esa-sceva/satcom-chunk-collection huggingface dataset:
# ATOSE: Audio Tagging with One-Sided Joint Embedding
## 4 Experiments
### Additional Mixed Experiment and Further Analysis  
We conducted experiments with datasets with both high inter-tag correlations and zero correlations. Naturally, it would be expected that we would also investigate the case of intermediate tag correlations. However, due to the limited availability of public audio datasets, we chose to conduct our experiments using the DCASE 2017 dataset. Despite not being an ideal fit for the intermediate case, it is one of the more frequently used datasets in this field.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Feature Extractor** & \multicolumn{2}{c}{**Accuracy**} \\ \cline{2-3}  & **Original** & **With JE** \\ \hline SampleCNN & 0.9557 & 0.9483 \\ SampleCNN (+se) & 0.9595 & 0.9537 \\ SampleCNN (+res) & 0.9635 & 0.9476 \\ SampleCNN (+res) & 0.9580 & 0.9312 \\ HarmonicCNN & 0.9635 & 0.9695 \\ Tag-SincNet & 0.9421 & 0.9038 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Performance comparison with Speech Command dataset.  
Figure 3: The correlation of tags in the Speech Commands dataset.  
DCASE 2017 is a challenge dataset that was used for a sound event detection task in the Detection and Classification of Acoustic Scenes and Events workshop in 2017 [24]. The dataset was built using a part of Google's AudioSet [25], which contains about 53,000 audio files each 10 s long classified into 17 categories of sound events, such as warning sounds and vehicle sounds. We used the F1-score as an evaluation metric to measure the model's performance with the DCASE 2017 dataset.  
Figure 4 shows the correlations among the top 10 highly correlated tags in relation to the \"Fire truck (siren)\" tag. We can observe correlations among the tags related to sirens. Note that the correlations are relatively weak, with values of less than 0.15.  
At the experimental results in Table 3, SampleCNN (+se) and HarmonicCNN showed performance improvements, with F1-scores rising from 0.5134 to 0.5312 and from 0.5052 to 0.5341, respectively. However, the results for the other models indicated degradation. We were unable to identify any specific trends when utilizing the semantic information of the tags with weak intermediate correlations.  
Figure 4: The correlation of tags in the DCASE 2017 dataset.  
In summary, through three different experiments, we confirmed that utilizing semantic tag information could enhance performance with high tag correlations. However, in cases with low tag correlations, we observed that performance could decrease. Additionally, we tried to understand why performance improved compared to the the existing models when there were high tag correlations. For this purpose, for each given tag, we visualized the audio feature vectors with t-SNE and examined the differences compared to the original models. Although we discovered some meaningful changes in certain cases, we were unable to find consistent evidence across multiple trials.
## 3 Results
### Evaluation Parameters  
#### 3.2.1 Tilted Angle Error during the Parallelization Step  
The first parameter used to evaluate was the tilted angle error. For this, the C-arm marker and the FAD marker of the optical tracking system were attached to the plane of the image panel of the C-arm and the plane of the guiding pattern block, respectively, as shown in Figure 8. The coordinate systems [C-arm] and [FAD] were also assigned to markers, as defined in Figure 8. The \(X\)-axis of [C-arm] was defined as the normal vector of the C-arm plane, and the \(Z\)-axis of [FAD] was defined as the normal vector of the guiding pattern block of the FAD. Thus, the tilted angle error was defined as the error between the two axes that are marked as red arrows, which can be obtained using the formula of the Euclidean dot product as in Equation (1).  
\[\theta=\text{acos}\left(\frac{\overset{\text{-}}{\underset{\text{C-arm}}{ \parallel}}\cdot\overset{\text{-}}{\underset{\text{FAD}}{\parallel}}}{\overset{ \text{-}}{\underset{\text{C-arm}}{\parallel}}\cdot\overset{\text{-}}{ \underset{\text{FAD}}{\parallel}}}\right) \tag{1}\]  
Figure 6: Confirmation of position of the two holes relative to each other. (**a**) Misaligned state. (**b**) Aligned state.  
Figure 7: Experimental setup. (**a**) Whole setup. (**b**) Enlarged.
## 4 Discussion
#### Limitations  
There are also some obvious limitations for fine blood flow detection with H-Doppler.
