esa-sceva/satcom-chunk-collection huggingface dataset:
# Improved Food-Processing Techniques to Reduce Isoflavones in Soy-Based Foodstuffs
## 3 Results
### Pilot Scale
#### 3.2.2 Effect of Granulometry  
The industrial process of dehulling generated different types of particles. Their sorting by size gave the following proportions in number: 11.53 \(\pm\) 1.09% whole beans and 88.47 \(\pm\) 1.16% half-beans. When the proportion was given by mass, whole beans represented 14.35 \(\pm\) 2.50%, dehulled beans 72.25 \(\pm\) 3.44%, seed fragments 13.32 \(\pm\) 4.92% and envelope residues 0.09%. The morphological analysis of the particle sizes generated by dehulling showed solid densities of 1.2-1.4 and 1.2-1.8 whether bounded or not, respectively. It was highest for the fractions 1-2.24 mm and 0.5-1 mm for bounded and unpounded beans, respectively. The details of the particle size sorting are given in Figure 2, with a 2D photograph for each fraction. It can be seen that the particles have an ellipsoidal shape which is more pronounced as the size decreases. Pounding made the particles' shapes more irregular. The particle sizes generated have dimensions either over 4 mm, between 4 and 2.24 mm, or between 2.24 and 0.5 mm. The 4-6.23 mm fraction corresponds to the most represented mass, i.e., 94% of the total mass of particles recovered on the sieves.  
With regard to the impacts of granulometry on the process, the following correlations were observed. The washing effectiveness on deflavonoidisation was slightly better with magnetic than orbital stirring, but the difference was only significant with the smallest particle sizes. The IF content decrease between raw and washed matter was significant, regardless of the particle size. It was also noted that the smaller the particle size, the more efficient the deflavonoidisation, independent of the hydration of the samples, since the values were calculated according to the dry matter (see Figure S2 in the Supplementary Materials). However, in return, the drenching waters were all the more contaminated by solids, the smaller the particle size. This indicates a loss of water-soluble substances that are also present in soybeans, i.e., proteins, sugars, fibres, etc.
###### Abstract
[PERSON], et al. [13] noted that the CLSMs prepared from coarse-grained soils with PCE showed superior flowability compared to the mixture with sulfonated acetone formaldehyde (SAF) within 30 min. They pointed out that the water-reducing effect of PCE was superior to that of SAF. Nevertheless, practical application revealed that the clay particles in sand and gravel aggregates can compromise PCE's water-reducing effectiveness [14]. In specific instances, the plasticizer Pozzolith 475N, applied at a dosage of 1.5% by weight of cement, did not significantly impact flowability due to the high water content and low cement content in CLSMs [15]. [PERSON], et al. [16] confirmed that PCE cannot effectively reduce water consumption in soil-based CLSMs. Clay minerals, particularly montmorillonite, exhibit a higher PCE adsorption capacity than cement particles, attributed to their layered structure [17]. The competitive adsorption of PCE between clay particles and cement ultimately diminishes PCE's effectiveness. Therefore, the effectiveness of PCE in mixtures containing clay particles remains controversial. The water-reducing effect of PCE in soil-based CLSMs on the flowability and strength of soil-based CLSMs needs to be further investigated. The existing literature on soil-based CLSMs predominantly focuses on their preparation and strength, while research on the pore structure of soil-based CLSMs is still in its early stages. Mercury intrusion porosimetry (MIP) is a traditional method for quantitatively studying the microscopic pore structure of tested samples [18]. The intrusion of mercury under high pressure in MIP can disrupt the sample structure, leading to inaccurate estimates of the pore size distribution and porosity [19]. Additionally, the presence of clusters of smaller pores within a continuous network of larger pores can result in the pore shielding effect, causing temporary mercury entrapment and increasing uncertainty in the test results [20]. In contrast, low-field nuclear magnetic resonance (NMR) utilizes hydrogen protons as probes to measure the pore size distribution and porosity of samples in a low external magnetic field [21]. This technique offers advantages such as rapidity, accuracy, reusability, and non-destructive. Low-field NMR has been successfully applied to study porous materials like cement-based materials [21], coal [22], and rock [23]. This study explored the feasibility of producing soil-based CLSMs using excavated soil as the sole fine aggregate and cement. PCE was employed as a substitute for water to enhance the flowability of soil-based CLSMs. The influence of varying cement contents, initial water contents, and the curing time on the mechanical properties and corrosivity of soil-based CLSMs with PCE was investigated. Furthermore, the transverse relaxation time \(T_{2}\) distribution, pore size distribution and porosity were analyzed through low-field NMR, revealing the microscopic mechanism of strength development in soil-based CLSMs with PCE.
