esa-sceva/satcom-chunk-collection huggingface dataset:
# Advancing Spanish Speech Emotion Recognition: A Comprehensive Benchmark of Pre-Trained Models
## 2 Related Works  
### State of the Art in Spanish SER  
Recent progress in Spanish SER has leveraged diverse datasets and methodologies with notable success. [PERSON] et al. [62] used MFCC and MS features with classifiers like RNN, SVC, and MLR on the ELRA-S0329 dataset, reaching 90.05% accuracy with RNN and MFCC+MS. [PERSON] et al. [64] applied ComParE and eGeMAPS features with SVC/XGBOOST, achieving 64.2% precision. [PERSON] et al. [65] combined CNN-1D, CNN-2D, and MLP architectures with spectral and spectrogram features, obtaining 96% accuracy, recall, and F1-score on MESD. [PERSON] et al. [63] employed DeepSpectrum with attention (DS-AM), achieving 98.4% on ELRA-S0329 and 68.3% on EmoMatchSpanishDB. [PERSON] and [PERSON] [68] reported a 90.06% weighted F1-score on MEACorpus using Late Fusion and feature concatenation. In contrast, [PERSON] et al. [66] obtained only a 40.7% F1-score on the older EmoWisconsin dataset.  
In the multimodal domain, IberLEF 2024 showcased the strength of combining audio and text. The best-performing model, proposed by BSC-UPC [67], reached an 86.69% F1-score using RoBERTa and XLSR-Wav2 Vec 2.0, outperforming text-only approaches by nearly 10 percentage points. Table 1 provides a comparative overview of these studies.  
Despite this progress, several limitations hinder the generalization and robustness of current approaches. Some studies still rely on handcrafted features such as MFCCs and spectral descriptors [62; 63; 64; 65; 66]. Others employ deep models but limit evaluation to few datasets, without considering speaker-independence or cross-corpus scenarios [67; 68]. Additionally, while some use PTMs, they often overlook in-depth analyses, such as layer-wise exploration or selective fine-tuning [67; 69; 70; 71]. These gaps highlight the need for broader benchmarking across acted, natural, and elicited Spanish corpora, and deeper PTM-based exploration to ensure consistent performance in real-world conditions.
# SDN Based Collaborative Scheme for Mitigation of DDoS Attacks
## 4 Testbed and Evaluations
### Effect of Deployment Approaches on Attack Mitigation
#### 4.3.1 Linear
We added AS-to-AS communication latency and processing delays for large size attack definitions. For AS-to-AS communication latency, we ran traceroute for arbitrary domains and took worst case estimates of 150 ms avg. delays (On average, we observed 3 to 5 router hops in any AS or ISP) using a 500 kb/s Internet connection (to simulate low available bandwidth during an ongoing DDoS). It took on avg. 137 ms to process a payload containing 100,000 IP addresses (see Section 4.4). The results generated via this setup are shown in Figure 8. In this experiment, the whole operating procedure remains the same as thoroughly described above. The results for four and eight network setup resembles the previous pattern except for the values (see Figure 8a,b). The effect of mitigation transferred from destination to the source with two intermediate networks is approx. 660 ms and with six intermediate networks it increases to approx. 2141 ms or 2.14 s.  
The larger number of Intermediate networks have proportional increase in the mitigation time. Nevertheless, our proposed framework and C-to-C protocol is lightweight with instantaneous effect. It only requires somewhere between 290 to 330 ms to process and forward attack definitions from one network to another in realistic ISP settings. Figure 8: Mitigation Effect in ISP Setting: Shows the effect of mitigation transferred from destination to the source in real ISP settings with AS-AS Latency and Processing Delays. (**a**) Two Intermediate Networks; (**b**) Six Intermediate Networks.
## 3 Method
### Basic Metric and Inner-Class Metric
#### 3.1.1 Cosine Similarity  
The cosine similarity (CS) is a classical similarity measurement method that measures the similarity between two vectors. With the increase of dimensionality, similarities based on the Euclidean distance face the curse of dimensionality and their characterization ability cannot be guaranteed. Unlike the Euclidean distance, the cosine similarity can effectively measure the relationship between high-dimensional data. The cosine similarity (CS) can be formulated as follows:  
\[CS(x,y)=\frac{x\cdot y}{\|x\|\|y\|}, \tag{4}\]  
where \((\cdot)\) denotes the dot-product of two vectors.
