esa-sceva/satcom-chunk-collection huggingface dataset:
# Emotion Recognition from Videos Using Multimodal Large Language Models
## 4 Methods
### Integrating MLLM-Generated Description Features into a Transformer-Based Architecture  
This approach generates textual descriptions of the videos using Video-LLaVA [12] with the same prompts employed in the probing strategy. These descriptions are then used to extract textual features, which were integrated into a state-of-the-art multimodal architecture that combines visual, acoustic, and textual information.  
Figure 3 shows how textual features from Video-LLaVa [12] are integrated. First, it generates detailed textual descriptions for each video. These descriptions aim to capture the emotional nuances expressed by the subjects in the videos. Then, a text embedding model, i.e., RoBERTa, extracts meaningful textual features from the generated descriptions. These embeddings capture semantic information relevant to the emotions. Subsequently, the extracted textual features are integrated into an existing multimodal framework, namely ViPER [9], whose architecture is specifically designed to leverage visual, acoustic, and textual features to address the video emotion recognition task. This integration is achieved by replacing the textual embeddings produced by ViPER [9] with those extracted from Video-LLaVA-generated texts. Notably, Video-LLaVA [12] creates a single textual description for the entire video, whereas ViPER [9] exploits frame-level representations. To inject the new textual embeddings into the existing ViPER [9] framework, the new textual embedding is replicated to enrich each ViPER [9] multimodal token.  
We also explore an alternative approach where, instead of using Video-LLaVA [12] to describe the entire video, LLaVA [11] is used to describe individual video frames separately. The aim is to enrich each multimodal token with a different textual embedding tailored to the specific frame rather than replicating the same embedding across all tokens. This method provides us with a more granular alignment of textual and visual information, potentially enhancing the model's ability to capture frame-specific emotional nuances. A sketch of this approach is depicted in Figure 4.  
Figure 2: Sketch of the probing pipeline. Video-LLaVA produces meaningful video embeddings through token average pooling, to which a probing network is applied to predict emotion scores. The circle on the right side focuses on the internal structure of the probing network.  
Figure 4: Integration of LLAVA textual features into the state-of-the-art ViPER [9] architecture. The video is sampled to extract 32 equally spaced frames, used to feed the LLAVA model. Then, it produces a different description for each frame. Finally, these descriptions are encoded and concatenated to the corresponding input token of the Perceiver module of ViPER [9].  
Figure 3: Integration of Video-LLAVA [12] textual features into the state-of-the-art ViPER [9] architecture. First, the entire video is passed as input to the Video-LLAVA [12] model. This MLLM produces a unique description that is encoded and concatenated to each input token of the Perceiver module of ViPER [9].
## 4 Pre-Processing and Classification
### Pre-Processing  
The dataset can be refined using multiple pre-processing techniques, including data augmentation, removing null values, eliminating outliers, and selecting key features. The application of such techniques varies according to the need and performance. However, these techniques have proven beneficial in achieving superior performance when implementing ML and DL methods. The presented review also investigated some of the pre-processing techniques adopted by several authors in asthma and environmental condition prediction. Data enhancement using the Synthetic Minority Oversampling Technique, which produces synthetic samples for the minority classes to address class imbalance, was exploited in the work by [25; 45]. [PERSON] et al. [54] employed a generative adversarial network, which is an ML framework with two pertinent components, including a generator and discriminator, and is famous for data augmentation purposes. Similarly, audio data enhancement using the PyDub library for respiratory sounds was considered by [PERSON] et al. [35]. In converting audio sounds to spectrograms for the DL methods using the CNN, [PERSON]. [38] considered the Mel-transformation technique to realize the time-frequency analysis. Since the textual dataset, i.e., EHRs and environment are commonly influenced by various limitations, the most familiar techniques are outlier removal and null values handling [25; 39; 48; 55; 56; 57]. Reducing irrelevant features and selecting important ones is a common pre-processing step, which can be achieved using various libraries. [PERSON] et al. [32] used a correlation
## 3 Experiment Results and Analyses
### Dynamic Elastic Modulus of Sample  
Under the action of axial compression, the change in the dynamic elastic modulus of the prefabricated fractured rock samples vs. the average strain rate is illustrated in Figure 11.  