## 4 Implementation Details of Field Arithmetic
### Optimization Strategy on 32-Bit ARM  
The ARM Cortex-M is a family of 32-bit RISC ARM processors for microcontrollers. The Cortex-M4 processor is a high-performance Cortex-M processor with digital signal processing (DSP), SIMD, and MAC instructions. It based on the ARMv7-M architecture and equipped with 16 32-bit general registers that are numbered from R0 to R15. Registers R13 to R15 are special-purpose registers that are used for the stack pointer (SP), link register (LR), and program counter (PC), respectively. The Cortex-M4 instruction set provides multiply and MAC instructions, such as UMULL, UMLAL, and UMAAL. The UMULL instruction multiplies two unsigned 32-bit operands to obtain a 64-bit result. The UMLAL and UMAAL instructions multiply two unsigned 32-bit operands and accumulate a single 64-bit value and two 32-bit values.  
In our implementations, we used the STM32F407-DISC1 board, which contains a 32-bit ARM Cortex-M4 STM32F407 VGT6 microcontroller [37]. This microcontroller is equipped with 1 MB of flash memory, 192 KB of SRAM, and 64 KB of core-coupled memory (CCM) data RAM and can be clocked up to 168 MHz.  
For integer multiplication and squaring, we implemented the operand scanning method by using efficient MAC operations. Additionally, these MAC operations facilitate an efficient implementation of modular reduction. The first reduction computes \(c^{\prime}\equiv c_{l}+2\cdot 5997\cdot c_{h}\), where \(0\leq c_{h},c_{l}<2^{128}\). For example, the intermediate values \(c_{h}\) are loaded in R9 to R12 and \(c_{l}\) are loaded in R5 to R8.  
The constant \(11994=2\cdot 5997\) is loaded in R3 and 0 is loaded in R4. The computation \(c^{\prime}\equiv c_{l}+2\cdot 5997\cdot c_{h}\) is performed as follows:  
MOV R3,#11994,  
MOV R4,#0,  
UMLAL R5,R4,R3,R9,  
UMAAL R4,R6,R3,R10,  
UMAAL R6,R7,R3,R11,  
UMAAL R7,R8,R3,R12.  
The results of the first reduction \(c^{\prime}\) are held in (R5,R4,R6,R7,R8). The second reduction can be computed using simple multiplication (MUL) and addition (ADD, ADC) instructions.  
For the further improvement of field arithmetic, we implemented field arithmetic over \(\mathbb{F}_{p^{2}}\) at the assembly level [24; 38]. In the case of field multiplication over \(\mathbb{F}_{p^{2}}\), we utilized the operand scanning multiplication with a lazy reduction method. This operation computes \(a\cdot b=(a_{0}+a_{1}i)(b_{0}+b_{1}i)=(a_{0}b_{0}-a_{1}b_{1})+(a_{0}b_{1}+a_{1 }b_{0})i\), where \(a=a_{0}+a_{1}i,b=b_{0}+b_{1}i\in\mathbb{F}_{p^{2}}\). The operand scanning method results in better performance than the Karatsuba multiplication in our case. The field squaring over \(\mathbb{F}_{p^{2}}\) is implemented using \(a^{2}=(a_{0}+a_{1})(a_{0}-a_{1})+2a_{0}a_{1}i\) at the assembly level.
