esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 The NAPS and NAPS BE Affective Picture Databases
The pictures are illustrated in Figure 3.  
Figure 3: Examples of 6 pictures from the NAPS repository used in the experiment annotated with their dimensional emotion values: mean (_val_SAM_M_, _aro_SAM_M_) and standard deviation (_val_SAM_SD_, _aro_SAM_SD_). From top to bottom row, the pictures belong to the subgroups faces, landscape, and animals, respectively. Reproduced with permission from [22]. In later efforts to expand NAPS, an erotic subgroup was introduced for the Nencki Affective Picture System (NAPS ERO) with an additional 200 stimulus pictures that were provided with self-reported ratings of emotional valence and arousal by homosexual and heterosexual men and women (\(N=80\), divided into four equally sized subgroups) [43]. Finally, Children-Rated Subset is the most recent extension of NAPS and includes 1128 images from the original database that were rated as appropriate for children based on various criteria and expert judgment. In the Children-Rated Subset, affective ratings were collected from a sample of \(N=266\) children aged 8-12 years [44]. In our experiment, we used the NAPS and its first extension NAPS BE. Both databases have a typical architecture and file structure and use a data model standard of most affective picture databases [5]. Their combined dataset is sufficiently large to make inferences about the relationship between affective variables. Moreover, they were developed relatively recently. As such, they are the best common representatives of affective image sets using the dimensional and basic models of emotion. Therefore, we believe that these two data sets were the optimal choice for the presented study.
# A Novel Reconfigurable Vector-Processed Interleaving Algorithm for a DVB-RCS2 Turbo Encoder
## 1 Introduction  
In the era of modern digital communication, the demand for high-speed, reliable, and energy-efficient data transmission is ever increasing. Technologies such as satellite communications, the broadband Internet, and digital video broadcasting rely heavily on robust encoding and decoding mechanisms to maintain data integrity and efficiency in the presence of noise and bandwidth limitations [1]. The Digital Video Broadcasting-Return Channel via Satellite 2 (DVB-RCS2) standard has become a cornerstone for interactive satellite communication systems, where Turbo Codes are widely employed for their near-Shannon-limit error correction capabilities [2, 3].  
To meet the stringent performance and latency requirements of such systems, there is a growing emphasis on optimizing the computational aspects of Turbo encoders. A significant bottleneck in this domain arises from the interleaving operation, which plays a critical role in spreading burst errors but often involves irregular memory access patterns that hinder efficient parallel execution [4].  
Modern computing architectures, including multicore CPUs and SIMD (Single Instruction Multiple Data) vector units, offer substantial potential for accelerating encoding operations. However, many encoding algorithms remain inherently sequential or are poorly adapted to these hardware capabilities, resulting in the suboptimal utilization of computational resources [4]. To address these limitations, it is essential to devise generic and reconfigurable strategies that restructure traditional sequential implementations into parallelizable and vector-friendly formats [5; 6].  
Such approaches involve not only rethinking memory access patterns but also creating algorithmic frameworks that align with the architectural strengths of modern processors. In this context, we propose a novel Reconfigurable Vector-Processed Interleaving Algorithm tailored specifically for the DVB-RCS2 Turbo encoder. The proposed approach is designed to exploit data-level parallelism, enhance memory access efficiency, and support reconfiguration for scalability across varying hardware platforms.
## 3 Materials and Methods
### Proposed Approach
#### 3.1.2 The Considered Inter-Histogram Distances  
Evaluation of the dissimilarity between the target histogram and that obtained from an unknown test image is a highly important issue.  
Figure 1: Proposed approach: (**a**) evaluation of the target histogram, and (**b**) inference phase for an unknown image.  
In the literature, the similarity between the two histograms is evaluated by several distance measurements over the underlying distributions [104; 105]. Regarding the aims of this paper, after denoting by \(p\) and \(q\) the two \(N_{bin}\)-dimensional vectors representing the involved histogram distributions, defined over the set of interval bins \(\mathcal{I}=\{1,\ldots,N_{bin}\}\), we have selected the following four distances.  
1. [label=0., ref=1]
2. Cosine distance: It is formally defined as: \[d_{c}=1-\frac{\sum_{i\in\mathcal{I}}p_{i}q_{i}}{\sqrt{\sum_{i\in\mathcal{I}}p _{i}^{2}}\sqrt{\sum_{i\in\mathcal{I}}q_{i}^{2}}},\] (3) and it normally ranges in the interval \([0,\,2]\). However, since we are considering probabilities, each bin value is non-negative (i.e., \(p_{i},q_{i}\geq 0\), for all \(i\)), so that the distance in (3) is limited to the interval \([0,\,1]\). A distance equal to zero means that the two histogram are identical, while a distance equal to one denotes orthogonal histograms.