# A Deep Learning Approach for MIMO-NOMA Downlink Signal Detection
###### Abstract  
As a key candidate technique for fifth-generation (5G) mobile communication systems, non-orthogonal multiple access (NOMA) has attracted considerable attention in the field of wireless communication. Successive interference cancellation (SIC) is the main NOMA detection method applied at receivers for both uplink and downlink NOMA transmissions. However, SIC is limited by the receiver complex and error propagation problems. Toward this end, we explore a high-performance, high-efficiency tool--deep learning (DL). In this paper, we propose a learning method that automatically analyzes the channel state information (CSI) of the communication system and detects the original transmit sequences. In contrast to existing SIC schemes, which must search for the optimal order of the channel gain and remove the signal with higher power allocation factor while detecting a signal with a lower power allocation factor, the proposed deep learning method can combine the channel estimation process with recovery of the desired signal suffering from channel distortion and multiuser signal superposition. Extensive performance simulations were conducted for the proposed MIMO-NOMA-DL system, and the results were compared with those of the conventional SIC method. According to our simulation results, the deep learning method can successfully address channel impairment and achieve good detection performance. In contrast to implementing well-designed detection algorithms, MIMO-NOMA-DL searches for the optimal solution via a neural network (NN). Consequently, deep learning is a powerful and effective tool for NOMA signal detection.  
**Keywords:** 5G; non-orthogonal multiple access (NOMA); multiple-input multiple-output (MIMO); deep learning (DL); wireless communication +
Footnote †: journal:
## 3 Frequency Response
In slow-light modulators, on the other hand, this issue is more complicated due to the large differences between group velocities. Let us assume that \(f\) = 12.5 GHz is a critical frequency for 25 Gbps modulation. Then, for \(n_{\rm g}\) = 20, \(n_{\rm RF}\) = 2 and \(L\) = 200 \(\upmu\)m, \(\eta(f)\) is calculated to be higher than 0.95 when \(\theta_{0}\) is optimized. Thus, the phase mismatch issue is not so severe. However, if either \(n_{\rm g}\), \(L\) or \(f\) is larger than their present values, it will become a limiting factor to the frequency response; \(\eta(f)\) will be less than 0.9 when \(n_{\rm g}\)\(\geq\) 32, \(L\)\(\geq\) 340 \(\upmu\)m or \(f\)\(\geq\) 22 GHz. We measured the frequency response spectrum \(R(f,\lambda)\) by changing \(\lambda\) in steps of 0.02 nm in the LD band within the range \(\lambda\) = 1550-1558 nm. Figure 4 shows the measured results as a color plot, as well as the measured \(n_{\rm g}\) spectrum and calculated \(f_{\rm 3 dB}\) spectrum from Equations (2) and (3). \(R(f,\lambda)\) exhibits fluctuations, which are considered to reflect the \(n_{\rm g}\) spectrum, although they do not agree completely. The \(n_{\rm g}\) spectrum shows a relatively large and slow oscillation with an averaged period of 0.7 nm, and a small and fast oscillation with an averaged period smaller than 0.2 nm. This large and slow oscillation roughly corresponds to the fluctuations in \(R(f,\lambda)\) at \(f\) > 20 GHz, and therefore may correspond to the effect of the phase mismatch at higher \(f\). On the other hand,\(f_{\rm 3 dB}\) depicted by the boundary between red and yellow regions only exhibits the small and fast fluctuation, lying between 15 and 20 GHz, with the exception of three wavelengths that show \(f_{\rm 3 dB}\) = 12-13 GHz. Thus, the frequency response of this device satisfies the requirement for the 25 Gbps operation over the wide wavelength range. Figure 3: Measured \(S\) parameters: (**a**) \(S_{11}\); (**b**) EO \(S_{21}\) at \(\lambda\) = 1554 nm. Gray line shows raw data. Black line shows averaged values within each \(\sim\)0.5 GHz range.
## Preliminaries
### Isolation Kernel  
The isolation kernel is a special kernel function that uses the data isolation mechanism to design a similarity measurement method. It can increase the similarity between points in low-density areas and reduce the similarity between points in high-density areas. Different from traditional distances or kernels, isolation kernel [39; 40] has no closed expressions and is stemmed directly from a dataset without learning [41]. The similarity between the two points is measured based on the partitions created in the data space.  
