esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results  
### Performance of the Multi-LSTM-Sentiment Models  
The performance of the Multi-LSTM-Sentiment model (from Section 3.3.1) was evaluated using error metrics, including mean absolute error (MAE) and root-mean-square error (RMSE), to measure its predictive accuracy on unseen test data. The model achieved an MAE of 0.0093 and an RMSE of 0.0099. These low values indicate that the model generally performed well, with minimal deviations from the actual Bitcoin prices during testing.  
A graph comparing the actual Bitcoin prices and the predicted prices over the test period is shown in Figure 7. The Multi-LSTM-Sentiment model successfully captured the overall trend of Bitcoin price fluctuations. However, the model struggled with sudden price spikes or drops, highlighting areas for improvement in handling high volatility.  
The differences between the actual and predicted values, called residuals, were used to measure the model's errors. The standard deviation of these errors helped estimate how much the predictions might vary. Confidence intervals were calculated by multiplying this standard deviation by a specific value (for example, 1.96 for 95% confidence). These  
Figure 6: Training and validation error trends for the tuned Hybrid LSTM-Volume model across metrics: MSE, MAE, and MAPE. The decreasing trends indicate effective learning and generalisation.  
Figure 7: Multi-LSTM-Sentiment: actual vs. predicted prices.  
intervals are shown as a shaded area around the predicted values on the plot, giving a visual sense of how uncertain the model's predictions were. All figures in this section now have a shaded area demonstrating the confidence score, enhancing the interpretability of the visualized trends and the reliability of the predictions.  
The tuned Multi-LSTM-Sentiment model, which, as described in Section 3.3.2, was derived from fine-tuning the hyperparameters of the Multi-LSTM-Sentiment model, exhibited superior performance. The MAE and RMSE values decreased significantly to 0.00196 and 0.00304, respectively. This highlights the effectiveness of hyperparameter tuning in improving model accuracy.  
Figure 8 compares the actual and predicted prices for the tuned Multi-LSTM-Sentiment model. The predictions aligned closely with the actual prices, demonstrating the model's ability to generalise well. Minor deviations were observed, particularly during periods of rapid price changes, but overall, the tuned model performed exceptionally well.  
A comparative analysis of the performance of the Multi-LSTM-Sentiment and the tuned Multi-LSTM-Sentiment models on test data is presented in Table 2, along with results from the literature. The tuned Multi-LSTM-Sentiment model's improvements in MAE and RMSE confirmed it as the most accurate and reliable.
###### Abstract  
As a new type of earth observation satellite approach, video satellites can continuously monitor an area of the Earth and acquire dynamic and abundant information by utilizing video imaging. Hence, video satellites can afford to track various objects of interest on the Earth's surface. Inspired by the capabilities of video satellites, this paper presents a novel method to track fast-moving objects in satellite videos based on the kernelized correlation filter (KCF) embedded with multi-feature fusion and motion trajectory compensation. The contributions of the suggested algorithm are multifold. First, a multi-feature fusion strategy is proposed to describe an object comprehensively, which is challenging for the single-feature approach. Second, a subpixel positioning method is developed to calculate the object's position and overcome the poor tracking accuracy difficulties caused by inaccurate object localization. Third, introducing an adaptive Kalman filter (AKF) enables compensation and correction of the KCF tracker results and reduces the object's bounding box drift, solving the moving object occlusion problem. Based on the correlation filtering tracking framework, combined with the above improvement strategies, our algorithm improves the tracking accuracy by at least 17% on average and the success rate by at least 18% on average compared to the KCF algorithm. Hence, our method effectively solves poor object tracking accuracy caused by complex backgrounds and object occlusion. The experimental results utilize satellite videos from the Jilin-1 satellite constellation and highlight the proposed algorithm's appealing tracking results against current state-of-the-art trackers regarding success rate, precision, and robustness metrics.  
