esa-sceva/satcom-chunk-collection huggingface dataset:
# Characterization of a Quantum Random Number Generator Based on Vacuum Fluctuations
## 4 Results and Discussion  
To verify that there is a preponderance of quantum noise over electronic noise, the spectral density power was taken both with and without the LO, considering an input power of 12 dBm, as can be seen in Figure 3.  
Figure 3: The power spectral density (PSD) when the LO is on (upper lines) and off (lower lines). Inset focus on low-frequency contributions. The introduction of the DC block suppresses the observed low frequency contributions, with no visible difference in the remaining spectral noise.  
For both cases, the high-frequency domain is defined by the cut-off frequency from the detector's bandwidth at 45 MHz. Moreover, strong spectral contributions were observed at low frequencies. These can arise both from electric hum and an imperfect subtraction of the photocurrents since the HD has a finite common-mode rejection ratio of 30 dB. To suppress these frequencies, as well as flicker noise associated with the electrical circuit, an 8535 Imnet RF DC Block was applied after the amplification step. It acts as a high-pass filter, blocking all frequency components inferior to 7 kHz. Consequently, the spectral curve below 40 MHz yields the relatively flat power density required for unbiased random number generation. In fact, no strong spectral peaks are observed for this interval, and the total noise clearance over electronic noise was measured to be at least 5 dBm. This clearly shows the difficulty in obtaining an expressive quantum to classical noise ratio (QCNR), which limits the available quantum entropy.
# Millimeter-Wave Smart Antenna Solutions for URLLC in Industry 4.0 and Beyond
## 5 Research Opportunities and Future Directions
### System-on-Chip Design  
The advancement in CMOS, SiGe, and IC design has led to the design of on-chip antennas (AoC) and antennas-in-package (AiP). AoC has always remained a bottleneck for the realization of true system-on-chip (SoC) RF solutions. Efficiency is the main issue in AoC due to the low resistivity and high permittivity of a silicon substrate [102]. However, some solutions, such as CMOS-MEMS (microelectromechanical systems), to suspend the antenna structure in air and keep a gap from the silicon substrate, may increase the efficiency and gain of AoC [98; 102]. Moreover, the characterization and measurements of AoC are also different than those of conventional PCB-based antennas. This is because on-chip antennas are fed using small wafer probes, which are fragile and prone to damage. Any mishandling may affect the measurement results. A microscope is required to accurately place the probes on the miniature on-chip antennas or the lines feeding the antenna. Microscopes are not standard anechoic chamber equipment, so they must be carefully placed so that they do not interfere with the antenna radiation or obstruct its movement [102].
# Comparison of Cooling Different Parts in a High Pressure Ratio Centrifugal Compressor
## 4 Conclusions and Remarks  
In this study, a three-dimensional CFD model has been used in order to study the performance of a high pressure ratio centrifugal with cooling on different parts. Important conclusions about the cooling approach in a centrifugal compressor are listed as follows.  
1. Shroud cooling increases the pressure ratio the most. The cooling power of about 2400 W on the shroud increases the pressure ratio by about 4.6%.  
2. Only back plate cooling reduces the solid impeller temperature. Impeller temperature will drop by 45 K with the cooling power of 2400 W on the back plate. Cooling the other walls does not affect the impeller temperature.  
3. Shroud cooling has the largest effect on the efficiency, while back plate and diffuser cooling have almost identical effects on the efficiency. Shroud cooling with the cooling power of 2400 W increases the polytropic efficiency by 1.49%. With increasing each 500 W of cooling power on the shroud, the efficiency increases roughly about 0.3%.  
4. Heating should have a contrariwise effect on each wall. For example, heating the shroud should reduce the performance the most, and heating the back plate will increase the impeller temperature. It is very important to prevent heat transfer to the shroud and back plate to preserve efficiency and reliability, respectively.  
This research was supported by the National Natural Science Foundation of China (Grant No. 51176087). [PERSON] acquired the data and wrote the paper and [PERSON] revised the paper and offered useful suggestions to write and revise the paper.  
The authors declare no conflict of interest.  
Figure 11: Streamlines for the compressor in the adiabatic condition and with shroud cooling.
## 4 Results and Discussion
### RQ4: What Are the Gaps in the Research on NAS in GANs?
Understanding why certain architectures perform better than others can provide valuable insights and guide future design improvements. Research efforts should aim to develop techniques that enhance the interpretability of NAS-generated GAN architectures. * A further aspect that warrants deeper exploration is the integration of NAS with emerging AI paradigms, such as self-supervised learning, which could significantly enhance the robustness and generalizability of GANs. * Another domain that calls for more research is the environmental and computational costs associated with NAS processes, promoting the development of more energy-efficient and sustainable AI models. * There is also a need to investigate the impact of NAS on GANs in other domains beyond image generation, such as natural language processing or time-series data, which could open new avenues for GAN applications. * Additionally, the application of NAS techniques to transformer architectures within GANs remains unexplored. Given the success of transformers in various machine learning tasks, exploring NAS for transformer-based GAN architectures could lead to significant advancements. This presents a promising research direction that could leverage the strengths of transformers for improved GAN performance.
