esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract
With the rapid advancement of deep learning technology and increasing diversity and complexity of model architectures, there is an urgent need for mixed-precision training techniques that offer broad applicability and can quickly and efficiently adapt to different models. To discover universally applicable mixed-precision training strategies across different DNN architectures, a systematic investigation of the patterns affecting low-precision training effectiveness is essential. Rather than greedily minimizing low-precision errors during training or pursuing optimization for specific tasks, our paper approaches the problem from the perspective of low-precision matrix computations in DNN training, analyzing how matrices with different characteristics affect computational results under low-precision quantization. By examining three key factors--the number of accumulations in matrix calculations, element usage frequency, and the depth of matrices within the model--we reveal how these factors influence model convergence and stability under various low-precision training configurations, and explore the underlying universal patterns. We design and implement a series of comparative experiments, including both typical DNN model experiments and customized experiments aimed at isolating individual factors. Through these experiments, the following key patterns emerge: First, layers with higher accumulation counts and matrices with lower usage frequencies demonstrate greater tolerance to low-precision quantization noise without significantly impacting model training effectiveness. Second, while matrix depth influences the stability of matrix operations to some extent, its impact on overall training results is relatively minimal. These universal patterns eliminate the need for detailed analysis and optimization of computational processes, facilitating efficient DNN training and deployment. Our contributions in this work are twofold. First, we provide a systematic analysis of the impact of quantization noise on various computational factors within DNNs. This analysis offers a theoretical framework that can guide the implementation of low-precision training strategies across different models, ensuring that these strategies are both effective and adaptable. Second, we introduce a set of generalizable principles for low-precision training that can be readily applied to a wide range of DNN architectures. By addressing the need for a universally applicable and theoretically grounded approach to low-precision training, our work paves the way for more efficient and effective deep learning models that are capable of meeting the demands of modern applications while operating within the constraints of limited computational resources.
# New Methods for the Comprehensive Analysis of Bioactive Compounds in _Camabis sativa_ L. (hemp)
## 3 Materials and Methods
### HPLC-UV/DAD Conditions
#### 3.4.1 HPLC-UV/DAD Analysis of Non-Psychoactive Cannabinoids  
The chromatographic conditions for the analysis of non-psychoactive cannabinoids were set on the basis of a previous work [9]. HPLC analyses were carried out on an Ascentis Express C\({}_{18}\) column (150 mm \(\times\) 3.0 mm I.D., 2.7 \(\upmu\)m, Supelco, Bellefonte, PA, USA), with a mobile phase composed of 0.1% HCOOH in both (A) H\({}_{2}\)O and (B) ACN. The gradient elution was modified as follows: 0-13 min 60% B, 13-17 min from 60% to 80% B, 17-22 min from 80% to 90% B. The post-running time was 15 min. The flow-rate was 0.4 mL/min. The column temperature was set at 30 \({}^{\circ}\)C. The sample injection volume was 3 \(\upmu\)L. The UV/DAD acquisitions were carried out in the range 190-600 nm, while chromatograms were acquired at 210 nm (for decarboxylated cannabinoids) and at 220 nm (for cannabinoic acids) [9]. Three injections were performed for each sample.
## 2 Materials and Methods  
### Data Sources and Preprocessing  
This study incorporated the time series data from two distinct research projects, each with 10 different participants, exploring various aspects of human gait over treadmill walking. The 'Footwear Study' focused on the effects of shoe design on gait under 15 conditions, using 200 Hz for motion capture and 2000 Hz for GRF data [21]. In this study, ten healthy male participants (age 24.9 \(\pm\) 2.7 years, mass 75.7 \(\pm\) 13.4 kg, height 173.2 \(\pm\) 6.4 cm; mean \(\pm\) s.d.) were recruited. The 'Hip Exoskeleton Study' assessed the biomechanical impact of hip exoskeletons under 12 conditions, using 120 Hz for motion capture and 1000 Hz for GRF data acquisition [22]. In this study, ten healthy adults (four males, six females; age: 27.6 \(\pm\) 5.9 years, body mass: 65.3 \(\pm\) 13.1 kg, height: 1.66 \(\pm\) 0.08 m) participated. Both experiments were performed at a constant walking speed of 1.25 m/s on a treadmill. One dataset included variations in outside geometry, slopes, and treadmill inclination, while the other involved walking with an exoskeleton providing different hip assistance timings. The equipment used included a bilateral semi-rigid exoskeleton for hip motion assistance, and precise sensor calibration ensured high accuracy in data collection. In both studies, the net metabolic rate for walking, expressed in watts per kilogram (W/kg), was determined by the indirect calorimetry method [23]. These computed metabolic cost values serve as the 'target values' in our research. Combining the two studies, 270 trials of biomechanical data were collected, including the GRF, normalized as N/kg, and joint moments, normalized as N\(\cdot\)m/kg for biological relevance. Data were preprocessed to calculate stride-average time series, ensuring consistency and comparability across the various trials and conditions. To maintain uniformity and ensure comparability across the 270 trials, we standardized the sample rates; GRF signals were normalized to a sample rate of 1000 Hz and motion capture data to 120 Hz. Additionally, we transformed each time series from the trials into 100 data points, reflecting the percentage of the gait cycle. This approach ensured consistency in data analysis and allowed us to focus on the biological moments in the joint moment time series, providing a clear and comparable biomechanical insight across the 270 trials from the two distinct studies.
