esa-sceva/satcom-chunk-collection huggingface dataset:
# HyFusER: Hybrid Multimodal Transformer for Emotion Recognition Using Dual Cross Modal Attention
## 4 Hybrid Multimodal Transformer for Emotion Recognition
### Extract Text and Speech Features  
This section includes the preprocessing steps for embedding text and speech data to effectively learn the features of different modalities. This process enhances interaction by transforming both modalities to have the same dimensions and categories.  
To accurately capture the grammatical and semantic context of the Korean language, text data are embedded using the KoELECTRA base-v3 model. KoELECTRA is a pre-trained language model specialized for Korean, transforming each word within a sentence into a 768-dimensional vector. Consequently, the embedded output for a given sentence is a vector with dimensions corresponding to the number of tokens in the sentence and the embedding dimension of KoELECTRA (768). For instance, a sentence with 10 tokens is converted to a (10,768) vector, while one with 30 tokens becomes a (30,768) vector. To train deep learning models, all sentences must have consistent dimensions. First, the maximum number of tokens across the dataset is determined. The text data are then tokenized using KoELECTRA's tokenizer, and each sentence is padded to match the maximum token count. Padding involves adding [PAD] tokens to sentences with fewer tokens, ensuring uniform lengths. The maximum token count identified is 126, and all sentences are normalized to this length. As a result, the final text data are transformed into fixed-dimensional vectors  
Figure 3: Research structure.  
of size (number of samples, 126,768), where 126 represents the number of tokens and 768 is the embedding dimension of KoELECTRA. This preprocessing ensures that the text data have a consistent input format for deep learning model training.  
Speech data are embedded using the HuBERT ls960 model. HuBERT learns temporal and spectral characteristics of speech, generating refined speech representation vectors. The vectors extracted by HuBERT vary in dimensions depending on the temporal length of the speech data. To input both text and speech data into the same deep learning model, the embedding sizes of the two modalities must match. To achieve this, linear interpolation is applied, where data with insufficient length are expanded by filling data points between existing ones, and data with excess length are compressed by reducing intervals between data points. Consequently, the speech data are reshaped to match the dimensions of the text data, resulting in vectors of size (number of samples,  
The feature-extracted text and speech data are transformed into vectors of shape (number of samples, 126,768) and then passed into the transformer encoder within the proposed model. The text data, embedded with features learned through KoELECTRA, and the speech data, embedded with features learned through HuBERT, are individually processed by separate transformer encoders. Subsequently, the Dual Cross Modal Attention mechanism facilitates the learning of interactions between the two modalities, enabling effective integration of their complementary features.
## Appendix B Cumulative Surface Leaching of Contaminants  
Notations for Table A4. The applied methodology is based on the three approaches:  
* Standards SS-EN ISO 10523:2012 with measurement uncertainty of \(\pm\)0.05 pH-enh;
* SS-EN 27888-1:1994 with measurement uncertainty \(\pm\)2.9%;
* SGI-method with measurement uncertainty pf \(\pm\) 25%.  
