esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methods
### The Calculation Method of Insertion Loss  
The center frequency with the minimum mean deviation between the 1/3 rd-octave-band diffraction and the total diffraction is the equivalent frequency [36]. The equivalent frequency could be used in the approximate calculation of barrier attenuation. The results of the approximate calculation are close to the direct calculation using the whole spectrum as parameters, and the total error is always less than 1 dB. The procedures for calculating the insertion loss in this paper are presented below. Generally, several path length differences were preset first to calculate the 1/3 rd-octave-band diffraction and the total diffraction. The detailed calculations are shown as follows:  
1. Seven path length differences from 0.01 m to 10 m were preset first: 0.01 m, 0.1 m, 0.5 m, 1 m, 2.5 m, 5 m, 10 m;
2. The data were collected at the noise measurement site without the noise reduction effect of the barrier, and contained the 1/3 rd-octave-band SPL \(L_{i}\) and the total SPL \(L\), where \(i\) is the sequence number of the 1/3 rd-octave-band center frequency;
3. Through the diffraction formula, the diffraction \(\Delta L_{di}\) of every \(1/3\)rd-octave-band center frequency could be calculated as follows (Equation (4)): \[\Delta L_{di}=20\text{lg}\frac{\sqrt{2\pi N}}{\text{tanh}\sqrt{2\pi N}}+5 \text{dB},N=\frac{\delta\cdot f}{170}\] (4) where \(\delta\) is the path length difference and \(f\) is the 1/3 rd-octave-band center frequency;
4. For a certain path length difference, the total SPL \(L^{\prime}\) with the reduction effect of noise barrier can be calculated as (Equations (5) and (6)) \[L_{i}^{\prime}=L_{i}-\Delta L_{di}\] (5) \[L^{\prime}=10\text{lg}(\sum_{i}10^{L_{i}^{\prime}/10})\] (6) where \(L_{i}^{\prime}\) is the 1/3 rd-octave-band SPL with the reduction effect of barrier;
5. The following equation is used to yield the total diffraction \(\Delta L_{d}\)(Equation (7)): \[\Delta L_{d}=L-L^{\prime}\] (7)After calculating the total diffraction \(\Delta L_{d}(\delta_{j})\) and the 1/3 rd-octave-band diffraction \(\Delta L_{di}(\delta_{j})\) with seven preset path length differences, the mean deviation \(\Delta d_{i}\) between \(\Delta L_{d}(\delta_{j})\) and \(\Delta L_{di}(\delta_{j})\) can be obtained (Equation (8)).  
\[\Delta d_{i}=\frac{1}{7}\sum_{j=1}^{7}\bigl{|}\Delta L_{d}(\delta_{j})-\Delta L _{di}(\delta_{j})\bigr{|} \tag{8}\]
## 1 Introduction  
Speech Emotion Recognition (SER) is a focused area of research within affective computing, aiming to develop models capable of identifying and understanding human emotions from speech. Recognizing emotions plays and essential role in enhancing human-computer interaction (HCI), enabling applications in mental health monitoring, customer service, entertainment, and more [1]. Traditional SER models often focus on either discrete emotion representations, which classify emotions into distinct categories like joy, sadness, or anger [2], or dimensional emotion representations, which capture emotions along continuous dimensions such as valence, arousal, and dominance. This exclusive focus limits the models' ability to capture the full complexity and nuances of human emotions, as emotions can simultaneously exhibit characteristics of both discrete categories and continuous dimensions with varying degrees of intensity and subtlety [3].  
The significance of SER lies in its potential to enhance HCI and facilitate communication in various domains. For instance, in mental health monitoring, SER models can identify stress, depression, or anxiety from speech patterns, supporting healthcare professionals in diagnostics and treatment planning [4]. In customer service, understanding the emotional state of callers allows agents to tailor their responses, providing more empathetic and effective support [5]. SER can also be integrated into AI-powered chatbots and virtual assistants to respond appropriately based on users' emotions [6]. In the entertainment and gaming industries, emotion-aware systems can create more immersive experiences by adapting content or gameplay based on the player's emotions [1]. Furthermore, in education and training environments, SER models can help instructors understand learners' emotional states, leading to more personalized and effective teaching strategies [6].  
