esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
The most used and well-known acoustic features of a speech signal, the Mel frequency cepstral coefficients (MFCC), cannot characterize emotions in speech sufficiently when a classification is performed to classify both discrete emotions (i.e., anger, happiness, sadness, and neutral) and emotions in valence dimension (positive and negative). The main reason for this is that some of the discrete emotions, such as anger and happiness, share similar acoustic features in the arousal dimension (high and low) but are different in the valence dimension. Timbre is a sound quality that can discriminate between two sounds even with the same pitch and loudness. In this paper, we analyzed timbre acoustic features to improve the classification performance of discrete emotions as well as emotions in the valence dimension. Sequential forward selection (SFS) was used to find the most relevant acoustic features among timbre acoustic features. The experiments were carried out on the Berlin Emotional Speech Database and the Interactive Emotional Dyadic Motion Capture Database. Support vector machine (SVM) and long short-term memory recurrent neural network (LSTM-RNN) were used to classify emotions. The significant classification performance improvements were achieved using a combination of baseline and the most relevant timbre acoustic features, which were found by applying SFS on a classification of emotions for the Berlin Emotional Speech Database. From extensive experiments, it was found that timbre acoustic features could characterize emotions sufficiently in a speech in the valence dimension.  
timbre acoustic features; valence dimension; affective computing; emotion recognition; neural networks; speech processing
## 4 Results
### Accuracy of Synthetic Data Generation
#### 4.1.2 Atrial Fibrillation  
The analysis revealed that the median values for most HRV parameters in the synthetic dataset were comparable to the real dataset. Notably, the following parameters showed significant differences between the two datasets (\(p<0.05\)), suggesting potential variations:  
* NNxx (beats): Number of successive RR interval pairs that differ more than xx ms;
* pNNxx (%): NNxx divided by the total number of RR intervals;
* Very low frequency (VLF) (Hz) AR spectrum: The overall activity of the various slow mechanisms of the sympathetic function.  
See the parameter differences in Table 4.  
\begin{table}
\begin{tabular}{c c c} \hline
**Property** & **Atrial Fibrillation (AF)** & **Cardiovascular Events (CE)** \\ \hline Column shapes & 82.05\% & 79.61\% \\ Column pair trends & 95.74\% & 96.12\% \\ Overall score & 88.89\% & 87.86\% \\ \hline \end{tabular}
\end{table}
Table 3: Statistical similarities between real and synthetic datasets of AF and CEs.
# Multi-Objective Message Routing in Electric and Flying Vehicles Using a Genetics Algorithm
###### Abstract  
With progressive technological advancements, the time for electric vehicles (EVs) and unmanned aerial vehicles (UAVs) has finally arrived for the masses. However, intelligent transportation systems need to develop appropriate protocols that enable swift predictive communication among these battery-powered devices. In this paper, we highlight the challenges in message routing in a unified paradigm of electric and flying vehicles (EnFVs). We innovate over the existing routing scheme by considering multi-objective EnFVs message routing using a novel modified genetics algorithm. The proposed scheme identifies all possible solutions, outlines the Pareto-front, and considers the optimal solution for the best route. Moreover, the reliability, data rate, and residual energy of vehicles are considered to achieve high communication gains. An exhaustive evaluation of the proposed and three existing schemes using a New York City real geographical trace shows that the proposed scheme outperforms existing solutions and achieves a 90%+ packet delivery ratio, longer connectivity time, shortest average hop distance, and efficient energy consumption.  
