esa-sceva/satcom-chunk-collection huggingface dataset:
# Audio Anti-Spoofing Based on Audio Feature Fusion
## 4 Results
### Comparison with Models That Do Not Use the Pre-Trained Model  
As shown in Table 2, in this study, we used the experimental results provided by the ASVspoof 2021 LA partition to compare our model with the baseline models, including Rawnet2 [36], LFCC-LCNN, CQCC-GMM, and LFCC-GMM. Among these models, LFCC-LCNN had the best performance, with an EER of 8.90%, while the worst model, LFCC-GMM, had an EER of 21.13%. Our proposed model showed significant improvements compared to LFCC-LCNN and LFCC-GMM, indicating good performance on the ASVspoof 2021 LA partition.  
AASIST [29] is a model that performed well in the ASVspoof 2019 LA partition. It applies heterogeneous graphs for synthetic speech recognition and has the characteristics of few parameters and a high recognition accuracy. Due to the excellent performance of AASIST in the ASVspoof 2019 LA partition, many current studies, including SASV [39],  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Partition** & **EER (\%)** & **Min t-DCF** \\ \hline LA (3) & 3.71 & 0.2880 \\ \hline LA (5) & 1.18 & 0.2171 \\ \hline DF (3) & 2.62 & \\ \hline DF (5) & 4.72 & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Our model obtained these results using two different Rawboost data augmentation methods, mode 3 and mode 5, on LA and DF partitions, respectively.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **EER (\%)** & **Min t-DCF** \\ \hline SSL\_Anti-spoofing [13] & 0.82 & 0.2066 \\ \hline ours & 1.18 & 0.2171 \\ \hline T23 [8] & 1.32 & 0.2177 \\ \hline wav2+FF layer [37] & 3.54 & 0.2780 \\ \hline fusion [38] & 4.66 & 0.2882 \\ \hline wav2+LLGF [12] & 7.18 & 0.3590 \\ \hline LFCC-LCNN & 8.90 & 0.3152 \\ \hline Rawnet2 [36] & 9.49 & 0.4192 \\ \hline AASIST [29] & 11.47 & 0.5081 \\ \hline CQCC-GMM & 15.80 & 0.4948 \\ \hline LFCC-GMM & 21.13 & 0.5836 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The results of our model and other studies on the ASVspoof 2021 LA partition.  
use AASIST as the baseline. However, the EER of AASIST on the ASVspoof 2021 LA task was 11.47%, which is much worse than our model's performance.  
According to the results on the official website of ASVspoof 2021 LA, the best result was achieved by team T23 [8], with 1.32%, and our model also outperformed this result.
## 2 Material and Methods
### Experimental Set Up
#### Application of Tested Compounds  
Cattle manure was collected from an organic farm in Ham-Sud, Quebec, at the end of June 2009. Before the start of the experiment, all lysimeters were irrigated, bringing them to saturation. The drainage pipe at the bottom of each lysimeter was left open throughout the study. The next day (Day 0), with the lysimeter's soil at field capacity, homogenized manure (10 mg ha\({}^{-1}\)) was applied to the surface of all lysimeters and manually mixed into the top 30 mm of soil [37]. Metribuzin was then sprayed on to each lysimeter's soil surface (1 July 2009) at the locally recommended rate for potatoes (1 kg a.i. ha\({}^{-1}\)). Twelve liters (70 mm) of irrigation water, containing one of three different concentrations of Brij35 (0, 0.5 or 5.0 g L\({}^{-1}\)) was applied on day 0, day 21, day 42, day 63, according to recommended irrigation practices for potatoes. Each treatment was replicated three times. While these surfactant concentrations may appear to be high, they are well within concentrations found in wastewater and industrial wastewater. Indeed, concentrations of up to 10 g L\({}^{-1}\) have been used to study the effect of surfactants on atrazine movement in soils [38].