3. Kullback-Leibler (KL) divergence [106]: It is defined as: \[d_{KL}=\sum_{i\in\mathcal{I}}p_{i}\log\!\left(\frac{p_{i}}{q_{i}}\right),\] (4) where \(p_{i}\) and \(q_{i}\) are the values of the \(p\) and \(q\) histogram in the \(i\)-th bin, respectively. By definition, the contribution of the \(i\)-th term in the summation in (4) is zero when \(p_{i}\) vanishes. It is always non-negative, and it is zero when the two distributions are equal.
4. Bhattacharyya distance [107]: It is defined as: \[d_{B}=-\log\!\left(\sum_{i\in\mathcal{I}}\sqrt{p_{i}q_{i}}\right).\] (5) The Bhattacharyya distance, such as the KL divergence, is always non-negative, while it is vanishing when the two distributions are equal.
5. \(\chi^{2}\) distance: It is defined as: \[d_{\chi^{2}}=\sum_{i\in\mathcal{I}}\frac{(p_{i}-q_{i})^{2}}{p_{i}+q_{i}}.\] (6) In addition, the \(\chi^{2}\) distance is a non-negative measure.  
These distances, except the cosine one, have been normalized by the number \(N_{bins}\) of used bins, in order to render them independent of the \(N_{bin}\) setting. The Bhattacharyya distance is widely used in several application, such as image processing, and, unlike the KL one, has the advantages of being insensitive to the zeros of distributions.  
In the proposed approach, if the distance (chosen between the cosine, KL, Bhattacharyya, and \(\chi^{2}\) ones) between the target and a test histogram is above the set threshold \(TH\) of Equation (2), then, the image under test is classified as COVID-19 (CNP); otherwise, it is classified as the target class (normal or CP, depending on the used training set).
## 4 LiCSAR Applications for Measuring Tectonic and Volcanic Deformations  
Since the initial development of the LiCSAR system, numerous research projects have been carried out to study deformation of the Earth's crust using LiCSAR products. Typical deformation sources include magma chambers, dike intrusions, and faults that slip during different phases of the earthquake cycle. In this section, we highlight some case studies where LiCSAR has contributed to the monitoring of tectonic activity and volcanic events.
# Prediction and Control of Broadband Noise Associated with Advanced Air Mobility--A Review
## 6 Discussion
Future work could extend ML applications to the mechanisms of broadband noise sources, which could be a key focus for further research. Several passive control methods have been reviewed, including leading and trailing edge serrations, leading edge trips, leading edge notches, blade shape, and tip designs. These methods have shown some success in mitigating blade noise but often reduce the aerodynamic performance of the rotors. These still have some trade-offs that may workas intended but may result in difficulty in manufacturing or may present some structural difficulties. For example, serrations, trips, and tip designs involve 3D geometries with numerous unconstrained parameters, making it challenging to determine the optimal shape design. Additionally, they have thin and complex structures, which can pose manufacturing challenges and may be prone to damage during use. Among these active control methods, suction and blowing techniques have demonstrated considerable success in reducing broadband noise for airfoils and turbine fans. These methods effectively manipulate the boundary layer to diminish noise generation, as seen in the studies by [PERSON] et al. [85] and [PERSON] et al. [86]. However, applying these techniques to propellers presents significant design challenges. The thin structural profile of propeller blades complicates the integration of hollow channels necessary for suction or blowing, making these methods less feasible for propeller noise control. In response to these challenges, two novel approaches show great promise for broadband noise control. One such approach is the trim control strategy proposed by [PERSON] et al. [89]. By adjusting the pitch angle and RPM of the front and rear rotors, this strategy optimizes the overall system performance while significantly reducing noise levels. This approach not only mitigates BBN but also maintains the aerodynamic efficiency of the rotors. Another approach involves the use of dielectric barrier discharge (DBD) plasma actuators, as explored by [PERSON] et al. [90]. DBD plasma actuators generate electric wind by ionizing air particles, which can be used to control the boundary layer flow on blade surfaces. The experimental study conducted on helicopter model blades demonstrated that DBD actuation could significantly reduce broadband noise, particularly in the low-to-mid-frequency range, while also enhancing the overall aerodynamic performance.
