esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Fabrication of Gelatin/CuSNP nanocomposite Films  
The films were fabricated by a solution casting process [43]. First, CuSNP suspensions were prepared by adding various quantities of CuSNP (0.0, 0.5, 1.0, and 2.0 wt % by weight of polymer) to 150 mL DI water with strong agitation for 1 h, and then sonicated for 20 min and shear mixed for 10 min at 5000 rpm. Subsequently, as a plasticizer, 1.2 g of glycerol (30% by weight of polymer) and then 4 g of gelatin were gradually mixed at 80 \({}^{\circ}\)C for 20 min with continuous stirring at 500 rpm to prepare the film-forming solutions, cast on a Teflon film-coated glass plate and dried. The films were conditioned (25 \({}^{\circ}\)C and 50% relative humidity (RH)) for 48 h before further testing. All film specimens were fabricated in triplicate and designated Gelatin, Gel/CuS\({}^{0.5}\), Gel/CuS\({}^{1.0}\), and Gel/CuS\({}^{2.0}\), respectively, according to the content of CuSNP (Table 1).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Films** & **Gelatin (g)** & **Glycerol (g)** & **CuSNP (mg)** \\ \hline Gelatin & 4 & 1.2 & 0 \\ Gel/CuS\({}^{0.5}\) & 4 & 1.2 & 20 \\ Gel/CuS\({}^{1.0}\) & 4 & 1.2 & 40 \\ Gel/CuS\({}^{2.0}\) & 4 & 1.2 & 80 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Composition of gelatin/CuSNP nanocomposite films.
# Recent Advances in the Targeting of Head and Neck Cancer Stem Cells
## 2 Cancer Stem Cells
### Stemness-Associated Signaling Pathways
#### 2.2.3 Sonic-Hedgehog (SHh) Signaling Pathway  
Although the SHh signaling system is critical for tissue homeostasis and embryonic development, cancer cells, especially those in HNSCC, have found a way to utilize it. In the absence of Hh ligands, the signaling is inactivated because Smoothened (Smo) is inhibited by Patched (Ptch). In the presence of Shh ligands, Ptch suppression of Smo is abolished, resulting in the nuclear accumulation of Gli1 and activation of target genes [20]. Excessive SHh signaling in HNSCC is typically brought on by mutations in PTCHI, SMO, GLI1, and GLI2 or increased SHh ligand expression. Angiogenesis, EMT, and tumor development are all aided by abnormal SHh signaling, which makes HNSCC more aggressive [21]. Previous research indicates that SHh is essential for the maintenance and function of cancer stem cells. It has also been linked with chemotherapy and radiotherapy resistance [22; 23; 24]. The Notch, SHh, and Wnt pathway interactions further increase the intricacy of HNSCC [25].  
Figure 1: Current models of CSC-induced tumorigenesis. Classical model presumes the self-renewal of the cancer stem cells by symmetrical division, and heterogeneity is introduced by asymmetrical division. In the plasticity model, CSCs are interchanged from differentiated or transient cancer cells to CSCs in response to niche. Monophyletic model presumes the effect of inflammation and wound healing factors in CSC dynamics and heterogeneity. Nevertheless, all these models postulate the same result: a heterogeneous tumor.