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline
**Period** & **6 h** & **1 Days** & **2.25 Days** & **4 Days** & **9 Days** & **16 Days** & **36 Days** & **64 Days** \\ \hline Sample No & 2325 & 2326 & 2327 & 2328 & 2329 & 2330 & 2331 & 2332 \\ Al & 1.86 & 3.6 & 5 & 6.32 & 7 & 7.6 & 8.02 & 8.41 \\ As & 0.0581 & 0.133 & 0.219 & 0.284 & 0.362 & 0.444 & 0.501 & 0.584 \\ Ba & 6.04 & 12.9 & 20.6 & 27.2 & 40.3 & 53.8 & 74 & 94.6 \\ Ca & 15,900 & 33,300 & 52,900 & 72,000 & 96,300 & 121,000 & 15,300 & 190,000 \\ Cd & 0.0042 & 0.00927 & \textless{}0.01 & \textless{}0.02 & \textless{}0.02 & \textless{}0.02 & \textless{}0.03 & 0.0328 \\ Co & 0.037 & 0.0663 & 0.0902 & 0.117 & 0.159 & 0.186 & 0.215 & 0.256 \\ Cr & \textless{}0.04 & \textless{}0.08 & \textless{}0.1 & \textless{}0.2 & \textless{}0.2 & \textless{}0.2 & \textless{}0.3 & \textless{}0.3 \\ Cu & 1.3 & 2.23 & 2.91 & 3.45 & 4.04 & 4.64 & 5.53 & 6.5 \\ Fe & 0.859 & 1.52 & 2.1 & 2.57 & \textless{}3 & \textless{}3 & \textless{}3 & \textless{}4 \\ Hg & \textless{}0.002 & \textless{}0.003 & \textless{}0.005 & \textless{}0.006 & \textless{}0.008 & \textless{}0.009 & \textless{}0.01 & \textless{}0.01 \\ K & 16,000 & 31,600 & 49,100 & 65,400 & 81,300 & 96,700 & 113,000 & 128,000 \\ Mg & 39,400 & 78,800 & 122,000 & 163,000 & 197,000 & 231,000 & 258,000 & 285,000 \\ Mn & 1.33 & 2.35 & 2.9 & 3.46 & 3.65 & 3.92 & 4 & 4.05 \\ Mo & 0.782 & 1.5 & 2.29 & 3 & 3.87 & 4.66 & 5.7 & 6.9 \\ Na & 380,000 & 747,000 & 1,170,000 & 1,560,000 & 1,930,000 & 2,290,000 & 2,600,000 & 3,010,000 \\ Ni & 0.411 & 0.726 & 0.95 & 1.25 & 1.66 & 1.9 & 2.07 & 2.23 \\ Pb & \textless{}0.02 & \textless{}0.03 & \textless{}0.05 & \textless{}0.06 & \textless{}0.08 & \textless{}0.09 & \textless{}0.1 & \textless{}0.1 \\ V & 0.141 & 0.35 & 0.591 & 0.833 & 1.18 & 1.4 & 1.57 & 1.69 \\ Zn & 0.278 & \textless{}0.4 & \textless{}0.6 & \textless{}0.7 & \textless{}0.9 & \textless{}1 & \textless{}1 & \textless{}1 \\ B & 144 & 288 & 445 & 594 & 722 & 846 & 947 & 1050 \\ Sb & 0.044 & 0.0924 & 0.143 & 0.194 & 0.282 & 0.354 & 0.469 & 0.579 \\ Se & \textless{}0.2 & \textless{}0.5 & \textless{}0.7 & \textless{}0.9 & \textless{}1 & 5.35 & \textless{}6 & \textless{}6 \\ Naphthalene & \textless{}0.002 & \textless{}0.005 & \textless{}0.007 & \textless{}0.009 & \textless{}0.01 & \textless{}0.01 & \textless{}0.02 & \textless{}0.02 \\ Acy & \textless{}0.0008 & \textless{}0.002 & \textless{}0.002 & \textless{}0.003 & \textless{}0.004 & \textless{}0.005 & \textless{}0.005 & \textless{}0.006 \\ Acenap
## Abbreviations  
The following abbreviations are used in this manuscript:3 GPPThird Generation Partnership Project
* 5 GPP Fifth Generation Partnership Project
* 1 BN Intent Based Networking LTE Long-Term Evolution EPC Evolved Packet Core RAN Radio Access Network SDN Third Generation Partnership Project NFV Network Function Virtualization VNFs Virtual Network Functions GAN Generative Adversarial Network OSM Open Source MANO VIM Virtual Infrastructure Manager VNF Manager OAI OpenAirInterface DL Deep Learning IETF Internet Engineering Task Force SDR Software Defined Radio
# On the Local and String Stability Analysis of Traffic Collision Risk
## 2 Preliminary on Conventional Traffic Stability Analysis
### Definition of Traffic Local Stability  
The traffic local stability is defined in the same way as bounded-input-bounded-output (BIBO) stability in system engineering [30]. Traffic local stability focuses on the disturbance evolution over time, indicating whether the vehicle's state can recover to the equilibrium state in a sufficiently long period of time. Specifically, after a minor disturbance (e.g., a sudden deceleration) is forced on vehicle \(n\) at time \(t\), if the disturbance decays over time and the vehicle recovers to its initial state \(x_{n}^{eq}\), eventually, the vehicle is considered traffic local stable. The mathematical expression of local stability in the time domain is written as  