A key requirement for an isolation kernel is a spatial partitioning mechanism that isolates a point from other points in the sample set. There are many kinds of spatial segmentation mechanisms, the commonly used ones are isolation forest [42; 43], Voronoi Diagram [44; 45] and isolating hyperspheres [46; 47]. The definition of isolation kernel is as follows [39; 40]: Suppose \(\mathbb{H}_{\psi}(X^{D})\) represents the set of all admissible partitions of \(H\) derived from a given finite dataset \(X^{D}\), where each \(H\) is from a random subset \(X^{{}^{\prime}}\subset X^{D}\), and each point in \(X^{{}^{\prime}}\) has an equal probability of being chosen from \(X^{D}\), and \(|X^{{}^{\prime}}|=\psi\). The role of the isolation partition \(\theta(\theta\in H)\) is to isolate the point \(z\in X^{{}^{\prime}}\) from other points in \(X^{{}^{\prime}}\) in a given random subset \(X^{{}^{\prime}}\). The union of \(\theta\) covers the entire dataset.  
Given a data space \(X^{D}\), the isolation kernel about \(X^{D}\) between any two points, \(x,y\in X^{D}\) is defined as the density distribution expectation that all \(H\), \(x\), and \(y\) belong to the same isolation partition \(\theta\), as shown in Equation (3).  
\[\mathrm{K}_{\psi}\Big{(}x,y\mid X^{D}\Big{)}=\mathbb{E}_{\mathbb{H}_{\psi}(X^ {D})}[\mathbb{I}(x,y\in\theta\mid\theta\in H)] \tag{3}\]  
where \(\mathbb{I}(B)\) is the indicator function; if \(B\) is true, it outputs 1, otherwise, it outputs 0.  
In fact, the isolation kernel \(K_{\psi}\) is computed through a partition \(H_{m}\in\mathbb{H}_{\psi}(X^{D})\)\((m=1,2,\cdots,t)\), as shown in Equation (4).  
\[K_{\psi}(x,y|X^{D})=\frac{1}{t}\sum_{m=1}^{t}\sum_{\theta\in H_{m}}\mathbb{I} (x\in\theta)\mathbb{I}(y\in\theta) \tag{4}\]  
where \(K_{\psi}(x,y)\in[0,1]\).
# Reflections on the Limited Pervasiveness of Augmented Reality in Industrial Sectors
## 2 Investigating the Causes of the Lack of Pervasiveness
### Main Factors for Lack of AR Pervasiveness  
As possible to see in Figure 4, Department/Unit not available to handle AR projects (88-92%), IT Infrastructures (85-90%), and Digitalization Time (84-87%) were considered the main factors for a lack of pervasiveness. It is valuable to stress that those three parameters underline how industrial companies are still not fully capable to support the adoption of AR technologies, due to lack of proper company units or departments that can fully devote working time on developing and maintaining these solutions. Organizational issues are important when considering industry; a similar conclusion has been highlighted in [4; 12]. In second place, a not proper IT Infrastructure (meaning AR hardware and software limitation) was also considered a main cause. Finally, the time needed for creating valuable digital information to show through the devices was considered a concrete bottle neck.  
In order to analyze the differences among the 11 factors in relation with industries and to compare whether the perceptions of employees from railway, automotive, and manufacturing sectors differ significantly, a one-way analysis of variance (ANOVA) has been conducted. The results showed that for the nine factors (Department/Unit not able to handle AR projects, Skill not present in the company to handle AR projects, IT Infrastructures, Ergonomics, Corrective Lenses/Glasses, Distraction, Internet Connection Digitalization Time) the perception of employees from different industries did not differ significantly. However, the employees' perception for two factors (Image Recognition Method, [F(2, 53) = 4.09, p = 0.022] and Responsiveness, [F(2, 53) = 10.16, p <= 0.001]) varied significantly for different industries. In Table 1 are summarized the main outcomes of the ANOVA analysis.  
The reason why 2 mentioned factors have discrepancy in the perception values can be found in the characteristics that the different industrial sectors have in affecting the deployment of AR solutions such as more/less complicated parts to recognize from a software perspective and more/less complicated setup of the operation/task to perform.  
To improve the understandability of the survey, its results, and to indicate possible measures to take, the 11 factors, already originated by a first round of grouping, have been further clustered to three final factors, and considered as overall limitations to the AR pervasiveness. As shown in Figure 5, these were the organizational limitations (accounted for 40%), the human interaction limitations (accounted for 24%), and the technological/technical limitations (accounted for 36%). With different weight, each part contributes to reduce the possible deployment of AR technologies in the industrial sectors. AR and IT in general still remain \"technology push\" in the relatively conventional operations and maintenance domains.