## 3 Analysis and Handling Methods of Uncertain Factors
### Analysis of Uncertain Factors
#### 3.1.2 Population Density  
Population density is an important factor in calculating the risk of infection. The population density in traditional risk models is generally given a certain value based on past experience. However, there is significant uncertainty in population density itself. On the one hand, accurate statistics of population density are difficult, and on the other hand, high population mobility leads to dynamic changes in population density. The uncertainty of population density can affect the accuracy of infectious risk assessment.
# A Review on Rainfall Measurement Based on Commercial Microwave Links in Wireless Cellular Networks
## 4 Signal Processing from RSL to Rainfall Map
### Wet/dry Classification
#### 4.1.3 Machine Learning Algorithms  
[PERSON] et al. [57] used support vector machine (SVM) to distinguish the wet/dry period. Specifically, the average, minimum, and maximum attenuation are used as the feature vectors, and the radial basis function is used to convert the data. It is pointed out that the classification accuracy increases as the carrier frequency and the length of the link increases. Other machine learning algorithms, such as convolutional neural networks (CNNs) and LSTM, can also be used to establish classification models based on the RSL data, and we will discuss these algorithms in Section 5, which covers machine learning.  
In addition, the wet/dry classification methods in current studies also include Fisher discriminant analysis based on kernel function [58] and multi-family likelihood ratio test [59].
# Studies of General Precipitation Features with TRMM PR Data: An Extensive Overview
## 5 Rainfall Observation of PR Data
### Climate Characteristics of Rainfall
#### 5.3.1 Rainfall in Monsoon Seasons  
The temporal and spatial distribution of rainfall during monsoon seasons is an important issue in meteorology, and the change of rainfall distribution with the onset of the monsoon could be analyzed. Studies found that the stratiform precipitation and the convective precipitation evidently increased and occurred simultaneously in time and space after the monsoon onset [134; 135; 136]. It seemed that, in the  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Reference** & **Period** & **Location** & **Products (Version) of PR** \\ \hline [PERSON] et al. [122] & Dec. 1997 to Mar. 2004 & tropics & 3G68 (version5) \\ [PERSON] et al. [106] & Jun. to Aug. of & & \\  & 1998–2007 & Asia & 2A25 \\ [PERSON] et al. [128] & 1998–2006 & Tibetan Plateau and surrounding & 2A25 (version6) \\ [PERSON] and [PERSON] [132] & 1998–2002 & Bormeo area & 2A23 and 2A25 (version5) \\ [PERSON] and [PERSON] [126] & 1998–2006 & tropics & 3G68 \\ [PERSON] and [PERSON] [127] & 1998–2006 & tropics & the University of Utah \\  & & & precipitation feature database \\ [PERSON] and [PERSON] [131] & Jun. to Aug. of & & \\  & 1998–2007 & southern China & 2A25 (version6) \\ [PERSON] et al. [121] & 1998–2000 & Indonesian Maritime Continent & 3G68 \\ [PERSON] and [PERSON] [120] & Dec. 1997 to Nov. 2000 & tropics & 2A23 and 2A25 (version5) \\ [PERSON] et al. [130] & 2002–2008 & Upper Blue Nile basin & 2A25 \\ [PERSON] et al. [123] & 1998–2000 & tropics & 3G68 (version5) \\ [PERSON] et al. [129] & 1998–2009 & Asian monsoon region & the University of Utah \\ [PERSON] and [PERSON] [124] & 1998 & tropics & precipitation feature database \\ [PERSON] and [PERSON] [125] & 1998–2005 & tropics & 2A25 and 2B31 (version6) \\ \hline \hline \end{tabular}
\end{table}
Table 9: Key information of the studies on diurnal variation of rainfall from PR data.  
