esa-sceva/satcom-chunk-collection huggingface dataset:
# A Unified Speech Enhancement System Based on Neural Beamforming With Parabolic Reflector
## 4 Discussion  
To illustrate the effectiveness of our speech enhancement system more clearly, an utterance corrupted by mixed noise (destroyer engine + speech interference) at \(-2\) dB from test data and enhanced by our proposed system is presented, as shown in Figure 6.  
Compared with the spectrogram of PR enhanced speech, speech processed by neural beamformer removed most of the interference parts, while losing a lot of high-frequency information. It also verified the description that there are some similarities, differences, and supplementary parts in these twofold signals. Although the PR model also introduced some speech distortions to the target speech, the spectrogram indicated that the PR model has significant performance in data cleaning.  
Figure 6: (**a–d**) are the spectrograms and waveforms from clean speech, noisy speech, PR model, and multi-channel beamforming preprocessing, respectively. The spectrogram of our final output is listed in (**e**). (**a**) Spectrogram and waveform from clean speech; (**b**) Spectrogram and waveform from noisy speech; (**c**) Spectrogram and waveform from IRM-BF results; (**d**) Spectrogram and waveform from PR results; (**e**) Spectrogram and waveform from the proposed method.  
The processed speech makes a trade-off between speech distortion and speech intelligibility by fusing operation, yielding better enhancement performance. As seen in Figure 6e, the power and the strength of noise are largely suppressed.
# Research on Public Service Request Text Classification Based on BERT-BiLSTM-CNN Feature Fusion
## 4 Experiments and Results
### Experimental Comparison and Result Analysis
#### 4.3.1 Model Comparison
As shown in Table 5, when the Attention mechanism is incorporated into the models, F1 scores of the models are improved; Table 6 shows that when incorporating the Attention mechanism into hybrid models like CNN, the precision is somewhat enhanced by the influence of recall. However, the overall metric F1 score decreases; this is because when Attention is combined with CNN, the Attention mechanism will cause the model to overlook other significant aspects in favor of paying excessive attention to local features, resulting in some helpful information being ignored, thus affecting the performance of the model. BERT and Word2 Vec exhibit significant differences in language representation, directly influencing their performance in hybrid neural network models. Word2 Vec relies on static embeddings where each word has a fixed vector representation, which remains constant at the lexical level and fails to capture semantic variations of words across different contexts. In contrast, BERT, based on the Transformer architecture, is a deep bidirectional model that uses context-aware dynamic embeddings. It dynamically generates representations for each word based on context, thereby better capturing semantic changes in words across different contexts and providing richer and more accurate language representations. Table 7 illustrates the comparison of different hybrid neural network models based on BERT and Word2 Vec on the test set. The results show that hybrid neural networks based on BERT consistently outperform those based on Word2 Vec. This further demonstrates that BERT outperforms Word2 Vec for classification tasks in natural language processing. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline BERT-BiGRU-CNN & **0.9420** & **0.9385** & **0.9344** & **0.9322** \\ BERT-BiGRU-Attention-CNN & 0.9299 & 0.9274 & 0.9062 & 0.9125 \\ \hline BERT-CNN & **0.9509** & 0.9354 & **0.9503** & **0.9395** \\ BERT-Attention-CNN & 0.9446 & **0.9440** & 0.9287 & 0.9335 \\ \hline BERT-BiLSTM-CNN & **0.9536** & **0.9541** & **0.9519** & **0.9509** \\ BERT-BiLSTM-Attention-CNN & 0.9505 & 0.9528 & 0.9457 & 0.9470 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Attention and CNN models’ comparison. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline BERT-BiGRU & 0.9345 & **0.9405** & 0.9136 & 0.9223 \\ BERT-BiGRU-Attention & **0.9469** & 0.9386 & **0.9448** & **0.9387** \\ \hline BERT-BiLSTM & 0.9306 & 0.9080 & 0.9270 & 0.9129 \\ BERT-BiLSTM-Attention & **0.9391** & **0.9382** & **0.9309** & **0.9314** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Attention but no CNN models’ comparison.
