esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Plugging Agent Optimization Results  
#### 3.2.1 High-Temperature Consolidation Performance  
The consolidation performance of high-temperature plugging agents HTP, HTE, HTF, and HTG, was analyzed and compared at different temperatures through experiments (test conditions: the proportion of quartz sand was 70% and the aging time was 8 h), and the results are shown in Figure 8.  
It is shown in Figure 8 that the HTP outperformed the other three agents. Temperature has a great influence on the compressive strength of the consolidated body formed by quartz sand and plugging agent [24]. When the temperature was 80 \({}^{\circ}\)C, the compressive strength of the consolidated body was very low, but with the increase of temperature, the compressive strength of the consolidated body continued to increase, and when the temperature reached 160 \({}^{\circ}\)C, the compressive strength of the consolidated body using HTP reached 11.1 MPa. However, when the temperature rose again, the further increase in the compressive strength of the consolidated body decreased. It was found that when the temperature was higher than 180 \({}^{\circ}\)C, the consolidated body had volume expansion phenomenon and was tightly and firmly cemented with glass tube, indicating that HTP can  
Figure 8: Consolidation performance of HTP at different temperature levels.  
Figure 7: Liquid rate proportion at different steam injection periods. (**a**) 4.7 h (**b**) 8.0 h (**c**) 11.3 h.  
be well cemented with sandstone formation and has a high consolidation strength (higher than maximal steam injection pressure 12.8 MPa), while the maximal pressure resistance of the other three plugging agents was lower than 11 MPa. Therefore, HTP is suitable as a plugging agent for the steam-based thermal recovery of heavy oil.
## 3 Measurement and Discussion
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Ref.**} & **UWB** & \multirow{2}{*}{**Size/mm2**} & \multirow{2}{*}{**Unwanted Bands**} & \multirow{2}{*}{**Rejection Bands**} & \multirow{2}{*}{**Band Error/\%**} & \multirow{2}{*}{**Bandwidth**} \\  & & & **(Bandwidth)/GHz** & & **(Bandwidth)/GHz** & \\ \hline \multirow{4}{*}{[8]} & \multirow{4}{*}{2.7–10.7} & \multirow{4}{*}{25\(\times\) 22} & 3.3–3.7 (0.4) & 3.58–4.32 (0.74) & 8.5, 16.8 & 85 \\  & & & 5.15–5.35 (0.2) & 5.36–5.57 (0.21) & 4.1, 4.1 & 5 \\  & & 5.725–5.825 (0.1) & 5.98–6.10 (0.12) & 4.5, 4.7 & 20 \\  & & 7.8–8.2 (0.4) & 8.12–8.85 (0.73) & 4.1, 7.9 & 83 \\ \hline \multirow{4}{*}{[13]} & \multirow{4}{*}{2.8–10} & \multirow{4}{*}{32\(\times\) 27} & 3.3–3.7 (0.4) & 3.06–3.83 (0.77) & 7.2, 3.5 & 93 \\  & & 5.15–5.35 (0.2) & 5.05–N/A (N/A) & 1.9, N/A & N/A \\  & & 5.725–5.825 (0.1) & NA–5.96 (N/A) & N/A, 2.3 & N/A \\ \hline \multirow{4}{*}{[20]} & \multirow{4}{*}{2.8–11} & \multirow{4}{*}{N/A} & 3.3–3.7 (0.4) & 3.3–4.0 (0.7) & 0, 8.1 & 75 \\  & & 5.15–5.35 (0.2) & 5.05–N/A (N/A) & 9, N/A & N/A \\  & & 5.725–5.825 (0.1) & N/A–5.9 (N/A) & N/A, 1.2 & N/A \\ \hline \multirow{4}{*}{[23]} & \multirow{4}{*}{2.45–12} & \multirow{4}{*}{31.8 \(\times\) 26} & 3.3–3.7 (0.4) & 3.27–3.57 (0.3) & 0.9, 3.5 & 25 \\  & & 5.15–5.35 (0.2) & 5.01–5.45 (0.44) & 