# Track-Bridge Interaction of CWR on Chinese Large-Span Bridge of High-Speed Railway
## 4 Special Problems of Track-Bridge Interaction
### Shrinkage and Crep
Compared with the rail stress and deformation under the Eurocode shrinkage and creep mode, the calculation results under the Chinese Railway code shrinkage and creep mode are larger. When selecting the standard calculation, we should consider the situation of the calculation object and select the applicable standard [74; 75; 77; 78]. Based on the short-term concrete creep test data, a creep prediction model suitable for practical engineering is established. As the basis of structural calculation, the creep development of the actual structure can be more accurately predicted, and the prediction accuracy of long-term shrinkage and creep of the long-span railway concrete bridge can be improved [79]. The results show that the additional force of shrinkage and creep is more sensitive to the beam storage time, the rail stress and vertical displacement decrease with the increase of the beam storage time, and the beam storage time has little effect on the horizontal force on the pier top [74; 77]. After using the small resistance fastener, the stress of the rail can be obviously improved, the additional force of the rail near the peak value is significantly reduced, and the additional force of the rail shrinkage and creep and the additional force of the rail expansion can be significantly reduced [75]. Strengthening the curing of concrete can also reduce the effect of shrinkage and creep of prestressed concrete on the stress and deformation of the rail. The rail stress, vertical displacement, and pier top horizontal force decrease rapidly with the increase of the concrete curing relative humidity [74]. Figure 21: Rail stress due to shrinkage and creep model under different codes.
# S2p: Provenance Research for Stream Processing System
## 7 Experimental Evaluation
### General Settings  
**Dataset**: We constructed a diverse set of data sets based on the data benchmark in [43], which consists of Tweets in six months, and the data benchmark in [44]. For Twitter data sets, we filtered out coordinates and timestamps; only the Twitter messages were left. Then, we randomly sampled text lines and formed a new text collection to simulate the scenario in which some people send Tweets independently, and DSP ingests the data for further analysis. For the movie rating data set, we extend it by randomly sampling them and appending them to a new data file. Basing on these two kinds of data sets, we constructed our data sets in different sizes ranging from 2 GB to 500 GB.  
**Object Applications**: In terms of benchmark applications, we choose three subject applications from earlier works [45; 46], some of which are adapted and extended with stream processing features (e.g., process data with time windows). These subject applications are listed in Table 2 (the extended applications are tagged with an asterisk).  
**Hardware and Software Configuration**: Our experiments were carried out on a cluster that contains ten i3-2120 machines, each running at 3.30 GHz and equipped with two cores (two hyperthreads per core), 4 GB of RAM. Among these machines, eight of them are as the slave nodes with 500 GB of disk capacity for each. One is the master node. Furthermore, the left one works as a data node with 3 TB of disk capacity. The data node stimulates users to send data continuously. All ten of these machines are connected via a TL-SG1024 Gigabit Ethernet Switch.  
The operating system is 64-bit Ubuntu 18.04. The data sets are all stored in HDFS with version 2.8.3. We built **s2p-flink** on Apache Flink version 1.9.2, the baseline version with which we will compare our system. We also set the parallelism of jobs to 16.
# Advancements in EEG Emotion Recognition: Leveraging Multi-Modal Database Integration
## 3 Multi-Modal System EEG with Facial Expressions--Proposed Model
### EEG Topographies and Brain Heat Maps
The two-dimensional topography of EEG contains spatial information, making it particularly useful for optimizing electrode placement on the scalp. Consequently, these topographies serve as the input to an objective function aiming to maximize the discriminative ability between various emotional states.
# A 920-MHz Dual-Mode Receiver with Energy Harvesting for UHF RFID Tag and IoT +
###### Abstract  
A low-power dual-mode receiver is presented for ultra-high-frequency (UHF) radio frequency identification (RFID) systems. The reconfigurable architecture of the tag is proposed to be compatible with low-power and high-sensitivity operating modes. The read range of RFID system and the lifetime of the tag are increased by photovoltaic, thermoelectric and RF energy-harvesting topology. The receiver is implemented in a 0.18-\(\upmu\)m standard CMOS process and occupies an active area of 0.65 mm \(\times\) 0.7 mm. For low-power mode, the tag is powered by the rectifier and the sensitivity is \(-\)18 dBm. For high-sensitivity mode, the maximum PCE of the fully on-chip energy harvester is 46.5% with over 1-\(\upmu\)W output power and the sensitivity is \(-\)40 dBm with 880 nW power consumption under the supply voltage of 0.8 V.  