In Figure 11, the dynamic elastic modulus of the prefabricated fractured rock samples grows with the growth in the mean strain rate. The reason is that the mean strain rate grows with the growth in impact pressure. Due to the growth in impact pressure, the interior of the sample is closer and the resistance to deformation is greater, so the dynamic elastic modulus increases.  
Formula (4) shows the quadratic function relationship between the mean strain rate and the dynamic elastic modulus of the prefabricated fractured sandstone samples.  
\[E=18.43+0.087\dot{\varepsilon}+3.53\times 10^{-5}\dot{\varepsilon}^{2}\quad(R ^{2}=0.9949) \tag{4}\]  
where \(E\) is the dynamic elastic modulus of the prefabricated fractured sandstone specimens.
# Non-Maximum Suppression Performs Later in Multi-Object Tracking
## 1 Introduction
In conclusion, the main contributions of this paper are as follows:  
* In order to avoid good detection boxes being filtered out and reducing the impact of detection tasks on tracking, a method of performing NMS later in tracking is proposed. NMS does not play a role in the detection stage, but plays a role later, serving the tracking and prediction stage, effectively improving the performance of MOT. * Experimental results show that the method proposed in this paper can effectively solve the occlusion problem. * The effectiveness of the method proposed in this paper is verified through a large number of experiments, and good results have been achieved on the MOT17 and MOT20 datasets, and the state-of-the-art FP and ML metrics have been achieved.
## 5 Analysis of Hydrodynamic Coefficients  
The hydrodynamic coefficients of floating OWTs are primarily dominated by the wave exciting force and the radiation force (including added-mass and damping terms), which are usually solved by the potential-flow theory or the Morison equation. The choice of the potential-flow theory or the Morison equation is dependent on the size of the floating platform and the flow field. When the ratio of the diameter of the floating platform to the wave length cannot be neglected, the potential-flow theory would be adopted rather than the Morison equation, and vice versa. More specifically, Morison equation is applicable for calculating the hydrodynamic loads on slender cylinders when (1) the diffraction effects are negligible; (2) radiation damping is negligible; (3) flow separation may occur. The definition of a slender cylinder implies that cylinder diameter, \(D\), is small relative to the wave length, \(\lambda\), which is generally met when \(D/\lambda<\)0.1-0.2 in moderate to severe sea states.  
In order to acquire convergent hydrodynamic coefficients, the grid-independence test would be carried out for surge, heave and pitch modes, respectively. Accordingly, the wave angular frequency is set within the range between 0.01 rad/s to 10 rad/s. The wave incidence angle is 0\({}^{\circ}\) pointing to the positive \(X_{0}\)-axis. The ocean current speed, the moving speed of the floating OWTs and the wind speed are neglected in the simulation. The grid-independence tests of hydrodynamic coefficients for four individual floating OWTs are introduced in the following. It is known that the viscous damping induced by the hull drag plays a significant role in calculating hydrodynamic forces of the OC3-Hywind spar or TLPs under the most relevant wave conditions. The other alternative is that the hydrodynamic forces of the floating OWTs can be augmented with additional linear damping, which can be obtained from the free decay test. Although linear damping and viscous damping were not considered in the present hydrodynamic model, the discrepancies of RAOs between our model and other codes would be realized by discussing the damping effect.
## 4 Empirical Quasi-Static Model of Single cable
### Relationship between \(T_{z}\) and \(T_{x}\)  
In Statement 2, we state that \(T_{z}=a_{\pi}T_{x}+b_{\pi}\) with \(a_{\pi}\) and \(b_{\pi}\) depend on \(\mathbf{B}^{d}\) and \(\rho\). According to Equations (26) and (27), the relationship between \(T_{z}\) and \(T_{x}\) can be expressed as Equation (29).  
\[T_{z}=\tan(\beta)T_{x}+h_{b}D \tag{29}\]  
Substituting the parameter (28) to (29), the affine line \(\pi\) can be expressed as the set (30).  
\[\pi=\left\{(T_{x},T_{z}):T_{z}=\tan(\beta)T_{x}-\frac{\rho gD}{2}\right\} \tag{30}\]  
The second statement pointed out that the solution of the quasi-static problem of single cable is not unique and the particular solution just can be resolved when the cable unstrained length \(L\) is determined. In Section 5, we will try to establish the relationship between cable tension act on the extremity \(\mathbf{B}\) and the unstrained length of cable by using both analytical approach and empirical approach.