electric vehicles; UAVs; multi-objective optimization; genetics algorithm; EnFVs
###### Abstract  
This study identified differences in the prevalence of antibiotic resistance genes (ARGs) between wastewater treatment plants (WWTPs) processing different proportions of hospital and municipal wastewater as well as various types of industrial wastewater. The influence of treated effluents discharged from WWTPs on the receiving water bodies (rivers) was examined. Genomic DNA was isolated from environmental samples (river water, wastewater and sewage sludge). The presence of genes encoding resistance to sulfonamides (_sul1_, _sul2_) and fluoroquinolones (_qcp_A, _aac(6')-Ib-cr_) was determined by standard polymerase chain reaction (PCR). The effect of the sampling season (summer - June, fall - November) was analyzed. Treated wastewater and sewage sludge were significant reservoirs of antibiotic resistance and contained all of the examined ARGs. All wastewater samples contained _sul_ and _aac(6')-Ib-cr_ genes, while the _qep_A and _sul2_ genes occurred less frequently. These observations suggest that the prevalence of ARGs is determined by the type of processed wastewater. The Warmia and Mazury WWTP was characterized by higher levels of the _sul2_ gene, which could be attributed to the fact that this WWTP processes agricultural sewage containing animal waste. However, hospital wastewater appears to be the main source of the _sul1_ gene. The results of this study indicate that WWTPs are significant sources of ARGs, contributing to the spread of antibiotic resistance in rivers receiving processed wastewater.  
antibiotic resistance; wastewater; WWTP; ARGs; sulfonamides; fluoroquinolones +
Footnote †: journal: Applied Sciences
# Transformer Self-Attention Change Detection Network with Frozen Parameters
## 2 Methodology
### Global Spatial Attention Unit (GSAU)  
Self-attention possesses powerful capabilities for global information extraction by computing the relationships between each pixel and the other pixels in an image. However, this computation approach can lead to significant memory consumption. Furthermore, as mentioned earlier, the feature maps often contain a significant amount of noise and interference for various reasons. If a straightforward and coarse application of self-attention is used to attend to global information, it can lead to efficiency issues. Therefore, we proposethe Global Spatial Attention Unit (GSAU), which retains the global information extraction capabilities of self-attention while reducing memory consumption.  
Figure 3 shows a schematic diagram of the GSAU, where the input of the GSAU is set to \(f\in R^{B\times C\times H\times W}\). First, \(f\) is sent to the pooling layer that is globally averaged in the horizontal and vertical directions to obtain \(f_{1}\in R^{B\times C\times H\times 1}\) and \(f_{2}\in R^{B\times C\times 1\times W}\); then, we perform dimension extrusion on \(f_{1}\) and \(f_{2}\) to obtain \(f_{1}\in R^{B\times C\times H}\) and \(f_{2}\in R^{B\times C\times W}\). Then, we send \(f_{1}\) and \(f_{2}\) to two multi-head self-attention modules to obtain \(f_{3}\in R^{B\times C\times H}\) and \(f_{4}\in R^{B\times C\times W}\), and we perform dimension expansion on \(f_{3}\) and \(f_{4}\) to obtain \(f_{3}\in R^{B\times C\times H\times 1}\) and \(f_{4}\in R^{B\times C\times 1\times W}\). Then, we use the broadcast mechanism to sum \(f_{3}\) and \(f_{4}\) and multiply them by \(f\). Finally, the product is convolved with \(1\times 1\) and then added to itself to obtain the final output \(f_{out}\in R^{B\times C\times H\times W}\). The specific arithmetic expression is as follows:  
\[f_{1}=\text{MHSA}(\text{HSqueeze}(\text{AvgPool2}d(f))), \tag{4}\]  
\[f_{2}=\text{MHSA}(\text{VSqueeze}(\text{AvgPool2}d(f))), \tag{5}\]  
\[f_{c}=(\text{HUnsqueeze}(f_{1})+V\text{Unsqueeze}(f_{2}))\times f, \tag{6}\]  
\[f_{out}=\text{Conv}_{1\times 1}(f_{c})+f_{c}. \tag{7}\]  
In this context, \(\text{MHSA}(\cdot)\) represents the multi-head self-attention module, \(\text{HSqueeze}(\cdot)\) denotes the dimension compression along the vertical direction, \(\text{VSqueeze}(\cdot)\) denotes the dimension compression along the horizontal direction, \(\text{HUnqueeze}(\cdot)\) represents the dimension expansion along the vertical direction, and \(\text{VUnqueeze}(\cdot)\) represents the dimension expansion along the horizontal direction.