2.7, 1.8 & 120 \\  & & 5.725–5.825 (0.1) & 5.55–6.05 (0.5) & 3.0, 3.8 & 400 \\  & & 7.25–7.75 (0.5) & 7.05–7.45 (0.4) & 2.7, 3.8 & 20 \\  & & 8.025–8.4 (0.375) & 7.83–8.19 (0.36) & 2.4, 2.5 & 4 \\ \hline \multirow{4}{*}{[24]} & \multirow{4}{*}{N/A} & \multirow{4}{*}{28.5 \(\times\) 28} & 3.3–3.7 (0.4) & 3.15–3.62 (0.47) & 4.5, 2.1 & 17.5 \\  & & 5.15–5.35 (0.2) & 5.1–5.38 (0.28) & 0.9, 0.8 & 40 \\  & & 5.725–5.825 (0.1) & 5.72–6.12 (0.4) & 0.1, 5.1 & 300 \\ \hline \multirow{4}{*}{[25]} & \multirow{4}{*}{2.6–23} & \multirow{4}{*}{15\(\times\) 15} & 3.3–3.7 (0.4) & 3.1–3.9 (0.8) & 6, 5.4 & 100 \\  & & 5.15–5.35 (0.2) & 5.1–N/A (N/A) & 0.9, N/A & N/A \\ \cline{1-1}  & & 5.725–5.825 (0.1) & NA–6 (N/A) & N/A, 3 & N/A \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of proposed and reference antenna about unwanted bands and achieved rejection bands. Figure 16: Gain of the proposed antenna with notched bands.
## 3 Results  
Using the SMAP/CYGNSS TB downscaling algorithm (Equation (3)), TB spatial heterogeneity depends on the spatial variations in CYGNSS reflectivity (\(\Delta\Gamma\)) and is scaled by \(\beta\). We first present SMAP/CYGNSS TB spatial heterogeneity results, then we discuss the spatial patterns in \(\Delta\Gamma\) values and \(\beta\) values. Finally, we present 3 km SMAP/CYGNSS TB results.
# Survey of Dense Video Captioning: Techniques, Resources, and Future Perspectives
## 3 Techniques for Dense Video Captioning
### Attention-Based Approaches
#### 3.5.2 Temporal Attention Models  
Temporal attention models have emerged as a prominent approach in the field of video captioning, enabling dynamic focus on the most relevant frames or temporal segments  
Figure 12: The STAT unit (redrawn by the authors, adapted from [43]) is depicted, receiving local features, global-motion features, and model status as inputs. It generates dynamic visual features for LSTM decoder iterations. Layer 1 applies spatial attention to local features. Layer 2 focuses on temporal attention for both feature types. Layer 3 fuses two temporal representations.  
within a video. [PERSON] et al. [19] were pioneers in this domain, introducing a model that utilizes a soft attention mechanism. This mechanism assigns weights to frames based on their relevance to the current word being generated, allowing the model to selectively attend to various temporal regions for improved caption prediction.  
Building on this foundation, [PERSON] et al. [16] proposed the Spatial-Temporal Attention Mechanism (STAT) for video captioning. As illustrated in Figure 13, their framework integrates three types of features (CNN, C3D, and R-CNN) through a two-stage attention mechanism. Initially, given the current semantic context, a spatial attention mechanism guides the decoder to select local features with higher spatial attention weights, representing significant regions within frames. Subsequently, a temporal attention mechanism enables the decoder to select global and motion features, along with the previously identified local features. Ultimately, these three types of features are fused to represent the information of keyframes, enhancing the model's ability to generate more detailed descriptions.