Figure 7: The unitless ratio of daytime to nighttime monthly rainfall given by PR algorithm 2A25. ([124] © American Meteorological Society. Used with permission.)total rainfall amounts, the convective precipitation contributed more than the stratiform precipitation for the United States Monsoon [137], the stratiform and the convective precipitation contributed almost equally for the Indian Monsoon [134], and the stratiform precipitation contributed more than the convective precipitation for the East Asian Monsoon [135; 136].
## 3 Results
### Research on the Frequency of Use of Keywords (RQ-2 and RQ-3)
#### 3.2.1 The Most Common Keywords  
The authors created a keyword map with the highest frequency of use, shown in Figure 3. To compile it, the authors set the minimum number of mentions of keywords to 11 (which corresponds to 20% of all detected keywords according to the Pareto distribution). A total of eight views and 43 keywords were highlighted that met these criteria. The 10 most frequently used keywords include electronic voting (433), security (318), cryptography (314), blockchain (281), protocol (193), privacy (149), authentication (139), networks (85), consensus (67), internet (65), and smart contract (58).  
Figure 3 shows that these key terms were divided into four groups, each represented by a distinct color in the visualization. VOSviewer performs clustering based on minimizing the distances between keywords, which means that more closely related keywords are combined into one group. Therefore, the five most closely related keywords in each of these six groups are as follows: (1) algorithms, management, and consensus; (2) blockchain, decentralization, and smart contract; (3) protocol and anonymity; (3) cryptography and authentication; and (4) electronic voting, privacy, cloud computing, computer networks, and zero knowledge. It can be concluded that the first group reflects algorithms, the second group is blockchain and smart contracts, and the third group is cryptography and authentication; that is, user authentication is aimed at confirming the truth of the presented identity to make sure that this person is who he claims to be. The color scheme utilized in the visualization aids in distinguishing between these thematic clusters, providing a visual representation of the relationships between keywords within each group.  
Figure 2: Temporary distribution of research in the field of electronic voting.  
Figure 4 shows part of the keyword match matrix; the columns and rows of keywords in the matrix reflect the degree of co-occurrence of two key terms. In addition, the intensity of the green color indicates that keywords tend to occur together. The richer the green color, the stronger their relationship.  
To highlight the most common keywords, Python programming language code was implemented. The program code was then visualized using the matplotlib library version 3.7.1. It is a library for creating static, animated, and interactive graphs in the Python programming language. It is widely used for data visualization and scientific results. Matplotlib provides many functions for creating various types of graphs, charts, and maps. The corresponding fragment of the program code is shown in Figure 5.  
Figure 4: Matrix of coincidences of the most common key terms in the field of electronic voting.  
Figure 3: Graphic representation of the keywords most commonly encountered.  
The most common keywords are voting and electronic (324), voting and machines (87), voting and internet (77), network and security (59), voting and systems (58), and system and voting (58).
## 4 Problem Definition and Research Methodology
### Methodology and Performance Metric  
Figure 2 shows the methodology in assessing the ML model. During data collection, the required data are gathered for the studied cells using the PM systems, as discussed previously. The data set analyzed in this research was collected from a Tier 1 Mobile Operator with quite a mature LTE network, which consists of more than 3000 base stations operating in various LTE frequency bands (800 MHz, 900 MHz, 1800 MHz, 2100 MHz, 2300 MHz and 2600 MHz).  
Figure 2: The workflow investigating alternative models.  
Data were collected during a period of three months with hourly resolution, with more than 2000 records per cell. In pre-processing the PRB utilization in surrounding cells, the number of surrounding cells, the average PRB utilization in surrounding cells, the heavy data factor, and the higher order modulation are calculated from the raw data. The ML model being studied is trained to predict the average CQI using these inputs. During testing, the performance of the model is evaluated on unseen data. The performance is then compared to the other models.  