## 3 Materials and Methods
### Machine Learning Models
#### 3.1.1 Random Forest  
RF was introduced by [PERSON] [29]. In the RF model, \(N\) trees are constructed by the model using four-step iteration. In the first step, the bootstrap dataset is used for training purposes which is a part of the real dataset. The second step includes tree construction and the third step involves random selection of attributes. At the last step, the result is finalized using majority voting [30]. Equations (1) and (2) present the workflow of RF.  
\[p=modeT_{1}(y),T_{2}(y),\dots,T_{m}(y) \tag{1}\] \[p=mode\sum_{m=1}^{M}T_{m}(y) \tag{2}\]  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Ref.** & **Methods** & **Dataset** & **Contribution** & **Limitations** \\ \hline
[12] & SVM-KNN-PSO & KDD 99 & Ensemble model using a weighted algorithm for high accuracy. & Multi-class problem is not handled \\
[13] & SC4 ID algorithm & UNM \& ADFA-LD & Abnormal system calls by a novel algorithm with higher accuracy & Attack detection problem with sub-sequences attacks. \\
[14] & SVM-ANN & NSL-KDD & Hybrid model, high performance & Computational complexity \\
[18] & Deep learning & KDD-CUP 99 \& NSL- & Deep learning with robust results & Low performance for R2L attacks \\
[19] & MDRA & KDD-CUP 99 & Real-time attack detection & Low dimensional data cause errors \\
[20] & Sparse autoencoder & CICIDS 2017 & Uses trigonometric simplexes & Sparsity constraints \\
[21] & Memetic & NSL-KDD \& KDD99 & PSO with higher accuracy & R2L attacks have a low detection accuracy \\
[22] & SVM & NSL-KDD & The logarithmic marginal density ratio & Configuration for different datasets is difficult \\
[23] & MSML & KDD-CUP 99 & Multi-level intrusion detection & Optimization for unknown pattern discovery \\
[16] & MINDFUL & KDD-CUP 99, UNSW-NB 15, CICIDS 2017 & Multi-channel for deep feature learning & Class imbalance leads to lower accuracy \\
[17] & Deep hierarchical & NSL-KDD \& UNSW-NB15 & Data balancing using SMOTE & High training time needed \\
[15] & PCA-LDA-SVM & KDD-CUP 99 & Dimensionality reduction & Principal component selection for non-Gaussian \\
[28] & DT-RFE & KDD-CUP 99 \& NSL-KDD & Stacked approach & U2R has low accuracy \\
[25] & Q learning & CRAWDAD & Federated jamming & Asynchronous communication \\
[26] & DT, KNN, NB \& DNN & KDD-CUP 99, open-stack cloud & Socket programming and Open-Stack firewall & Limited to detection of a small range of DDoS \\
[27] & Fuzzy logic & DDoS attack (T-shark) & Dynamic DDoS attack detection & Manual setting of iterations for T time \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparative analysis of the existing approaches.  
where \(p\) is the final prediction which is calculated by of trees, \(T_{1}\), \(T_{2}\), and \(T_{m}\) using majority votes [31].
## 2 Related Works
### Data Prefetching, Caching, and Data Replication  
Data prefetching or caching are techniques that are highly used mostly in databases [12; 13] in order to boost the execution performance by fetching data in the memory before it is needed. These methodologies are widely used in database systems or the prefetching of small-sized data such as websites [14; 15] or Internet of Things (IoT) data [16].  
More precisely, in [14], an implementation of caching and prefetching algorithms in a clustered network is proposed that will improve the retrieval of web objects. In a similar manner, [PERSON] et al. in [15] proposed a prefetching scheme in mobile cloud computing infrastructures enriched with machine learning capabilities. A set of machine learning algorithms are used, such as C4.5 (and its implementation in WEKA, the algorithm J48), naive Bayes, and random trees that generate decision trees which cluster the users' objectsand predict which one will be requested. Prefetching and caching methodologies are also used in data derived from IoT devices, as also presented in [16]. In more detail, the authors proposed a graph prefetching framework named Grap which analyzes a building's topologies and prefetches location-related IoT data for the user without knowing the user's destination during navigation.  
While prefetching works great with files that are smaller in size, when it comes to large files that cannot be manipulated or stored even shortly in the cache, other technologies such as data replication [17] are used instead. Several works exist that tried to utilize this paradigm and adopt it in the cloud and web services domains. To begin with, the authors in [18] proposed a data replication strategy in order to identify the correlation of the data files stored in the cloud using historical data, predicting which data were going to be requested and replicating it in order to reduce transmission delays. In more detail, the proposed data replication strategy in [18] identifies groups of popular files within the cloud infrastructure, namely files with strong correlations among them, and replicates them in the requested site, thus improving both the access time and the bandwidth usage overall.  
