esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
As we measured, the mRNA expression levels of SOD, CAT, GSH-px, HO-1, NQO1, and CGLC were significantly up-regulated, further reducing the excess intracellular ROS. Further investigation revealed that BAL1 could effectively reduce MMP1 activity and mRNA expression levels, avoiding the degradation of COL-I due to excessive MMP1. Besides, Smads are regulators of TGF-\(\beta\) signaling, in which Smad2/3 is phosphorylated by the TGF-\(\beta\) receptor, which then polymerizes with Smad4 and continues signaling, but Smad7 acts as an inhibitor that blocks the transmission of Smad2/3 [31; 32]. The regulation of BAL1 in TGF-\(\beta\)/Smad signaling is extremely significant. As shown inFigure 6, BAL1 promoted the mRNA expression of TGF-\(\beta\), smad2, smad3, and smad4, and the increase of COL-I content and the up-regulation of mRNA expression level verified that the TGF-\(\beta\)/Smad pathway was promoted. In summary, the anti-photoaging effect of BAL1 may be closely related to biochemical indicators, Nrf/Keap1 signaling pathway, and TGF-\(\beta\)/Smad signaling pathway in photoaging skin tissues. We can roughly speculate the potential mechanism of anti-photoaging of BAL1 based on the results obtained from the experiments. As shown in Figure 7, prolonged UVA irradiation leads to a large production of ROS, which then leads to oxidative stress and elevated levels of matrix metalloproteinase enzyme activity and mRNA expression in fibroblasts, followed by the disruption of Nrf2/Keap1 and TGF-\(\beta\)/Smad signaling pathways, impaired cellular function, and degradation of the extracellular matrix. In contrast, BAL1 could promote the nuclear displacement of Nrf2 to activate the transcription of antioxidant enzyme genes to a certain extent, thus reducing the cellular damage caused by oxidative stress due to UVA. It also upregulated the mRNA expression of the transducer and target gene type I pre-collagen in the TGF-\(\beta\)/Samd pathway, which slowed down the degradation of COL-I by MMP-1 to some extent. This suggests that BAL1 can effectively reduce UV light damage to human skin in daily life, thus supporting the significance of its application.
## 3 Experimental Setup (Materials and Equipment)
### Compounding on Twin-Screw Extruder  
A variety of equipment was utilized for compound processing and sample preparation, characterization, color quantification and microstructural characterization. The extruder employed was an intermeshing co-rotating twin-screw extruder (TSE) manufactured by Coperion Germany. It featured a 25.5 mm screw diameter, an L-to-D ratio of 37 and a 27 kW motor. The extruder also included nine heating zones for the barrel along with one for the die. The system configuration is depicted in Figures 1 and 2.  
After exiting the die, the extruded material underwent quenching in cold water, followed by drying using air and was subsequently converted into pellets via a pelletizer. These pellets were then molded via injection molding into three rectangular color chips (3 \(\times\) 2 \(\times\) 0.1\({}^{\prime\prime}\)). Kawaguchi Co. manufactured the injection-molding machine utilized, specifically a KM100 model with a clamp tonnage of 85 tons. This machine comprised two main components: an injection component and a clamping component. The KM100 was employed to produce samples for further characterizations and color quantification, processed at approximately 1000 PSI (28 MPa) and 280 \({}^{\circ}\)C.  
Subsequently, the specimens were dried at room temperature in the lab for further optical microscopic tests and characterization measurements.  
Experimentation took place at the Industrial IP plant in SB. The materials were extruded using an intermeshing twin co-rotating screw extruder (refer to Figure 1). The total weight of the color additives (pigment and additive) was 86%. The two PC resins, R1 and R2, were utilized in a ratio of 30 and 70 wt.%, respectively. Table 3 outlines the formulation utilized. The additives and pigments were blended with the resins at a ratio of 100:86 and were batch-mixed using a super floater.  
The steps involved in the compounding process are depicted in Figure 2. The samples were processed using a co-rotating twin-screw extruder throughout the extrusion process.  
The pellets were subsequently molded using the injection-molding machine to create rectangular color chips measuring 3 \(\times\) 2 \(\times\) 0.1\({}^{\prime\prime}\) in dimensions. The injection pressure and temperature were maintained at approximately 28 MPa (1000 psi) and 280 \({}^{\circ}\)C, respectively. Using a spectrophotometer, color measurements were conducted at three different locations on each specimen (coupon) to obtain the tri-stimulus values (L*, a* and b*). Target values for L*, a* and b* were predefined. Color differences were then determined as dL*, da*, db* and dE*. A spectrophotometer was used to record color spectra for each coupon at three separate locations per coupon
## 2 Materials and Methods
### Jammer Characterization  
The experiment employs two jammers to impair GNSS signals in the lower and upper L-bands. Jamming signal is injected through a cable into the front-end to keep the outside environment unaffected.  