## 2 Materials and Methods
### Data Source and Processing  
#### 2.2.1 Meteorological Data  
The meteorological data required for this study were the daily value dataset (V3.0) of China's surface climate data from the China Meteorological Data Network. The daily temperature and precipitation data from 47 meteorological stations from 2001 to 2020 were downloaded from the China Meteorological Data Sharing Service Center.
# Critical Review of Techniques for Food Emulsion Characterization
## 3 Techniques for Emulsion Characterization
### Thermal Properties Analysis
The peak area in the resulting thermogram signifies the enthalpic change, while the direction of the peak indicates whether the thermal event is exothermic or endothermic [162]. DSC proves particularly useful in monitoring the solidification (crystallization) and melting processes of emulsion components [18]. A notable advantage of DSC analysis is its ability to distinguish the thermal characteristics of different constituents (such as oils, proteins, etc.) within a single thermal cycle, obviating the need for their extraction. DSC is considered a standard method for determining phase diagrams, which reveal information about transition temperatures and the melting enthalpy. It allows evaluation of the thermal behavior of oils, water, and emulsifiers within emulsions, and tracing of the mass transfer within these complex mixtures [163]. However, it can be time-consuming, and expensive, and requires careful sample preparation before measurement.
## 2 Methodology  
### Feature Selection Based on ReliefF Algorithm  
Feature selection plays a critical role in data-mining techniques. When processing a vast amount of data, datasets with irrelevant and redundant features may hinder subsequent analysis, such as key information extraction, classification, and prediction. Irrelevant and redundant features can weaken model performance. Hence, it is necessary to perform feature selection on the data to remove these irrelevant and redundant features. Feature selection algorithms help to build simpler and more understandable models, reduce training time, and improve model performance by identifying the most distinctive subset of features from a large number that are relevant to the problem under study [14]. Feature selection algorithms combined with classification models fall into three categories: filter, wrapper, and embedded [15].  
Filter feature selection algorithms are independent of any learning algorithm; they evaluate and select features based on the data's characteristics to determine the relevance of each feature [16]. The Relief algorithm is a typical filter feature selection method. It is a sample-based learning algorithm for calculating feature weights, measuring the distinctiveness power of features by examining their differences between similar near-neighbor samples and dissimilar samples. If a feature shows a small difference between similar samples and a large difference between dissimilar samples, it is considered to have a strong distinctiveness ability. The Relief algorithm is regarded as one of the most straightforward and effective filter feature selection methods available [17]. The ReliefF algorithm, an extension of Relief, is a multi-category feature selection algorithm that extends the study object from a two-category problem to a multi-category problem by transforming the multi-category problem into multiple one-to-many two-category problems [18]  
According to the Relieff algorithm, bridge inspection data can be analyzed, and feature parameters with a high impact on bridge condition ratings can be classified and selected. The purpose of this classification is to group bridge features with similar causes of deterioration to enhance the identification of deterioration patterns in bridges.
## 2 Principle and Mathematical Model
### Modulus Back-Calculation Method  
The current pavement modulus back-calculation employs a simplified stress-strain relationship (Hooke's Law), assuming that the material is linearly elastic and isotropic and the pavement deformation is small:  
\[\sigma_{z}=\lambda\varepsilon_{x}+\lambda\varepsilon_{y}+(\lambda+2G) \varepsilon_{z} \tag{5}\]  
\[G=\frac{E}{2(1+\mu)} \tag{6}\]  
\[\lambda=\frac{2\mu G}{1-2\mu} \tag{7}\]  
where \(\sigma_{z}\) = vertical stress; \(\varepsilon_{x}\), \(\varepsilon_{y}\), \(\varepsilon_{z}\) = transverse, longitudinal, and vertical strains, respectively; \(\mu\) = Poisson's ratio of the material; \(\lambda\) = [PERSON]'s first parameter; and \(G\) = shear modulus.  
In substituting Equations (6) and (7) into Equation (5) and simplifying, the following is obtained:  
\[E=\frac{(1-2\mu)(1+\mu)\sigma_{z}}{\mu\varepsilon_{x}+\mu\varepsilon_{Y}+(1- \mu)\varepsilon_{z}} \tag{8}\]  
According to the requirements of specification, Poisson's ratio of the pavement asphalt mixture is defined as \(\mu\) = 0.25. In substituting this value into Equation (8) and simplifying, the back-calculation formula for the elastic modulus can be derived as  
\[E=\frac{2.5\sigma_{z}}{\varepsilon_{x}+\varepsilon_{Y}+3\varepsilon_{z}} \tag{9}\]  
In comparing the back-calculated elastic modulus results across sections treated with different grouting materials, the effectiveness of grouting rehabilitation can be quantitatively evaluated.