\[x_{n}(t)-x_{n}^{eq}(t)=y_{n}(t),\quad\lim_{t\to\infty}y_{n}(t)\to 0 \tag{4}\] \[\dot{x}_{n}(t)-\dot{x}_{n}^{eq}(t)=\dot{y}_{n}(t),\quad\lim_{t\to \infty}\dot{y}_{n}(t)\to 0\]  
where \(x_{n}^{eq}(t)\) and \(\dot{x}_{n}^{eq}(t)\) denote the equilibrium state of \(x_{n}(t)\) and \(\dot{x}_{n}(t)\), respectively; \(y_{n}(t)\) represents the disturbance, which is assumed to be differentiable; and \(\dot{y}_{n}(t)\) is its first derivative.  
With the linearized car-following model in Equation (2), the local stability criterion is derived as follows:  
\[f_{\Delta x}>f_{x}\quad\text{and}\quad f_{\Delta x}>0 \tag{5}\]  
Figure 1: Demonstration of a vehicle platoon.
# Context-Aware Beam Selection for IRS-Assisted mmWave V2I Communications
## 5 Results and Discussion
### The Impact of the Traffic Light Context on Connection Time  
Our simulation has generated various insightful examples, some of which are straightforward while others are more subtle. Generally, vehicles approaching a red traffic light tend to have longer connections compared to those approaching green lights. However, multiple context variables and numerous connection points are evaluated simultaneously, creating interactions that can amplify or diminish individual effects, thus increasing the complexity of the analysis. Figure 6 specifically evaluates the combined contexts related to traffic lights. To produce this figure, simulation data was processed by calculating the average connection duration for each combination of contexts observed. The range between the shortest and longest bars indicates the discriminatory power of these combined contexts--wider variations signify stronger predictive capabilities. For instance, the bar with the longest average connection time corresponds to a scenario labeled \"Green, Small,  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Case** & **Exploration** & **Explotation** \\ \hline With IRS & 5.78 & 13.51 \\ Without IRS & 2.28 & 3.28 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The impact of IRS usage on connection time with comparison of average connection time (in seconds) during exploration and exploitation phases with and without IRS assistance.  
Figure 5: Connection frequency distribution across different beam sectors during (**a**) the exploration phase and (**b**) the exploitation phase after decision given by C-MAB algorithm.  
Away, Far\", reaching approximately 24 s, although intuitively a vehicle moving away from a green light that is about to change does not have a relationship directly. This result emerges, in the considered scenario where two traffic lights are at the end main road, when a vehicle moving away from one traffic light is simultaneously approaching another traffic signal at the opposite end of the main road and eventually waits at the second light for an extended period.  
Conversely, scenarios positioned at the shorter end of the chart, such as \"Green, Mid, Towards, Close,\" yield predictably shorter connection durations due to vehicles swiftly passing through a green light without stopping. A representative middle-range scenario, for example, the eighth-longest bar (\"Red, Big, Towards, Close\"), illustrates a vehicle approaching a nearby red traffic signal that will remain red for a longer time, thus extending the connection duration slightly beyond the waiting time at the light. These nuanced findings underline the value of leveraging machine learning to determine, through iterative exploration, which combinations of contexts genuinely maximize connectivity performance. Rather than relying solely on intuitive assumptions, this empirical approach ensures that subtle yet critical interactions between multiple contextual variables are effectively identified and utilized.