## 3 Results
### Effects of KRG-P on MAPKs and NF-\(\kappa\)B Phosphorylation in RAW264.7 Cells  
[PERSON] [23] reported that macrophages are activated by plant polysaccharides through MAPK and NF-\(\kappa\)B phosphorylation and ultimately regulate gene transcription. [PERSON] et al. [30] also reported that RAW264.7 cells treated with LPS can activate MAPK pathways to produce NO, IL-6, and TNF-\(\alpha\). To study the mechanism of macrophage activation by KRG-P, RAW264.7 cells were treated with KRG-P at concentrations of 5-50 \(\upmu\)g/mL for 30 min. As shown in Figure 3, P38, ERK, and JNK were significantly phosphorylated by KRG-P treatment compared with the control group in a concentration-dependent manner. The p65 subunit of NF-\(\kappa\)B was also phosphorylated by KRG-P treatment in a concentration-dependent manner compared with the control group. The levels of the total p38, JNK, ERK, and p65 protein were not affected. These results indicate that MAPK and NF-kB pathways are significantly phosphorylated by KRG-P in a concentration-dependent fashion (Figure 3).  
Figure 1: Cytotoxic effect of KRG (**a**) and KRG-P (**b**) isolated from Korean red ginseng on RAW264.7 macrophage cells in vitro. RAW264.7 macrophage cells (1.0 \(\times\) 10\({}^{5}\) cells/well) were treated with KRG or KRG-P using a series of concentrations (7.8–250 \(\upmu\)g/mL) in 96-well flat-bottomed microplates for 24 h. Cytotoxicity was determined using the EZ-Cytox based colorimetric assay. CTL; control. Data are presented as the means \(\pm\) SD of three independent experiments.  
Figure 3: _Cont._  
Figure 2: Effect of KRG and KRG-P on the production of IL-6 **(a,b)**, TNF-\(\alpha\) (**c,d**), and NO (**e,f**) in RAW264.7 macrophage cells in vitro. RAW264.7 macrophage cells (1.0 \(\times\) 10\({}^{5}\) cells/well) were treated with KRG and KRG-P using a series of concentrations (7.8–250 ug/mL) or LPS (500 ng/mL) in 96-well flat-bottomed microplates for 24 h. The supernatants were collected for the measurement of cytokines including IL-6 **(a,b)**, TNF-\(\alpha\) (**c,d**), and NO (**e,f**). CTL; control, LPS; lipopolysaccharide. Data are presented as the means \(\pm\) SD of three independent experiments. ***\(p\) < 0.001 vs. the control group.
# Evaluation of Combat Helmet Behavior under Blunt Impact
## 3 Results
### Curbstone Anvil Surface
#### 3.2.2 Head Injury Criterion (HIC)  
For the head surrogate, the maximum HIC values obtained for the lateral, rear, and top impacts are lower than the threshold established by the NHTSA standard (HIC = 1000), Table 9. In the case of  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Head Model**} & \multirow{2}{*}{**Location**} & \multirow{2}{*}{**HIC**} & **AIS 1** & **AIS 2** & **AIS 3** & **AIS 4** & **AIS 5** & **AIS 6** \\  & & & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** \\ \hline \multirow{4}{*}{**Head**} & Top & 981 & 100 & 90 & 55 & 18 & 3 & 0 \\  & Frontal & 1245 & 100 & 96 & 77 & 33 & 7 & 0 \\ \cline{1-1}  & Rear & 868 & 98 & 83 & 42 & 11 & 0 & 0 \\ \cline{1-1}  & Lateral & 941 & 99 & 88 & 50 & 12 & 2 & 0 \\ \hline \multirow{4}{*}{**Human Head**} & Top & 2198 & 100 & 100 & 98 & 94 & 82 & 50 \\ \cline{1-1}  & Frontal & 2017 & 100 & 100 & 97 & 90 & 70 & 28 \\ \cline{1-1}  & Rear & 995 & 100 & 91 & 56 & 19 & 4 & 0 \\ \cline{1-1}  & Lateral & 1173 & 100 & 95 & 70 & 30 & 5 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 7: HIC and AIS results.