esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Works
### Image Emotion Prediction/Annotation
#### 2.2.2 Dimensional Emotion Estimation in Images  
Image-based dimensional emotion estimation seeks to enable machines to accurately and comprehensively interpret human emotions from visual data. Unlike traditional categorical emotion recognition, which classifies emotions into discrete labels (e.g., \"happy\", \"sad\", and \"angry\"), this approach draws from psychological theories of dimensional emotions to represent emotions on continuous scales. These dimensions allow for a nuanced understanding of emotional states, capturing subtleties and complexities beyond categorical boundaries. Inspired by psychological theories on dimensional emotions [64] and foundational theories such as [PERSON] and [PERSON]'s PAD model [65], dimensional emotion estimation focuses primarily on valence, arousal, and, in some cases, dominance [65].  
Valence: Represents the positivity or negativity of an emotion, ranging from unpleasant to pleasant. Valence reflects the subjective experience of feeling good or bad in a specific context. For example, a smile might indicate a positive valence, while a frown could signify a negative one.  
Arousal: Indicates the level of excitement or activation associated with an emotion, ranging from calm to excited [64] (refer to Figure 2). It captures emotions' physiological and psychological activation, such as increased heart rate or heightened alertness. For instance, fear and excitement may share high arousal levels but valence differences.  
Dominance: Reflects the degree of control or influence one feels over a scenario or one's emotions, ranging from submissive to dominant.  
Utility: Refers to an emotion's perceived usefulness or value in a given context. It also encompasses the sense of control or influence. Utility helps evaluate how emotions impact decision-making and behavior, such as confidence in a dominant emotional state or hesitation in a submissive one.  
Goal of dimensional emotion estimation: The primary aim is to provide a comprehensive and accurate representation of emotions by capturing subtle variations and interdependencies among emotional states. Specific goals include (i) Enhancing Emotional Understanding by providing a detailed, nuanced picture of emotional experiences, especially for complex or mixed emotional states; (ii) Improving Machine Perception by enabling machines to interpret and respond to human emotions in a way that feels natural and empathetic; (iii) Facilitating Multidisciplinary Applications in psychology, marketing, healthcare, and robotics, where emotional intelligence is critical; and (iv) Adapting to Dynamic Contexts by incorporating temporal modeling to capture how emotions evolve over time in response to external stimuli.  
Figure 2: The 2D and 3D emotion coordination. (**Left**): example of an environmental psychology model with two axes that show various adjectives to indicate the level of pleasure (X-axis) and arousal (Y-axis). (**Right**): pleasure (valence), arousal, and dominance (PAD or DVA) model (3D) reproduced based on [64].
## 3 Efficient Dynamic Perception Net
### Image Segmentation
#### 3.2.2 Ldfm  
We feed the ice-covered transmission line image to the encoder to extract image features at different levels and then fuse these features with the decoder features through cross-layer connections. In U-Net, feature fusion is achieved by cascading encoder and decoder features. This approach of only concatenating two features is relatively simple, resulting in incomplete fusion between encoder and decoder features. We propose a lightweight deep fusion module that uses lightweight attention to deeply fuse the features of the encoder and decoder.  
ECA-Net is an efficient and lightweight attention module proposed by [PERSON] et al. [39] in 2020. ECA-Net uses a local cross-channel interaction strategy to significantly reduce the complexity of the attention module while maintaining performance. Figure 3 is a schematic diagram of ECA-Net. First, after using Global Average Pooling (GAP) without dimensionality reduction to aggregate convolution features, the one-dimensional convolution kernel size K is adaptively selected. After one-dimensional convolution, the channel attention is calculated using the Sigmoid function.  
Figure 2: Schematic diagram of the LMRC.  