# The Frequency Response Characteristics of Ge-on-Si Photodetectors Under High Incident Power
###### Abstract  
This study explores the mechanisms responsible for the bandwidth reduction observed in germanium photodetectors under high signal light power. We investigate the impact of the carrier-shielding effect on the bandwidth through simulations, and we mitigate this effect by increasing the applied bias voltage. The increase in the concentration of photogenerated carriers leads to a reduction in the carrier saturation drift velocity, which reduces the bandwidth of the germanium photodetector; this phenomenon is studied for the first time. The bandwidth is determined primarily by the carrier saturation drift velocity when the incident light power is below 2.5 mW. The decrease in bandwidth that is calculated based on the decrease in carrier saturation drift velocity is consistent with the experimental results. However, when the signal light power exceeds 3 mW, both the carrier-shielding effect and the reduction in the carrier saturation drift velocity contribute to the bandwidth reduction. This study provides good theoretical guidance for the design of high-power germanium photodetectors.  
Keywords:photodetectors; high-power optical signals; bandwidth; carrier-shielding effect; carrier saturation drift velocity
## 3 System Model
### Computing Model  
The MLLM inference process involves three sequential phases: uplink transmission of multimodal input data, inference computation, and downlink transmission of generated results. This section models the latency and energy consumption for both UAV and LEO satellite computing.
## 3 Establishment of the Test Rig Calculation Model
### Establishment of the Dynamic Model  
#### 3.2.1 Model Condensation  
The finite element method (FEM) model of the test rig primarily includes the piston body, the connecting rod (including bushings at both the small and big ends), the crank pin, and the piston pin. Geometric features such as receding grooves, oil supply channels, and bores on journals or bushings, chamfers, and rounded corners on each structure were ignored in the geometric model, which has less influence on the dynamic characteristics, and material properties were assigned to each part after meshing was completed.  
To improve the calculation efficiency, the free interface substructure method (Craig-Chang method) was used to determine the lubrication characteristics by solving the mean Reynolds equation. The FEM and condensed model comparisons of the piston, the piston pin, the connecting rod and the crank throw are presented in Table 2. The numbers of elements of the piston, connecting rod and piston pin are 167,734, 79,369, and 736,592, respectively. The retained DOFs of the condensed model are 4023, 2163, and 138, respectively. The damping of the parts was obtained according to the empirical value of the damping ratio in Rayleigh damping.
# PFSegIris: Precise and Fast Segmentation Algorithm for Multi-Source Heterogeneous Iris
## 4 Conclusions  
With respect to the problem that various iris segmentation methods work well for distinct iris datasets and poorly for multi-source heterogeneous iris images, a precise and fast segmentation algorithm, PFSegIris, was proposed for multi-source heterogeneous iris images. The algorithm fully extracted the information of iris images using the lightweight iris feature extraction module designed in this paper. The efficient parallel dual attention module suppressed the influence of noise and other interference factors, and further enhanced the discriminability of pixels in the iris region. A low-level skip connection supplemented the edge feature information. Experimental results on seven heterogeneous iris datasets from different sources showed that the proposed algorithm is superior in precision and speed to existing deep-learning-based iris segmentation algorithms and traditional positioning algorithms, with F1-score up to 99.13% and mIoU up to 98.27%. The shortest time was 0.06 s. The Params, Storage Space, and FLOPs of PFSegIris were only 1.86 M, 0.007 GB, and 0.65 G.  
The algorithm proposed in the paper is able to segment multi-source heterogeneous iris quickly and precisely, providing sufficient and reliable feature information for the subsequent recognition process, which is of good practical application value on low-performance computing devices such as mobile terminals.  