## 3 Research Methodology
### K-Nearest Neighbor (K-NN)  
K-nearest neighbor is known for its simplicity among other ML algorithms. It is a supervised learning-based non-parametric method that makes assumptions about the similarity between the new data and known data and classifies the new data into the category that is closer to known categories. The k-NN algorithm is either used for regression or classification tasks. K-NN is also called a \"lazy learner algorithm\" due to the fact that instead of learning from the training set immediately, it stores the data, and then during the data classification, it classifies the instances into a category that is much similar to the new data (Figure 6) [29; 30; 31; 32].  
The k-NN algorithm operation can be broken down into six steps:  
1. Select the number K of the neighbors.
2. Calculate the Euclidean distance of K number of neighbors.
3. Take the k-nearest neighbors based on the measured Euclidean distance.
4. Among the k-nearest neighbors, measure the number of the data points in each category.
5. Assign the new data points to the category for which the number of neighbors is maximized.
6. The model is ready for application.  
Euclidean distance:  
In an \(n\)-dimensional space, the Euclidean distance between two points \(P\) = (\(p_{1}\), \(p_{2}\),...,\(p_{n}\)) and \(Q\) = (\(q_{1}\), \(q_{2}\),...\(q_{n}\)) is given by (10), (11) (Figure 7).  
\[d(P,Q)=\sqrt{\sum_{i=1}^{n}(p_{i}-q_{i})^{2}} \tag{10}\]  
\[d(X,Y)=\sqrt{(X_{2}-X_{1})^{2}+(Y_{2}-Y_{1})^{2}} \tag{11}\]  
Figure 6: K-Nearest Neighbors (K-NN).
# Risk Assessment of Soil Contamination with Heavy Metals from Municipal Sewage Sludge
## 1 Introduction
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Metal**} & \multicolumn{4}{c}{**Permissible Values for Heavy Metals Intended for Natural Use, mg\_kg-1 d.m.**} \\ \cline{2-7}  & **Poland** & **EU Directive** & \multicolumn{2}{c}{**Chinese Regulation GB**} & \multicolumn{2}{c}{**USA Regulation**} & \multicolumn{2}{c}{**South African**} \\  & **Regulation** & **86/278/EEC [11]** & **86/278** & **18918–2002** [13] & **40 CFR Part 503,** & **Guideline (Pollutant** \\  & [8-10] & **9H \textless{} 6.5** & **pH \textgreater{} 6.5** & **503.13 [11]** & **Class a) [14]** \\ \hline Cd & 20 & 20–40 & 5 & 20 & 39 & 40 \\ Ni & 300 & 300–400 & 100 & 200 & 420 & 420 \\ Zn & 2500 & 2500–4000 & 500 & 1000 & 2800 & 2800 \\ Cu & 1000 & 1000–1750 & 250 & 500 & 1500 & 1500 \\ Cr & 500 & - & 600 & 1000 & - & 1200 \\ Pb & 750 & 750–1200 & 300 & 1000 & 300 & 300 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Normative limit values for heavy metals in sewage sludge for natural use. \begin{table}
\begin{tabular}{c c} \hline \hline \multicolumn{1}{c}{**Metal**} & \multicolumn{2}{c}{**Industry Branches**} \\ \hline \multirow{3}{*}{Cadmium} & Galvanising plants, manufacture of dyes, batteries, accumulators, paints and plastics, polymer stabilisers, chemical industry, manufacture of plant protection products, graphic and printing works \\ \hline \multirow{2}{*}{Lead} & Production of dyes, accumulators, batteries, fertilisers, automotive industry, energy industry, plant protection products, electrochemical industry \\ \hline \multirow{2}{*}{Chromium} & Electroplating; tanning; wood impregnation; textile, dye and plastic manufacturing; printing and graphic arts plants \\ \hline Copper & Metallurgical, paint, textile, plant protection products and fertiliser industries \\ \hline \multirow{2}{*}{Mercury} & Production of batteries, phosphoric acid, caustic soda, pulp mills, production of plant protection products, metallic mercury \\ \hline Nickel & Electroplating industry, paper industry, refineries, steelworks, fertiliser factories \\ \hline \multirow{2}{*}{Zinc} & Production of batteries, paints, textile industry, plastics, polymer stabilisers, printing and graphic arts \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Industry branches which are a source of heavy metal emissions to the environment [16; 17]. (Igeo), the potential environmental risk index (PERI), the risk assessment code (RAC), the environmental risk factor (ERF), and the environmental risk determinant (ERD) proposed by the authors were calculated based largely on the analysis of the mobility of heavy metals.
