esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Discussion
The phase change occurred at a different time for each row, depending on the distance from the hot surface. As in approach A, the largest errors were observed in the solid phase and decreased as the PCM melted. In approaches B and C, the differences between experimental and simulated temperature values in row 5, the closest to the hot surface, increased with time, showing the emergence of convective flows in the liquid phase, which had not been considered in these studies and noted in approach A. Three simulation approaches showed the highest deviation from the experimental data at the lowest temperatures, around the first hour of the simulation. As seen in Figure 10, the three approaches calculated temperatures lower than the experimental ones. The error was \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Thermocouple’s Row** & **Model** & \begin{tabular}{c} **Phase-Change Start** \\ **(hh: mm)** \\ \end{tabular} & \
\begin{tabular}{c} **Phase-Change End** \\ **(hh: mm)** \\ \end{tabular} &
\begin{tabular}{c} **Duration** \\ \end{tabular} \\ \hline \multirow{2}{*}{Row 1} & EP & 19:16 & - & Not completed \\  & EHC & 19:15 & - & Not completed \\ \hline \multirow{2}{*}{Row 2} & EP & 12:39 & 26:57 & 14 h 18 min \\  & EHC & 12:44 & 25:58 & 12 h 14 min \\ \hline \multirow{2}{*}{Row 3} & EP & 7:50 & 15:35 & 7 h 45 min \\  & EHC & 7:57 & 15:12 & 7 h 16 min \\ \hline \multirow{2}{*}{Row 4} & EP & 4:03 & 7:35 & 3 h 32 min \\  & EHC & 4:09 & 7:29 & 3 h 22 min \\ \hline \multirow{2}{*}{Row 5} & EP & 1:18 & 2:14 & 56 min \\  & EHC & 1:20 & 2:14 & 54 min \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Time of start and end of the phase change obtained by the numerical model used in approaches B and C for each of the thermocouple rows. \begin{table}
\begin{tabular}{c c c} \hline \hline
**CFD Model** & **RMSD (K)** & \(\mathbf{R^{2}}\) \\ \hline Approach A & 1.24 & 0.9882 \\ Approach B & 0.78 & 0.9965 \\ Approach C & 0.92 & 0.9951 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Statistical indices of the simulation performance for approaches A, B, and C.  
reduced throughout the simulation, resulting in temperatures that were calculated slightly higher than experimentally observed values. The error might be due to close deviations in the properties of lauric acid at temperatures above the melting point. The phase-change time estimated by each approach was close to the experimental one, showing the validity of the models in the study of the melting process.
# Trellis Tone Modulation Multiple-Access for Peer Discovery in D2D Networks
## 2 System Model and Conventional FDMA-Based D2D Discovery  
In this section, we briefly explain the system model for peer discovery in D2D networks and review the conventional FDMA-based discovery method that is based on contents of various papers and patents [24; 25; 26; 27].
## 4 Remote Sensing Platforms Used in Peatland Research
### Satellite-Based Remote Sensing
#### Challenges and Limitations
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**References** & \begin{tabular}{c} **Peatland** \\ **Type*** \\ \end{tabular} & \begin{tabular}{c} **Peatland** \\ **Status** \\ \end{tabular} & **Dominant Vegetation** & **Platform** & **Sensor/Product** & **Indices** & \
\begin{tabular}{c} **Sensing** \\ **Type** \\ \end{tabular} &
\begin{tabular}{c} **Multi/** \\ **Hyper** \\ \end{tabular} & **Application** \\ \hline
[58] & \begin{tabular}{c} **Peatland** \\ **Postland** \\ \end{tabular} & \begin{tabular}{c} **Restored** \\ **Pure**, willow-birth, bare peat \\ \end{tabular} & \begin{tabular}{c} **Pure**, willow-birth, bare peat \\ \end{tabular} & \begin{tabular}{c} Landstat, Sentinel-2, \\ Spot-5 \\ \end{tabular} & \begin{tabular}{c} ETM+, MSI, \\ HRG \\ \end{tabular} & \begin{tabular}{c} RED-NIR, \\ SWIR-2-SWIR \\ \end{tabular} & \
\begin{tabular}{c} Passive \\ **Multi** \\ \end{tabular} &
\begin{tabular}{c} Multi \\ **Fire risk reduction** \\ \end{tabular} \\ \hline