Conceptualization, L.D. and X.Z.; methodology, L.D.; software, L.D.; validation, L.D.; formal analysis, L.D., Y.L. and X.Z.; investigation, L.D.; resources, L.D., Y.L. and X.Z.; data curation, L.D.; writing--original draft preparation, L.D.; writing--review and editing, L.D.; visualization, L.D.; supervision, L.D.; project administration, L.D.; funding acquisition, Y.L. and X.Z. All authors have read and agreed to the published version of the manuscript.  
Figure 14: Test results comparison of PFSegIris and other three algorithms on five hard samples from JLU-7.0, CASIA-Iris-Lamp-v4, UBIRIS.V2, MICHE-I, and Mmu2, respectively: (**a**) original images; (**b**) ground truth; (**c**) segmentation results of PFSegIris; (**d**) segmentation results of MFFIris-Unet; (**e**) segmentation results of PI-Unet; (**f**) segmentation results of FRED-Net.  
**Funding:** This research was funded by the Natural Science Foundation of Jilin Province, grant number YDZJ2010 ZYS144; and the National Natural Science Foundation of China (NSFC), grant number 61471181.  
**Institutional Review Board Statement**: Not applicable.  
**Informed Consent Statement**: Not applicable.  
**Data Availability Statement**: Not applicable.  
**Acknowledgments**: Thanks to the Jilin Provincial Key Laboratory of Biometrics New Technology for supporting this project.  
**Conflicts of Interest**: The authors declare no conflict of interest.
# AI Approaches in Computer-Aided Diagnosis and Recognition of Neoplastic Changes in MRI Brain Images
## 4 Discussion  
Both Convolutional Neural Networks and Vision Transformers were able to solve the task of brain cancer classification based on the MRI scans and achieved an accuracy of approximately 90%, which is close to the state-of-the-art methods. In combination with reasonable looking heatmaps indicating the reason for decisions, this performance allows the inference that building CAD systems helping with brain tumor diagnosis based on such artificial intelligence algorithms is possible.  
Attention-based models proved well suited for the task and even outperformed traditional CNNs at the cost of complexity. However, even the model comparably simple to the four-layer CNN handled the problem to a similar degree as one of the most complex CNN models to date. ViTs proved to be harder to train than CNNs, requiring a greater amount of data and additional precautions during the training process, which makes the accuracy gain costly, despite a shorter training time. As a bonus to the excellent scores achieved by ViTs, they also provide exceptionally detailed heatmaps that can be used for diagnosis support. All of these factors show that Vision Transformer is the architecture that should be further developed in the context of computer vision as it is a powerful and general model whose possibilities have yet to be fully explored.  
To enhance opportunities for ViT-based CAD systems, there is a need for more extensive and high-quality datasets of labeled MRI images, which might allow experimenting with different types of architectures. Models using attention mechanisms can be further upgraded with the use of hybrid methods, different attention calculations, novel initialization and optimization methods, or adaptive attention. In addition, further research could result in a wider variety of available pre-trained ViT models, which could help to productize the solution to different IoT fields, including medicine.  
Summarizing the conclusions gathered here, computer-aided diagnosis systems based on neural networks producing classification labels and heatmaps are possible to create. They might prove helpful in speeding up and raising the quality of a diagnosis and lowering the risk of overlooking neoplastic changes. Vision Transformer models and other attention-based architectures seem to be excellent choices for this task. Presented techniques can be also applied for semantic pattern classification in other IoT areas [35].  
J.K.: conceptualization, methodology, software, validation, formal analysis, investigation, writing--original draft, visualization. M.R.O.: conceptualization, methodology, validation, writing--review and editing, supervision. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the AGH University of Science and Technology Research, Grant No 16.16.120.773.  
Not applicable.  
The dataset used for training can be acquired from [9]. Other data available on request from authors.  
The authors have no conflict of interest to declare for this manuscript.