# Repurposing Olive Oil Mill Wastewater into a Valuable Ingredi for Functional Bread Production
## 2 Materials and Methods
### Volatile Organic Compounds
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{\(\mathbf{n}^{\circ}\)} & \multirow{2}{*}{**CAS**} & \multirow{2}{*}{
\begin{tabular}{c} **Chemical** \\ **Compounds** \\ \end{tabular} } & \multirow{2}{*}{**Samples**} & & \multirow{2}{*}{**Aroma**} & \multicolumn{2}{c}{**Ddor**} \\  & & & & & & & **\#/kg in** \\ \hline \multicolumn{8}{c}{**CTR\({}_{B}\)**} & \multicolumn{1}{c}{**EXP\({}_{B}\)-1**} & \multicolumn{1}{c}{**EXP\({}_{B}\)-2**} \\ \hline \multicolumn{8}{c}{Acids} \\
1 & 64-19-7 & Acetic acid & 1.65 \(\pm\) 0.76 & n.d. & n.d. & pungent, & 22,000– & [47] \\  & & Alcohols & & & sour & 320,000 & [48] \\
2 & 64-17-5 & Ethanol * & 20.34 \(\pm\) 0.00 & 42.62 \(\pm\) 0.17 & 42.55 \(\pm\) 0.66 & sweet & 100,000 & [48] \\
3 & 18,409-17-1 & 2-Octen-1-ol (E) * & n.d. & 0.11 \(\pm\) 0.01 & 0.13 \(\pm\) 0.01 & green & 1 & [48] \\  & & & & & bitter & & & \\
4 & 111-87-5 & 1-Octanol * & n.d. & 0.19 \(\pm\) 0.00 & 0.14 \(\pm\) 0.01 & burnt & 110–130 & [48] \\  & & & & & & & & \\
5 & 123-51-3 & 3-methyl-butanol & 19.81 \(\pm\) 0.04 & 9.36 \(\pm\) 2.4 & 18.81 \(\pm\) 0.95 & basalamic, & 250–300 & [48] \\  & & & & & & & banana, & \\
6 & 111-27-3 & 1-Hexanol & 2.55 \(\pm\) 0.32 & 2.80 \(\pm\) 0.86 & 3.96 \(\pm\) 0.16 & flower, & 2500 & [48] \\  & & & & & & grass, herb & & \\
7 & 78-27-3 & Cyclohexanol, 1-ethynyl- & 0.41 \(\pm\) 0.30 & n.d. & n.d. & odorless & & [48] \\
8 & 104-76-7 & 1-Hexanol, 2-ethyl & 0.69 \(\pm\) 0.22 & 0.31 \(\pm\) 0.02 & 0.29 \(\pm\) 0.03 & rose, green & - & [48] \\
9 & 60-12-8 & Phenylethyl Alcohol * & 8.55 \(\pm\) 0.00 & 4.09 \(\pm\) 0.93 & 4.58 \(\pm\) 0.46 & lilac, rose, wine & 750–1100 & [48] \\
10 & 40,642-37-3 & Z-4-dodecenol & 0.41 \(\pm\) 0.11 & 0.41 \(\pm\) 0.11 & 0.27 \(\pm\) 0.05 & waxy & & [48] \\
11 & 513-85-9 & 2,3-Butanediol & n.d. & 1.49 \(\pm\) 2.02 & n.d. & fruit, onion & - & [48] \\
12 & 78-83-1 & Isobutyl alcohol & n.d. & n.d. & 1.95 \(\pm\) 0.75 & apple, bitter, & 7000 & [49] \\
13 & 4798-44-1 & 3-Hexen-1-ol & n.d. & n.d. & 0.58 \(\pm\) 0.04 & green & 70 & [48] \\  & & & & & & apple, & & \\
14 & 123-92-2 & Isoamyl acetate & n.d. & n.d. & 0.22 \(\pm\) 0.03 & banana, & 2 & [49] \\  & & & & & & pear (fruity) & & \\  & & & & & & & & \\  & & & & & & & & \\
15 & 66-25-1 & Hexanal & 2.39 \(\pm\) 1.09 & 1.10 \(\pm\) 0.68 & 1.10 \(\pm\) 0.03 & apple, fat, & & \\  & & & & & & & & fresh, green, & oil & 4.5–5 & [48] \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Volatile aromatic compounds expressed as a percentage of three replicates expressed as (peak area of each compound/total area of significant peaks) \(\times\) 100, and odor descriptors identified in \(B\) process breads, produced using a biga-like fermentation agent.