# Co-Seismic Magnetic Field Perturbations Detected by Swarm Three-Satellite Constellation
###### Abstract  
The first 5.3 years of magnetic data from three Swarm satellites have been systematically analyzed, and possible co-seismic magnetic disturbances in the ionosphere were investigated just a few minutes after the occurrence of large earthquakes. We preferred to limit the investigation to a subset of earthquakes selected in function of depth and magnitude. After a systematic inspection of the available data around (in time and space) the seismic events, we found 12 Swarm satellite tracks with co-seismic disturbances possibly produced by ten earthquakes from Mw5.6 to Mw6.9. The distance of the satellite to the earthquake epicenter corresponds to the measured distance-time arrival of the disturbance from the surface to the ionosphere, confirming that the identified disturbances are most likely produced by the seismic events. Secondly, we found a good agreement with a model that combined a propagation of the disturbance to the F2 ionospheric layer with an acoustic gravity wave at a velocity of about (\(2.2\pm 0.3\)) km/s and a second faster phenomenon that transmits the disturbance from F2 layer to the Swarm satellite with a velocity of about (\(16\pm 3\)) km/s as an electromagnetic scattering propagation.  
geomagnetic field; co-seismic magnetic disturbance; Swarm satellites; earthquakes; gravity waves +
Footnote †: journal: Remote Sensing, 2020, 12, 1166; doi:10.3390/rs12071166
# Diagnosis by SAM Linked to Machine Vision Systems in Olive Fitting Machines
## 3 Results
### Validation of SAM Segmentation Accuracy
#### 3.3.7 Comparative Benchmark with an HSV-Based OpenCV Baseline Under Ideal Lighting  
Deep-learning segmenters, such as UNet, DeepLab v3+ or Mask R-CNN, are deliberately excluded from the quantitative benchmark because their deployment would negate the main advantage of the present workflow--namely operating without any hand-labelled data. Training a lightweight UNet on Gordal olives alone would require at least 500 pixel-accurate masks (\(\approx\)8 h of expert annotation) and \(\approx\)45 min of GPU fine-tuning; a larger backbone (ResNet-101 + DeepLab) pushes that effort beyond 2000 masks and several GPU-hours. In contrast, zero-shot SAM achieves \(\geq\) 0.90 IoU across all lighting conditions with zero annotation and zero training. We therefore restrict the head-to-head experiment to the HSV-based OpenCV baseline proposed by [PERSON] et al. (2025) [39], as it is the only alternative that, like SAM, can be deployed immediately without a dedicated training set (see Table 9).  
\begin{table}
\begin{tabular}{c c c} \hline
**Metric** & **Mean \(\pm\) SD** & **Interpretation** \\ \hline IoU\({}_{\text{ellipse}}\) & 0.945 \(\pm\) 0.011 & Excellent spatial overlap \\ MAE\({}_{\text{n}}\) (px) & 0.49 & \(\sim\)0.1\% error in major semi-axis \\ MAE\({}_{\text{b}}\) (px) & 1.22 & \(\sim\)0.2\% error in minor semi-axis \\ MAE\({}_{\text{AR}}\) & 0.067 & Aspect-ratio bias \(<\) 7\% \\ RMSE\({}_{\text{\theta}^{\circ}}\) & 6.09 & Small angular deviation \\ \hline \end{tabular}
\end{table}
Table 7: Validation metrics for the Hoijblanca dataset.  