###### Abstract  
In recent years, more and more reports have shown that the miR156-_SPL_ module can participate in the regulation of anthocyanin synthesis in plants. However, little is known about how this module responds to hormonal signals manipulating this process in grapes. In this study, exogenous GA, ABA, MeJA, and NAA were used to treat the 'Wink' grape berries before color conversion, anthocyanin and other related quality physiological indexes (such as sugar, aroma) were determined, and spatio-temporal expression patterns of related genes were analyzed. The results showed that the expression levels of VvmiR156b/c/d showed a gradually rising trend with the ripening and color formation of grape berries, and the highest expression levels were detected at day 28 after treatment, while the expression level of _VvSPL9_ exhibited an opposite trend as a whole, which further verifies that VvmiR156b/c/d can negatively regulate _VvSPL9_. Besides, VvmiR156b/c/d was positively correlated with anthocyanin content and related genes levels, while the expression pattern of _VvSPL9_ showed a negative correlation. Analysis of promoter cis-elements and GUS staining showed that VvmiR156b/c/d contained a large number of hormone response cis-elements (ABA, GA, SA, MeJA, and NAA) and were involved in hormone regulation. Exogenous ABA and MeJA treatments significantly upregulated the expression levels of VvmiR156b/c/d and anthocyanin structural genes in the early stage of color conversion and made grape berries quickly colored. Interestingly, GA treatment downregulated the expression levels of VvmiR156b/c/d and anthocyanin structural genes in the early color-change period, but significantly upregulated in the middle color-change and ripening stages, therefore GA mainly modulated grape berry coloring in the middle- and late-ripening stages. Furthermore, NAA treatment downregulated the expression levels of VvmiR156b/c/d and anthocyanin structural genes and delayed the peak expression of genes. Meanwhile, to further recognize the potential functions of VvmiR156b/c/d, the mature tomato transient trans genetic system was utilized in this work. Results showed that transient overexpression of VvmiR156b/c/d in tomato promoted fruit coloring and overexpression of _VvSPL9_ inhibited fruit coloration. Finally, a regulatory network of the VvmiR156b/c/d-VvSPL9 module responsive to hormones modulating anthocyanin synthesis was developed. In conclusion, VvmiR156b/c/d-mediated _VvSPL9_ participated in the formation of grape color in response to multi-hormone signals.  
VvmiR156b/c/d-_VvSPL9_; hormone signal; anthocyanin; grape
# Insider Threat Detection Based on User Behavior Modeling and Anomaly Detection Algorithms
## 2 Dataset and User Activity Modeling
### User Activity Modeling Based on Daily Activity Summaries
\begin{table}
\begin{tabular}{c c} \hline \hline
**Relc** & **Number of Anomalous Records** \\ \hline Salesman & 32 \\ IT Admin & 23 \\ Electrical Engineer & 10 \\ Computer Programmer & 3 \\ Manager & 2 \\ Director & 1 \\ Production line worker & 1 \\ Software developer & 1 \\ Total & 73 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The number of anomalous records according to role. The performance of machine learning models, including anomaly detection, is strongly affected by the input variables used to train the model [24]. Theoretically, the performance of machine learning models improves as the number of variables increases when independence between input variables is ensured. However, when applied to a real-world dataset, a large number of input variables sometimes degenerate the model performance because of the high dependency between input variables (multi-collinearity) and the existence of noise. Hence, it is necessary to select a set of effective variables rather than using all variables to secure better performance. In this study, we used the univariate Gaussian distribution to select possible beneficial variables to detect malicious instances. For each variable, we first estimated the parameter of Gaussian distribution (mean and standard variation). Then, if at least one of the abnormal activities was located in the rejection region with the significance level \(\alpha=0.1\) for a certain variable, we included the variable as an input variable for further anomaly detection modeling. Table 5 shows the selected variables obtained by the univariate Gaussian distribution test.