[40] & \begin{tabular}{c} **Mire** \\ \end{tabular} & \begin{tabular}{c} **Disturbed** \\ **Postland** \\ \end{tabular} & \begin{tabular}{c} **_Molina earthquake_** \\ _Potentiale ectu_ \\ \end{tabular} & \begin{tabular}{c} Landstat, Sentinel-2, \\ Spot-5, Spot-6 \\ \end{tabular} & \begin{tabular}{c} ETM+, OLI \\ MSI, HRG \\ \end{tabular} & \begin{tabular}{c} RED-NIR, \\ SWIR-2-SWIR \\ \end{tabular} & \begin{tabular}{c} Passive \\ **Multi** \\ \end{tabular} & \begin{tabular}{c} **Passive** \\ \end{tabular} & \
\begin{tabular}{c} Multi \\ **monitoring** \\ \end{tabular} &
\begin{tabular}{c} **Peak** \\ \end{tabular} \\ \hline
[79] & \begin{tabular}{c} **Peatland** \\ \end{tabular} & \begin{tabular}{c} NA \\ \end{tabular} & \begin{tabular}{c} NAV \\ \end{tabular} & \begin{tabular}{c} Sentinel-1, Sentinel-2 \\ SA/R/MSI \\ \end{tabular} & \begin{tabular}{c} C-band \\ \end{tabular} & \begin{tabular}{c} NAV/N, NDII \\ \end{tabular} & \
\begin{tabular}{c} Mixed \\ **Multi** \\ \end{tabular} &
\begin{tabular}{c} Multi \\ **Widfire dynamics** \\ \end{tabular} \\ \hline \hline \end{tabular}  
* \({}^{*}\)Note: in this table, the term “_postland_” is used comprehensively to refer to all wetlands considered in the study unless otherwise specified.  
\end{table} 
Table 3: _Cont._
# An Overview of GEO Satellite Communication Simulation Systems
## 3 Resource Management and Scheduling
### Optimization Algorithms  
Optimization problems are typically solved using various algorithmic approaches, including integer linear programming [28], heuristic algorithms, and artificial intelligence methodologies. Heuristic optimization algorithms (e.g., genetic algorithms, particle swarm optimization) combine global search with local refinement to produce near-optimal solutions for NP-hard scheduling problems. DRL techniques [19; 21; 29; 30], such as the deep Q-network (DQN) [31] and the asynchronous advantage actor--critic (A3C) algorithm [32], dynamically adapt scheduling policies through continuous interaction with the environment, demonstrating strong adaptability and generalization capabilities.
# Setonix: Blockchain-Based Hierarchy Domain Name System for Web3
## 5 Implementation
### DNS Part  
Since the DNS must be uploaded to a blockchain network, as described earlier, the first step is to decide which blockchain to use. In this paper, Ethereum was selected due to its high activity and widespread use, as well as its support for development on the blockchain. Additionally, development was carried out using Solidity, the smart contract language that is supported on this blockchain.  
The domain parser, domain register, and domain resolver described in the architecture were each implemented as functions, while the domain checker was implemented as logic operating within the actual register function. Once all the operations shown in Figure 3 are completed and the final registration is performed in the smart contract, the data are stored in a map structure. The reason for using a map structure is that it has an internal structure similar to a hash table, allowing data to be stored efficiently. Moreover, retrieving the stored data can be performed just as quickly as insertion, with the same O(1) time complexity. Since it is implemented based on a map structure, the function for resolving is also designed to retrieve matching data from the map when requested or to return an error message if no match is found.
## 2 Methodologies and Dataset
### Rf-Imf
#### 2.3.2 RF-IMF  
The original annual rainfall time series is broken down into five intrinsic mode functions and a residual series. Then, these subseries are modeled using the random forest regression model so that the future values can be predicted. The predicted values of all the IMFs are aggregated to forecast the annual rainfall. The training dataset comprises annual rainfall values from 1871 to 2000, whereas the testing dataset is selected to be from 2001 to 2020. Empirical mode decomposition was implemented using the PyEMD package in Python. The implementation of sifting algorithm was carried out using the emd.sift module in the package. The algorithm for RF-IMF forecasting model is shown in Algorithm 3.  
Figure 1: Flowchart for empirical mode decomposition.  