satellite videos; object tracking; correlation filter; multi-feature fusion; subpixel positioning; adaptive Kalman filter +
Footnote †: journal: remote sensing  
Article
## 4 Discussion
### PEF in Production of Liquid Whey Foods  
In the context of whey protein product manufacture, the conventional methodology (Figure 14a) and a proposed method utilizing pulsed electric fields (PEFs) for liquid whey production (Figure 14b) were juxtaposed. The incorporation of a dual-stage PEF pasteurization process presents a potentially innovative and effective approach in the production line of liquid whey protein products. The initial stage necessitates PEF pasteurization of raw milk prior to the addition of rennet and inoculation. Given the non-thermal nature of PEF technology, there exists no requirement to cool the pasteurized milk before inoculation, as is custom in traditional whey protein powder production. This could feasibly facilitate a reduction in energy expenditure due to the elimination of the cooling process for lactic acid bacterial survivability. The secondary stage of PEF is suggested to be implemented subsequently to the ultrafiltration and diafiltration processes, thereby providing an additional layer of pasteurization and enhancing both the shelf life and safety of the product. In contrast, the traditional methodology involves several additional stages that are energy-intensive, such as evaporation and spray-drying. In the proposed PEF-based production of liquid whey, the principal energy-consuming stage post-ultrafiltration and diafiltration would be refrigeration, hence potentially offering substantial energy savings.
# Grouting Process Simulation Based on 3D Fracture Network Considering Fluid-Structure Interaction
## 2 Methodology
### Fluid-Structure Interaction Model  
The fluid-structure interaction model mainly consists of two parts: computational fluid dynamics (CFD) and computational structure dynamics (CSD). The grouting process is simulated by CFD and the deformation of fractures is calculated by CSD.  
The solution of fluid-structure interaction includes a directly coupled solution and partitioned solution. The first one solves the governing equations of fluid and structure simultaneously in the same solver by coupling the governing equations of fluid and structure to the same equation matrix, so its advantage is that there is no time lag problem. However, a direct coupling solution may result in poor convergence and huge computational cost. As a consequence, it is difficult to realize in fact [38]. On the contrary, the second one solves the fluid governing equations and the structure governing equations in different solvers, and the results are exchanged and transmitted through the fluid-structure interface. In this study, we choose the partitioned solution to solve the fluid-structure interaction between grout and fractures.
# A Bibliometric Analysis and Visualization of In-Vehicle Communication Protocols
## 1 Introduction
Despite the growing number of reviews and studies on intelligent transportation systems (ITS), few have employed a comprehensive bibliometric approach that synthesizes multiple dimensions--such as co-citation analysis, institutional collaboration networks, and co-occurrence keyword mapping--over a robust and updated dataset spanning 2018 to early 2025. This study distinguishes itself by analyzing 2919 scholarly works and identifying not only dominant trends but also emerging under-represented themes, including Time-Sensitive Networking (TSN), CAN FD, and the evolving role of AI-driven intrusion detection systems (IDS). Additionally, unlike earlier reviews that focus on a single aspect or timeframe, our approach integrates diverse bibliometric perspectives and applies VOSviewer-based visualization to reveal latent structures within the scholarly landscape.
## 2 Antenna Design  
Figures 1 and 2 show the geometry of the proposed coplanar antenna and NB antennas for CR applications. The antennas are printed on an FR-4 substrate (\(\varepsilon_{r}\) = 4.4, tan\(\delta\) = 0.02) with a thickness of 0.8 mm. Considering the space required for the NB antennas, the ground dimensions are designed as 42 mm \(\times\) 50 mm (0.88 \(\lambda_{g}\) \(\times\) 1 \(\lambda_{g}\) at 3 GHz). The guided wavelength (\(\lambda_{g}\) wavelength within a dielectric substrate) can be approximated by the following equations:  
\[\lambda_{g}=\frac{\lambda_{0}}{\sqrt{\varepsilon_{eff}}} \tag{1}\]  
\[\varepsilon_{eff}=\frac{\varepsilon_{0}+\varepsilon_{r}}{2} \tag{2}\]  
where 'g' in \(\lambda_{g}\) stands for 'guided', \(\varepsilon_{eff}\) is the effective permittivity, \(\varepsilon_{0}\) is the permittivity of the air, \(\varepsilon_{r}\) is the permittivity of the substrate, and \(\lambda_{0}\) is the desired wavelength in free space. The proposed coplanar antenna consists of a UWB antenna and three NB antennas. The UWB antenna is a CPW-fed tapered slot antenna with a trapezoidal tuning stub, which is connected to Port #1 (P1). The NB antennas are loop-type ground radiation antennas. NB Antenna #1 is fed by Port #2 (P2). It has an interdigital capacitor to achieve low-frequency operation, near the 3 GHz band, and impedance matching [30]. The position of NB Antenna #1 is chosen preferentially because it operates at the lowest frequency and is the longest of the three NB antennas. NB Antenna #1 is located on the upper right corner of the ground to prevent the performance degradation of the other NB antennas, such as impedance matching and radiation characteristics. Ports #3 (P3) and #4 (P4) are assigned for NB Antennas #2 and #3, which are positioned at the lower part of the ground plane. Instead of using an interdigital capacitor, NB Antennas #2 and #3 use a gap with a width of 0.3 mm.