* To successfully capture the signals encoded in the ultrasound data with Gaussian weighted Hermite polynomials kernels, the frequency bands of the ultrasound RF signal are assumed to contain the information on both large and small scattering objects. Therefore, the probe used in the H-Doppler method should be broadband.
* According to the basic assumption of the H-scan, the emission waveform needs to be approximated as a G\(H_{4}\) kernel (13). This is also why an arbitrary waveform generator module was adopted in the experiment to obtain the best performance of tissue scattering classification for the H-scan.
* Another limitation of the current H-scan method is that it cannot compensate for frequency-dependent attenuation. Since the G\(H_{2}\) and G\(H_{8}\) convolution outputs represent different frequency-dependent scattering, i.e., the B channel is contributed by higher frequency ultrasound signals reflected from small scatterers; the corresponding image quality will be diminished in deep tissue. These influences were ignored in this paper, but recently, some have proposed that the attenuation correction method can enhance the ability of tissue characterization for H-scan imaging, especially for deep tissue [33]. An adaptive attenuation correction that can match to the changing spectrum undergoing attenuation will be investigated in a future study to improve the visualization of the H-Doppler results in deep tissue.
* Additionally, there is a lack of a ground truth of the actual vessel sizes and distributions in the in vivo images for comparison to the ultrasound images. The future work also includes obtaining histological sections of the rabbit brain together with the ultrasound imaging plane to visually verify the size and location of blood vessels for comparison with different beamforming approaches and the H-Doppler method.
## 4 The Proposed Algorithm
### Markov Decision Process Modeling
#### 4.1.3 Reward Function  
As analyzed above, minimizing the task processing delay and energy consumption of each UAV contributes to minimizing the overall objective function. Therefore, the reward for UAV \(u\) at time slot \(t\) is defined as \(r_{u}\), which is the negative value of the weighted sum of task processing delay and energy consumption. A balancing factor \(\zeta\) is introduced to normalize the scales of delay and energy. The reward function is formulated as  
\[R_{t}=(r_{1},\ldots,r_{u},\ldots,r_{M}), \tag{39}\]  
\[r_{u}=r(s_{u},a_{u})=-\eta_{l,u}(t)[w_{0}t_{u}(t)+w_{1}e_{u}(t)\zeta]. \tag{40}\]  
This MDP framework provides a systematic approach to optimize task offloading and resource allocation in multi-UAV MEC systems, balancing both delay and energy efficiency.
# Modeling Semantic-Aware Prompt-Based Argument Extractor in Documents
## 4 Experiment
### Ablation Experiments  
To validate the contribution of different components of the SPARE model, an ablation study was conducted on two datasets, with the results shown in Table 4. After removing the event type detection model, the Arg-I and Arg-C scores of RAMS decreased by 1.3% and 1.3%, respectively, and the Arg-I and Arg-C scores of WikEvents decreased by 0.9% and 0.8%, respectively. This indicates that the detection of event types is crucial for enhancing the precision of event argument extraction. After removing the position-awareness semantic role attention mechanism, the Arg-I and Arg-C metrics of RAMS decreased by 2.0% and 2.4%, respectively, and the Arg-I and Arg-C metrics of WikiEvents decreased by 2.0% and 2.0%, respectively. This demonstrates that reinforcing the location information of the arguments can boost the model's capacity to perceive the semantic context within a specific area. When constructing table column headings without using prompts but instead using the connections of argument roles, the Arg-I and Arg-C metrics decreased by 2.6% and 3.0% for RAMS and 2.4% and 2.4% for WikiEvents, respectively. This is consistent with the finding of [PERSON] et al [34] that hand-crafted prompts are very helpful for EAE tasks. When the pre-encoded embedding of the input form was replaced with the token embedding of RoBERTa, the Arg-I and Arg-C metrics of RAMS decreased by 5.5% and 7.1%, respectively, and the Arg-I and Arg-C metrics of WikiEvents decreased by 5.4% and 6.0%, respectively. This demonstrates the necessity of initializing the input form embedding with encodings computed by the encoder.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**RAMS**} & \multicolumn{2}{c}{**WikiEvents**} \\ \cline{2-5}  & **Arg-I** & **Arg-C** & **Arg-I** & **Arg-C** \\ \hline w/o Event type & 56.5 & 52.2 & 73.1 & 68.5 \\ w/o SRL Attention & 56.1 & 51.6 & 72.3 & 67.7 \\ w/o Prompts & 55.8 & 51.3 & 72.0 & 67.4 \\ w/o PET & 54.1 & 49.1 & 69.8 & 64.9 \\ \hline SPARE & 57.3 & 52.9 & 73.8 & 69.1 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Results of ablation experiment. PET: Pre-computed Encodings of the input Table.