\(\hat{i}_{i}(n+1)\) is the \(i\)th predicted IMF, \((n+1)\)th IMFs are predicted using the \((n)\)th adjacent IMFs. Since \(\hat{i}_{6}(n+1)\) is the trend signal, it is predicted using the auto regression of \(\hat{i}_{6}(n)\). The expressions for predicted IMFs are:  
\[\hat{i}_{i}(n+1)=R\big{(}Y,\hat{i}_{1}(n),\hat{i}_{2}(n)\big{)}, \tag{11}\]  
\[\hat{i}_{2}(n+1)=R\big{(}\hat{i}_{1}(n),\hat{i}_{2}(n),\hat{i}_{3}(n)\big{)}, \tag{12}\]  
\[\hat{i}_{3}(n+1)=R\big{(}\hat{i}_{2}(n),\hat{i}_{3}(n),\hat{i}_{4}(n)\big{)} \tag{13}\]  
\[\hat{i}_{4}(n+1)=R\big{(}\hat{i}_{3}(n),\hat{i}_{4}(n),\hat{i}_{5}(n)\big{)} \tag{14}\]  
\[\hat{i}_{5}(n+1)=R\big{(}\hat{i}_{4}(n),\hat{i}_{5}(n),\hat{i}_{6}(n)\big{)}, \tag{15}\]  
\[\hat{i}_{6}(n+1)=AR\big{(}\hat{i}_{6}(n)\big{)} \tag{16}\]  
\[\hat{\chi}(n+1)=\sum_{i=1}^{6}\hat{i}_{i}(n+1). \tag{17}\]  
Figure 2 displays the RF-IMF forecasting model, which predicts each IMF using RF and combines them to obtain the forecasted annual rainfall.
## 4 The Dataset Usage
### Data Extraction and Feature Selection  
Initially, commit histories were parsed to extract code snippets corresponding to added_lines and deleted_lines. These represent modifications made to the source code during each commit. In this context, deleted_lines represent vulnerable code snippets, while added_lines denote the corrected code addressing the vulnerabilities. The extracted data was preprocessed to ensure consistency and quality. Preprocessing steps included the following:  
Tokenization--Using regular expressions, all lines of code were split into meaningful tokens, which included keywords, variable names, function calls, and operators.  
Normalization--Code-specific artifacts, such as indentation and formatting, were normalized to reduce noise.  
Feature Engineering--Semantic and structural features were derived from the code, such as function call counts, try-catch block occurrences, and usage of critical APIs. These features provide valuable insights into the code's functional and security characteristics.
###### Abstract  
There is no standardized protocol for the assessment of microbial air contamination in museums and other cultural heritage sites. Therefore, most museums conduct such assessments based on their own guidelines or good practices. Usually, microbial air contamination is assessed using only classical microbiology methods with the application of a single growth medium. Therefore, this medium should be carefully selected to limit any selective cultivation bias. Metabarcoding, i.e., a next-generation sequencing (NGS)-based method, combined with classical microbiological culturing was used to assess the effectiveness of various media applications in microbiological screening at the Museum of King John III's Palace at Wilanow (Warsaw, Poland). The obtained results indicated that when using a classical microbiology approach to assess the microbial air contamination at the museum, the selection of a proper growth medium was critical. It was shown that the use of rich media (commonly applied by museum conservators) introduced significant bias by severely underreporting putative human pathogens and the bacterial species involved in biodeterioration. Therefore, we recommend the use of other media, such as Frazier or Reasoner's 2A (R2A) medium, as they could yield more diverse communities and recovered the highest number of genera containing human pathogens, which may be suitable for public health assessments.  
air quality; cultural heritage; museum; next-generation sequencing; PCR amplicon 20202010 7128 10.3390/app10207128
## 6 Conclusions  
Methodologies for predicting the material removal rate (MRR) and tool wear rate (TWR) are crucial for precisely controlling the hole depth in micro-EDM drilling. In this work, the capacity and advantages of multiple linear regression (MLR) models, in which data collected through process monitoring are used, have been tested and verified. Data collected by following a Design of Experiments (DoE) have been used for training the MLR models. Validation experiments by applying random processing parameters within the process window defined by the DoE have been used. This way, the prediction accuracy of the different models could be tested in experimental conditions which differ from the ones applied when recording the training data.  