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Head Model**} & \multirow{2}{*}{**Location**} & \multirow{2}{*}{**PLA**} & **AIS 2** & **AIS 3** & **AIS 4** & **AIS 5** & **AIS 6** \\  & & & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** \\ \hline \multirow{4}{*}{**Head**} & Top & 981 & 100 & 90 & 55 & 18 & 3 & 0 \\  & Frontal & 1245 & 100 & 96 & 77 & 33 & 7 & 0 \\ \cline{1-1}  & Rear & 868 & 98 & 83 & 42 & 11 & 0 & 0 \\ \cline{1-1}  & Lateral & 941 & 99 & 88 & 50 & 12 & 2 & 0 \\ \hline \hline \multirow{4}{*}{**Human head**} & Top & 2198 & 100 & 100 & 98 & 94 & 82 & 50 \\ \cline{1-1}  & Frontal & 2017 & 100 & 100 & 97 & 90 & 70 & 28 \\ \cline{1-1}  & Rear & 995 & 100 & 91 & 56 & 19 & 4 & 0 \\ \cline{1-1}  & Lateral & 1173 & 100 & 95 & 70 & 30 & 5 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Peak linear acceleration (PLA) and AIS for the curbstone anvil case.  
frontal impacts, this parameter is very close to the threshold. In the human head configuration, all the cases are higher than HIC = 1000, and therefore severe injuries may be caused. However, all HIC values for both configurations do not exceed the maximum threshold, HIC = 2400, allowed by the ECE R22/05 standard.  
The results obtained with both models also show high probabilities for causing injuries from the AIS 1 level to the AIS 2 level. For the head surrogate configurations, the probability of suffering traumatic brain injuries is significantly reduced; however, there may be significant potential for causing traumatic brain injury at the AIS 4 level and even at the AIS 5 level.
# Advances in Toughening Modification Methods for Epoxy Resins: A Comprehensive Review
## 5 Flexible Chain Segment Toughening Method
### Current Research
#### 5.2.2 Incorporation of Flexible Chains into Epoxy Resins  
Through molecular design strategies, traditional rigid chains can be replaced with flexible chains, fundamentally enhancing the material's toughness. The core of this design approach lies in minimizing or avoiding the use of rigid structures, instead employing more flexible molecular architectures. By adopting non-cyclic or low-cyclic molecular structures, the intermolecular interactions can be effectively reduced, thereby increasing the overall flexibility of the material.  
[PERSON] et al. [94] conducted a study where polydimethylsiloxane (PDMS) oligomers were reacted with bisphenol A epoxy resin to prepare a silicone-modified epoxy resin (ESDG) copolymer. The results demonstrated that, when the PDMS content was 10%, the impact strength of the modified epoxy resin reached 22.0 J/m, representing a 115% increase  
Figure 7: (**A**) Photographs of EP0, EP0.25, EP0.5, EP0.75, and EP1 bending test; (**B**) tensile stress–strain curve, tensile test results, and compression test results. Reproduced from Ref. [19] with permission of John Wiley and Sons.  
compared to the unmodified resin. When the PDMS content was increased to 40%, the impact strength further increased to 59.0 J/m, a 478% improvement.  
The addition of reactive diluents to epoxy resins introduces flexible chain segments while reducing viscosity and enhancing mechanical properties. [PERSON] et al. [95] synthesized a low-viscosity reactive modifier, diethylene glycol diglycidyl ether (DGEG), and incorporated it into epoxy resin (EP) via a solvent-free method. The mechanical properties of the modified epoxy resin were investigated through tensile and impact tests. The results showed that the elongation at break and impact strength of EP significantly increased with higher DGEG content, while an optimal DGEG content also enhanced the tensile strength of EP.  