# GDC-Based Infiltrated Electrodes for Solid Oxide Electrolyzer Cells (SOECs)
## 4 Conclusions  
Performing cathodes were fabricated by GDC infiltration into a two-layered LSCM/YSZ backbone. Different infiltrating solutions using water and ethanol as solvents (GDCW and GDCE) were employed, resulting in different microstructures. Specifically, the non-aqueous solution greatly favored the GDC nanoparticles' dispersion by reducing the grain agglomeration and increasing the TPB sites for the process of CO\({}_{2}\) reduction. The GDCE also improved the GDC particles' interconnection, favoring the transfer process at the electrode/electrolyte interface. EIS measurements at OCV performed on both the LSCM/GDCW- and LSCM/GDCE-infiltrated cathodes showed that both the ohmic and polarization resistances of the LSCM/GDCE values were lower than those of the LSCM/GDCW at all temperatures investigated. Moreover, the polarization resistance value of the LSCM/GDCE cathode (0.3 \(\Omega\)-cm\({}^{2}\) at 900 \({}^{\circ}\)C) is comparable to the best performing cathodes. Thus, in agreement with the relevant literature, this work confirms that infiltrated cathodes are promising for SOEC applications. Further improvements can be achieved by optimizing the infiltration parameters of the fabrication procedure.  
Conceptualization, L.S. and E.D.B.; Methodology, E.D.B.; Software, F.Z.; Validation, G.D. and E.D.B.; Formal Analysis, L.S.; Investigation, L.S. and F.Z.; Resources, E.D.B.; Data Curation, L.S. and F.Z.; Writing--Original Draft Preparation, L.S.; Writing--Review and Editing, G.D. and E.D.B.; Supervision, G.D. and E.D.B. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
No conflicts of interest to declare.  
Figure 8: Arrhenius plot (and related linear fitting) of the LSCM/GDCE working with CO/CO\({}_{2}\).
## 4 Methodology
### Vision-Based Localization Information Acquisition
#### 4.2.1 Keyframe Insertion Mechanism  
A keyframe is forcibly inserted when the step length estimation model detects a complete gait cycle (e.g., peak in the gait segmentation signal, as shown in Figure 7), and the number of feature matches in the current visual frame \(N_{\text{f}}\) is lower than a threshold \(N_{\text{T}}\). This ensures that at least one visual keyframe corresponds to each walking step, maintaining map density. The threshold is dynamically adjusted based on the historical mean (\(u_{\text{f}}\)) and standard deviation (\(\sigma_{\text{f}}\)) of feature matching counts using the following formula:  
\[N_{\text{T}}=\max(u_{\text{f}}-k\cdot\sigma_{\text{f}},N_{\text{T\_min}}) \tag{13}\]  
where \(k\in[1,2]\) and \(N_{\text{T\_min}}=15\). When \(N_{\text{f}}\) exceeds the threshold, keyframes are inserted conventionally; otherwise, keyframes are forcibly inserted. This method effectively increases the threshold in texture-rich scenes to reduce redundant keyframes, while lowering  
Figure 11: Diagram of vision-based position tracking principle with step length constraint.  
the threshold in texture-poor scenes (e.g., indoor corridors) to ensure environmental features are recorded.