## 3 Results and Discussion
### Effect of pH on Antioxidant Activity  
The effect of the pH on the antioxidant capacity of the AS extracts was tested at pH values of 3 and 9. As shown in Figure 1, a significant drop in the TEAC was observed in both acidic and basic conditions when compared to the unmodified extract. This agrees with previous studies that observed a pH-dependent effect on the antioxidant activity of flavonoids [36]. These results are key when exploring future applications of AS extracts. For example, to benefit from its additional antioxidant properties in the food industry, the colored extract must be incorporated in a matrix with a relatively neutral pH, which excludes processed dairy products such as yogurt. The stabilization of the phytochemical compounds found in the extract through encapsulation and emulsion technologies should be considered to ensure the bioactivity when incorporated into cosmetics or food products [16].  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Parameter Tested** & \begin{tabular}{c} **Semi 34 Seed** \\ **Extract** \\ \end{tabular} & [20] & [21] & [19] & [18] \\ \hline Extraction Method & \begin{tabular}{c} Ultrasound- \\ assisted \\ extraction (UAE) \\ \end{tabular} & \multirow{2}{*}{Maceration} & \multirow{2}{*}{Maceration} & \multirow{2}{*}{\
\begin{tabular}{c} Solid-liquid \\ extraction (SLE) \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} Microwave- \\ assisted extraction \\ (MAE) \\ \end{tabular} } \\ \hline TEAC (\(\mu\)M/g seed) & 1743.3 \(\pm\) 52.3 a & 18.4 \(\pm\) 0.9 d & 32.51 \(\pm\) 9.07 f & 500 \(\pm\) 10 i & 266.56 \(\pm\) 2.76 j \\ \hline TPC (mg gallic acid \\ equivalents/g seed) & 25.86 \(\pm\) 2.17 b & 1303.0 \(\pm\) 67.7 e & 232.36 \(\pm\) 12.25 g & - & 307.09 \(\pm\) 14.16 b \\ \hline TPC (mg quercetin \\ equivalents/g seed) & 2.09 \(\pm\) 0.10 c & - & 2.13 \(\pm\) 0.22 h & 60 \(\pm\) 10 e & - \\ \hline \hline \end{tabular}
\end{table}
Table 1: Total phenolic compounds, total flavonoids, and antioxidant capacity of Semi 34 seed extract. Different letters represent different expressions of the concentration: a = (\(\mu\)M/g extract); b = (mg GAE/g extract); c = (mg QE/g extract); d = (mmol Trolox/g dried extract); e = (mg GAE/g dried extract); f = (mM Tolrox eq./100 g of extract); g = mMol GA/100 g of extract; h = mMol Cat. eq./100 g of extract; i = \(\mu\)g TE/g dried extract; j = mg TE/g extract.  
Figure 1: pH effects on antioxidant activity of Semi 34 seed extract. pH 6.1 is observed in the processed extracts of Semi 34 avocado seeds.
## 1 Introduction
By incorporating \(IQR\) and \(EWMA\_SPRR\) into link quality assessment, the proposed method improves robustness against transient fluctuations and provides a more stable estimation of link quality trends, ensuring accurate and reliable evaluation in dynamic underwater environments. 3. The FLRAF routing protocol builds a nested fuzzy logic reasoning system to complete the selection of candidate sets in the forwarding region. The inner inference system obtains the \(LQI\) between nodes, while the outer inference system comprehensively considers the output of inner inference, the residual energy (\(RE\)), and the effective advance distance (\(AD\)) of nodes to obtain the priority ranking. By incorporating a novel multimetric fuzzy logic decision model, the method improves the packet delivery rate of the network and some parameters related to energy consumption. The rest of this article is structured as follows. Section 2 presents the detailed implementation of the FLRAF protocol, including its forwarding process and fuzzy-logic-based decision strategy. Section 3 provides a comprehensive performance comparison with existing routing protocols under various simulation scenarios. Section 4 summarizes the main findings, discusses current research limitations, and outlines future work directions to enhance the adaptability and intelligence of underwater routing protocols.
# Chemical Characterization of Human Body Odor Headspace Components
###### Abstract  
This study focused on evaluating human body odor volatiles using a chamber approach. Ten participants were asked to sit inside the chamber for 1 h, while using SPME as the extraction technique for vapor sampling. A total of 105 compounds were detected across participants, with nonanal having the highest frequency. PCA statistical analysis depicted tighter clustering in female whole-body odor profiles when compared to males, thus corroborating gender odor differences. Concurrently, various biospecimens (hand, axillary, breath) from the same participants allowed for a comparison between whole-body and individual biospecimen odor signatures. When comparing whole-body sampling and distinctive biospecimens, nonanal and decanal were the only odor volatiles shared. Statistical clustering depicted higher similarity within the odor profiles of individual biospecimens compared to odor profiles of the whole body, indicating distinctiveness of the odor chemical landscape as a function of sampling region. Overall, this study demonstrated that SPME-GC/MS methodology was successful in the extraction, detection, and identification of previously reported human scent volatiles when employing the human chamber for whole-body sampling. Our presented testing paradigm allows for a direct comparison of odor volatiles across the full body and specific body locations that allows odor markers to be furthered exploited for diagnostic and biological detection contexts.  