RFID; receiver; energy harvesting; cross-coupled voltage multiplier; charge pump; ultralow-power circuits and systems Article
## 4 Testbed Results  
The results of the testbed are separated into the GOOSE results and the results for the Modbus and DNP3 protocols. With the GOOSE protocol, all five network configuration  
Figure 7: Packet delay latency measurement.  
results are compared, contrasting the latency of LAN configurations with the 5G configurations. Optimization is then performed on the 5G components of the testbed to evaluate these changes for any achievable improvements to the testbed's latency. For Modbus and DNP3, initial tests are performed, and the results are compared to the GOOSE results. Given the similarity of results between each protocol, different packet sizes were also tested and evaluated in order to isolate the cause of latency related to gNB wireless scheduling.
## 2 Materials and Methods
### Amplitude and Phase Feature Extraction  
To fully exploit the information in EEG signals, the features in the phase domain are integrated to improve engagement recognition accuracy. To extract the phase angle of EEG signals, the Hilbert transform [38] is employed to an EEG signal \(\mathbf{X}(t)=[x_{1}(t),x_{2}(t),\cdots,x_{C}(t)]\), in which \(C\) is the number of channels:  
\[\hat{x}(t)=\mathcal{H}[x(t)]=\frac{1}{\pi}\int_{-\infty}^{+\infty}\frac{x( \tau)}{\tau-t}d\tau. \tag{17}\]  
The analytic signal is then formed as  
\[\Theta(t)=x(t)+i\cdot\hat{x}(t)=A(t)\cdot e^{i\cdot\theta(t)}, \tag{18}\]  
where \(i\) denotes the imaginary unit. \(A(t)\) and \(\theta(t)\) stand for the amplitude and phase (AP), respectively:  
\[\begin{cases}A(t)=\sqrt{x^{2}(t)+\hat{x}^{2}(t)},\\ \theta(t)=\arctan\frac{\hat{x}(t)}{x(t)}.\end{cases} \tag{19}\]
# A Location-Time-Aware Factorization Machine Based on Fuzzy Set Theory for Game Perception
## 3 Problem Formulation and Algorithm
### Similarity Calculation  
Game user perception is a multidimensional nonlinear problem. In actual experiments, we found that under similar network conditions, different players or other conditions may lead to different final user perceptions. The features of each instance and its corresponding label are represented as an association, which is considered a correlation [20; 21], similarity, or likelihood distribution [22]. The relationship between each sample and its corresponding label can also be represented by the correlation. To address this issue, we introduce the fuzzy set theory, which uses a membership function to determine the similarity between data points and the centroid vector of each label.  
In our work, the membership function is based on the similarity point of view, by the distance to the perfect sample-based similarity point of view [23]. We adopt a multidimensional Gaussian function as the membership function [24], which is widely used in many applications because of its simple form [25]. The multidimensional membership function in our proposed method is defined as:  
\[u_{ij}=\exp\left(-\frac{\|x_{ij}-c_{ij}\|^{2}}{2\sigma^{2}}\right),i\in\{1,2, \ldots,N\},j\in\{1,2,\ldots,L\} \tag{2}\]  
where \(u_{ij}\) is the membership value and \(0\leq u_{ij}\leq 1\). Here \(\sigma\) is the dispersion of the radius, \(x_{ij}\) is the inner product value of the feature interaction and \(c_{ij}\) is the center vector of the corresponding label. The centroid vector \(c_{ij}\) is calculated as follows:  
\[c_{ij}=\frac{1}{|q_{j}|}\sum_{q_{j}}x_{i} \tag{3}\]  
where \(q_{j}=\{x_{i}|x_{i}\in l_{j}\}\), \(l_{j}\) is the \(j\)-th label corresponding to the instance \(x_{i}\), and \(|q_{j}|\) is denoted as the element number of the set \(q_{j}\). The membership function in Equation (2) is used to measure the similarity between the feature interaction and the centroid vector. If the sample value is close to the centroid vector, then its membership is higher. Conversely, its membership is lower the farther it is from the centroid vector.