Figure 4 is a schematic diagram of the lightweight deep fusion module. The following is the pseudocode implementation of Algorithm 3, namely LDFM:  
```
1: Feature = Add(Encoder Input, Decoder Input)
2: Attention = ECA-Net(Feature):
3: Feature = AvgPool(Feature)
4: Attention = Conv_1\(\times\)1(Feature)
5: Attention = Sigmoid(Attention)
6: Encoder Output = Attention \(\times\) Encoder Input
7: Decoder Output = Attention \(\times\) Decoder Input
```  
**Algorithm 3** LDFM  
The lightweight deep fusion module first adds the encoder and decoder features to fully fuse the feature information, then sends the fused features to ECA-Net to extract the fused attention information. The fused features undergo average pooling along the spatial dimensions to compress spatial information. Then, 1 \(\times\) 1 convolution is used to extract channel attention, and the attention weights are multiplied with the encoder and decoder features to obtain the attention-weighted features. Finally, the cascaded encoder and decoder features are sent to the decoder to generate the ice segmentation image.  
The attention weights extracted after adding the encoder and decoder are calculated from the fused features and contain information about the encoding and decoding features. Assigning attention weights to both helps the decoder pay more attention to the important features.
# Fuzzy Sliding Mode Control of Vehicle Magnetorheological Semi-Active Air Suspension
## 5 Simulation Analysis of Fuzzy Sliding Mode Control
### Impacted Pavement Excitation Response  
Vehicle driving is not only the existence of random continuous vibration; sometimes, there will be low-lying, protrusion sections, which bring strong impact to the vehicle. Therefore, in order to fully reflect the vibration reduction ability of the vehicle suspension system, the impact road surface model was established to replace the impact vibration of the vehicle on the road surface. Figure 19 shows the simulation curve of impact road surface input, and its mathematical model is as follows:  
\[x_{0}(t)=\begin{cases}\frac{H}{2}\Big{[}1-\cos\Big{(}\frac{2\pi u}{W_{0}}t \Big{)}\Big{]}&\frac{W_{0}}{u}\leq t\leq\frac{2W_{0}}{u}\\ 0&\text{other}\end{cases} \tag{46}\]  
where \(x_{0}(t)\) represents the input displacement of the impact road, \(H\) is the height of the impact road with a value of 0.07 m, \(W_{0}\) represents the width of the impact road with a value of 0.08 m, \(u\) donates the vehicle speed, and the value is 3.08 km/h.  
The simulation curve of the 2-DOF quarter vehicle semi-active air suspension controlled by FSMC impacting the road surface is shown in Figure 20. It reflects that the peak value of the FSMC-controlled semi-active air suspension is significantly reduced, compared with that of the passive and SMC-controlled semi-active air suspension under the same impact road excitation.  
Compared with passive suspension, the peak reduction percentages of body acceleration, suspension dynamic travel and wheel dynamic load of semi-active suspension under SMC control and FSMC control are shown in Figure 21, in which the body acceleration under SMC control and FSMC control are 13.9% and 27.5%, respectively, lower than that of passive suspension. The suspension dynamic travel is reduced by 12.8% and 26.5%, respectively, while the wheel dynamic load is reduced by 5% and 9.6%, respectively.  
Figure 19: Impacted pavement input simulation curve.  
Figure 21: Percentage reduction of peak response under impacted pavement surface.  
Figure 20: Simulation curve of air suspension system under impact road excitation. (**a**) Body acceleration, (**b**) suspension dynamic displacement, (**c**) tire dynamic load.
# A Hybrid Deep Learning Model Based on FFT-STL Decomposition for Ocean Wave Height Prediction
## 4 Conclusions  
In response to the multi-scale dynamic challenges of ocean wave height prediction, this study proposes a hybrid prediction framework that integrates signal processing and machine learning. The characteristics of the wave height series were extracted by Fourier spectrum analysis and STL decomposition, and BiLSTM, SARIMA and 1D-CNN were used to model the trend, seasonality and remainder, respectively. In the training site scenario, the model showed high prediction accuracy. The mean square error (MSE) of the model prediction was calculated to be 0.0087 and the root mean square error (RMSE) was 0.0935,  
Figure 14: Comparison of predicted wave height and actual wave height (46047 station).  