In a similar manner, the authors in [19] proposed another correlation-aware replication strategy based on a rule-based management system identifying the data to be replicated. They identified high-valued files (i.e., files that were more likely to be requested), and based on this, they identified which files should be replicated in a prefetch pool. In addition, if the storage of the prefetch pool is not enough, a decision is made based on the abovementioned values in order to remove files already existing in the prefetch pool to free space for the new replicas.
# Space-Time Prediction of PM\({}_{2.5}\) Concentrations in Santiago de Chile Using LSTM Networks
## 7 Conclusions  
In this work we have used the LSTM recurrent network for a prediction of PM\({}_{2.5}\) concentration levels for the city of Santiago de Chile with 11 meteorological stations. By using a recurrent model composed of space-time pollutant and meteorological data, we were able to predict the concentrations of PM\({}_{2.5}\) in Santiago during the next hour and the next 24 h, reaching values of 0.74 and 0.38 for average \(R^{2}\) when considering 7 and 11 stations, respectively. This research shows that the task of spatio-temporal prediction of PM\({}_{2.5}\) pollutant concentration is a more difficult task than typical temporal forecasting because the historical data of the target station are not used in the input data of the training process of the model.  
As future work, we propose to improve the prediction quality of these networks by increasing the number of training variables and looking for the relationship that exists in each of the monitoring stations through the distance and positioning of each of these. We also plan to consider different input variables, such as the outputs of meteorological models. Finally, the use of new attention-based architectures or the use of spatial information through convolutional networks stacked with recurrent networks could be proposed for further improving the results. In conclusion, we think that the proposed model constitutes a new approach for space-time pollution prediction that could be used by governmental decision makers for implementing restrictive measures which could prevent negative effects on the human health.  
**Author Contributions:** Conceptualization, B.P.; methodology, T.S., B.P. and O.N.; software, T.S. and B.P.; validation, T.S., O.N. and L.C.; formal analysis, O.N.; investigation, T.S., B.P. and O.N.; resources, T.S.; data curation, B.P. and T.S.; writing--original draft preparation, T.S.; writing--review and editing, B.P., O.N. and L.C.; visualization, T.S. and B.P.; supervision, B.P. and O.N.; project administration, B.P. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
**Information Consent Statement:** Not applicable.  
**Data Availability Statement:** The dataset is available from: [[https://ldrv.ms/u/stAnkU8l4](https://ldrv.ms/u/stAnkU8l4) kGM9]([https://ldrv.ms/u/stAnkU8l4](https://ldrv.ms/u/stAnkU8l4) kGM9) wlhwy22 fexCGzHBPp?e=ZQ9 Hdq, accessed on 1 January 2020.  
**Acknowledgments:** [PERSON] appreciates the support of the National Center for Artificial Intelligence CENIA FB210017, Basal ANID. [PERSON] appreciates the support of the Research Center for Integrated Disaster Risk Management (CIGIDEN), ANID/FONDAP/15110017 and the ANID-FONDECYT 1201478.  
The authors declare no conflict of interest.
## 4 Results
### F-N Curve Assessment of Case Studies
In addition, the curves for these cases (noHOE) gradually move upward from TD01 to TD04, indicating that as the number of active systems is reduced; the system becomes risker for no human intervention case. Also, for all cases in TD16 (no active system), the F-N curve remains above the tolerability limit (Supplementary Material--Figures S1-S10). For partial or standalone active systems (TD5 to TD15) again, parts of F-N curve remain above tolerability limits prescribed by ABCB and PD7974-7:2019 resulting in unacceptable societal risks. * By and large, risk is lower for cases where stairwells and active safety systems are present. The lowest risk levels were realized in Case #1 and #5 (with two stairwells and installation of sprinklers, smoke detection, and BOWS) and Case #2 (with single stairwell, sprinklers, smoke detection, and BOWs). Risk levels did not exceed the tolerability limit in these cases when HOEs were included. In cases (#1, 2, and 5) risk only increased by 20% for with and without HOEs (nearly half that of Cases #4 and #7). * To lower the curve to an acceptable ALARP region, additional fire safety measures or alternatives are required. So, for Case #4 where HOEs edged the F-N curve towards the tolerability limit and also Case #7, additional fire safety measures. However, if the installation of further fire safety features is no longer available, alternatives might be to improve the reliability of active systems, accept a lower reliability interval or redesign the building plans to introduce passive means to improve tenability. * As all cases fall within the ALARP region, Cost-Benefit Analysis (CBA) will need to be carried out  
The CBA would use disproportionality factor, \(D\) set as 1 per PD7974-7:2019 guidelines [6], however, this is beyond the scope of this paper.