The upper L-band jammer (also referred as L1 jammer) transmits out a chirp signal with an approximate center frequency of 1.565 GHz and a bandwidth of 28.20 MHz. The detailed specifications of the L1 jammer can be found in [19]. The second jammer used in the jamming experiments operates in the lower L-band (also known as L5 Jammer). The spectrums of both jammers as obtained by the spectrum analyzer are shown in Figure 4. The L5 jammer is a wideband jammer with center frequency at approximately 1.1953 GHz and a bandwidth of 200 MHz, as can be seen in Table 3.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **Frequency Bands (L1/E1/B1)** & **Frequency Bands (L5/E5a, E5b)** \\ \hline Center frequency (MHz) & 1574.890625 & 1192.50 \\ Sampling rate (MHz) & 40.5 & 81 \\ Data type & Complex & Complex \\ Sample bit width & 8 bit + 8 bit (I + Q) & 8 bit + 8 bit (I + Q) \\ Bandwidth (MHz) & 38 & 54 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Front-end configuration for TeleOrbit GTEC® dual-band GNSS signal receiver.  
Figure 3: Block diagram of hardware setup used during the data collection on Megastar.
# Research on Optical System of Dim Target Simulator Based on Polarization Stray Light Suppression
## 4 Experimental Results Verification
### Star Position Simulation Error Calibration  
A star position simulation error calibration test was set up using the theodolite and most of the adjustment tables. Before the test, the relative positions of the target simulator and the theodolite were adjusted to ensure that their optical axes coincided, as shown in Figure 18.  
The star position simulation error is obtained from the difference between the measured angular distance between stars \(\gamma_{reality}\) and the theoretical angular distance between stars \(\gamma_{ideal}\) relative to the central star position [21], as shown in Equation (22):  
\[\begin{split}\rho=\arccos[\cos\beta\cos\beta_{O^{\prime}}\cos( \alpha-\alpha_{O^{\prime}})+\sin\beta\sin\beta_{O^{\prime}}]\\ -\arccos\left[\frac{d\left(\left(x-x_{0}\right)^{2}+\left(y-y_{ 0}\right)^{2}\right)+2f_{0}{}^{2}}{\sqrt{d\left(\left(x-x_{0}\right)^{2}+ \left(y-y_{0}\right)^{2}\right)+f_{0}{}^{2}}}\right]\end{split} \tag{22}\]  
where \(\alpha\) and \(\beta\) are the azimuth and pitch angle of the measured star point, \(\alpha_{O^{\prime}}\) and \(\beta_{O^{\prime}}\) are the azimuth and pitch angle of the measured central star point, \(x\) and \(y\) are the theoretical star point coordinates, respectively, and \(f_{0}\) is the focal length of the collimating optical system.  
The display area is divided into a 10 \(\times\) 10 grid, with a total of 100 measured star points. The theodolite is used to measure the azimuth angle \(\alpha\) and pitch angle \(\beta\) of each star point, respectively, and the simulation error \(\rho\) of each star point position is calculated according to Equation (22). The same method was used to obtain the simulation error \(\rho^{\prime}\) of each star position before depolarizing stray light. The test results are shown in Figure 19.  
Figure 18: Test site of star position simulation error of dim target simulator based on polarized stray light suppression.  
The maximum error of each star position is taken as the simulation error of star position of the target simulator. The test results show that the star position simulation error of the target simulator without depolarizing stray light is 18.99\", while the star position simulation error of the depolarizing stray light dim target simulator in this study is 9.90\", and the star position simulation accuracy is improved to 1.92 times of that before depolarizing stray light, meeting the accuracy requirements of dim target simulation. The comparison of main parameters of the target simulation system before and after depolarization stray light is shown in Table 4.