## 5 Experiments
### Sensitivity Analysis
#### 5.4.1 Experimental Design  
The analysis was performed on a medium-scale shop layout (comprising 10 machines and 2 charging stations) using benchmark instances MK03 and MK04 from the MK dataset. Three key hyperparameters were examined: \(N_{\text{candidates}}\), \(T_{\text{KL}}^{\text{target}}\), and \(\epsilon_{\text{clip}}\). Specifically, \(N_{\text{candidates}}\) was tested with values of 3, 5, and 7; \(T_{\text{KL}}^{\text{target}}\) with 0.1, 0.2, and 0.3; and \(\epsilon_{\text{clip}}\) with 0.1, 0.2, and 0.3, each with a clipping range of \(\pm\)0.1. While holding other parameters constant, we systematically varied one parameter at a time and evaluated performance based on the mean Optimality Deviation Rate (ODR), ODR standard deviation, and the number of training epochs required for convergence.
## 3 Results
### Results and Evaluation of Trajectory Estimation
#### 3.4.3 Trajectory Estimation in Real-World Environments  
To evaluate whether estimation is feasible in an actual indoor environment, walking trajectory estimation was conducted during hand-held walking indoors. The environment used for estimation included desks, chairs, PCs, TVs, refrigerators, etc., inside a wooden structure. The following Figure 15 shows the actual walking trajectory and the environment. The arrows indicate the order in which the trajectory is walked. By following the arrows that are not opposite to the direction of movement, return to the starting point. The thin black lines in the figure are spaced at 50 cm intervals, the thick black lines represent walls, and the gray areas indicate places where desks or shelves are located so as to prevent walking.  
Figure 14: Measurement results of geomagnetic disturbance caused by other devices (**Left**: 0.5 m; **Center**: 0.3 m; **Right**: 0.2 m).  
Figure 13: Measurement trajectory of geomagnetic disturbance caused by other devices (**Left**: 0.5 m; **Center**: 0.3 m; **Right**: 0.2 m).  
Figure 16 shows the estimated results obtained by walking along the trajectory shown in Figure 15. In the figure, \"S\" represents the starting point and \"G\" represents the estimated position when returning. The estimation results shown in Figure 16 were obtained by simultaneously estimating with (proposed method) and without (conventional method) dynamically changing \(\beta\) while using exactly the same sensor values.  
Figure 16: Estimation R = results of walking trajectory.  
Figure 15: Trajectory and environment used for estimation.  
From the results shown in Figure 16, it is evident that preventing geomagnetic disturbances caused by other devices and equipment improves the accuracy of walking trajectory estimation. This is probably because the geomagnetic values vary slightly from room to room depending on the other devices, leading to the direction gradually deviating if this variation is not properly addressed.
# Resource-Efficient Multicast URLLC Service in 5G Systems
###### Abstract  
Many emerging applications, such as factory automation, electric power distribution, and intelligent transportation systems, require multicast Ultra-Reliable Low-Latency Communications (mURLLC). Since 3 GPP Release 17, 5G systems natively support multicast functionality, including multicast Hybrid Automatic Repeat Request and various feedback schemes. Although these features can be promising for mURLLC, the specifications and existing studies fall short in offering guidance on their efficient usage. This paper presents the first comprehensive system-level evaluation of mURLLC, leveraging insights from 3 GPP specifications. It points out (i) how mURLLC differs from traditional multicast broadband wireless communications, and (ii) which approaches to provide mURLLC require changing the paradigm compared with the existing solutions. Finally, the paper provides recommendations on how to satisfy strict mURLLC requirements efficiently, i.e., with low channel resource consumption, which increases the capacity of 5G systems for mURLLC. Simulation results show that proper configuration of multicast mechanisms and the corresponding algorithms for mURLLC traffic can reduce resource consumption up to three times compared to the baseline solutions proposed for broadband multicast traffic, which significantly increases the system capacity.  
5G; multicast; URLLC; massive MIMO; MCS +
Footnote †: journal:  