Figure 13: Data decomposition results through STL (46047 station).  
which verified the effectiveness of the collaboration between multi-scale decomposition and heterogeneous models. The model has limited generalization ability across locations. When applied to different sites, the MSE increases to 0.1123, indicating that the model is sensitive to local environmental characteristics and needs to be combined with physical prior knowledge to enhance spatial adaptability.  
The contributions of this paper are threefold. First, it introduces a novel hybrid model that combines STL decomposition and an LSTM network, which provides a balanced approach for wave height prediction and improves interpretability and prediction accuracy. Second, the proposed framework has practical significance for marine engineering, offshore safety, and renewable energy development, as accurate wave height forecasting is essential. Finally, this study bridges the gap between data-driven methods and traditional physics-based models, establishing a framework that combines machine learning with oceanographic research. Future research will prioritize the integration of multi-site heterogeneous datasets and physical constraints based on spectral wave propagation models to alleviate domain adaptability limitations in spatially heterogeneous marine environments.  
Y.S.; writing--original draft preparation, L.Y.; review and editing, D.Z.; data curation. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (Grant No. 42365006).  
The data supporting this study are available from the corresponding author upon reasonable request.  
The authors declare no conflicts of interest.
# Phased Array Antenna Analysis Workflow Applied to Gateways for LEO Satellite Communications
## Abbreviations  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} LEO & Low Earth Orbit \\ ADS & Advanced Design Software \\ EIRP & Effective Isotropic Radiated Power \\ SOTM & SATCOM On The Move \\ GEO & Geostationary Earth Orbit \\ IoT & Internet of Things \\ TT\&C & Tracking, Telemetry and Commands \\ MEO & Medium Earth Orbit \\ MSS & Mobile Satellite Services \\ MIMO & Multiple Input Multiple Output \\ HTS & High Throughput Satellite \\ FCC & Federal Communications Commission \\ EM & Electromagnetic \\ UAN & User-Defined Antenna \\ RF & Radiofrequency \\ NF & Noise Figure \\ PCB & Printed Circuit Board \\ \end{tabular}
## 3 Hardware Implementation
### Feature Extraction Unit  
After R-peak detection, we adopt a 128-sample window (at 360 Hz sampling rate) for each heartbeat for feature extraction, which spans from 48 samples before the R peak to 79 samples after the R peak. This window covers the QRS segments that are essential for the arrythmia detection task.  
To perform so, we have the discrete wavelet transform (DWT) as our principal analysis method. To keep the computational complexity low, we have selected the Haar wavelet--the simplest wavelet function--as the wavelet function. And based on our Matlab simulation results, the Haar wavelet only results in negligible performance loss.  
The HDWT can be calculated by filtering the signal, followed by a down-sampling process as shown in Figure 10. The signal is partitioned into approximation coefficients, which correspond to the low-frequency signal, and detail coefficients related to the high-frequency portion. After interactions, we achieve the desired resolution level.  
The main feature of each heartbeat is captured by the scale-3 approximation coefficient, which we select as the feature vector for each heartbeat and apply as input to the next classification stage. In the hardware implementation, the idea of pruning is used to reduce the computational complexity; we only need to execute the specific calculation (represented in blue in the above figure) to obtain a3 and ignore the computation of d1, d2, and d3, as they are not relevant to our analysis.  
Mathematically [39], given an input signal, \(X(n)\) = {x1, x2, x3,..., xn}, where n is the number of elements in the vector, the approximation coefficients of the first level resolution will be calculated according to the following equation:  
\[a1=\left\{\frac{x_{1}+x_{2}}{\sqrt{2}},\frac{x_{3}+x_{4}}{\sqrt{2}},\frac{x_{5 }+x_{16}}{\sqrt{2}},\ldots\ldots,\frac{x_{n-1}+x_{n}}{\sqrt{2}}\right\} \tag{15}\]  
By applying the above equation three times, we can obtain the calculation equation for a3.  
\[a3=\left\{\frac{\sum_{i=1}^{8}x_{i}}{2\sqrt{2}},\frac{\sum_{i=9}^{16}x_{i}}{2 \sqrt{2}},\frac{\sum_{i=17}^{24}x_{i}}{2\sqrt{2}},\ldots\ldots,\frac{\sum_{n- 7}^{n}x_{i}}{2\sqrt{2}}\right\} \tag{16}\]  
Figure 10: DWT filter bank with 3 levels.  