## 4 Experimental Results
### Waveforms  
Figures 5 and 6 demonstrate two examples of waveform plots for performance comparisons. Speech signals uttered by a male and a female speaker were interfered by helicopter-cockpit and factory noise with _Avg_SegSNR = 5 dB. In Figures 5c-e and 6c-e, a clipped signal is absent at the output waveforms of the denoised speech. This is attributed to all noise estimators that do not over-estimate the level of noise power spectra for each sub-band, yielding denoised speech not suffering from serious speech distortion. By comparing Figure 5c-e, interference noise can be effectively removed by using the three noise estimators for speech denoising. The proposed method can preserve a greater quantity of speech components than the other two methods during speech presence regions, including weak vowels, the onset and offset of a vowel, and consonants marked by ellipses. This is due to the adaptation of harmonic properties for the determination of segment length and the thresholds for speech presence as given in (5) and (17).  
By observing Figure 6, a speech signal is corrupted by factory noise as shown in Figure 6b. Factory noise is non-stationary. It is a challenge to remove this noise interference noise in noisy speech. By comparing the denoised speech shown in Figure 6c-e, the proposed approach (Figure 6e) is better to preserve speech components for weak vowels and consonants marked by ellipses. Accordingly, the proposed method can improve the performance of the MCRA noise estimator by the preservation of weak speech components.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Noise Type**} & \multicolumn{2}{c}{**SNR**} & \multicolumn{3}{c}{**PESQ**} \\ \cline{2-6}  & **(dB)** & **Noisy** & **MCRA** & **FB\_MCRA** & **Proposed** \\ \hline \multirow{3}{*}{White} & 0 & 1.64 & 2.13 & 2.11 & 2.24 \\  & 5 & 1.94 & 2.48 & 2.48 & 2.60 \\  & 10 & 2.28 & 2.77 & 2.80 & 2.94 \\ \hline \multirow{3}{*}{F16} & 0 & 1.86 & 2.31 & 2.30 & 2.32 \\  & 5 & 2.20 & 2.65 & 2.64 & 2.72 \\  & 10 & 2.56 & 2.95 & 2.97 & 3.08 \\ \hline \multirow{3}{*}{Factory} & 0 & 1.84 & 2.23 & 2.22 & 2.24 \\  & 5 & 2.18 & 2.59 & 2.59 & 2.63 \\  & 10 & 2.55 & 2.90 & 2.92 & 2.98 \\ \hline \multirow{3}{*}{Helicopter} & 0 & 2.05 & 2.44 & 2.46 & 2.45 \\  & 5 & 2.39 & 2.78 & 2.80 & 2.87 \\  & 10 & 2.75 & 3.08 & 3.13 & 3.20 \\ \hline \multirow{3}{*}{Car} & 0 & 3.43 & 3.24 & 3.43 & 3.38 \\  & 5 & 3.86 & 3.42 & 3.63 & 3.72 \\  & 10 & 4.14 & 3.55 & 3.78 & 3.87 \\ \hline \multirow{3}{*}{Babble} & 0 & 1.91 & 2.09 & 2.08 & 2.07 \\  & 5 & 2.26 & 2.49 & 2.48 & 2.46 \\ \cline{1-1}  & 10 & 2.62 & 2.85 & 2.87 & 2.86 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparisons of perceptual evaluation of speech quality (PESQ) for the denoised speech in various noise corruptions.
## 2 Materials and Methods
### Soluble Components Analyzed by UHPLC Q-TOF
#### 2.6.3 Q-TOF Mass Spectrometry  
The sample was separated by UHPLC and subjected to a Triple TOF 5600 mass spectrometer (AB SCIEX). positive and negative ion modes of electrospray ionization (ESI) were used for detection. The settings of the ESI source were as follows: Ion Source Gas1 (Gas1): 60; Ion Source Gas2 (Gas2): 60; curtain gas (CUR): 30; source temperature: 600 \({}^{\circ}\)C; IonSapary Voltage Floating (ISVF): \(\pm\)5500 V (positive and negative modes); TOF MS scan m/z range: 60-1000 Da; product ion scan m/z range: 25-1000 Da; TOF MS scan accumulation time: 0.20 s/spectra; product ion scan accumulation time: 0.05 s/spectra. The secondary mass spectra were obtained using information-dependent acquisition (IDA) with high-sensitivity mode, a declustering potential (DP) of \(\pm\)60 V (positive and negative modes), collision energy of 35 \(\pm\) 15 eV, and IDA settings were as follows: exclude isotopes within 4 Da, candidate ions to monitor per cycle: 6. Six sample replicates were performed in each group.  
The raw data were converted to mzXML format by ProteoWizard, and then the XCMS program was used for peak alignment, retention time correction, and peak area calculation. Metabolite structures were retrieved in a self-built database and identified using a method combining accurate primary mass matching (<25 ppm) and secondary spectra matching. For the data extracted by the XCMS, ion peaks with >50% missing values in the group were deleted.