## 4 Result
### Comparison of Result of ML Techniques
For each model, the basic statistical characteristics of the residuals (mean, median, variance, Max-Min range) were calculated and their normality was tested. The results of the residual analysis are shown in Table 9. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Parameter** & **Regression Model** & **Decision Tree Regression Model** & **Random Forest Model** & **ANN Model** \\ \hline MSE & 2.901 & 9.172 & 1.76 & 0.248 \\ MAE & 1.328 & 2.442 & 1.01 & 0.383 \\ RMSE & 1.703 & 3.029 & 1.22 & 0.498 \\ R\({}^{2}\) & 0.952 & 0.848 & 0.963 & 0.996 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Final summary of model accuracy. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Prediction Accuracy** & **Interpretation** & **Robustness** & **Complexity** \\ \hline Regression model & Medium & Simple & Low & Low \\ Decision tree regression model & Lower & Simple & Low & Low \\ Random forest model & High & Medium complex & High & Medium \\ ANN model & Highest & Complex & High & High \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Final summary of models. The average residual value is close to zero. Through testing (single-sample \(t\)-test, or Wilcoxon's test depending on whether the sample has a normal distribution), it was tested whether the mean (or median) of the residuals was different from zero to a statistically significant level. The results of the tests are in Table 10.  
It is shown that the random forest model, ANN model and equally the decision tree regression model do not have systematic errors (shifts in residuals around zero), which is helpful. Conversely, the regression model has a systematic shift (overestimation), which could be a problem in its application. In addition, the variance of the residuals was compared between the two selected sets. Where both sets of residuals are from normal distributions, the F-test (comparison of the residuals of the regression model and the decision tree regression model) is used, in other cases the Levene test is used. The results are shown in Table 11.  
In almost all cases (except for E\({}_{\text{E,R}}\) vs. E\({}_{\text{E,RF}}\)), the \(p\)-value is less than the significance level \(\alpha\), which means that we reject the null hypothesis of equal variances. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Rezidual** & **Normal Distribution** & **Test** & \(p\)**-Value** &
\begin{tabular}{c} **Shift in Residuals** \\ **from Zero** \\ \end{tabular} & **Interpretation** \\ \hline E\({}_{\text{E,R}}\) & Yes & \(t\)-test & 0.013 & Yes (kladny) & Systematic overestimation of results \\ \hline E\({}_{\text{E,T}}\) & Yes & \(t\)-test & 0.655 & No & No systematic shift in mean of residuals \\ \hline E\({}_{\text{E,RF}}\) & No & Wilcoxon & 0.716 & No & No systematic shift in median of residuals \\ \hline E\({}_{\text{E,ANN}}\) & No & [PERSON] & 0.767 & No & No systematic shift in median of residuals \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Residual Test Results (\(\alpha=0.05\)).
# Enhancement of English-Bengali Machine Translation Leveraging Back-Translation
## 4 Experiments
### Datasets  
For English-Bengali machine translation, there are limited parallel corpora (\(\sim\)1 k to \(\sim\)0.1 M sentences) compared with rich resource language pairs like English-Chinese (\(\sim\)10 M to \(\sim\)100 M sentences). Most of the datasets have parallel contents that are translated or checked by humans, while some are automatically crawled and scored by algorithms. We obtain the following freely accessible datasets from OPUS [88], except SUPara.  
* WikiMatrix [89] is collected from Wikipedia in 1620 language pairs by data mining technology. The pages describing the same entity in different languages can be related, but it may be hard to construct a sentence-to-sentence alignment. Therefore, the corresponding contents are \"comparable\" rather than \"parallel\". For English-Bengali, it has 0.7 M pairs of sentences. In practice, we can separately use its English part and Bengali part as a monolingual corpus.
* GlobalVoices [90] is extracted from Global Voice news articles. The constructors leverage human evaluation to rate the aligned contents and then filter out low-quality translations. For the English-Bengali language pair, it contains 0.1 M pairs of sentences.  
* Tanzil (Tanzil translations [[https://tanzil.net/trans/](https://tanzil.net/trans/)]([https://tanzil.net/trans/](https://tanzil.net/trans/)) accessed on 1 August 2024) is a project that provides the Quran in different languages, including 0.2 M sentence pairs in English-Bengali. The translations are submitted by users. Notably, aligned bilingual versions can be downloaded from OPUS.
* Tatoeba (Tatoeba [[https://taoteba.org/](https://taoteba.org/)]([https://taoteba.org/](https://taoteba.org/)) accessed on 1 August 2024) is an open and free multi-language collection of sentences and translations. Its name means \"for example\" in Japanese. The version of the English-Bengali corpus we use was updated on 12 April 2023, and contains 5.6 k pairs of sentences. Most of the sentences in this dataset are selected from short daily conversations.