[PERSON] et al. [96] synthesized and characterized two novel epoxy resins--diglycidyl ether of ethoxylated bisphenol A (BPA) with two and six oxyethylene units (DGEBAEO-2 and DGEBAEO-6). DGEBAEO-6 was employed to toughen the conventional epoxy resin diglycidyl ether of BPA (DGEBA), and the thermal and mechanical properties of the modified system were systematically investigated. As the DGEBAEO content increased, the modified resin exhibited reduced viscosity, a lower glass transition temperature (Tg), and enhanced impact strength, while maintaining favorable thermal stability and mechanical performance. SEM analysis revealed that DGEBAEO-6, with elastomeric characteristics, effectively improved the toughness of DGEBA. The toughening mechanism is attributed to the flexible chain segments promoting plastic deformation within the epoxy network.
## 5 Ultimate Capacity Test Results and Discussion
### Influence of Cold-Bending Curvature on Ultimate Capacity and Ultimate Deflection  
The ultimate capacity (\(q_{\text{b}}\)) and ultimate deflection (\(\Delta\)) of the specimens are demonstrated in Table 5. During the ultimate capacity test, the maximum deflection of the glass panels occurred at the panel center.  
By comparing the \(q_{\text{b}}\) and \(\Delta\) of the specimens with identical interlayer thicknesses and different cold-bending curvatures, it was found that both parameters of cold-bent SGP laminated glass panels decreased with greater cold-bending curvatures. When the cold-bending curvature increased by 0.2%, \(q_{\text{b}}\) and \(\Delta\) decreased by 14.7% and 7.9% on average, respectively. Consequently, it can be concluded that the cold-bending curvature has a great influence on \(q_{\text{b}}\) and \(\Delta\) of cold-bent SGP laminated glass panels.  
The data in Table 5 were plotted into the relationship curves in Figure 11, with Figure 11 depicting the relationship curves between \(q_{\text{b}}\) and cold-bending curvature and Figure 11b showing the relationship curves between \(\Delta\) and cold-bending curvature. Both figures showed a nonlinear negative correlation between the parameters they involve, and the rate of decrease became smaller as the cold-bending curvature rose.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Specimens** & \(q_{\text{b}}\)**/**kN** & \(\Delta\)**/mm** \\ \hline SJ1 & 35.6 & 50.38 \\ SJ2 & 27.9 & 43.79 \\ SJ3 & 24.1 & 40.71 \\ SJ4 & 21.5 & 38.61 \\ SJ5 & 33.3 & 48.26 \\ SJ6 & 26.6 & 42.94 \\ SJ7 & 23.3 & 40.14 \\ SJ8 & 21.0 & 38.37 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The results of ultimate capacity and ultimate deflection.
## 2 Materials and Methods
### Statistical Analysis  
The fNIRS data were analyzed using commercially available software (Oxysoft, Artinis Medical SystemsTM, The Netherlands). A moving average window of 1 s was applied to the O\({}_{2}\)Hb signals to filter out the noise of the heartbeat frequency. The fNIRS data were excluded from the analyses when the standard deviations of oxy-Hb during the pre-task period exceeded 0.035 [38]. For the fNIRS signals, the value at the start of the first trial was taken as zero. Changes of [O\({}_{2}\)Hb] were calculated for approximately 105 s from this point. Therefore, the 1-back task period for each subject was divided into four time-segments of approximately 105 s each. To establish the hemodynamic and systemic responses to cognitive performance, the mean values of oxy-Hb for each subject were calculated for four time segments of 105 s. The signals were linearly detrended and were filtered using a band-pass filter with a cutoff frequency of 0.01-0.1 Hz. The time series was cubically interpolated at 1 Hz to obtain uniformly spaced time series for spectral and transfer function analysis.  
The behavioral data were analyzed with the SPSS 20.0 software package. We considered the hit rates, false-alarm rates, d-primes, and RT of the 1-back task as the behavioral dependent variable and the oxy-Hb values as the neural dependent variable. The independent variables were time measurements (pre-test and post-test) and groups (the experimental group and control group). We used the repeated measures analysis of variance (Repeated ANOVA) to analyze the data. For RT across all tasks, trials with values more than 2.5 standard deviations above or below the individual mean and less than 100 ms were excluded before computing the overall mean and standard deviation.