# Potentiality of Rod-Type Chitosan Adsorbent Derived from Sewage Sludge
## 3 Results and Discussion
### Adsorption Study  
#### 3.3.1 Kinetics  
Figure 2 shows the kinetics of metalloid/metal adsorption on RCS as a function of batch contact time. The time to reach equilibrium was different for each metal. For cationic Cd(II), equilibrium was reached after 1 h. As(V) and Cr(VI) sorption was achieved after 3 h of contact time, respectively. The Mn(VII) sorption was finished after 6 h. Table 3 shows that the pseudo-second-order equation, which agrees that chemisorption is the rate-controlling mechanism, was better able to describe the adsorption of As(V), Cd(II), Cr(VI), and Mn(VII) onto RCS. Comparing the amount of adsorbed metal at equilibrium, the order is Mn(VII) \(>\) Cr(VI) \(>\) As(V) \(>\) Cd(II). Notably, the removal of Mn(VII) did not fit the kinetic models for adsorption (Table 3). This means that the Mn(VII) removal mechanism is different from other metals. To investigate the Mn(VII) removal characteristics of RCS, changes in total Mn concentration were investigated (Figure S3). Even after the complete removal of Mn(VII), some Mn remained in the aqueous phase. That is, the Mn remaining in the aqueous phase was Mn obtained by oxidation of Mn(VII). That is to say, some of the Mn(VII) was reduced to other oxidation states of Mn [25]. Cd(II) showed the fastest adsorption, although its adsorption capacity was smaller than that of other metals. In general, carboxyl groups present on biomass are known to be involved in the adsorption of Cd(II) [26].
# Defect Detection in Striped Images Using a One-Dimensional Median Filter
## 2 Materials and Methods
### One-Dimensional Median Filter  
The two-dimensional median filter is commonly used in image processing to remove salt-and-pepper noises. Due to the characteristics of striped images in this research, this study extended the two-dimensional median filter to a one-dimensional median filter to remove the noises along with the stripes. The idea is similar to that of the temporal median filter used in [15]. The application of the one-dimensional median filter is explained as follows. In order to obtain the one-dimensional array, as shown in Figure 2, it is first necessary to find the median of the grayscale values of all elements. Then, the grayscale values of all the elements are replaced with the median. It is then possible to calculate the difference between the new array and the original array, and the elements with significant discrepancies could be noises.  
Figure 1: Defects in the striped images include: (**a**) scratches and stains (marked with boxes). (**b**) Enlargement of a scratch. (**c**) Enlargement of a stain.  
Figure 2: (Top) The original one-dimensional array, in which the median of all the elements is 50. (bottom) The new one-dimensional array consists of the median, 50, for all its elements.
## 1 Introduction  
Cardiac magnetic resonance imaging (MRI) has been widely adopted for the non-invasive assessment of cardiac morphology, function, and tissue characteristics because of its superior soft-tissue contrast compared to other imaging modalities. Although continued technical advancements and developments have pushed the envelope for expanding clinical applications, cardiac MRI remains time-consuming and requires proficient operators, contributing to its overall costs and limiting its more widespread global use.  