## 2 Background
### WaveCluster
#### 2.2.2 WaveCluster Parameters  
As previously mentioned, the WaveCluster algorithm is parameterized by the number of cells in the grid (inversely proportional to cell size), the wavelet function used to perform the transformation, and the density threshold to apply in order to determine whether a cell is \"significant\" after performing the wavelet transform. The density threshold can be an absolute cell value, but is often more convenient to think about in terms of a percentile among cell values, since malignant regions tend to manifest as regions of localized density relative to surrounding tissue. Unlike k-means, WaveCluster is not parameterized by the number of clusters, which is determined automatically by the algorithm.  
Figure 2: Illustrated step-by-step operation of the WaveCluster algorithm.  
Varying the parameters has several effects:  
* Increasing the number of cells increases the resolution of the analysis, allowing the algorithm to detect finer patterns but reducing the amount of neighborhood information which is incorporated in the clustering.
* Increasing the density threshold reduces the number of cells which are considered \"significant\", resulting in fewer \"bridges\" between clusters and a smaller number of \"stronger\" but more isolated clusters. The optimal value of this parameter is application-dependent: too low and the entire image may be merged into one cluster, too high and regions of the same cluster will break apart. At extremely high values, no clusters are detected whatsoever.
* The wavelet function can be thought of as a convolution kernel applied to each pixel of the image. \"Hat-shaped\" wavelets are useful for clustering applications because they emphasize dense regions and suppress sparse ones, but more complex wavelet functions may be useful in niche applications, such as clustering highly anisotropic data.
# Speech Act Theory as an Evaluation Tool for Human-Agent Communication
## 1 Introduction  
Human societies rely on communication. Human-agent communication in a collaborative virtual environment (VE), where both the human and the agent should collaborate together to complete a shared goal, is a particularly challenging task as the communication should be real-time, goal-driven, and the verbal and non-verbal communication should be interleaved in order to achieve the common goal [1]. Most natural communication situations require integrating communication channels [2]. Much of the agent-based research concerned with communication and teamwork focusses on agent-to-agent communication [3]. Agent-to-agent communication tends to be manageable especially if the agents involved in communication are designed around Beliefs, Desires, and Intentions (BDI). The BDI approach has been introduced as a means of modelling and allowing agents to do complex and anonymous reasoning [4]. While there are some models to aid human-agent communication, these models tend to consider only a single communication channel, i.e. the verbal (e.g., [5]) or non-verbal communication channel (e.g., [6]). The few models that combine both channels of communication are either situation-dependent (e.g., [PERSON], [PERSON] [7] offer a model to be used in a role-specific static environment) or lack an objective evaluation of its effectiveness (e.g., [3]).  
Speech Act Theory (SAT) is a well-known theory in the field of the philosophy of language that involves the study of the performative function of language and communication. SAT proposes that any form of expression, either verbal or textual, represents acts to be performed, and there are some actions that need to be performed by the receiver [8]. In other words, SAT tries to understand how an utterance can be used to achieve actions. [PERSON] [9] described three characteristics, or acts, of human speech: locutionary acts, illocutionary acts, and perlocutionary acts. Locutionary acts refer to the uttering of a sentence. Illocutionary acts denote the action of expressing the speaker's intention. Perlocutionary acts refer to what is achieved by saying something.  
In order to evaluate the impact of the communication model on human-agent collaboration performance, SAT is used in this paper as a novel evaluation technique. The foundation of SAT will be used to analyse the components of an agent's speech and find the possible impact on collaboration performance with a human teammate. To investigate the influence of the agent's verbal communication, as evaluated by SAT, on human-agent collaboration, the following research questions are proposed:  
1. Using SAT concepts, how can an agent's locutionary act be evaluated?
2. Using SAT concepts, how can an agent's illocutionary act be evaluated?
3. Using SAT concepts, how can an agent's perlocutionary act be evaluated?
4. Could the agent's evaluated illocutionary and perlocutionary acts impact collaboration performance?