* Shahjalal University Parallel (SUPara) corpus [91] is a collection of aligned English-Bengali sentences. It is constructed with a balance of text sources, including literature, administrative texts, journalistic texts, and so on. It has 21,158 pairs of sentences. However, the only source we find for this corpus is not freely available. Therefore, we only obtain 500 pairs of sentences (known as the test set of the SUPara benchmark) and employ them as the final test set.  
We refer to Table 1 for some example sentences from the aforementioned datasets. It can be observed that some of the translations do not convey the exact same meaning. We plan to address this issue.
# MAC/PHY Comprehensive Visible Light Communication Networks Simulation
###### Abstract  
In this paper, the effect of channel conditions on the global behavior of a wireless Visible Light Communications (VLC) optical network are studied. It presents a system-level simulator that considers jointly a channel propagation model and the MAC mechanisms to have a realistic description of the network, even in situations where the emitted signal is heavily affected by reflections in any close surface or obstacle. The resulting platform also accurately evaluates both Line-Of-Sight (LOS) and Non-LOS (NLOS) contributions on each node and enables the effective use of Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) schemes as defined by IEEE 802.15.7r1 standard, as well as allows a correct evaluation of lifelike problems such as the effect of hidden nodes. This work shows the necessity of accurately modeling VLC MAC layer performances, taking also into account the physical nature of visible light propagation in indoor scenarios.  
Keywords:visible light communications; IEEE 802.15.7; channel simulation; network simulation; MAC layer simulation; MAC/PHY simulation +
Footnote †: journal: Sensors
# Deep Learning-Based Network Intrusion Detection Using Multiple Image Transformers
## 2 Related Work
### Early ML-Based NIDS  
Various traditional ML models have been applied to NIDS to address the high management cost and slow response time of NIDS, which are used to detect intrusion based on existing thresholds or patterns. Detecting intrusions using thresholds or patterns can be slightly vulnerable to detection bypass attacks; therefore, a ML-based NIDS detects intrusions using overall characteristics rather than some characteristics of intrusive traffic [23; 24; 25]. For example, traffic is divided into sessions, and various statistical characteristics are extracted for each session and used to distinguish between intrusive and normal traffic. Because a ML-based NIDS uses approximately 20-90 features, it comprehensively uses the overall characteristics of the session to detect intrusion [24; 25; 26]. Hence, the detection efficiency is not affected by only a few parameters, e.g., the threshold or pattern, rendering it unfeasible for attackers to circumvent a ML-based NIDS.  
In the early days, many simple decision tree algorithms were applied, and high-speed classifier algorithms, such as J48, C4.5, and C5.0, were selected because NIDS required a fast classification speed to handle large amounts of traffic [27; 28]. NIDS and firewalls require a packet classification process. In contrast to routing or switching, which is classified based on one existing field, packet classification in a NIDS or firewall is performed using five tuples. Thus, the packet classification process was implemented using a sophisticated high-speed algorithm. The high-speed packet classification algorithm is similar to the decision tree-based algorithm with a field number of five. Therefore, the decision tree algorithm can be easily implemented using high-speed packet classification algorithms; thus, the decision tree algorithms are most suitable for early ML-based NIDS.  
As the CPU/GPU hardware platform using multiple threads was applied in the NIDS, it even used RF to apply multiple decision trees. RF is a superior alternative for increasing the performance of NIDS, as it can prevent the overfitting of decision trees and increase classification accuracy. In addition to RF, attempts have been made to increase classification performance by applying the ensemble technique to various MLs. Most studies have been conducted to increase classification accuracy rather than classification speed.
## 11 Conclusions  
In this paper, we have presented a novel multi-feedback matched filter (MBMF) strategy for coded iterative receivers for OFDM systems over rapidly time-varying channels. The proposed approach employs a multiple ICI cancellation strategy and approximates the residual interference inside the band by introducing multiple cancellation feedback candidates, which are generated by two different mechanisms: Gibbs sampling-based generation (GSG) and tree search-based generation (TSG). The BER performance of the proposed algorithm was analyzed, and the simulation results indicated that the proposed MBMF-PIC can achieve a close to Ungerboeck-MAP performance and outperforms other existing soft ICI cancellation techniques at the expense of a moderate complexity increase. For imperfect channels, the proposed MBMF-SIC can still achieve an acceptable BER performance at high normalized Doppler frequencies and provides an attractive tradeoff between performance and complexity.