This paper proposes a novel Speech Emotion Recognition model based on the joint modeling of discrete and dimensional emotion representations. By leveraging the strengths of both representation types, the proposed approach aims to create a more comprehensive and accurate SER model. For example, a model that fuses acoustic and deep features by using a genetic algorithm (GA) to optimize the feature weights has shown improved performance. This method, combined with decision tree-support vector machine (DT-SVM) classification, helps in the selection of the most distinguishable feature set at each classification node, contributing to significant improvements in emotion recognition [7]. This highlights the potential of strategic feature fusion in enhancing SER performance, even with limited training data.
# Study of the Phenolic Compound Profile of _Arbutus unedo_ L.
## 3 Results and Discussion
### Analysis of Phenolic Compounds by HPLC-TQ-MS/MS
#### 3.3.1 Development of the HPLC-TQ-MS/MS Method
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Class**} & \multirow{2}{*}{**Analyte**} & \multirow{2}{*}{**Rt (min)**} & \multicolumn{2}{c}{**Molecular Ion;**} & \multicolumn{2}{c}{**Linear**} & \multirow{2}{*}{**Linearity; R2**} & \multirow{2}{*}{**LOD (mg**} & \multirow{2}{*}{**LOQ (mg**} \\  & & & **Product Ions** & & & & & L\({}^{-1}\)) & L\({}^{-1}\)) \\  & & & **(Coll. Energy, eV) (m/z)** & & & & & \\ \hline \multirow{8}{*}{Flavonols} & Caffe acid & \multirow{2}{*}{9.4} & 178.9; & 178.9; & \multirow{2}{*}{0.05–10.0} & y = 2.94E8x + 4.86E7; & \multirow{2}{*}{0.015} & \multirow{2}{*}{0.050} \\  & Caffe acid & & & 178.6(8V)/134.7*(16V) & & & & & \\  & & & 310.9; & & & & & \\  & & & 178.8(14V)/148.8*(12V) & & & & & \\  & & & 134.6(26V) & & & & & \\  & Quercetin & 12.5 & 178.6(18.0V)/150.6*(22V) & & & & & \\  & & & 463.1; & & & & & \\  & & & 301.0(20V)/299.7*(26V) & & & & & \\  & & & 270.9(45V) & & & & & \\  & & & 609.5; & & & & & \\  & & & 10.7 & 300.1*(34V)/271.3*(45V) & & & & & \\  & & & 150.2(40V) & & & & & \\  & Kaempfer & 13.4 & 284.9; & & & & & \\  & & 288.37(14V)/226.3*(30V) & & & & & & \\  & & 288.9; & & & & & & \\  & (+) Catechin & 9.3 & 244.7(12V)/108.8*(22V) & & & & & \\  & (–)- & & 457.1; & & & & & \\  & Epigallocatechin & 9.6 & 168.5(18V)/124.4*(36V) & & & & & \\  & gallate & & 304.9; & & & & & \\  & Epigallocatechin & 8.9 & 178.7(14V)/218.4(16V)/ & & & & & \\  & & & 166.8(16V)/164.6*(20V) & & & & & \\  & & & 577.2; & & & & & \\  & & & 288.9; & & & & & \\  & & & 288.9*(24V) & & & & & \\  & Flavanones & Naringin & 10.5 & 270.8(36V)/151.0*(45V) & & & & & \\  & & 431.1; & & & & & \\  & Flavones & Vitexin & 10.0 & 429.9(12V)/311.1*(22V)/ & & & & & \\  & & 282.83(4V) & & & & & \\  & & 11.4; & & 226.9; & & & & \\  & Resveratrol & 13.1 (cis) & 184.8(12V)/142.8*(32V) & & & & & \\  & & 10.7; & & 390.0; 227.4*(20V) & & & & & \\  & & & & & & & & \\  & & & & & & & & \\ \hline \hline \end{tabular}  
* Product ions used for quantification. Chromatographic conditions with the optimized gradient elution: t = 0 min 5% A –95% B, t = 18 min 100% A, t = 20 min 5% A –95% B (2 min) (ACN as mobile phase A and H\({}_{2}\)O as mobile phase B, both containing 0.1% formic acid and 2 mM ammonium acetate). The flow rate was 0.2 mL min\({}^{-1}\).  