1 Telecommunication Systems Lab, HSE University, Moscow 101000, Russia; [EMAIL_ADDRESS] (A.K.)
# Light Harvesting and Optical-Electronic Properties of Two Quercitin and Rutin Natural Dyes
## Results and Discussion
### Reorganization Energies  
The charge transfer performance can be evaluated according to the reorganization energy (\(\lambda\)). \(\lambda\) can be categorized into intramolecular/intermolecular reorganization energy (\(\lambda_{intra}\)/\(\lambda_{inter}\)). \(\lambda_{intra}\) has greater influence on electron transfer reaction when compared with \(\lambda_{inter}\). \(\lambda_{intra}\) can be divided into electron/hole reorganization energy (\(\lambda_{e}\)/\(\lambda_{h}\)) for different charge transport materials [62; 63]. Table 4 lists the calculated \(\lambda_{h}\) and \(\lambda_{e}\). \(\lambda_{h}\) of S-Q and S-R are S-Q (0.6861) \(<\) S-R (0.8812); \(\lambda_{e}\) of S-Q and S-R are S-Q (0.5321) \(<\) S-R (0.5932). S-Q has the smallest \(\lambda_{h}\) and \(\lambda_{e}\). Therefore, S-Q has a stronger hole transportation ability and electron transportation ability, which are beneficial for the easy photocurrent formulation of S-Q.  
\begin{table}
\begin{tabular}{c c c} \hline
**Dyes** & \(\lambda_{h}\) & \(\lambda_{e}\) \\ \hline S-Q & 0.6861 & 0.5321 \\ S-R & 0.8812 & 0.5932 \\ \hline \end{tabular}
\end{table}
Table 4: The reorganization energies of hole (\(\lambda_{h}\)) and electrons \(\lambda_{e}\) of S-Q and S-R.
## 5 Conclusions  
In this study, we have shown that the peptide sequence YHWYGYTPQN, previously known to bind to EGFR, is also capable of binding to ER\(\alpha\) and PPAR\(\alpha\), making it suitable for targeting multiple over-expressed receptors in tumor cells. We have further demonstrated that four point mutations of this peptide sequence and conjugation of both this sequence and mutated sequences to polyphenols, such as gallic acid and rosmarinic acid, can further enhance binding interactions of with the three receptors. In the case of the EGFR receptor, most of the conjugates appeared to bind to the kinase domain binding pocket region, while, in the case of ER\(\alpha\), binding occurred at the alpha-helical sandwich region of the binding pocket. Of the twenty novel peptide-polyphenol conjugates studied, those found to be most promising for ER\(\alpha\), PPAR\(\alpha\), and EGFR targeting based on molecular docking and molecular dynamics results were rosmarinate-YHWYGYTPQN, gallate- YIWYGYTPQN and gallate-FHWYGYTPQN. MMGBSA studies revealed that the lowest binding energies (therefore, the highest binding interactions) were found to be with Pep (YHWYGYTPQN) and Mut1 (FHWYGYTPQN), GLAMut1 and GLAMut2 for the EGFR receptor, while GLAMut1 also showed low binding energy with PPAR-\(\alpha\) receptor. Mut4 and CGAMut4 showed the lowest binding energies with ER-\(\alpha\). Thus, each of these peptides and conjugates can also be fine-tuned to bind to the specific receptors. ADME studies revealed that the lipophilicity was increased upon conjugation of the peptides with the polyphenols. Most of the designed compounds showed medium permeability for MDCK cells. Though all of the conjugates formed Pgp substrates, none formed CYPA1/A2 substrates or inhibitors. Overall, the polyphenol peptide conjugates (particularly GLAMut2) and (RMAPep) designed here can be potentially explored for further synthesis and exploration as drug candidates for targeting breast cancer cells. The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app12010515/s1](https://www.mdpi.com/article/10.3390/app12010515/s1)]([https://www.mdpi.com/article/10.3390/app12010515/s1](https://www.mdpi.com/article/10.3390/app12010515/s1)), Figures S1-S13: RMSDs, Trajectory images for MD simulations of Pep; the four mutated peptides as well as the selected polyphenol-peptide conjugates at 0, 25, 50, 75 and 100 ns showing interactions of the peptides with each of the receptors; as well as schematic of ligand-atom interactions with each of the receptors. Table S1--Binding energies obtained from FireDock analysis; Tables S2-S7 shows PLIP results for each of the peptides and the polyphenol-peptide conjugates.
## 4 Results and Discussion
### Results and Discussions of Attribute Bidirectional Performance
#### 4.3.2 Comparative Analysis of Attributes' Bidirectional Performance
For value, the effect of its good performance on high customer ratings is stronger than the effect of its poor performance on low customer ratings in low-star hotels, while quite the opposite is true for high-star hotels. Therefore, the  
Figure 5: The percentage of negative sentiment values concerning six attributes. Figure 6: The percentage of positive sentiment values concerning six attributes. results indicate that the effect of attribute performance on customer ratings is asymmetric, consistent with many previous studies [8; 10; 23; 24; 71; 76]. Furthermore, the asymmetric effect of values' performance on customer ratings is different between high-star and low-star hotels.