After this, the next step is to optimize the hardware architecture of the HDWT by leveraging approximate computing. Considering the complexity introduced by the presence of \(\sqrt{2}\) in the above formula, implementing it in hardware design becomes challenging. Therefore, we approximate \(\sqrt{2}\) using the following formula, and the symbol \"\(>\)\" in the formula represents a bit shift operation.  
\[\frac{X}{2\sqrt{2}}\approx 181*2^{-9}=(128+32+16+4+1)\gg 9 \tag{17}\]  
The hardware architecture of the HDWT-based feature extraction engine, as shown in Figure 11, only contains addition and bit shift operations.
# Renewable Energy-Based Energy-Efficient Off-Grid Base Stations for Heterogeneous Network
## 4 Results' Analysis
### Economic  
The requirement of the total battery for the proposed model has increased with the growth of the total load. As macro BSs has to deal with the highest load, a total of 32 of the batteries for the energy storage purpose is required. The number of batteries required for micro, pico, and femtocells are 16, 8, and 8, respectively, to back the load request of the BS throughout the shortage or absence of RES. The number of batteries are not feasible less than 8 because the proposed model uses the \"Trojan L16P\" battery. The DC bus bar voltage is 48V. The terminal voltage of each battery is 6V. HOMER calculates the battery bank autonomy using Equation (1). The annual battery bank autonomy under different network configurations are illustrated in Figure 16. The battery bank autonomy is inversely proportional to the BS load or energy demand. For the same reason, higher system bandwidth reveals lower battery autonomy. For a reliable and cost-effective supply system, these results are justified.  
Bio-feedstock is the raw material of BG. In our proposed system, only macro and micro BSs are connected to both Solar PV and BG, so they are liable for bio-feedstock consumption. Bio-feedstock consumption is linearly proportional to the amount of energy harvested from the BG, and for our proposed model, the annual consumption has shown in Figure 17. It  
Figure 16: Battery autonomy under different system bandwidth.  
Figure 15: Energy breakdown under different system BW for the pico (**left**) and femto (**right**) BS.  
is clear from the previous energy breakdown figures that total energy consumption has proportionally been increased with the increases in BW, but BG is not the only source for the energy generation of macro and micro BSs. As a result, bio-feedstock consumption has not proportionally increased with the BW, though macro BSs' average bio-feedstock consumption is far more than micro BSs.
# Postural Strategies Used While Donning a Simulated xEMU Spacesuit
## 2 Materials and Methods  
Research experiments were conducted at the Center of Biomechanical & Rehabilitation Engineering (CBRE) at the University of the District of Columbia (UDC), in Washington, DC, and at the Anthropometry & Biomechanics Facility (ABF) at the NASA Johnson Space Center (JSC) in Houston, TX. Within the UDC CBRE, the experimental protocol was approved by the Institutional Review Board (Protocol ID: 1837316-1) and informed consent was given by the participants prior to engaging in the study. An overview of the research study is shown in Figure 1.  
Participants were recruited via flyer postings around the University campus, as well as by word of mouth. Seventeen physically fit and healthy individuals (6 males and 11 females) aged 20-40 years old (26.53 \(\pm\) 6.51 years old), participated in the study. None of the participants had a previous injury or condition, nor were they taking any medications that affected their balance and/or gait. No prior training was required for the experiments. Two participants had prior experience in a spacesuit.  