\end{table} 
Table 3: \(Cont.\)
## 3 Experiment 1 Results  
All data were analysed using SPSS version 21 (IBM North America, New York, NY, USA). Data were tested for normal distribution by [PERSON]'s test and for homogeneity of variance using [PERSON]'s test. Following confirmation of parametricity, an analysis of variance (ANOVA) with repeated measures was used to test for differences in rating, with order group (1, 2, 3, or 4) and sex (male or female) as a between-subjects factor, and size (large or small) and congruency (congruent or incongruent) as within-subjects factors. Subsequently, post hoc paired samples t-tests with Bonferroni corrections were performed to locate differences. We report alpha levels as exact P values, without dichotomous interpretation of'significant' or 'non-significant' as advised by the American Statistical Association [9]. Effect sizes are reported using partial eta squared (\(\eta\)p\({}^{2}\)) for ANOVA and [PERSON]'s \(d\) (difference in means \(\div\) pooled standard deviation [SD]) for pairwise comparison. \(\eta\)p\({}^{2}\) was interpreted as small (0.02), medium (0.13), and large (0.26) effects. [PERSON]'s \(d\) was interpreted as small (0.2), medium (0.5), and large (0.8) effects [10]. Figures were generated in GraphPad Prism (GraphPad Prism 8.4.3, GraphPad Software Inc., San Diego, CA, USA) and the display grouped dot plots with mean and 95% confidence intervals (CIs)  
Figure 1: (**A**) Habituation event, (**B**) Large ball congruent, (**C**) Large ball incongruent, Bottom: (**D**) Small ball congruent, (**E**) Small ball incongruent event outcomes.  
as recommended by [PERSON] [11] and [PERSON] et al. [12]. Data are presented in the text as mean \(\pm\) SD.  
Test trials  
The main effect of size from the ANOVA was (F (1, 16) = 12.40, \(p\) < 0.01), np\({}^{2}\) = 0.44. The main effect of congruency from the ANOVA was (F (1, 16) = 8.31, \(p\) = 0.01, np\({}^{2}\) = 0.34), with adults rating the congruent test events (7.10 \(\pm\) 2.55) more likely compared to incongruent test events (2.68 \(\pm\) 2.40). The interaction effect between size and congruency from the ANOVA was (F (1, 16) = 24.69, \(p\) < 0.001, np\({}^{2}\) = 0.61). Adults rated the small congruent event (6.71 \(\pm\) 2.52) more likely than the small ball incongruent event (2.26 \(\pm\) 2.36; \(p\) < 0.001: \(d\) = 1.82; Figure 2). Furthermore, adults rated the large congruent event (7.54 \(\pm\) 2.59) more likely than the large incongruent event (3.08 \(\pm\) 2.77; \(p\) < 0.001: \(d\) = 1.66; Figure 2). There was no effect of order group (F (3, 16) = 1.12, \(p\) = 0.37, np\({}^{2}\) = 0.25) nor sex (F (1, 16) = 0.78, \(p\) = 0.39, np\({}^{2}\) < 0.10).
## 2 Theory
### Theory of an ANN  
An ANN, which is a kind of intelligence model, is proficient at adapting to changes in the environment, learning from experience, and improving its performance [57; 58; 59]. An ANN is composed of neurons. Multi-layer perceptron (MLP) and radial basis function (RBF) networks are two common types of ANNs.  
A common MLP structure has the following steps: (1) an input layer, (2) one or more hidden layers, and (3) an output layer. There are some elements (or neurons) in each layer. By using optimization algorithms, the number of neurons used in the hidden layer should be determined [60].  