\begin{table}
\begin{tabular}{c c c} \hline
**Step** & **Potential Bias Introduced** & **Quantitative Impact** \\ \hline Mask selector & Olive might be missed \(\Rightarrow\) false negatives & Recall = 1.000/1.000 (none observed) \\ Hole filling & Adds \(\leq\) 2-px rim inside cavities \(\Rightarrow\) slight & Precision—IoU = +0.03/+0.09 \\  & over-segmentation & \\ Speck removal & Deletes stray white pixels; if threshold too strict, may & Bias already accounted for in values above \\  & cut true edge \(\Rightarrow\) under-segmentation & \(\text{MAE}_{\text{a}}\) = 2.6/0.5 px\({}_{\text{Y}}\) MAE\({}_{\text{b}}\)= 3.0/1.2 px \\ Ellipse fitting & Smooths jagged contour; may shrink or expand axes & \(\text{RMSE}_{\text{\theta}^{\circ}}\) = 5.1\({}^{\circ}\)/6.1\({}^{\circ}\); \\  & IoU\({}_{\text{ellipse}}\) = 0.938/0.945 \\ Mask selector & Olive might be missed \(\Rightarrow\) false negatives & Recall = 1.000/1.000 (none observed) \\ \hline \end{tabular}
\end{table}
Table 8: Bias introduced by each post-processing step and its quantitative impact on final segmentation accuracy (dataset means for Gordal/Hoijblanca).
## 3 Demand Forecasting Models and Power Theft Detection Strategy
### Power Theft Detection Algorithm  
Energy is the fundamental resource to make possible every application in domestic, commercial, and industrial environments. The electric grid refers to the combination of transformers, transmission lines, substations, and other components that make energy delivery possible, from the source layout to the field of work in each sector. The complexity of energy generation and distribution systems leads to the necessity of managing and solving several possible issues and challenges. In this context, one of the most significant issues to be addressed is power theft. Particularly in India, power theft is a serious issue that the country has dealt with for many years. No effective solutions have yet been found. A recent survey by the Central Electricity Authority suggests that over 27 percent of the total produced energy from various sources is lost due to the illegal practice of power colony [36]. Consequently, this affects over 5 percent of the country's GDP (gross domestic product). To address the issue, a new scheme is proposed in this paper for analyzing consumer energy and thereby detecting the source of theft, according to the procedure shown in Figure 5. To detect the source of power theft, the developed system is trained with recent data of energy consumption of the past 90 days from the available dataset. The system starts evaluating the mean of the collected data. The statistical mean of the given data is the sum of all the energy consumption values divided by its frequency, which is 90 in our case. The calculated mean 'A' is then stored in the memory manager of the proposed system. Afterwards, the system starts estimating the standard deviation of the data fed.  
Figure 5: Flowchart of power theft detection procedure.  
Standard deviation is defined as the individual differences of the data values with its mean. The purpose of standard deviation in our system is to define the degree of deviation between the adjacent data points. All the odd values of standard deviation are recorded. Among the recorded values of standard deviation, the values of maximum and minimum deviation are considered 'd' and '\(D\)', respectively. Now, for testing the developed system, we consider the current day energy expenditure. In particular, the current energy expenditure '\(B\)' is subtracted with the recorded mean value of the trained data '\(A\)'. The obtained value is denoted by '\(K\)'.  
The final step of the theft detection scheme compares \(K\) and \(D\) values. If the \(K\) value is greater than the \(D\) value, then it can be concluded that power theft is occurring. The intensity of power theft can also be determined from the magnitude of calculated difference '\(D\)'.
# A Novel Dual-Component Radar-Signal Modulation Recognition Method Based on CNN-ST
## 1 Introduction
As can be known from the above references, some methods of radar-signal modulation recognition at the same signal-to-noise ratio (SNR) have been proposed. However, the dual-component radar signal recognition method under different SNRs has not been reported to date. A majority of the proposed radar-signal modulation recognition methods cannot accurately classify dual-component radar signals under different SNRs. The recognition accuracy of radar-signal modulation recognition was relatively lower, particularly under a lower SNR. There is an urgent need to propose an efficient method for recognizing dual-component radar signals under different SNRs and to improve the recognition accuracy. Moreover, for promoting the actual applications in modern electronic warfare, the proposed method would also be used to recognize multi-component radar signals under different SNRs by training the network parameters and designing a multi-label classifier. Therefore, to solve the above deficiencies, this paper presents a novel network model called CNN-swin transformer (CNN-ST) for recognizing randomly overlapping dual-component radar signals under different SNRs, which integrates the swin transformer into the designed CNN model and improves the recognition accuracy. The important contributions include the following: (1) a novel randomly overlapping dual-component radar signal recognition method under different SNRs based on CNN-ST is first presented; (2) it is inspired by the swin transformer with powerful global modeling capability, which integrates into the CNN; (3) for extracting detailed radar signal features, an inverted residual structure and lightweight depthwise convolutions are adopted; and (4) the proposed CNN-ST model achieves better recognition accuracy under different SNRs. The related works of radar-signal modulation recognition are reviewed in Section 2; the dual-component radar signals and data preprocessing are explained in detail in Section 3; the novel network CNN-ST for classifying dual-component radar signals under different SNRs is fabricated in Section 4; The influences of dual-component radar signals under different SNRs on the recognition performance are fully investigated in Section 5; and some important conclusions are summarized in Section 6.