human scent; volatilome; chemical characterization; whole-body odor; SPME
## 4 Performance Evaluation in Edge Scenarios
### Performance in Network Programming
#### 4.4.3 Impact of File Size on Latency  
When measuring the impact of file size on download latency, a single-threaded mode was used, and native Linux, Gramine and Occlum were all tested. The client concurrency was set to 1, and the file size gradually increased from 1 KB to 100 KB to observe the changes in download latency. The results are shown in Figure 7. As the file size increases, there is no significant change in the latency of native Linux. For Occlum, latency increases both in virtual and native environments. Gramine is the most affected by file size, with latency rising significantly as the file size increases. Comparing server performance in single-threaded mode, Occlum demonstrates better performance in handling file downloads, particularly with larger files. Compared to native Linux, Occlum's latency is approximately 2 to 3 times higher, while Gramine, due to the impact of file size, can experience latency increases of over 10 times.  
According to the results of the network programming performance tests, although Occlum performs better in file downloads in single-threaded mode, it still exhibits 2 to 3 times higher latency compared to native Linux, and is similarly affected by latency fluctuations due to file size. Additionally, Occlum's compatibility issues  
Figure 6: Comparison on CPU usage.  
cess creation mode can lead to usability problems. Gramine, on the other hand, has high download latency and is severely impacted by the size of the load.
###### Abstract
However, the weak robustness of PCA restricts the application of signal de-noising in PWA. Another modified method named robust principal component analysis (RPCA) has better robustness than PCA in the field of multi-channel arterial pulse signal de-noising [19]. [PERSON] et al. developed an RPCA-based method named weighted robust principal component analysis (WRPCA) to process multi-channel pulse signals and indicated that WRPCA could achieve better three-dimensional visual performance than RPCA [20]. However, these researchers extracted signal features separately for every channel without taking advantage of the internal associations between these channels. Since 3 DPI is a pulse signal with a spatial and temporal correlation of each channel [6], this study introduces a new de-noising algorithm called cross-channel DWRPCA, which produces a channel-scaled factor (CSF) technique to manipulate the weights of WRPCA. This paper uses a convolutional neural network to adaptively tune the CSF; therefore, the factors can adjust the weights in the nuclear norms according to the signal pattern of each channel. This technique can optimize the feature extraction for multi-channel signals such as 3 DPI to eliminate the noise in the original signals. In addition, this paper employed a complete and standard pulse signal processing system, including preprocessing, de-noising algorithm implementation, signal reconstruction, and display. Then, we evaluate the performance in the time domain and frequency domain. The idea of the proposed cross-channel DWRPCA algorithm can be generalized to other biomedical signal de-noising cases. In other words, our work provides a novel and promising thought to de-noise biomedical signals [21; 22; 23; 24; 25; 26]. The remainder of this paper is structured as follows: Section 2 introduces the dataset employed in this work and the overall signal processing framework; Section 3 describes our de-noising algorithm cross-channel DWRPCA, as well as the evaluation method used throughout the experiments; Section 4 mainly presents the time-domain and frequency-domain analysis results of the experiments. The final two sections discuss the experimental results and present the conclusions of this paper.