# Effects of Epigallocatechin-3-O-Gallate on Bone Health
###### Abstract  
Tea is one of the most consumed beverages in the world, belonging to the category of compounds known as tannins and flavonoids. One of the polyphenols found in large amounts in green tea leaves (_Camelia sinensis_) is epigallocatechin-3-O-gallate (EGCG). Though EGCG has shown some pharmacological effects, to date, it has not been utilised as a therapeutic agent. This is attributed to the fact that EGCG lacks adequate stability, and it is known to degrade through epimerization or auto-oxidation processes, especially when it is exposed to light, temperature fluctuations, some pH values, or the presence of oxygen. Consuming green tea with EGCG can alleviate the effects of bone diseases, such as osteoporosis, and support faster bone regeneration in the case of fractures. Therefore, this review focuses on the current state of research, highlighting the effects of EGCG on bone biology, such as enhancing osteoblast differentiation, promoting bone mineralisation, improving bone microarchitecture, and inhibiting osteoclasts through the modulation of the RANK/RANKL/OPG pathway. Additionally, EGCG exerts antioxidant, anti-inflammatory, and dose-dependent effects on bone cells. It also downregulates inflammatory markers (TNF-\(\alpha\), IL-1B, and COX-2) and reduces oxidative stress via the inhibition of reactive oxygen species generation and the activation of protective signalling pathways (e.g., MAPK and NF-\(\kappa\)B). Studies in animal models confirm that EGCG supplementation leads to increased bone mass and strength. These findings collectively support the further exploration of EGCG as an adjunct in the treatment and prevention of metabolic bone diseases. The authors aim to present the relationship between EGCG and bone health, highlighting issues for future research and clinical applications.  
bone health; drug delivery systems; (-)-epigallocatechin-3-O-gallate (EGCG); green tea catechins (GTC); green tea extract (GTE) +
Footnote †: journal: Applied sciences
# Text Augmentation Using BERT for Image Captioning
## 4 Results and Discussion
### Quantitative Analysis  
We selected several examples of captions generated by the resulting models on a test data. They are presented in Figure 4. Here, \"Ground truth\" denotes a real human-generated capture from a test set used in a quality measuring. \"Original\" denotes a caption that is generated by a model trained on an original dataset without augmentation. We can see that augmentation helps the model trained  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Augmentation** & **BLEU-4** & **METEOR** & **ROUGE-L** & **CIDEr** & **SPICE** \\ \hline No augmentation & **38.3**[1] & 28.6 & 58.0 & 126.1 & 22.6 \\ BERT, \(d=2,p=0.1\) & 37.8 & **28.9** & **58.3** & **128.8** & **22.8** \\ BERT, \(d=2,p=0.3\) & 37.3 & 28.5 & 57.8 & 125.4 & 21.9 \\ BERT, \(d=2,p=0.5\) & 37.3 & 28.8 & 58.0 & 126.5 & 22.3 \\ BERT, \(d=3\) & 37.9 & 28.6 & 57.9 & 127.2 & 22.4 \\ Synonyms, \(d=2\) & 37.7 & 28.7 & 57.8 & 127.4 & 22.2 \\ \hline \hline \end{tabular}  
* The best result for each metric is marked with bold.  