A kind of feed-forward net is sketched using iterative localized basis functions, and the function approximation algorithms are RBF-ANNs. RBF-ANNs and MLP-ANNs differ not only in design but also in their responses to patterns; with more details, simple design, and very precise response to patterns that are not used for training, RBF-ANNs have an advantage over MLP-ANNs [61]. Because the RBF-ANN training process is much faster than for an MLP-ANN and the structure of an RBF-ANN is simpler, it is an acclaimed alternative to an MLP-ANN [62]. The RBF-ANN structure contains three layers: (1) an input layer, (2) a hidden layer with a non-linear active RBF function, and (3) an output layer. The following equation shows the RBF-ANN output:  
\[y_{i}(x)=\sum_{k=1}^{h}w_{ki}\varphi(\|x-c_{k}\|), \tag{2}\]  
where \(x\) represents an input pattern, \(y_{i}(x)\) denotes the \(i\)th output, \(w_{ki}\) refers to the connection weight between the \(k\)th hidden unit and the \(i\)th output, \(\|\|\) represents the Euclidean norm, and \(c_{k}\) represents the center of the \(k\)th hidden unit. The Gaussian function was chosen as the RBF (\(\varphi\)), where the Gaussian function is presented below:  
\[h(x)=\exp\left(-\frac{(x-c)^{2}}{r^{2}}\right). \tag{3}\]  
The center (c) and radius (\(r\)) are known as Gaussian parameters. The offered MLP-ANN approach utilizes the log-sigmoid transfer function (logsig) and linear transfer function (purelin) in its hidden and output layers, respectively.
## 5 Applications of Activated Carbon from Waste Tyres
### Hydrogen Storage  
Waste tyre-based AC finds applications in hydrogen storage where the synthesis of AC following the mechanochemical or compactivation procedure has been successfully achieved. This procedure aims to increase reactive sites by the compaction of the solid char with an activating agent before the activation process. The solid char was first treated with water, HF, and HNO\({}_{3}\) before the mechanochemical approach was implemented. The surface area was found to have increased by 3% to 24% when compared to conventional activation methods. Hydrogen uptake measurements showed that the H\({}_{2}\) storage capacity of 1.4 wt% at 1 bar was achieved at the highest surface area carbon using the HNO\({}_{3}\)-compactivation method [69].
# Abnormal Behavior Detection in Uncrowded Videos with Two-Stream 3D Convolutional Neural Networks
## 5 Experimental Works
### Performance Evaluation  
The study measured the performance of the model in terms of the number of correct predictions made against each class. The results are shown in Table 5. One can see from the results that the model is more likely to misclassify other activities (none) as one of the abnormal behaviors (loitering). However, as previously explained in Section 4.4.2, it was deemed acceptable to have some false positives rather than misclassifying one of the abnormal conditions as \"none\". Another case of a higher misclassification rate can be seen in loitering being predicted as \"none\". As described in the previous section, this is mainly due to the essential difficulty of distinguishing between the two. Similarly, misclassifications are slightly high in the case of falling versus violence and vice versa. One possible reason for this could be that the most confounding activities to falling such as bending and lying are frequently found in the videos of violence.  
Table 6 shows the classification results obtained using the basic setup described in Section 5.2. Table 7 shows the results obtained after considering the distinguishing movement patterns and increasing the number of examples for each case. A comparison of results in these two tables shows the significant improvement achieved using the enhanced setup. Further, the results in Table 7 show that the model generalizes well and yields adequate classification performance for all abnormal behaviors. The \(F_{1}\) score (a combination of recall and precision) for falling, loitering, and violence i.e., 98%, 93%, and 97% respectively, is an indicator that the model can accurately distinguish between different events involving abnormal behavior.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Falling** & **Loitering** & **Violence** & **None** \\ \hline Recall & 0.92 & 0.73 & 0.88 & 0.87 \\ FP Rate & 0.04 & 0.02 & 0.02 & 0.11 \\ Precision & 0.88 & 0.91 & 0.92 & 0.72 \\ Accuracy & 0.95 & 0.91 & 0.95 & 0.88 \\ \(F_{1}\) & 0.90 & 0.81 & 0.90 & 0.79 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Overall classification test results for 4 classes with the basic setup.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Actual**} & \multicolumn{4}{c}{**Predicted**} \\ \cline{2-5}  & **Falling** & **Loitering** & **Violence** & **None** \\ \hline Falling & 98.03 & 0.05 & 1.36 & 0.56 \\ \hline Loitering & 0.01 & 93.34 & 0.11 & 6.54 \\ \hline Violence & 1.04 & 2.00 & 96.91 & 0.05 \\ \hline None & 0.92 & 4.61 & 1.62 & 92.85 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Confusion Matrix (%) for the proposed approach.