# Vitamin C in Plants: From Functions to Biofortification
## 5 Light-Dependent Vitamin C Accumulation in Plants
### Light Regulation of Vitamin C Accumulation  
The pivotal role of light-dependent vitamin C accumulation in green tissues is due to photosynthesis. In _Arabidopsis_ leaves and tomato green fruits, the photosynthetic inhibitor DCMU blocks vitamin C accumulation by reducing the expression of genes involved in its biosynthesis [70; 170]. Moreover, photosynthesis increases the amount of soluble carbohydrates, which are biosynthetic precursors of vitamin C. Three genes involved in carbohydrate accumulation and translocation have been related to a quantitative trait locus (QTL) associated with a 1.4-fold vitamin C increase in tomato [192]. On the other hand, [PERSON] et al. [193] found no correlation between vitamin C content and levels of soluble carbohydrates.  
Light can control vitamin C accumulation through different types of regulation (Figure 3).  
Light-dependent accumulation of vitamin C in plants seems to be principally due to the enhanced expression of different genes involved in the p-mannose/t-galactose biosynthetic pathway [70; 121; 142; 175]. It has been shown that in rice, GLDH and GPP contain light-responsive cis-elements (GT1 box and TGACG motif) in their promoters [194]. Concerning GLDH, light beyond regulating its expression can induce changes in respiration, indirectly modulating enzymatic activity [69; 159]. Light also influences the expression of genes involved in ASC recycling. During  
Figure 3: Light-dependent mechanisms involved in vitamin C accumulation (details are provided in the text). Abbreviations: PMM, phosphomannose mutase; GMP, GDP-n-mannose pyrophosphylase; GME, GDP-n-mannose epimerase; GGP, GDP-t-galactose-phosphorylase; GPP, t-galactose-1-phosphatephosphate; GDH, t-galactose dehydrogenase; GLDH, t-galactono-1,4-lactone dehydrogenase; APX, ascorbate peroxidase; MDHA, monodehydroascorbate; MDHAR monodehydroascorbate reductase; DHA, dehydrorosarcoma; DHAR, dehydroacorbate reductase.  
germination, corn seeds exposed to high light exhibited a higher DHAR expression along with an increased vitamin C content (Figure 3) [195].  
In tomato, light induces the expression of the transcription factor HZ24, which in turn activates GMP transcription in leaves and immature fruits [196]. High light also induces a decrease in the expression of AMR1, a transcription factor acting as a negative regulator of the last six genes of the d-mannose/t-galactose biosynthetic pathway, consequently increasing vitamin C biosynthesis (Figure 3) [197]. Finally, in the dark, CSN5B promotes GMP degradation via 26S proteasome, reducing vitamin C levels [198]. Additionally, darkness promotes vitamin C catabolism [44].  
The complexity of light-dependent vitamin C regulation (Figure 3) highlights that obtaining vitamin-C-biofortified plants by light treatments requires in-depth knowledge of the metabolic and physiological processes involved [199].
# Intrusion Detection for in-Vehicle Communication Networks: An Unsupervised Kohonen SOM Approach
###### Abstract  
The diffusion of embedded and portable communication devices on modern vehicles entails new security risks since in-vehicle communication protocols are still insecure and vulnerable to attacks. Increasing interest is being given to the implementation of automotive cybersecurity systems. In this work we propose an efficient and high-performing intrusion detection system based on an unsupervised Kohonen Self-Organizing Map (SOM) network, to identify attack messages sent on a Controller Area Network (CAN) bus. The SOM network found a wide range of applications in intrusion detection because of its features of high detection rate, short training time, and high versatility. We propose to extend the SOM network to intrusion detection on in-vehicle CAN buses. Many hybrid approaches were proposed to combine the SOM network with other clustering methods, such as the k-means algorithm, in order to improve the accuracy of the model. We introduced a novel distance-based procedure to integrate the SOM network with the K-means algorithm and compared it with the traditional procedure. The models were tested on a car hacking dataset concerning traffic data messages sent on a CAN bus, characterized by a large volume of traffic with a low number of features and highly imbalanced data distribution. The experimentation showed that the proposed method greatly improved detection accuracy over the traditional approach.  