# Hybrid Window Decoding for Joint Source Channel Anytime Coding System
## 2 Preliminaries
### Density Evolution (DE) Algorithms for SC-RA-Based JSCAC System  
In [18], the authors proposed a density evolution (DE) algorithm for an SC-RA-based JSCAC system with PJEWD scheme. Based on the DE analysis, the anytime properties of the SC-RA-based JSCAC system are provided, i.e., the error probability of any message block can exponentially decay to zero as the decoding delay approaches to infinity. Moreover, the lower bound of the delay exponent \(\alpha\) is also provided, which is proportional to the product of \(\lambda^{sc}\) and \(Q^{sc}\). The DE algorithm can also analyze the BP decoding threshold and the decoding complexity of the SC-RA-based JSCAC with PJEWD scheme. See more details of the encoding process, the decoding algorithms, and the DE algorithm of the SC-RA-based JSCAC system in [17; 18].
# WDS-YOLO: A Marine Benthos Detection Model Fusing Wavelet Convolution and Deformable Attention
## 4 Experiments
### Experiment Results
#### 4.2.3 Generalization Experiment  
To further assess the generalization capability of the WDS-YOLO model, a comparative study was conducted with mainstream models on the RUOD dataset [57]. The RUOD dataset comprises 14,000 images annotated with 10 categories: diver, starfish, coral, turtle, fish, echinus, holduurian, scallop, squid, and jellyfish. This comprehensive dataset covers diverse underwater scenarios featuring various marine organisms and challenging conditions such as color distortion and light interference, providing a robust platform for model performance evaluation. The experimental setup remained consistent with the previous section, and the corresponding results are summarized in Table 4.  
As demonstrated in Table 4, WDS-YOLO achieved a superior mAP@50 of 84.9% on the RUOD dataset, outperforming Faster R-CNN (75.2%), YOLOv7-tiny (81.5%), and YOLOv8n (83.8%) by considerable margins. These results demonstrate that WDS-YOLO exhibits enhanced generalization capabilities in handling complex underwater scenarios. The model maintains high detection accuracy even when handling a larger number of categories, attributed to its optimized network architecture and advanced feature extraction mechanism. These enhancements enable better adaptation to the diverse characteristics of underwater environments, thereby validating the robustness and practical applicability of the proposed method.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **mAP@50\%** & **Params/M** & **FLOPs/G** \\ \hline Faster R-CNN & 75.2 & 41.4 & 239.3 \\ YOLOv7-tiny & 81.5 & 6.0 & 13.2 \\ YOLOv8n & 83.8 & 3.0 & 8.1 \\ WDS-YOLO & 84.9 & 3.2 & 11.4 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of model performance on RUOD dataset.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **mAP@50\%** & **Params/M** & **FLOPs/G** \\ \hline Faster R-CNN & 78.6 & 41.4 & 239.3 \\ Cascade R-CNN & 80.0 & 69.2 & 119.0 \\ SSD & 75.1 & 26.3 & 63.4 \\ ATSS & 80.7 & 32.1 & 80.5 \\ YOLOv3 & 78.3 & 61.6 & 66.5 \\ YOLOv5s & 83.9 & 9.1 & 23.8 \\ YOLOv7-tiny & 84.6 & 6.0 & 13.2 \\ YOLOv8n & 83.5 & 3.0 & 8.1 \\ YOLOv9-t & 83.9 & 2.7 & 11.1 \\ YOLOv10n & 82.8 & 2.7 & 8.2 \\ YOLO11n & 80.0 & 2.6 & 6.3 \\ RT-DETR & 83.8 & 19.9 & 57.0 \\ WDS-YOLO & 85.6 & 3.2 & 11.4 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparative experiment results for different networks.
# Numerical Study on Track-Bridge Interaction of Integral Railway Rigid-Frame Bridge
## 5 Parameters Study  
### Longitudinal Resistance  
The longitudinal resistance of fasteners affects the additional stress of ballastless rails directly, while the models and values of longitudinal resistance specified in different codes differ greatly from each other [7; 8; 30]. The elastic-plastic model specified in UIC code 774-3R is now widely adopted around the world. Developed from a large number of model experiments and field tests, the calculating models and values of the longitudinal resistances of WJ-8 fasteners and small resistance fasteners on the ballast and ballastless tracks are included in the Chinese code TB 10015-2012 [30].  