The xEMU spacesuit raises the COG superiorly on the abdomen, while the portable life support system (PLSS), which is roughly the same weight as the xEMU spacesuit [1], further raises the COG and shifts it away from the body. To induce an analogous COG shift to the xEMU spacesuit [1], a weighted vest (or xEMU vest) or a hard upper body torso (or HUT) were used (Figure 2A,B). The xEMU Vest concentrates the weight on the torso and shifts the COG superiorly to mimic the superior shift of the COG observed with the xEMU spacesuit. The xEMU Vest weighed 20 lbs (9.072 kg), concentrated at the torso. The HUT is a rigid, 3D-printed torso section of the xEMU spacesuit that weighed approximately 11 lbs (4.98 kg). The HUT simulated the spacesuit's shift in COG, both superior to and away from the body and restricted the range of motion in the upper limbs and neck. The HUT altered the resting standing posture of the individual, shifting the shoulders forward, causing the participant to assume a more natural \"astronaut-like\" stance (Figure 2C); the wearer leans forward, and the arms and shoulders appear to be hunched over. Participants were tested unsuited (control) and while donning the xEMU Vest (Figure 2A), as well as using the HUT (Figure 2B).  
Figure 1: Overview of participant allocation, test configurations, and test conditions.
# An Access Control Model for Preventing Virtual Machine Hopping Attack
## 3 Preliminaries
### Access Control
#### 3.2.2 Biba Model  
The Biba model [25], proposed by [PERSON] in 1977, is the first model that involves the integrity of computer systems. The Biba model was developed after the BLP model and was used to address application data integrity issues.  
The Biba model supports five kinds of control policy: (1) Low-water-mark mandatory access control policy (LOMAC), (2) low-water-mark mandatory access control policy for object (LOMAC-O), (3) low-water-mark integrity audit policy (LO-IA), (4) ring policy (RP), and (5) strict integrity policy (SIP). Among these security policies, strict integrity is the most famous one, which is mathematically dual with the BLP security policy model. Since the strict integrity policy is most frequently used, the Biba model refers to this specific policy in most instances.  
Strict integrity policy is a mathematical dual of the confidentiality strategy based on Trusted Computer System Evaluation Criteria (TCSEC). The strict integrity policy provides No Read Down (NRD) and No Write Up (NWU) characteristics. From these two characteristics, the Biba and BLP models have exactly opposite characteristics. The BLP model provides confidentiality, while the Biba model guarantees the integrity of data.
# Influence of Cracking on the Durability of Reinforced Concrete with Carbon Nanotubes
## 1 Introduction
In fact, the incorporation of CNT in concrete is expected to essentially affect the source of time-dependent shrinkage and creep, by densifying the cement paste microstructure (nucleation and filler effect) and by controlling the propagation and development of microcracks (bridging effect) [18; 23]. Regarding the characterization of CNT-reinforced cementitious materials, various studies have been carried out focusing on the physical, mechanical and durability characterizations of cement pastes and mortars [20; 22; 23; 30], and few on those properties of CNT-reinforced concrete [16; 17; 18; 21; 24]. The studied properties of CNT-reinforced cementitious materials include compressive strength [16; 17; 18; 20; 21; 22; 19; 23; 17; 24], flexural strength [17; 19; 20; 23; 24], splitting tensile strength [17], modulus of elasticity [17; 18; 22; 23], ultrasonic pulse velocity [18; 23], fracture toughness [17; 23], steel-concrete bond [21], shrinkage [18; 22; 23], creep [18], microscopic analysis [17; 18; 20; 22; 23; 17; 24], capillary absorption [16; 22; 24], absorption by immersion [22; 24], carbonation [24], chloride ion penetration [24], and oxygen permeability [16]. However, despite its relevance, to the best of the authors' knowledge no study has been carried out on the durability of cracked CNT reinforced concrete. In this study, the influence of natural and artificial induced cracks on the durability of concrete reinforced with CNT is investigated. Pre-cracked concrete mixes, unreinforced or reinforced with 0.1% CNT, are characterized and compared to the uncracked reference concrete in terms of their main fresh properties, mechanical strength and, especially, durability (capillary absorption, carbonation, and chloride attack).