## 3 Methodology  
The main framework for ship trajectory prediction based on the TRFM model, as proposed in this paper, is illustrated in Figure 1. The data preprocessing section involves the extraction of ship trajectories, removal of anomalous and redundant points, imputation of missing points, equidistant time interval resampling, and dataset construction, all performed on the raw collected ship AIS data. Through this preprocessing procedure, the original AIS data become more robust, effectively addressing issues such as data anomalies, missing data, and discontinuous time intervals caused by network transmission, reception, and transmission. The inference section utilizes the TRFM model to predict ship trajectories. The TRFM model adopts an encoder-decoder structure, with its core component being the multi-head attention mechanism. It is able to effectively extract global spatio-temporal features of trajectories, thereby enhancing the accuracy of trajectory prediction. This mechanism effectively enhances the feature correlations between sequence points and enables parallel processing when handling temporal tasks, greatly improving the model's computational speed [25]. The model prediction uses the differential relative values of latitude and longitude as inputs, and the predicted results are obtained by regressing the absolute values of latitude and longitude to derive the final predicted trajectory. This can make the model's trajectory prediction more generalizable, rather than limited to trajectory predictions within the region where the training data are located. By constructing a trajectory prediction dataset and training and predicting the model on this dataset, the performance of the model in trajectory prediction tasks is compared with other state-of-the-art trajectory prediction models. Comprehensive evaluations of the model's prediction performance are conducted using metrics such as _MAE_, _RMSE_, _MSE_, _FDE_, and _ADE_.
# Fast Fractional-Order Terminal Sliding Mode Control for Seven-Axis Robot Manipulator
## 3 Controller Design
### Numerical Method  
In order to implement the fractional-order derivative, the Grunwald-Letnikov fractional derivative is applied in this research. The Grunwald-Letnikov fractional derivative is derived from the limit definition for derivatives and equivalent to the Riemann-Liouville definition [18; 19; 21]. Thus, the equivalent control input of the sliding mode control (27), which uses the Riemann-Liouville derivative, was implemented using the Grunwald-Letnikov derivative. The Caputo derivative term in the sliding surface (18) was transferred to a Riemann-Liouville derivative with the initial error first using the properties mentioned. Then, it was implemented on the real system by the Grunwald-Letnikov derivative method. The \(\alpha\) th-order Grunwald-Letnikov fractional derivative for \(f(t)\) is  
\[\begin{split}&{}_{GL}D_{t_{0},t}^{\alpha}f(t)=\underset{h\to 0}{ \lim}h^{-\alpha}\overset{(t-t_{0})/h}{\sum}\Gamma(k-a)\Gamma^{-1}(k+1) \Gamma^{-1}(-a)f(t-kh)\\ &\approx h^{-\alpha}\overset{(t-t_{0})/h}{\sum}\Gamma(k-a) \Gamma^{-1}(k+1)\Gamma^{-1}(-a)f(t-kh)\end{split} \tag{32}\]  
where \(h\) is the sampling time. For the gamma function, the property is obtained as [18]  
\[\Gamma(z+1)/\Gamma(z)=z,\forall z\in C \tag{33}\]  
Thus, \(\Gamma(k-a)/\Gamma(k+1)\) in (32) is rewritten as  
\[\begin{split}&\Gamma(k-a)/\Gamma(k+1)=\frac{\Gamma(k-a)}{\Gamma(k-a -1)}\frac{\Gamma(k-a-1)}{\Gamma(k-a-2)}\frac{\Gamma(k-a-2)}{\Gamma(k-1)}\frac{ \Gamma(k-1)}{\Gamma(k)}\frac{\Gamma(k)}{\Gamma(k+1)}\\ &=\frac{k-a-1}{k}\frac{k-a-2}{k-1}\frac{\Gamma(k-a-2)}{\Gamma(k- 1)}=\frac{\Gamma(-a)}{\Gamma(1)}\overset{k}{\underset{n=1}{\prod}}\frac{n-a- 1}{n})\end{split} \tag{34}\]  
