In this study, linear regression analyses between GPP and NDVI were used to quantify the strength of the coupling of these two variables during the annual growing season. Specifically, we used the average annual GPP and NDVI for each pixel during the growing season over 37 years (1982-2018) for fitting. The square of the correlation coefficient (R\({}^{2}\)) of the linear regression was used to quantify the coupling between GPP and NDVI [47]. Subsequently, the slopes of linear relationships between NDVI and GPP were defined as NDVI-GPP sensitivity for different aridity gradients and PFTs [48; 49]. Additionally, in order to investigate the inter-annual NDVI-GPP correlation across ecosystems and aridity gradients, CV (coefficient of variance: the ratio of standard deviation to mean) values were used to quantify the inter-annual variability (IAV) of NDVI and GPP. This index can be used to assess the asynchronous changes in vegetation productivity and greenness. The schematic illustration is shown in Figure 2.