The predictive performance of taking the mean CQI as the predicted value was assessed to establish a baseline naive method. Next, the predictive performance of popular ML models including linear regression [18] (implemented using the sci-kit learn library [19]), extreme gradient boosting (implemented using XGBoost) [20; 21], and feed-forward neural networks were compared. The neural network was implemented using the Keras library [22; 23], feed-forward architecture, three hidden layers, 256 neurons per layer, Relu activation function, and Adam optimizer.  
Mean Absolute Percentage Error (_MAPE_) was used as the performance metric throughout the study. _MAPE_ was calculated according to the equation shown below.  
\[MAPE=\frac{100}{N}\sum_{i=0}^{N}\left|\frac{predictedCQI_{i}-measuredCQI_{i}}{ measuredCQI_{i}}\right| \tag{1}\]  
Finally, the feature importance of the best performing is investigated. The features are scored by the number of times they are used to split nodes of trees in the ensemble. The features are then ranked by their score. Due to the stochastic nature of XGBoost, results may vary slightly.
## 2 The Proposed Schemes: DAE-EDMR and T-DAE-EDMR
### Method I: DAE-EDMR
#### 2.2.2 The Connectivity-Based Localization Process  
To obtain an estimate of \(\mathbf{X}\), which is denoted by \(\hat{\mathbf{X}}\in\mathbb{R}^{k\times N}\), based on the classical MDS method, we first define the geometric centering matrix as follows:  
\[\mathbf{C}=I_{N}-\frac{1}{N}\mathbf{1}_{N}\mathbf{1}_{N}^{T}\in\mathbb{R}^{N \times N}, \tag{13}\]  
where \(I_{N}\) is the identity matrix \(N\) by \(N\) in size. Next, the estimated centered Gram matrix (GM) is obtained as  
\[\hat{\mathbf{G}}_{c}=-\frac{1}{2}\mathbf{C}\hat{\mathbf{D}}\mathbf{C}\in \mathbb{R}^{N\times N}. \tag{14}\]  
Recalling the fact that \(\hat{\mathbf{G}}_{c}=\hat{\mathbf{X}}_{c}^{T}\hat{\mathbf{X}}_{c}\), where \(\hat{\mathbf{X}}_{c}\) is the centered \(\hat{\mathbf{X}}\), we can easily obtain \(\hat{\mathbf{X}}_{c}\) through the \(k\)-truncated EVD of \(\hat{\mathbf{G}}_{c}\). Based on this, we can finally obtain \(\hat{\mathbf{X}}\) through a rigid linear transform, i.e., rotation and translation, of \(\hat{\mathbf{X}}_{c}\) with the pre-knowledge of \([\mathbf{x}_{1},\cdots,\mathbf{x}_{P}]\in\mathbb{R}^{k\times P}\), which are the positions of the reference nodes.
## 2 Background and Literature Review
### Two-Stage FDH Model in Production Technologies  
In order to get FDH efficiency scores of DMUs with two-stage network structure, solving linear/nonlinear mixed integer programming problems is an essential part. Recent study by [PERSON] and [PERSON] [38] shows that FDH two-stage models can be solved by examining only some simple ratios, without solving any mathematical programming problem. Both cases of similar and diverse optional peers in both stages are provided by FDH models. In order to calculate the overall and stage efficiency scores based on dissimilar RTS assumptions, some closed form models are applied. Finally, some recent FDH models studies which have been proposed by many scholars can be addressed in literature [39; 40; 41; 42; 43].
## 5 Applications Across Diverse Sectors
### Recent Innovations
#### 5.1.3 Stereolithography and Microwave Sintering  
A groundbreaking development in additive manufacturing merges stereolithography with microwave sintering, enabling the rapid fabrication of intricate objects using powdered materials. This approach significantly reduces processing time while ensuring precision, allowing for quick consolidation and densification of printed items. This invention revolutionizes the production of intricate and useful products by expanding the variety of materials that can be utilized in 3D printing [62].  
Another notable advancement is continuous liquid interface production (CLIP), patented by Carbon3D in 2014. Unlike traditional stereolithography (SLA) and digital light processing (DLP), CLIP employs a continuous method to transform liquid resin into solid objects using light and oxygen. This method eliminates discrete printing steps and maintains a continuous liquid interface, enabling uninterrupted curing as the part is extracted from the resin. CLIP utilizes a digital projector and microcontrollers to optimize the printing process, resulting in a layer-less design and enhanced efficiency [46].  