Cardiac MRI is time-consuming because of the inherently low signals that are sampled in MRI on the one hand and due to the relatively complex patient setup and cardiac view planning procedures on the other hand. Inefficiency is further exacerbated if signal acquisition is limited to selected time points within the cardiac and/or respiratory cycle. The relatively complex preparations and cardiac view planning, combined with the need to manage both respiratory and cardiac motion, and therefore define additional parameters, make cardiac MRI not only time-consuming but also proficiency-demanding.  
An increasing effort is being undertaken to make cardiac MRI more efficient, less user-dependent, and faster--also in the interest of disseminating cardiac MRI beyond large urban academic centres. Among these efforts is the fully automated free-running framework (FRF), enabling highly efficient cardiac and respiratory motion-resolved whole-heart imaging without the need for electrocardiogram (ECG) electrode positioning, respiratory breath-holding, navigators, and complex cardiac view planning. This review aims to provide a comprehensive overview of the possibilities of the FRF, including its history and background, basic concepts and mechanisms, recent developments and improvements, various (clinical) applications, and general recommendations. Finally, conclusions on the current evidence and limitations are drawn, and new avenues for future research are discussed.
## 5 Experiments and Evaluation  
The empirical evaluation of the WS-UDA framework's performance is conducted in this section. To ensure a fair comparison and rule out the possibility that the observed benefits stem solely from the model's complexity, we maintain an identical configuration for the MAN model in both steps of the framework, as outlined in Table 1. Consequently, any observed improvement is attributed to the framework's capability to unveil additional deep features, enhancing performance through the proposed sequential approach. Like any deep neural network, all hyperparameters are determined through experimental tuning and optimization.  
For the evaluation, we adopt the following settings: cross-validation is employed, wherein each domain of the data set is divided into five folds. Three folds serve as the training set, one as the test set, and the remaining data act as the validation set. The classification accuracy reported in subsequent sections represents the average accuracy over the five folds.  
Two sets of assessments are conducted as follows: the first assessment set for the WS-UDA framework involves a multi-source/single-target configuration. Here, one of the four domains in the data set is identified as the target domain, while the other three serve as source domains. The second assessment set for the WS-UDA framework entails a single-source/single-target configuration. In this scenario, within each data set, one domain is designated as the source, and another as the target domain.
## 3 Comparison of Finite Element Simulation of Almen Test and Almen Test
### FE Simulation of Almen Test
#### 3.1.3 Mesh Size  
In finite element simulation of shot peening, the most challenging aspect is the evaluation of mesh size. In general, much finer elements have been preferred in the impacting area and coarser elements made in the area far from the impacting region. Since the size of the smallest element in explicit FE simulation determines run time, we cannot reduce thoughtlessly the element size. There are several studies [36; 37; 38] that assess the mesh size in FE simulation of the multiple impacts in terms of dimple (dent or indentation) diameter. The deformation of the mesh caused by the impact of shots is shown in Figure 4. The legend is the equivalent plastic strain (PEEQ) around the deformation area.  
[PERSON] et al. [35] chose an element size equal to 1/15 th of dimple diameter produced by a single shot impact. The dimple diameter indicates the magnitude of the concave surface due to the single shot impact. [PERSON] et al. [24] selected the size of elements that equals 1/10 th of dimple diameter. In the [PERSON] et al.'s [37] works, to predict the generation of a nanostructure surface layer of material, the element size in the impact zone was selected to be 1/20 th of dimple diameter.  
Figure 4: Schematic of dimple (dent or indentation) diameter on the Almen strip by a single shot.  
Figure 3: Variation of yield strength with strain rate.  
In this study, where 2-D multiple impacts simulation is performed, the size of elements in the impact zone is chosen to be 0.02 mm, which is about 1/6 th of dimple diameter.