The accuracy in predicting the MRR and TWR has been shown to significantly increase when using data-driven approaches rather than traditional methods based only on a correlation between the process performance indicators and the processing parameters. Experiments have also been performed by applying data-driven approaches for real-time tool wear prediction during the drilling process, showing that implementing data-driven MLR models into the process control strategy can lead to a more precise control of the hole depth.  
This work has confirmed the advantages of data-driven approaches for tool wear and material removal prediction. Future work will aim to extend the dataset by considering holes of higher aspect ratio (i.e. higher than 30) in order to investigate the applicability of data-driven regression models on a wider variety of industrial applications of the micro-EDM process. With more extensive datasets, future research will also focus on studying more advanced approaches for extracting information from the monitored signals rather than using the average values as well as on investigating different methods than linear regression.  
Conceptualisation, M.B. and M.W.; methodology, M.B. and M.W.; micro-EDM drilling experiments, M.B.; software, M.B.; writing--original draft preparation, M.B.; writing--review and editing, M.W., J.Q. and D.R.; supervision, J.Q. and D.R. All authors have read and agreed to the published version of the manuscript.  
The authors gratefully acknowledge the partial financial support from the European Union's Horizon 2020 projects Microman (Project ID: 674801) and ProSurf (Project ID: 767589) and from Flanders Make vzw.  
The authors declare no conflict of interest.
## 1 Introduction
### Performance Analysis  
We analyze the performances of the proposed schemes in comparison with those of the other schemes. We verify the number of operators in the proposed schemes as well as those in the existing studies, and we compare the number of clocks in an ARM simulator provided by IAR Embedded Workbench. We apply the proposed algorithm to SIMON and SPECK block ciphers and analyze their performances in comparison with existing studies. As a result, it is found that the SIMON cipher using constant ANDs and the SPECK cipher using constant ADDs exhibit performance improvements of approximately 33% and 37%, respectively.  
The remainder of this paper is organized as follows. In Section 2, we introduce PB methods by employing dual-rail-based encoding schemes. Section 3 is the core of the paper, in which we present new dual-rail-based PB methods without the application of LUTs. In Section 4, we evaluate the security against side-channel attacks. In Section 5, we present the performance metrics of our proposed method as well as those of existing methods. Finally, we present the conclusions in Section 6.
# Deducing Energy Consumer Behavior from Smart Meter Data +
## 5 Experimental Validation  
This section demonstrates the applicability of the framework via a comprehensive test case that was built in combination with an EU project called SmartHG [24]. The framework has been installed in 25 households in the city of Svebolle, Kalundborg, Denmark and 8 months of data has been collected. The meter's sampling rate is 15 min.
## 2 Materials and Methods
### Field Measurements
#### 2.2.2 Atmospheric Measurements  
The spectral transparency of the atmosphere was measured with an SPM Sun photometer [26] over 11 wavelengths \(\lambda\) (340, 379, 441, 501, 548, 675, 872, 1020, 1244, 1556, and 2134 nm). The obtained measurement data measurements were used to calculate the spectral AOT \(\tau_{\lambda}^{\alpha}\), the parameters \(\alpha\) and \(\beta\) of the Angstrom formula \(\tau_{\lambda}^{\alpha}=\beta\cdot\lambda^{-\alpha}\), and the optical depth of coarse- (\(\tau^{\rm c}\)) and fine-mode aerosol \(\tau_{500}^{\rm f}=\tau_{500}^{\rm a}-\tau^{\rm c}\) for the wavelength 500 nm. The methods and algorithms used to calculate these characteristics are presented in [26, 27, 29].  
The SPM Sun photometer is a portable instrument that is used to measure the spectral transparency in the transparency windows of the atmosphere and the absorption band of water vapor (940 nm). Data processing (calculation of physical characteristics) is performed using a special computer program.  
The SPM photometer receives direct radiation from the Sun as it passes through the Earth's atmosphere. In order to obtain the aerosol optical thickness due to scattering, molecular scattering and absorption are excluded from the signal. Earlier (from 2006 to 2015), in the Black Sea region, similar measurements were carried out using the CE-318 photometer within the framework of the AERONET international program. Since 2015, work under this program has been suspended and photometric measurements have been continued with the SPM. Based on previous research results, SPM measurements agree well with the data provided by the CE-318 photometer; however, the methods used to determine \(\tau_{\lambda}^{\alpha}\), \(\tau c\) and \(\tau_{0.5}^{\ell}\) for the CE-318 and SPM photometers differ [26; 27]. From the comparison of joint measurements using these photometers, the mean square of the deviation between the results (\(\tau_{\lambda}^{\alpha}\), \(\tau c\), and \(\tau_{0.5}^{\ell}\)) does not exceed the error of their measurements. The error of AOT when using SPM in the spectral range of 340-2134 nm does not exceed 0.01.