# Energy Harvesting Hybrid Acoustic-Optical Underwater Wireless Sensor Networks Localization
## 2 System Model and Proposed Technique
### Proposed Localization Technique
#### 2.4.2 Updating \(\Phi^{(u)}\) for Fixed \(\mathbf{L}\)  
In order to simplify the notation, (13) is re-written as  
\[\rho=\sum_{u=1}^{R}\Phi^{(u)^{\gamma}}\rho^{u}, \tag{28}\]  
where \(\rho^{u}=\sum_{i<j}v_{ij}\left(\tilde{d}_{ij}-d_{ij}(\mathbf{L})\right)^{2}\) is the \(u\)-th observation. To solve this sub-problem, we use Lagrange multiplier by taking the assumption that \(\sum_{u=1}^{R}\Phi^{(u)}=1\). The Lagrangian function of \(\rho\) is  
\[\mathsf{L}(\rho,\bar{\lambda})=\sum_{u=1}^{R}\Phi^{(u)^{\gamma}}\rho^{u}+\bar {\lambda}\left(\sum_{u=1}^{R}\Phi^{(u)}-1\right), \tag{29}\]  
where \(\bar{\lambda}\) is the rate of change of the function. The partial derivative of (29) with respect to \(\Phi^{(u)}\) is  
\[\frac{\partial\mathsf{L}(\rho,\bar{\lambda})}{\partial\Phi^{(u)}}=\gamma\Phi^ {(u)^{(\gamma-1)}}\rho^{u}-\bar{\lambda}. \tag{30}\]  
By setting (30) equal to zero, we get  
\[\Phi^{(u)}=\left(\frac{\bar{\lambda}}{\gamma\rho^{u}}\right)^{\frac{1}{ \bar{\lambda}-1}}. \tag{31}\]  
Using the constraint of \(\sum_{u=1}^{R}\Phi^{(u)}=1\), the multiplier term \(\bar{\lambda}\) is dropped out and the optimal value of \(\Phi^{(u)}\) is obtained as  
\[\Phi^{(u)}=\frac{\rho^{(u)^{\frac{1}{1-\gamma}}}}{\sum_{u=1}^{R}\rho^{(u)^{ \frac{1}{1-\gamma}}}}. \tag{32}\]  
Note that the choice of \(\gamma\) in (32) depends on the correlation between different observation views. If the observation views are highly correlated to each other, then a large value of \(\gamma\) is preferred because this results in getting equal weights. But if the observation views are highly uncorrelated then small value of \(\gamma\) is selected because this results in giving more importance to the accurate observation views.
# Single-Task Joint Learning Model for an Online Multi-Object Tracking Framework
## 3 Experiments
This performance suggests that our model is feasible for real-world surveillance applications, with potential for further optimization through model compression techniques or hardware acceleration to ensure efficiency in more resource-constrained environments. The feature model was trained as a pedestrian re-identification task using multiple widely cited datasets--CUHK03 [41], Market-1501, and DukeMTMC-reID--each selected for their unique representation of real-world tracking challenges and to ensure model robustness across diverse environments. CUHK03, captured from two non-overlapping cameras, includes challenges such as detection box misalignment and diverse pedestrian angles, which test the model's ability to adapt to variable perspectives and occlusions. Market-1501, filmed across six cameras, reflects real-world scenarios with frequent misalignments and varying angles, enhancing the model's ability to handle dynamic background changes and resolution inconsistencies. DukeMTMC-reID [35], featuring footage from eight synchro nized cameras, provides a range of scenes with frequent occlusions, lighting changes, and multi-view tracking scenarios, ideal for testing and refining multi-object tracking models in complex multi-camera systems. Together, these datasets helped evaluate the model's scalability and adaptability in handling larger numbers of objects and expansive tracking environments, simulating conditions akin to real-world surveillance networks.