The result of the Grunwald-Letnikov fractional derivative can be calculated using (34). When k is large (large t), a considerable amount of calculation is needed. As the term \(0<(n-a-1)/n<1\) for \(n>2\), \(\Gamma(k-a)/\Gamma(k+1)\) keeps decreasing. \(\Gamma(k-a)/\Gamma(k+1)\approx 0\) was assumed when \(k\geq 50\). Thus, the fractional-order derivative was calculated in the real application as  
\[{}_{GL}D_{t_{0},t}^{\alpha}f(t)\approx h^{-\alpha}\overset{50}{\underset{k=0} {\sum}}\Gamma(k-a)\Gamma^{-1}(k+1)\Gamma^{-1}(-a)f(t-kh) \tag{35}\]  
**Remark 3**.: _In the calculation of the fractional-order derivative with the numerical method, the singularity problem does not exist. Compared to the TSMC, the term in control input, such as \(-D_{0,t}^{1}(\lambda_{1j}sig(e_{j})^{r_{1j}})\), could have a singularity problem when \(e_{j}=0\) and \(\dot{e}_{j}\
eq 0\)._
# A Deep Learning Model for Data-Driven Discovery of Functional Connectivity
## 3 Results
### Region Selection  
The pooling layer added in our GNN module allows for us to reduce the number of regions. Functionality across brain regions differs significantly and not all regions are affected by a disorder or have any noticeable affect on classification. This makes it very important to know which regions are more significantly informative of the underlying disorder and study how they get affected or affect the disorder. Figure 6a shows the final 23 regions that were selected after the last pooling layer in the GNN model, which is just 20 percent of the total brain regions used. The relevance of these regions is further signified by the fact that the graph model has no residual connections and the final feature vector created after the last GNN layer is through the feature vectors of these regions. Figure 6b shows the location of the selected regions in the MNI brain space, regions are distinguished by color. Each region is assigned one unit from the color bar, which is used to represent signal variation in the fMRI data.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Test** & \(\mathbf{p}\)**Value** \\ \hline Mann-Whitney U Test & 0.0 \\ \hline Welch’s \(t\)-test & 0.0 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Statistical testing between weight matrices of healthy controls (HC) and schizophrenia (SZ). The test shows that weights of regions differ across HC and SZ subjects. Refer to Figure 4 for mean and deviation of these folds.  
Figure 5: Connectivity between regions of subjects of both classes using BrainGNN and sFNC (PCC method). **BrainGNN:** the similarity of connection between a class and difference across class is compelling. Weights of SZ class are more sparse than HC, highlighting the fact that fewer regions receive higher weights for subjects with SZ. Refer to Table 2 for results of statistical testing between weights of HC and SZ subjects. **sFNC**: the matrices are symmetric but are less informative than those that were produced by BrainGNN. Most of the regions are assigned unit weight.
## 6 Remaining Challenges
### Assumptions on the Threat Model  
In this work, it is assumed that 3 rd parties (or external adversaries) are the main risk sources of federated learning systems. However, it is important to recognize the various types of thread models that FL systems are subjected to [46].  
For example, it might not be the case that the attackers are foreign agents, as the vulnerabilities in a federated learning setup might emerge from within the group of participating clients that contribute data during the learning phase. Attacks originating from these clients pose a significant risk, potentially exerting greater influence than those during the inference phase. This is because adversarial clients in federated learning possess the ability to not only exploit the model service boundaries in production but also actively manipulate and alter the model's boundaries during its development stage [46].  