## 3 Results and Discussion
### Discrimination of Different Human Skin Tissues Using Vectorial Retardance
#### 3.2.1 Quantitative Classification
This is caused by factors such as the datasets, the feature selection of the datasets, model selection, kernel parameters, etc. [72]. Additionally, it can be found that the accuracy increases progressively as more retardance components are combined. In particular, classifier VIII based on three components (i.e., the vectorial retardance) shows the most attractive prediction accuracy of 96.19% in distinguishing MM, BE, and normal skin. In the future, we will prepare more samples, choose more valuable feature quantities combined with feature selection, establish different SVM models, or \begin{table}
\begin{tabular}{c c c c} \hline \hline
**SVM Classifier** & \begin{tabular}{c} **Retardance-Related** \\ **Parameters** \\ \end{tabular} & \
\begin{tabular}{c} **Cross-Validation** \\ **Accuracy** \\ \end{tabular} &
\begin{tabular}{c} **Prediction** \\ **Accuracy** \\ \end{tabular} \\ \hline Classifier I & \(R_{\text{H}}\) & 82.80\% & 81.64\% \\ Classifier II & \(R_{\text{45}}\) & 87.40\% & 84.30\% \\ Classifier III & \(R_{\text{C}}\) & 87.20\% & 85.92\% \\ Classifier IV & \(R\) & 88.33\% & 87.92\% \\ Classifier V & \(R_{\text{H}},R_{\text{45}}\) & 95.20\% & 84.11\% \\ Classifier VI & \(R_{\text{H}},R_{\text{C}}\) & 95.33\% & 82.97\% \\ Classifier VII & \(R_{\text{45}},R_{\text{C}}\) & 96.27\% & 85.16\% \\ Classifier VIII & \(R_{\text{H}},R_{\text{45}},R_{\text{C}}(R)\) & 98.60\% & 96.19\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The performance of eight SVM classifiers in distinguishing the three skin tissues. compare different machine learning methods to investigate the value of different retardance components. Currently, many statistical methods have been developed for diagnosing and classifying several diseases, including SVM [62; 73; 75], k-nearest neighbors (KNNs) [76], artificial neural networks (ANNs) [84], random forest (RF) [76; 85], naive Bayes classifier models [78; 86], convolutional neural networks (CNNs) [87], linear discriminant analysis (LDA) [88], etc. In this paper, the SVM has provided an appealing result, which is consistent with most reported on other disease classifications [73; 74; 75; 76]. More importantly, the results validate that vectorial retardance can visualize more useful details than scalar retardance. Although the accuracy provided by the SVM method is highly acceptable (\(\sim\)96.19%), we will compare the SVM method with other machine learning (ML) methods for the classification of the three types of skin tissues to further improve the accuracy in the future.