In Section 4 of this paper, the influence of the fastener resistance on various additional stresses was compared when describing the stress characteristics of the integral rigid-frame bridge, as shown in Figures 8-15. In order to show the influence of the fastener resistance on the TBI calculation results, various additional stresses are listed in Table 5.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Fasteners**} & **Design Stress-Free Rail** & **Maximum Temperature** & **Broken** & **Allowable Gap** \\  & **Temperature/\({}^{\circ}\)C** & **Drop of the Rail/\({}^{\circ}\)C** & **Gap /mm** & **/mm** \\ \hline WJ-8 fastener & 30 \(\pm\) 5 & 37.3 & 12.9 & 70 \\ Small resistance fastener & 30 \(\pm\) 5 & 37.3 & 38.6 & 70 \\ \hline \hline \end{tabular} Note: 37.3 \({}^{\circ}\)C = 35 \({}^{\circ}\)C \(-\)(\(-\)2.3 \({}^{\circ}\)C), where \(-\)2.3 \({}^{\circ}\)C is the lowest rail temperature listed in Table 3.  
\end{table}
Table 4: Broken gap of the continuous welded rail.  
Figure 15: Displacements of the rail in the case of rail fracture.  
Generally, when the longitudinal resistance is reduced, the additional expansion stress of the rail most obviously decreases (by about 55%), followed by the additional braking stress (by more than 10% under condition II), as shown in Figure 16. According to UIC 774-3R, the additional stress of the rail is required to be less than \(-92\) MPa/\(92\) MPa [8]. However, in the Chinese code TB 10015-2012, the total stress of the rail has a limitation while the additional stress has no specific requirements. Table 5 indicates that the total additional rail stress exceeds the limit values of the current relevant codes when the WJ-8 fastener is adopted. Therefore, for this integral rigid-frame bridge with a span of \(3\times 70\) m, small resistance fasteners are suggested to be selected in order to avoid the arrangement of the rail expansion joint. With the decrease of the fastener resistance, the rail broken gap increases greatly, by 199.2%.
## 3 Results
### Statistical Analysis Results
#### 3.2.1 Measured HRV Features  
The influence of group and session on the ECG features were examined to understand the effects of cognitive impairment and the experiment on the ANS activity of the subjects. To investigate these effects as well as the interaction between factor, Robust Mixed-model ANOVA. As mentioned, robust methods were used since most of the ECG features violated the normality assumption. The results of this test are shown in Table 4. For some of the ECG features (HR, RRI, RMSSD, HF, and LF/HF), there was a significant main effect on the session. There was no significant effect for the group factor, and the interaction between group and session was also not significant. Since there are no significant interactions, the main effects of the two variables, group and session, their main effects were interpreted separately.  
Since the sessions had a significant main effect on some of the ECG features, it was followed up with pairwise comparisons. The results of the pairwise tests using Wilcoxon signed-rank test are shown in Table 5. Here, the HRV measured from the cognitive attitude game was significantly different from Rest 1, Rest 2 and Games 2&3. Subjects have higher heart rates and LF/HF ratios and lower RRI, RMSSD, and HF, indicating a sympathetic activation during this session. The lack of significant difference between Rest 1 and Rest 2 indicates that HRV features returned to baseline.
## 3 Experimental Results
### Model Evaluation
This architecture discards Bi-LSTM, but uses a general fully connected network as the output. [PERSON] et al. [31] proposed that the XSleepNet network architecture is different from [29] and [30]. This architecture uses the original signal and the time-frequency signal as dual inputs to obtain the characteristics of the time-domain signal and the frequency-domain signal. [PERSON] et al. [32] proposed the multi-resolution convolutional neural network (MRCNN). The architecture of MRCNN introduces an attention mechanism, enabling it to adaptively focus on important information in the signal. [PERSON] et al. [33] proposed a lightweight LightSleepNet network architecture. This architecture uses a spectrogram as the signal input, and proposes two modules, the concatenation of max-pooling2D and average-pooling2D module and the multi-convolution block module, to extract various features from the spectrogram. Compared with various deep learning methods, the accuracy and F1-score of the proposed T-MCCFNN are 85.3% and 85.3%, respectively, and both are superior to other deep learning network methods. In Table 8, although the N1 and REM of the per-class F1-score in [PERSON] et al. [31] are 52.9% and 85.4%, respectively, which are better than the proposed method; the W, N2, and N3 of the per-class F1-score in the proposed T-MCCFNN are 92.7%, 88.5%, and 91.2%, respectively, which are better than the method of [PERSON] et al. [31]. Overall, the accuracy and F1-score of the proposed T-MCCFNN are superior to other deep learning network methods.