# Dynamic Spatial-Temporal Self-Attention Network for Traffic Flow Prediction
## 4 Methodology  
This research has developed a novel model called the Dynamic Spatial-Temporal Self-Attention Network (DSTSAN), which is depicted in Figure 1. The DSTSAN model comprises three main layers: the feature embedding layer, the spatial-temporal self-attention layer, and the output layer. The spatial-temporal self-attention layer is constructed by connecting \(l\) self-attention modules in parallel. In the subsequent sections, this paper will provide a comprehensive description of each component in the DSTSAN model.
# Intrinsic Interference Use for FBMC-IOTA Systems
## 5 Conclusions  
A novel algorithm for FBMC-IOTA systems with IOTA pulse-shaping is proposed to use the intrinsic interference in the demodulation process. The intrinsic interference is treated as parity symbols, which enables us to correct errors using the information offered by these parity symbols.  
Figure 3: Effect of imperfect channel estimation.  
Based on MPA, IIU is developed to improve the system performance. Effects of CFO and imperfect channel estimation on IIU are investigated.  
Conceptualization, L.W. and R.R.; methodology, J.L.; software, J.L.; validation, R.R. and J.L.; formal analysis, L.W.; investigation, R.R.; resources, J.L.; data curation, R.R.; writing-original draft preparation, J.L.; writing-review and editing, L.W.; visualization, R.R.; supervision, J.L.; project administration, L.W.; funding acquisition, L.W.  
This work was supported in part by the National Defense Technology Foundation under Grant 3101168, in part by the Hunan Natural Science Foundation under Grant 2017 JJ2303, and in part by National Natural Science Foundation of China under Grant 61603007 and Grant 61702536.  
The authors declare no conflict of interest.
# Rotation of Liquid Metal Droplets Solely Driven by the Action of Magnetic Fields
## 4 Conclusions  
We have demonstrated a novel method to manipulate the self-rotation of LMDs by solely utilizing magnetic fields which is smooth, simple, steady and, most importantly, does not produce a violent chemical reaction. The relative motion of the magnetic fields and LMDs generates an eddy current in the droplets and further induces the Lorentz force to actuate the self-rotation of the droplets. The motor speed, the LMD sizes, and the concentration of NaOH solution can be easily manipulated to regulate the self-rotational speed of droplet. Moreover, we demonstrated that such a technique can be used for the application of accelerating the cooling and mixing liquids. Nonetheless, bulky rotating magnets and a motor are still required in our current platform; however, we believe that introducing programmed electromagnetic fields can be helpful in resolving this problem in our future work. As such, utilizing magnetic fields to induce the self-rotation of LMDs could widely expand the application of LMDs to be used as MEMS devices.  
The following are available online at [[http://www.mdpi.com/2076-3417/9/7/1421/s1](http://www.mdpi.com/2076-3417/9/7/1421/s1)]([http://www.mdpi.com/2076-3417/9/7/1421/s1](http://www.mdpi.com/2076-3417/9/7/1421/s1)), Figure S1: Self-rotation of EGaIn droplets in different diameter tubes, Video S1: Self-rotation of liquid metal droplet, Video S2: Mixer based on self-rotation of liquid metal droplet.  
[PERSON], the first author, conceived the idea, performed the experiments, analyzed the data and wrote the paper. [PERSON]-[PERSON] conceived the idea, analyzed the data and wrote the paper. [PERSON]. ([PERSON]) performed the experiments. [PERSON] conceived the idea and analyzed the data. [PERSON] conceived the idea. [PERSON] analyzed the data and wrote the paper. [PERSON] conceived the idea, analyzed the data and wrote the paper. [PERSON]. ([PERSON]) conceived the idea, analyzed the data and wrote the paper.  
The authors acknowledge support from the National Natural Science Foundation of China (No. 51828503, U1713206, 61503270, 61873339), and the Shenzhen Science and Innovation Committee (Reference No. JCYJ20160427183958817). Dr. [PERSON] is the recipient of the Vice-Chancellor's Postdoctoral Research Fellowship funded by the University of Wollongong.  
There are no conflicts of interest to declare.  
Figure 4: Mixer based on a self-rotating LMD. (**a**) Schematic illustration of the mixing setup. (**b–d**) Continuous captures of mixing two drops of dye.