CLIP presents numerous advantages over conventional printing methods (see Table 3).
## 3 Results and Discussion
### Effect of N\({}_{2}\) Flow Rate on Ammonia Synthesis  
The gas flow rate affects the residence time of the active particles in the plasma discharge region, as well as the intensity of the reaction with the liquid film, so we investigated the effect of the gas flow state on the nitrogen fixation process. Unusually, we also investigated the characterization of nitrogen fixation products in the sealed gas flow state. The reaction conditions were the liquid flow rate of 100 mL\(\cdot\)min\({}^{-1}\), the peak-peak voltage of 31 kV, and the reaction time of 10 min.  
As illustrated in Figure 4a, increasing the gas flow rate from 50 SCCM to 300 SCCM resulted in a rise in NO3\({}^{-}\) concentration. NO2\({}^{-}\) initially increased but was undetectable in the product at a flow rate of 200 SCCM, while NH4\({}^{+}\) concentration exhibited an overall decreasing trend. The experimental results showed that high gas flow rates favored NO3\({}^{-}\) production, while low gas flow rates were more favorable for NH4\({}^{+}\) production. This may be due to the fact that the residence time of the gas flowing at high velocity in the reactor is shorter, with less opportunity for gas ionization and generation of reactive species, whereas at low flow rates, the gas has a longer residence time and reacts more fully with the liquid film, which is conducive to the formation of NH4\({}^{+}\). Therefore, we further investigated the effect of nitrogen fixation in the sealed airflow condition. In this state, the gas has the longest residence time in the reactor, and the ammonia synthesis rate reaches a maximum value of 11.24 umol\(\cdot\)min\({}^{-1}\) with an energy consumption of 400.32 MJ\(\cdot\)mol\({}^{-1}\) and a selectivity of 74.9%.  
Figure 3: (**a**) Concentrations of NH4\({}^{+}\), NO3\({}^{-}\), and NO2\({}^{-}\) produced in aqueous solutions under different liquid flow rates, and (**b**) the energy consumption and the selectivity of NH4\({}^{+}\).
## 8 System Implementation Experimental Studies  
Our research has been validated through experimental studies that examine various aspects of the developed QC-RL-LDPC system. These include encoding time; memory utilization of the encoder implemented with a microcontroller; investigation of decoding time and decoding parameter selection; error correction performance; and finally, normalized energy consumption estimation of the developed QC-RL-LDPC system. Due to the specifics of the design objective, which is an energy-efficient IoT uplink transmission scheme design, we investigated relatively short codes of length within \(100\ldots 1000\). We provide the microcontroller implementation results and a performance simulation for the channel with an unknown corruption level, represented by a wide range of SNRs (signal-to-noise ratios).  
Several different codes (\(\mathbf{H}1,\ldots,\mathbf{H}5\)) with diversified block lengths, chunk sizes and minimum rates, as listed in Table 1, were designed and used for this experimental study. As an example, Figure 6 shows a whole parity check matrix \(\mathbf{H}\) of the code \(\mathbf{H}1\).  
The parts of the designed communication system were implemented specifically with an STM32L476 microcontroller, for which we present the implementation results, including a case study of the power consumption and the energy efficiency calculations.
## 4 Simulation Results and Analysis
### Information Entropy  
Information entropy is a technique used to assess the randomness of variables in an image. When applied to both encrypted and original images, it can help to determine the variables present in the image. A high value of information entropy, close to 8, indicates a high level of randomness in the variables and a strong image encryption mechanism. Information entropy can be mathematically calculated using Equation (16).  
\[e=\sum_{i=1}^{256}p(i)\log\biggl{(}\frac{1}{p(i)}\biggr{)} \tag{16}\]  
In this study, we have analyzed the values of information entropy for both plain and encrypted images, and the results are presented in Table 4. In addition, Table 5 presents the average results of the Lena image encrypted with all image sizes used in this study and  
Figure 12: Spatiotemporal histogram of the plaintext image and the ciphertext image.  
compares them to those of other similar studies. These tables provide valuable insights into the security and performance of the proposed encryption method.