# Edge-Aided Reliable Data Transmission for Heterogeneous Edge-IoT Sensor Networks
## 2 Related Works
### Edge Computing for Sensor Networks  
Edge computing is a novel computing paradigm that provides a powerful solution to the resource-constrained sensor networks [13]. Edge servers are deployed in the sensor network such that the sensor nodes can offload the computational tasks to the edge servers and save much energy and computational delay. In this way, the sensor network can undertake much more complex computation tasks and much more tasks than traditional sensor networks.  
The existing works on edge computing for sensor networks mainly focus on the task offloading [17], resource allocation [16], and edge deployment [19]. In [17], the novel architecture of using edge servers to empower the low-power IoT networks is proposed. In [16], an adaptive scheme to offload the tasks from embedded devices to edge servers is proposed. The tasks are generalized as directed acyclic graphs (DAGs) and the dependency among different sub-tasks are considered. In [19],a task assignment scheme to allocate the task to the most appropriate edge servers considering the mobility of the underlying sensor nodes is proposed.
## 2 Stochastic Green's Function Method for 3D Ground Motion Simulation and Its Improvement
### Fourier Amplitude Spectra of Small Earthquakes in Subfaults
\(R_{\Phi\phi\text{m}}^{K}\) is the average radiation coefficient at high frequencies. \(R_{\Phi\phi\text{m}}^{\text{P}}\) and \(R_{\Phi\phi\text{m}}^{\text{S}}\) are taken as 0.52 and 0.63, respectively [27]. \(R_{\Phi\phi ij}^{K}\) is the theoretical radiation coefficient at low frequencies, taking the relative position relationship between the source and the observation point and the slip direction into account. The expressions are [28]:  
\[R_{\Phi\phi\text{0}ij}^{\text{P}} =\cos\lambda\sin\delta\sin^{2}i_{cij}\sin 2\big{(}\phi_{ij}-\phi_{ \text{s}}\big{)}-\cos\lambda\cos\delta\sin 2i_{cij}\cos\big{(}\phi_{ij}-\phi_{ \text{s}}\big{)} \tag{4}\] \[R_{\Phi\phi\text{0}ij}^{\text{SV}} =\sin\lambda\cos 2\delta\cos 2i_{cij}\sin\big{(}\phi_{ij}-\phi_{ \text{s}}\big{)}-\cos\lambda\cos\delta\cos 2i_{cij}\cos\big{(}\phi_{ij}-\phi_{ \text{s}}\big{)}\] (5) \[+\frac{1}{2}\cos\lambda\sin\delta\sin 2i_{cij}\sin 2\big{(}\phi_{ij}- \phi_{\text{s}}\big{)}-\frac{1}{2}\sin\lambda\sin 2\delta\sin 2i_{cij}\big{[}1+ \sin^{2}\big{(}\phi_{ij}-\phi_{\text{s}}\big{)}\big{]}\] \[R_{\Phi\phi\text{0}ij}^{\text{SH}} =\cos\lambda\cos\delta\cos i_{cij}\sin\big{(}\phi_{ij}-\phi_{ \text{s}}\big{)}+\cos\lambda\sin\delta\sin i_{cij}\cos 2\big{(}\phi_{ij}-\phi_{ \text{s}}\big{)}\] (6) \[+\sin\lambda\cos 2\delta\cos i_{cij}\cos\big{(}\phi_{ij}-\phi_{ \text{s}}\big{)}-\frac{1}{2}\sin\lambda\sin 2\delta\sin i_{cij}\sin 2\big{(}\phi_{ij}-\phi_{ \text{s}}\big{)}\]  
In Equations (4)-(6), \(\phi_{\text{s}}\) and \(\delta\) represent the strike and the dip angle of the fault plane, respectively, \(\lambda\) is the rate angle, \(\phi_{ij}\) is the azimuth angle from the center of the _ij_ subfault to the observation point, and \(i_{cij}\) is the takeoff angle, which is defined as the included angle between the direction of the seismic ray leaving the source and the vertical downward direction. Assuming that the propagation medium is homogeneous, the seismic wave travels along a straight line, and the takeoff angle refers to the angle between the line connecting the center of the _ij_ subfault and the observation point and the vertical downward direction. In order to avoid underestimating the amplitude of the simulated ground motion, the lower limit of the absolute value of \(R^{K}_{\Theta\phi 0 ij}\) is set to 0.1 [9; 10; 27].