## 2 Materials and Methods
### Methodology
#### 2.2.1 Pre-Processing  
In the staging phase, a 60 Hz infinite impulse response notch filter was applied to attenuate the power line, followed by a bandpass filter with lower and higher cut-off frequencies of 0.5 Hz and 75 Hz, respectively. This was followed by resampling to 256 Hz and re-referencing to the average of all electrodes. From the remaining EEG signal, 20 s fixed-length epochs were segmented.
# Energy-Efficient Multicast Precoding for Massive MIMO Transmission with Statistical CSI
###### Abstract  
In this paper, we investigate energy-efficient multicast precoding for massive multiple-input multiple-output (MIMO) transmission. In contrast with most previous work, where instantaneous channel state information (CSI) is exploited to facilitate energy-efficient wireless transmission design, we assume that the base station can only exploit statistical CSI of the user terminals for downlink multicast precoding. First, in terms of maximizing the system energy efficiency, the eigenvectors of the optimal energy-efficient multicast transmit covariance matrix are identified in closed form, which indicates that optimal energy-efficient multicast precoding should be performed in the beam domain in massive MIMO. Then, the large-dimensional matrix-valued precoding design is simplified into an energy-efficient power allocation problem in the beam domain with significantly reduced optimization variables. Using Dinkelbach's transform, we further propose a sequential beam domain power allocation algorithm which is guaranteed to converge to the global optimum. In addition, we use the large-dimensional random matrix theory to derive the deterministic equivalent of the objective to reduce the computational complexity involved in sample averaging. We present numerical results to illustrate the near-optimal performance of our proposed energy-efficient multicast precoding for massive MIMO.  
energy efficiency; physical-layer multicasting; massive multiple-input multiple-output (MIMO); statistical channel state information (CSI); beam domain transmission +
Footnote †: journal:  
_Article_  
Energy-Efficient Multicast Precoding for Massive MIMO Transmission with Statistical CSI
# A Neural-Network-Based Landscape Search Engine:
## 2 Methods and Data
### Data Extraction  
Two freely available remotely sensed data sources were utilized in this project: DEM data and NDVI data (see Figure 1). The DEM data comes from the Wisconsin Department of Natural Resources (DNR), and a 30m DEM resolution was selected to produce reasonable computation times given the size of the state of Wisconsin. This data is available for direct download as a single file from the Wisconsin DNR. Using a command line utility from the Geospatial Data Abstraction Library (GDAL), this single file was retiiled into individual.tif files, each 256 \(\times\) 256 pixels. Thus, the resulting extent of each.tif was about 7.5 km \(\times\) 7.5 km, which resulted in a total of 2510 observations after removing.tif files which were completely empty (i.e., those at Wisconsin's borders). This size balances ease of computation while keeping a user-friendly approach. Medium-sized cities such as Eau Claire and La Crosse can mostly be covered by 1-2 grid cells, whereas larger cities such as Madison and Milwaukee are encompassed by more cells. It also strikes a reasonable balance between substantial terrain and vegetation variation between grid cells on the application, without burdening users with an overwhelming number (i.e., tens of thousands) of small grid cells as selection options.  
The vegetation data comes from the National Air and Space Administration's (NASA) Moderate Resolution Imaging Spectroradiometer (MODIS) program, specifically, the 16-Day L3 Global 250 m SIN Grid. Similar to the DEM data, this dataset is available as a single HDF5 file. Using the R Project for Statistical Computing, the single raster was cropped by each of the 2510 DEM.tif files into individual vegetation.tif files. This ensured a one-to-one spatial match of each terrain and vegetation grid cell. The vegetation data come from 5 June 2021 which was selected for several reasons. First, by this point, all of the snow has melted in Wisconsin, and plants are actively growing. At the same time, crops have been planted but are not yet fully grown. The idea behind this was to effectively separate natural vegetation (i.e., prairie and forests) from agriculture. Experiments with vegetation data from later in the growing season did not effectively show the difference between the abundant coniferous forests of northern Wisconsin and the farms commonly found farther south.