Guaranteeing resilience against model backdoors is a critical concern. Within the realms of machine learning and computer security, various backdoor attack methods have emerged, targeting neural network models. Thus, questions such as \"Can we ensure robustness against these back doors by strategically designing backdoor-proof models?\" and \"Does the challenge of avoiding backdoors become self-referential, considering that models inherently learn from the dataset's distribution?\" arise. It is anticipated that approaches that gauge or maintain resilience [46] or leverage robust knowledge representations against such attacks display great potential in this direction.  
However, privacy is of lesser concern in federated learning (FL) because the data are distributed across multiple devices, and the model training process is performed locally on the devices. There are methods proposed to enhance privacy, such as differential privacy [31], homophobic encryption (HE) [47], and secure aggregation (SA) [48].  
For instance, similar to data obfuscation, differential privacy adds random noise to the data to mask user data and protect their privacy [31]. This ensures that the model's output does not depend on any single user's data by adding sufficient noise to the training data to protect the users' data, while still maintaining the overall quality of the model. In FL,the model is trained by aggregating the model updates from multiple devices, which can comprise privacy and potentially expose the users' data to the federated server (FS). Some approaches leverage homomorphic encryption, allowing computations to be performed on encrypted data without decrypting it. This is achieved in FL by encrypting the model before sharing it with the FS, and the FS can perform aggregation on encrypted models [47]. Another approach leverages secure aggregation methods based on multi-party computations. In FL, the FS computes the sums of model parameter updates from individual user devices in a secure manner, without revealing the individual user's data [48].
## 4 Result and Discussion
### Single Dynamic Impact Test
#### 4.1.5 Failure Pattern  
Figure 9 shows the failure state of fiber non-oriented specimens under a dynamic impact test with three fiber contents. When 2% fiber was added, the specimen can maintain its integrity under the conditions of a low strain rate and lower strain rate. However, when the impact velocity increased and the high strain rate reached 109.18 s\({}^{-1}\), the specimen could not bear it and fell apart. When the fiber content was 4%, the ability of the specimen to maintain its integrity after the impact compression load was significantly improved; at low strain rates, only a small number of concrete fragments in the appearance of the corners were peeled off. At the higher strain rate of 155.12 s\({}^{-1}\), only one corner of the specimen was broken, and the main crack appeared in the middle of the block, but it did not break completely. It only completely broke when the bullet hit at a higher velocity. At the same time, the increase of fiber content greatly improved the ability of the specimen to maintain its integrity after the impact load. When the fiber content was increased to 6%, the specimen still maintained great integrity even at a high strain rate.  
Figure 8: Influence of fiber content on energy absorption property in dynamic impact test (fiber-oriented).  
Fiber-oriented specimens had unique appearance characteristics in the form of impact compression failure, as shown in Figure 10. After undergoing impact compression loads, all cracks were in the same direction as the fiber center line. The D-SF2 specimen showed the clearest failure mode at the strain rate of 152.97 s\({}^{-1}\). The two main fracture surfaces are completely consistent with the fiber orientation, and several other fracture branches also maintained a small angle. This phenomenon was mainly because, in the direction of the fibers, even if the concrete produces great stress and transverse expansion, there were still a lot of fibers between the specimens to provide a bridging effect, which inhibits the development of cracks in the specimens. However, in the direction perpendicular to the fiber, the fiber can provide very little bridging force to the matrix. At the same time, the cracks in the fiber direction were also distributed in the vertical direction, so there was a large deformation in the vertical direction, resulting in parallel cracks or z-shaped cracks.  
Figure 9: Failure pattern of fiber in non-oriented UHPC impact compression test.
## 3 Results and Discussion
### Response Surface and Contour Analysis  
The effect of the interaction between SP, PFA, and SF on the compressive strength was analyzed using response surface and contour analysis based on the regression equation for the compressive strength of the manufactured sand concrete. Another factor was controlled for at an intermediate level when discussing the pattern of interaction effects on compressive strength. The intermediate levels of SP, PFA, and SF content in this work were 10%, 15%, and 5% of the cement mass, respectively, as described in the previous section.