## 5 Simulation Results and Discussion
### Network Parameters and Configurations  
As discussed, the simulation scenario under consideration includes a total of five networks, \(\mathcal{N}_{1}\), \(\mathcal{N}_{2}\), \(\mathcal{N}_{3}\), \(\mathcal{N}_{4}\), and \(\mathcal{N}_{5}\). These networks are implemented in ns-3 and are shown in Figure 9. Some of the key parameters used for the configuration of these networks in ns-3 are comprehensively summarized in Table 1.  
Each node in the network is configured with the dynamic global routing protocol to populate the routing data for routing. The OSPF protocol is used as a dynamic global routing  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Network**} & \multirow{2}{*}{
\begin{tabular}{c} **Network** \\ **Address** \\ \end{tabular} } & \multicolumn{2}{c}{**Parameters**} & \multirow{2}{*}{**Remarks**} \\ \cline{3-3} \cline{5-5}  & & **Data Rate** & & **Delay** \\  & & **(Kbps)** & **(ms)** & \\ \hline \(\mathcal{N}_{1}\) & 191.168.1.0 & 2000 & 2 & Dedicated network for PMU and PDC \\ \(\mathcal{N}_{2}\) & 191.88.1.0 & 2000 & 2 & Mimics the Internet \\ \(\mathcal{N}_{3}\) & 170.1.2.0 & 2000 & 2 & Backup path \\ \(\mathcal{N}_{4}\) & 170.1.3.0 & Variable & 2 & Backup path \\ \(\mathcal{N}_{5}\) & 170.1.1.0 & 2000 & 2 & Background traffic \\ \hline \hline \end{tabular}
\end{table}
Table 1: Key ns-3 configuration parameters for the SCN.  
Figure 9: SCN design using ns-3.  
protocol that works on link-state algorithms to discover networks, find optimum paths, and maintain the routing information. In ns-3, the dynamic global routing protocol in a node is configured using the following command: lpv4 GlobalRoutingHelper::PopulateRoutingTables(), which enables routing capability in all nodes during simulation. This function calls other ns-3 functions, BuildGlobalRoutingDatabase () and InitializeRoutes (), to build a routing database and initialize routing tables, respectively. Nodes \(v_{1}\) and \(v_{6}\) are used as the source and destination, respectively. The PMU is configured with the CBR applications supported by the UDP transport protocol to generate the application at a data rate of 300 Kbps. In particular, an on-off application helper is used to create an application in ns-3. The desired data rate in ns-3 can be set using Equation (21). To achieve a PMU data rate equal to 300 Kbps, the 25 packets/s that result in a packet transmission time of 1/25 s are considered, with each packet being equal to 1.5 KB. In ns-3, such applications with the desired data rate are configured using OnOffHelper.  
\[Data\ rate=\frac{Packet\ size}{Packet\ transmission\ time} \tag{21}\]
## 3 Data Set
### Land Surface Temperature (LST)  
LST is an indicative variable of the net surface energy balance driven by long-wave radiation surface emission [50]. LST is one of the key parameters in the physics of land-surface processes, combining surface-atmosphere interactions and the energy fluxes between the atmosphere and the land surface [51] and influencing processes such as evapotranspiration and vegetation stress [52]. The Terra MODIS 8-day LST with 1 km resolution (MOD11A2, collection v005) was used. The MODIS land surface temperature is derived from two thermal infrared band channels, _i.e._, 31 (10.78-11.28 \(\upmu\)m) and 32 (11.77-12.27 \(\upmu\)m) using the split-window algorithm [53] which corrects atmospheric effects and emissivity using a look-up table based on global land surface emissivity in the thermal infrared band. An accuracy assessment across a wide set of test sites indicates an accuracy better than 1 km with a root mean square (RMS) less than 0.5 K in most cases [54].