Keywords: intrusion detection systems; unsupervised learning; self-organizing maps; CAN bus; Kohonen SOM network; cyber-physical systems; security; vehicle safety; cyber-attacks +
Footnote †: journal:
## 3 Method
### Problem Formulation  
In this work, we address the challenge of reconstructing a 3D surface from a single-shot 2D image of an orthogonal sinusoidal fringe pattern reflected on a specular surface. To formulate the problem, we introduce the following notation:  
* \(I=\{I_{k}\mid k=1,\ldots,n\}\): The 2D reflected image of a pattern \(\mathcal{P}\) reflected by surface \(\mathcal{S}\).
* \(\mathcal{D}=\{\mathcal{D}_{k}\mid k=1,\ldots,n\}\): The ground truth depth map corresponding to surface \(\mathcal{S}\).
* \(\hat{\mathcal{D}}\): The estimated depth map produced by our network.
* \(f_{\text{MLP}}:\mathbb{R}^{H\times W}\times\mathbb{R}^{H\times W}\to \mathbb{R}^{H\times W}\): The MLP function that combines the outputs of the VAE and U-Net to produce \(\hat{\mathcal{D}}\).
* \(\mathcal{C}\): camera parameters.
* \(\mathcal{F}:\mathbb{R}^{3}\to\mathbb{R}^{2}\): The projection to render a 2D image \(I\) from surface \(\mathcal{S}\).
* \(\psi:\mathbb{R}^{3}\to\mathbb{R}\): The function mapping a 3D point to depth.  
The objective is to develop a hybrid depth estimator network, VUDNet, that integrates both generative and discriminative modeling approaches. To train and evaluate our model, we simulate a comprehensive dataset that includes 2D images \(I\) and corresponding ground truth depth maps \(\mathcal{D}\). The generated dataset captures the complex interactions of light with specular surfaces, providing a robust foundation for training our hybrid depth estimator network. This approach ensures that our model can generalize well to real-world scenarios involving specular reflections.
# Self-Supervised Spatio-Temporal Graph Learning for Point-of-Interest Recommendation
## 3 Methodology  
In this section, we first give the problem definition for the POI recommendation, and then introduce our proposed model SSTGL. As illustrated in Figure 1, our method consists of three main components, namely, the graph neural network backbone, the spatio-temporal-aware data augmentation, and the spatio-temporal-aware pre-text task.  
In detail, the role of the graph neural network backbone is to learn the node representations from the user-POI graph \(\mathcal{G}\) and use them for the recommendation loss \(\mathcal{L}_{main}\) and the generation of the final recommendation list \(\hat{P}_{U}\). Spatio-temporal aware data augmentation aims to generate multiple augmented graphs (i.e., \(\mathcal{G}^{\prime}\) and \(\mathcal{G}^{\prime\prime}\)) based on spatio-temporal prior knowledge, and these augmented graphs are also fed into the GNN backbone to generate multiple enhanced node representations. Spatio-temporal-aware pre-text tasks use spatio-temporal similarity to design optimization objectives \(\mathcal{L}_{ssl}\) for self-supervised learning, thus providing effective self-supervised information.