## 5 Results and Discussion
### Estimation of Sensor Values
#### 5.1.1 MAP Estimation
PSO and NLR produce nearly the same results. Moreover, the SEE value for NLR is 0.01 as compared to 0.02 and 0.024 in PSO and GA, respectively. It proves the higher accuracy of NLR over PSO and GA. To further justify the claim of the effectiveness of NLR, the optimization times are also calculated by using MATLAB, and it can be noted from Table 5 that the optimization time of NLR is shorter (0.46 s) when compared to PSO (6.71 s) and GA (4.25 s), which makes the NLR useful for time-sensitive applications and controllers with less computation power. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Throttle Angle**} & **Actual MAP** & \multicolumn{4}{c}{**Estimated MAP Values**} \\ \cline{3-5}  & **Value** & **PSO** & **GA** & **NLR** \\ \hline  
\hline \end{tabular}
\end{table} 
Table 4: Estimated MAP values with PSO, GA, and NLR. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **PSO** & **GA** & **NLR** \\ \hline Residual Sum of Squares (RSS) & 0.007 & 0.010 & 0.003 \\ Mean Square Error (MSE) & 0.0004 & 0.0006 & 0.0002 \\ \hline Coefficient of Determination (R\({}^{2}\)) & 0.99 & 0.99 & 0.99 \\ \hline Adjusted R\({}^{2}\) & 0.96 & 0.92 & 0.98 \\ \hline Standard Error of Estimation (SEE) & 0.02 & 0.024 & 0.01 \\ \hline Optimization Time (t) in seconds & 6.71 & 4.25 & 0.46 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Estimation statistics for MAP estimation.
###### Abstract  
Screening natural products for bacteriocin-producing bacteria may be the equilibrium point between the consumer demand for mild processing and the industry's need for hazard control. Raw unprocessed honeycombs filled with oregano honey from the alpine mountainous territory of Epirus, Greece were screened for bacteriocin lactic acid bacteria and _Bifidobacterium_ spp., with inhibitory action towards some pathogens and spoilage microorganisms isolated from fresh fruits and vegetables (number and type of strains: three _E. coli_, two _L. monocytogenes_, two _Salmonella_ spp., two _B.cereus_, two _Erwinia_ spp., _L. imocua_ (ATCC 33090 TM) and _E. coli_ 0157:H7 (ATCC 69373)). Among the 101 collected isolates (73 _Lactobacillus_, _8 Lactococcus_, _8 Leucostoc_ and 12 _Bifidobacterium_ species) from the oregano honeycombs (an original finding since there are no other reports on the microbial biodiversity of the flora of the oregano honey), 49 strains of lactic acid bacteria (LAB) and _Bifidobacterium_ spp. were selected and tested for their bacteriocin-producing capacity (34 _Lactobacillus_, 6 _Lactococcus_, 5 _Leucostoc_ and 4 _Bifidobacterium_). The antibacterial activity exerted by the tested LAB and _Bifidobacterium_ strains was not of the same potency. Our results suggest that the main molecules involved in the antimicrobial activity are probably bacteriocin-like substances (a conclusion based on reduced antibacterial activity after the proteolytic treatment of the cell-free supernatant of the cultures) and this antimicrobial activity is specific for the producing strains as well as for the target strains. The spoilage bacteria as well as the reference microorganisms showed increased resistance to the bacteriocin-like substances in comparison to the wild-type pathogens.  
Keywords:LAB; _Bifidobacterium_; BLS; fruits; vegetables; Oregano honey +
Footnote †: journal: Applied Sciences
# A Semantic Segmentation Dataset and Real-Time Localization Model for Anti-UAV Applications
## 3 Anti-UAV Semantic Segmentation Dataset
### Labels Generation  
The sampled image-label pairs are originally in object detection format, where labels are bounding boxes defined by \((x,y,w,h)\). Here, \(x\) and \(y\) represent the coordinates of the bounding box's top-left corner, while \(w\) and \(h\) denote the width and height of the object, respectively. However, semantic segmentation requires pixel-level masks as labels for model training. Therefore, converting bounding boxes into segmentation masks is necessary.  
In this work, we utilize the segment anything model (SAM) [38] provided by Ultralytics [39] to generate the segmentation masks for the UAV. The workflow of the mask generation process is shown in Figure 2 to generate segmentation masks for the UAVs. Figure 2 illustrates the workflow of this mask generation process. It begins by loading each sampled image and its corresponding bounding box. Next, the SAM2 model [40] is loaded via the Ultralytics library and used to predict masks.  
The loaded SAM2 model performs inference using the RGB image and bounding box as inputs. The model outputs an instance segmentation mask that identifies objects within the bounding box as the object of interest, while treating regions outside the box as background. Since SAM2 produces instance segmentation masks, we convert these into semantic segmentation masks by mapping all pixels to either 0 or 1, reflecting our binary classification task: UAV versus background. Specifically, pixels labeled 0 represent the background, and pixels labeled 1 represent the UAV.  