\end{table}
Table 1: Evaluation results of the [40] model trained with our augmentation methods.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline  & **BLEU-4** & **METEOR** & **ROUGE-L** & **CIDEr** & **SPICE** \\ \hline \(M^{2}\) Transformers [40], no augmentation & 38.3 & 28.6 & 58.0 & 126.1 & 22.6 \\ \(M^{2}\) Transformers, augmentation & 37.8 & 28.9 & 58.3 & 128.8 & 22.8 \\ \hline AoANet [41], no augmentation & 38.2 & 28.6 & 57.9 & 125.9 & 22.3 \\ AoANet, augmentation & 37.6 & 28.8 & 58.0 & 128.4 & 22.5 \\ \hline X-LAN [42], no augmentation & 38.8 & 28.9 & 58.2 & 126.1 & 22.8 \\ X-LAN, augmentation & 37.9 & 29.2 & 58.3 & 128.6 & 22.9 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Evaluation results of all of three tested models trained with our augmentation methods.  
Figure 3: Validation scores per epochs during a training phase oon a dataset with BERT augmentation with \(d=2\) and \(d=5\).  
on augmented datasets to construct more elegant and rich sentences than the model trained on an original dataset.  
Additionally, some examples of augmentation of the original captions are presented in Figure 5. Here, we can see that both augmentation methods using synonyms and BERT can diversify the captions adding a model potential to learn more complex and general ideas about textual description of the image. Additionally, since augmentation does not take into account the content of the image itself sometimes the augmented captions do not reflect the essence on the image well enough. This simulates the noise that may be present in the descriptions that are created by humans. Although augmentation is not perfect, in general captions are similar to the ground truth ones.
# Multiband and Lossless Compression of Hyperspectral Images
## 3 Lossless Multiband Compression for Hyperspectral Images (LMBHI)
### Review of the 2-D Linearized Median Predictor (2D-Lmp)  
The _2-D Linearized Median Predictor_ (2D-Lmp) [21] uses a prediction context that is composed by three neighboring pixels of \(X\), namely, \(I_{A}\), \(I_{B}\), and \(I_{C}\), as shown in Figure 1. In particular, the predictive structure is derived from the well-established _2-D Median Predictor_, which is used in JPEG-LS [22]. The 2-D Median Predictor has the following predictive structure outlined in the Equation (2).  
\[\hat{X}=\begin{cases}\max\left(I_{A},I_{B}\right)&if\ I_{C}\geq\min\left(I_{A},\ I_{B}\right)\\ \min\left(I_{A},\ I_{B}\right)&if\ I_{C}\leq\max\left(I_{A},\ I_{B}\right)\\ I_{A}+I_{B}-I_{C}&otherwise\end{cases} \tag{2}\]  
Basically, Median Predictor is in charge of selecting one of the above three options, depending on the context. By combining all the three options, it is possible to obtain the predictive structure of 2D-LMP, defined as in the Equation (3).  
\[\hat{X}=\frac{2\cdot\left(I_{A}+I_{B}\right)}{3}-\frac{I_{C}}{3} \tag{3}\]  
Figure 1: The prediction context of the 2D-LMP predictive structure. The gray part is already coded and the white part is not coded yet.
# Void Avoiding Opportunistic Routing Protocols for Underwater Wireless Sensor Networks: A Survey
## 5 Open Issues and Challenges in UWSNs  
* Energy efficiency: Due to the harsh underwater environment restrictions and limitations on recharging or replacing the deployed sensor node, energy efficiency is a major constraint that can restrict many applications from achieving their goals. The current focus of study is on energy conservation and routing process energy optimization. Future studies will continue to focus heavily on this topic.
* Channel utilization: effective and efficient channel usage is a significant area of investigation in UWSNs that has attracted a lot of attention; it has an effect on energy consumption and void areas that form easily in UWSNs. The channel must be used to its full potential to overcome its limitations, like interference, high error rates, continual sensor node mobility, and propagation latency.
* Void areas: Due to the reasons listed in Section 2.2, void areas result in more frequent packet drops, decreasing the network QoS. To ensure a high QoS for various UWSN applications and increase network reliability, more mechanisms to handle void areas and void communications effectively and establish trust for user apps are required.
* Security: A significant area of concern is the security of data exchanged between sensor nodes. The security of data is the most crucial issue in many UWSN applications, especially the military ones. In such applications, any information leak could lead to harmful effects and severe repercussions. To secure the connection between the sensor nodes in UWSNs as attacks and threats grow, investigations that consider security and privacy will be a continuing and extremely difficult effort.  
These are some of the most significant and active fields of research for UWSNs that need more investigation, and they will remain so in the upcoming years.