# A Dynamic, Cost-Aware, Optimized Maintenance Policy for Interactive Exploration of Linked Data
## 2 Background
### Semantic Web Technologies  
The Semantic Web technology defines the interconnection of data points as an expressive representation of heterogeneous data sources [18]. As described by [PERSON] [19], the Semantic Web enables the machine in such a way that data can be searched, interpreted and reused. Linked Data is another important concept in the Semantic Web, enabling the machine to browse the Web of data, such as DBpedia ([[https://wiki.dbpedia.org/](https://wiki.dbpedia.org/)]([https://wiki.dbpedia.org/](https://wiki.dbpedia.org/))). Linked Data is collaboratively built from the Web corpus to represent knowledge in a structured format that greatly facilitates the sharing of information around the world. Linked Data describes the inter-connection of Web pages's _URLs_ (_Uniform Resource Locator_) therefore facilitating the linking of the data from diverse sources. The Semantic Web is mainly composed of Knowledge Bases (KBs) such as Freebase [20], DBpedia [21] and Yago [22]. These KBs represent data as Linked Data according to a predefined schema, known as Resource Description Framework (RDF). The RDF is considered the standard representation for Linked Data, where relationships are represented in the form of triples (_subject, predicate and an object_). SPARQL ([[https://www.w3.org/TR/sparql11-overview/](https://www.w3.org/TR/sparql11-overview/)]([https://www.w3.org/TR/sparql11-overview/](https://www.w3.org/TR/sparql11-overview/))) is a widely used standard query language to retrieve and manipulate data that are stored in RDF format. SPARQL is a structured query language standardized by the W3C for querying RDF triglycerides ([[http://www.w3.org/TR/rdf-sparql-query/](http://www.w3.org/TR/rdf-sparql-query/)]([http://www.w3.org/TR/rdf-sparql-query/](http://www.w3.org/TR/rdf-sparql-query/))). A SPARQL query can be further decomposed into Basic Graph Patterns (BGPs) and the results are represented as a hierarchical tree. The syntax of SPARQL query contains different and disjoint query types such as SELECT, CONSTRUCT, ASK and DESCRIBE. The Linked Data cloud also providesa SPARQL endpoint for their datasets. However, querying SPARQL endpoints is cumbersome. Due to network instability often the connection to these endpoints is temporarily lost, affecting query performance. These endpoints do not provide any information about dataset modification. Therefore, long-running data analytics applications must resubmit queries for keeping the local data cache up-to-date.
## 4 Experimental Results  
In order to assess our pipeline, we applied the described workflow to a WebXR application developed by CNR ISPC (named \"/imagine\"), comprising several virtual environments populated by generative AI content. The web application tracked anonymous spatial data on the general public during two different exhibits in Italy: ArcheoVirtual 2023 in Paestum (3 days) and TourismA 2024 in Florence (2 days). This setup was particularly interesting for our assessment for multiple reasons:  
* the physical distance between the public hub (server located in main CNR research area, in Rome) and the actual location where users performed their sessions, thus involving an internet connection for data transmission;
* the number of visitors attending both events, with exhibit spaces focused on heritage and AI;
* the opportunity to study and investigate how people respond to and interact with generative AI content, using HMDs (immersive VR and MR).