# A Seismic Phase Recognition Algorithm Based on Time Convolution Networks
## 2 Data
### Data Preprocessing  
Data preprocessing is of great significance in the training of neural networks as preprocessing processes directly affect the model convergence effect. The original data in this study comprised continuous waveform segments with a time window length of 30 s and a data frequency of 100 Hz, so the data format was a two-dimensional matrix of \(3000\times 3\). Due to the existence of low-frequency long-term background disturbances and high-frequency noise in the waveform data, our model received too many useless signal features during the training process, which reduced the fitting effect. Therefore, the band-pass filtering method was first performed during the data processing on [0.1, 20] of the original data. A continuous wavelet transform was then used to decompose the signals into components at different scales [19; 20; 21; 22]. Finally, the amplitude differences between the time steps in the seismic data were too large, so to reduce the variations in the data and make the convergence process of the loss function smoother, it was necessary to normalize each component of the waveform data using the following calculation formula:  
\[A_{i}=\frac{A_{i}}{\max(|A_{i}|)} \tag{1}\]  
Figure 5: Data distribution before and after data processing ((**a**,**c**) are the original data, (**b**,**d**) are the label data after manual labeling.The red vertical line represents the arrival of the P-wave, the blue vertical line represents the arrival of the S wave, the purple box represents the label range of the P-wave, and the green box represents the label range of the S wave).  
where \(A_{i}\) is the amplitude of the i-th sampling point in each track component. An example original waveform is shown in Figure 6, where the red vertical line represents the arrival time of the P wave and the blue vertical line represents the arrival of the S wave (which were manually marked by seismological researchers).
## 2 Theoretical Background on the ANSYS DesignXplorer(c) Probabilistic Methods
### Six Sigma Analysis (SSA) Theory
#### 2.2.2 Latin Hypercube Sampling  
The Latin hypercube method, originally presented in Reference [66], is a method that can represent multiple variables avoiding overlapping datasets. According to Reference [55], stratified and Latin hypercube sampling methods use the common idea that the sampling space be divided into strata (or hypercubes) and that only a few of the many possible samples may be selected in each strata (or hypercube) for inclusion in the estimation of the integral \(J\). Application of the method initiates by dividing the distribution of each stochastic variable in \(n\), non-overlapping intervals with equal probability. For each variable, one value should be randomly selected from each interval, and the analysis point derived from each dataset is then associated. The homogeneous allocation of intervals on the probability distribution function yields relatively small variance in the response, compared to the conventional Monte Carlo sampling. At the same time, the analysis is much less computationally demanding to generate [49].  
The samples used from within each stratum (or hypercube) can be randomly selected from those that would otherwise be used; alternatively, for example, the mean or central point might be used as the one representative value. The evaluation of \(J\) then uses these selected values, weighted by their probability of occurrence. Latin hypercube sampling considers all the random variables [55; 64; 67; 68].  
For convenience, consider the special case in which all the random variables \(x_{i}\)\((i=1,\ldots,\ n)\) are each divided into the same number of strata, say \(N\). It is convenient also to make the divisions such that the probability of \(x_{i}\) falling in each stratum is \(1/N\). Now, select a representative value for each stratum and for each random variable. In principle, more values could be selected, but this can also be achieved by using a greater number of strata. To estimate \(J\), the procedure is to use just one of the \(N\) representative values for each random variable \(x_{i}\) (that is, selected from one of the strata) in combination with a selected representative value from each of the other random variables. This collection of representative values forms a sample set. The process is repeated, selecting from the remaining representative values, thereby forming another sample set, and so on. In the end, there will be \(N\) sample sets. These can be interpreted directly as the number \(N\) of sample vectors selected in Monte Carlo sampling and, thus, can be used directly in Equation (1), with evaluations of the limit state function using the present sample sets to estimate \(J\) (and, thus, \(p_{f}\)). Latin hypercube sampling can also be combined with other variance reduction techniques [68], such as importance sampling, although it is generally considered to be most suitable for small-scale simulation problems [55; 69; 56].
# Advanced Medical Image Segmentation Enhancement:
## 3 Materials and Methods
### Integrating PSO and HE for Image Segmentation
#### 3.4.2 Rationale, Benefits, and Experimental Rigor  
The rationale behind this integration is grounded in its capacity to enhance adaptive contrast, which is particularly beneficial for the diverse intensity landscapes found in medical images. This adaptive approach enhances feature discrimination, is vital for accurate segmentation, and addresses the challenge of overfitting commonly associated with generic enhancement techniques. Rigorous experimental validation becomes paramount, with quantitative metrics scrutinizing segmentation accuracy, precision, recall, and F1-score/Dice. Comparative analyses against traditional methods and alternative enhancement techniques provide nuanced insights into the advantages of PSO-HE integration and substantiate its efficacy across varied contexts.