## 2 Transmission Model
The transmit power used for information relaying at the relay \(R_{r}\) is expressed as  
\[P_{R_{r}}=\rho_{r}V|h_{SR_{r}}|^{2}G_{SR_{r}}P_{SD}, \tag{3}\]  
where \(0<\
u<1\) denotes the EH conversion efficiency of the receiver, which is assumed to be constant for all relay nodes. After receiving the messages from the source node in the first time-slot, the process of decoding and forwarding in the relays begins. The relay's ability to correctly decode the source information or not has a crucial impact on system performance. Error-free decoding at the relay nodes is not guaranteed in real practice. If the relay node does not succeed in decoding the message correctly, then the forwarded code-word will suggest a wrong information sequence at the destination node, which is referred to as error-propagation. In the following sections we present coded cooperation schemes with hard and soft information relaying strategies and relay selection to address the issue of error-propagation.
## 3 Prediction Method of Ground Heave Considering the 3D Space Effect  
### Basic Assumptions  
This section illustrates the prediction method of shield tunneling-induced ground heave considering the 3D space effect. Some assumptions are introduced to establish the deformation calculation method, which are as follows:  
1. The diaphragm wall is a linear elastic material, and underground water is not considered.
2. Under the process of tunneling, the soil volume behind the underground diaphragm wall remains constant.
3. Ground heave is induced by lateral deformation of the underground diaphragm wall, which is shown in Figure 3.
4. Diaphragm wall deformation volume \(V_{W}\) has a proportional relationship with deformation volume \(V_{S}\). This is example 1 of an equation: \[V_{S}=MuV_{W},\] (1) where \(Mu\) is coefficient proportionality, which depends on compressibility of soil [38]. Considering silty clay used in Metro Line 12, the value of \(Mu\) can be set as 0.8.
5. In order to simplify the calculation, the weighted average method is used to homogenize multilayer soils.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Soil**} & **Weight** & **Bearing** & **Medulus of** & **Poisson** & **Permeability** \\  & **\(\gamma\)** & **Capacity**\(f_{ak}\) & **\(\varphi\)**(°)** & **Compression** & **Ratio**\(\mu\) & **Coefficient**\(k\) **(cm/s)** \\ \hline miscellaneous & & & & & & \\ fill \(\textbackslash{}_{1}\) & 20.0 & - & 18 & - & 0.25 & \(5.0\times 10^{-3}\) \\ silty clay \(\textbackslash{}_{1}\) & 19.1 & 123 & 11 & 4.5 & 0.3 & \(3.5\times 10^{-3}\) \\ silty sand \(\textbackslash{}_{1}\) & 19.0 & 155 & 33.4 & 13.5 & 0.3 & \(2.8\times 10^{-3}\) \\ silty sand \(\textbackslash{}_{2}\) & 19.5 & 206 & 35.2 & 18.6 & 0.3 & \(3.1\times 10^{-3}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Physical parameters of soil layers (Reprinted/adapted with permission from Ref. [PERSON] et al. [37]).  
Figure 2: Space relationship of Metro Line 4 and Line 12.  
6. The diaphragm wall follows Kirchhoff plate theory [39]. The reason is that the ratio of width and depth of the wall is 1.2 m/41 m, which far less than 1/5. Hence, this wall is a typical Kirchhoff plane.
7. Due to high strength of the diaphragm wall, only elastic deformation is considered in the wall, which is widely adopted. Additionally, the diaphragm wall is considered as an isotropic material.  
Based on the above assumptions, a calculation model of ground heave induced by the diaphragm wall deformation is established.
# A Context-Aware Edge-Based VANET Communication Scheme for ITS +
## 3 Propose Scheme
### Edge Node Selection and Packet Forwarding with Edge Nodes  
Here, vehicles are used as edge nodes which conduct packet forwarding, computing, and content caching. We propose a concept that uses the same forwarder nodes for different traffic flows. As shown in Figure 1, three different traffic flows, specifically \"P1\(\rightarrow\)R1\", \"P2\(\rightarrow\)R2\", and \"P2\(\rightarrow\)R2-2\", use the same edges nodes for packet forwarding. The use of the same forwarder nodes results in two main benefits. First, the efficiency of wireless resource utilization is improved because the number of sender nodes can be reduced. Second, content caching at edge nodes becomes easier, resulting in a higher performance.