In the final semantic masks, pixel values are exclusively 0 or 1 to represent background and UAV classes, respectively. In images containing multiple UAVs, all UAV pixels are  
Figure 1: Example images sampled from various dataset sources. (**a**) RGB image pair from the Anti-UAV dataset. (**b**) IR image pair from the Anti-UAV dataset. (**c**) RGB image from the DUT-Anti-UUV dataset. (**d**) RGB image from the randomly sourced images.  
labeled 1, maintaining the binary mask format. Figure 3 shows examples of original images and their corresponding generated semantic segmentation masks.  
Figure 3: Sample original images and generated UAV masks. (**a–c**) Original images randomly sampled from the dataset. (**d–f**) Generated semantic segmentation masks from the proposed label conversion process.  
Figure 2: Workflow of semantic segmentation mask generation from object detection labels.
# Exploiting Outage and Error Probability
## 5 Conclusions and Future Work  
In this paper, incremental relaying with cooperative retransmission protocols; ACE and E-ACE, have been proposed for UWSNs along with outage and error probability performance analysis. Closed-form expressions for outage and error probability are determined and expression for the number of available relays is also derived. Results show that incremental relaying with triple retransmissions shows very less outage probability and so is BER as compared to regular incremental relaying cooperative diversity network having single retransmission mechanism. The proposed model and protocols are validated via simulations. We evaluated the proposed schemes by comparing them with another cooperative protocol called CARQ and non cooperative routing protocol referred as EEDBR. ACE and E-ACE substantially show better relative performance in terms of reliability and throughput efficiency. This improvement is likely due to the cooperative diversity benefits and the retransmission mechanism. However, this reliability is achieved at the cost of increased energy consumption which leads to decreased network lifetime. Future works include derivation of expression capacity of incremental relaying with \(m\) retransmissions. In addition, expressions for outage, BER and capacity using DF incremental relaying cooperative retransmissions will also be derived.  
This research work assumed non-LOS communication between static nodes, so, in future, we will extend our work for LOS communication between mobile nodes.  
The authors would like to extend their sincere appreciation to the Deanship of Scientific Research at King Saud University for funding this Research group No. RG-037-1435.  
[PERSON] and [PERSON] proposed the protocols. [PERSON] performed and wrote simulations. [PERSON], [PERSON] and [PERSON] did modelling work. [PERSON] and [PERSON] wrote and refined the manuscript.  
The authors declare no conflict of interest.
# A Comprehensive Evaluation Model for Optimizing the Sensor Array of Electronic Nose
## 5 Discussion
### Effect of Sensor Array Optimization on Recognition Accuracy of E-nose  
In order to investigate the influence of sensor array optimization on the recognition accuracy of the E-nose, this study established a gas recognition model using support vector machines (SVM), which selected RBF as the kernel function, and the hyperparameters C and gamma were determined through a network parametric search method. The experiment was carried out on the gas features extracted from the 90 CO-CH4 gas mixtures, in which 80% of the samples were randomly selected as the training dataset and 20% of the samples were used as the verification dataset, for which a fivefold cross-validation experiment was carried out. The classification accuracy of the nine mixed gases was taken as the recognition ability of the SVM model. Figure 13 shows the recognition accuracy before and after the  
Figure 12: PCA analysis of gas features extracted by the E-nose with the sensor array optimized based on EWM-TOPSIS model.  
sensor array optimization. It can be seen that after preliminary optimization based on the four single evaluation indicators, the highest recognition accuracy of the E-nose could reach to 93.3%, which was significantly improved compared with the 78.3% of the initial sensor array. After optimizing the sensor array through the EWM-TOPSIS model, the recognition accuracy of the electronic nose could be further improved to 96.5%. This proved that the comprehensive evaluation model could improve the recognition accuracy and was better than the single evaluation indicator in optimizing the sensor array for the E-nose.