# Research on Obstacle Avoidance Replanning and Trajectory Tracking Control Driverless Ferry Vehicles
## 3 Longitudinal Controller Design
### Fuzzy PID Acceleration Controller Design
#### 3.1.3 Fuzzy Rules  
The values of \(\Delta k_{p}\), \(\Delta k_{i}\), and \(\Delta k_{d}\) are adjusted online in real time by fuzzy rules. According to the expert experience, the fuzzy set is defined as {negative big, negative middle, negative small, zero, positive small, median, positive big} seven grades; that is, the fuzzy set is \(\{NB,NM,NS,0,PS,PM,PB\}\). Therefore, the selection of fuzzy rules is based on the following: When the speed deviation and the speed deviation change rate are large, in order to quickly reduce the system deviation, a larger \(\Delta k_{p}\) and \(\Delta k_{i}\) and a smaller \(\Delta k_{d}\) should be selected. When the rate of change of velocity deviation and velocity deviation is small but there is an obvious oscillation phenomenon, so as to quickly decrease the oscillation phenomenon and restore the stability of the system, a smaller \(\Delta k_{p}\) and \(\Delta k_{i}\) and a larger \(\Delta k_{d}\) should be selected. When both the velocity deviation and rate of change of velocity deviation are less than or equal to 0, the ferry is in deceleration or uniform motion, and the acceleration control should not work, so \(\Delta k_{p}\), \(\Delta k_{i}\), and \(\Delta k_{d}\) should be 0. The specific fuzzy rules are shown in Table 1.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{\(e\)} & \multicolumn{8}{c}{_ec_} \\ \cline{2-9}  & **NB** & **NM** & **NS** & **ZO** & **PS** & **PM** & **PB** \\ \hline NB & ZO, ZO, ZO & ZO, ZO & ZO, ZO, ZO & ZO, ZO, ZO & ZO, ZO, ZO & ZO, ZO, ZO \\ NM & ZO, ZO, ZO & ZO, ZO, ZO & ZO, ZO, ZO & ZO, ZO, ZO & ZO, ZO, ZO & ZO, ZO & ZO, ZO \\ NS & ZO, ZO, ZO & ZO, ZO, ZO & ZO, ZO, ZO & ZO, ZO, ZO & ZO, ZO, ZO & ZO, ZO, ZO & ZO, ZO \\ ZO & ZO, ZO, ZO & ZO, ZO, ZO & ZO, ZO, ZO & ZO, ZO, ZO & ZO, ZO, ZO & ZO, ZO, ZO & ZO, ZO, ZO \\ PS & PM, NB, PS & PM, NM, NS & PM, NS, NM & PS, NS, NM & ZO, ZO, NS & NS, PS, NS & NS, PS, ZO \\ PM & PB, NB, PS & PB, NB, NS & PM, NM, NB & PS, NS, NM & PS, NS, NM & ZO, ZO, NS & NS, ZO, ZO \\ PB & PB, NB, PS & PB, NB, NS & PM, NM, NB & PM, NM, NB & PS, NS, NB & PB, NB, NS & PB, NB, NS \\ \hline \hline \end{tabular}
\end{table}
Table 1: Fuzzy rule table.  
Figure 5: Membership function of \(\Delta k_{p}\).  
Figure 6: Membership function of \(\Delta k_{i}\) and \(\Delta k_{d}\).
# Electromagnetic Interference Shielding Anisotropy of Unidirectional CFRP Composites
###### Abstract  
Carbon fiber-reinforced polymer (CFRP) composites have excellent mechanical properties and electromagnetic interference (EMI) shielding performance. Recently, their EMI shielding performance has also attracted great attention in many industrial fields to resolve electromagnetic pollution. The present paper mainly investigated the EMI shielding anisotropy of CFRP materials using a specified set-up of free-space measurement. The electrical conductivity of unidirectional CFRP composites was identified to vary with the fiber orientation angles, and the formula was proposed to predict the results consistent with the experimental. The obvious EMI shielding anisotropy of unidirectional CFRP composites was clarified by free-space measurement. The theoretical formula can predict the EMI shielding value at different carbon fiber orientation angles, and the predicted results were highly consistent with the experimental results. A comparison of the free-space measurement and the coaxial transmission line method was also conducted, which indicated that special attention should be paid to the influence of the anisotropy of CFRP composites on the shielding results. With those results, the mechanism of EMI shielding anisotropy of CFRP composites is clarified, which will provide an effective design of EMI shielding products with a designable shielding direction and frequency.  
unidirectional CFRP; EMI shielding; anisotropy; free-space measurement Article Electromagnetic Interference Shielding Anisotropy of Unidirectional CFRP Composites. Materials20212141907[[https://doi.org/10.3390/ma14081907](https://doi.org/10.3390/ma14081907)]([https://doi.org/10.3390/ma14081907](https://doi.org/10.3390/ma14081907))