## 4 Proposed Method
### Multi-Scale Transformer Module
#### 4.3.1 Subframe-Level Transformer  
Subframes, as fundamental units of a speech frame, contain four codewords and exhibit strong local dependencies. The subframe-level transformer focuses on capturing these relationships, extracting essential features while filtering out irrelevant information. This targeted processing preserves critical details for higher-level modeling and enables efficient parallel computation.  
The subframe-level (SL) transformer extracts fine-grained features from individual subframes, each containing a single codeword. The first step is to apply the multi-head attention mechanism to model dependencies between subframes, following these steps:  
\[\mathbf{C_{mha-sl}}=Contact(H_{sl,1},H_{sl,2},\dots,H_{sl,h})\mathbf{W^{O}_{sl}}. \tag{12}\]  
where \(h\) indicates the number of the heads, and \(\mathbf{C_{sl}}\) indicates the output of the multi-head attention mechanism. And \(\mathbf{H_{sl,i}}(1\leq i\leq h)\) computes as follows:  
\[\mathbf{H_{sl,i}}=Softmax(\frac{\mathbf{Q_{sl,i}}\mathbf{K^{(T)}_{sl,i}}}{\sqrt{d_{k}}})\bm {V_{sl,i}}. \tag{13}\]  
where \(d_{k}\) indicates the dimension of the \(i\)-th head, \((T)\) represents the transition operation and the definition of \(\mathbf{Q_{sl,i}}\), \(\mathbf{K_{sl,i}}\) and \(\mathbf{V_{sl,i}}\) are as follows:  
\[\mathbf{Q_{sl,i}}=\mathbf{R_{A}}\mathbf{W^{Q}_{sl,i}},\mathbf{K_{sl,i}}=\mathbf{R_{A}}\mathbf{W^{K}_{ sl,i}},\mathbf{V_{sl,i}}=\mathbf{R_{A}}\mathbf{W^{V}_{sl,i}}. \tag{14}\]  
The second step is the residual connection and layer normalization operation, which are applied to stabilize learning as follows:  
\[\mathbf{C^{1}_{sl}}=LayerNorm(\mathbf{C}+\mathbf{C_{mha-sl}}). \tag{15}\]The third step is to utilize a two-layer FFN to refine the features as follows:  
\[\mathbf{C^{2}_{sl}}=ReLU(\mathbf{C^{1}_{sl}}\mathbf{W^{ffn}_{sl,1}}+\mathbf{b^{ffn}_{sl,1}})\mathbf{W^ {ffn}_{sl,2}}+\mathbf{b^{ffn}_{sl,2}}, \tag{16}\]  
where \(ReLU\) represents the rectified linear unit operation. Then, another residual connection and normalization performs on the \(\mathbf{C^{2}_{sl}}\) as described in the following equation:  
\[\mathbf{C^{3}_{sl}}=LayerNorm(\mathbf{C^{1}_{sl}}+\mathbf{C^{2}_{sl}}). \tag{17}\]  
The \(\mathbf{C^{3}_{sl}}\) is the output of the subframe-level transformer, which encodes local dependencies for each subframe.
## 3 Results
### LVP Performance
#### 3.1.2 Displacement Inputs  
The \(D_{in}\) showed differences between \(f_{set}\) (F = 713.88, \(p\)! 0.001, \(\eta_{p}^{2}\) = 0.974), postures (F = 113.27, \(p\)! 0.001, \(\eta_{p}^{2}\) = 0.856), and its interactions \(f_{set}\) \(\times\) postures (F = 69.53, \(p\)! 0.001, \(\eta_{p}^{2}\) = 0.785). The main effect was a significant reduction in \(D_{in}\) (\(p\)! 0.001) at 30 Hz (0.59 \(\pm\) 0.00, 95% CI: 0.583-0.601) versus 20 Hz (1.00 \(\pm\) 0.01, 95% CI: 0.978-1.013)  
Figure 4: Root mean squared (RMS) acceleration outputs and their transmissibility at different frequencies and across different body positions. Data are presented as mean (SD).  
and 25 Hz (0.96 \(\pm\) 0.01, 95% CI: 0.941-0.978). Overall, significant differences were found among postures (\(p\leq\) 0.006), except between TS-LU (\(p\) = 1). The LVP showed higher \(D_{in}\) in TS (0.90 \(\pm\) 0.00, 95% CI: 0.891-0.908) and LU (0.89 \(\pm\) 0.01 95% CI: 0.880-0.908), followed by DS (0.84 \(\pm\) 0.00, 95% CI: 0.833-0.849), HS (0.82 \(\pm\) 0.00, 95% CI: 0.813-0.829), and ER (0.78 \(\pm\) 0.01, 95% CI: 0.777-0.802).
These datasets are able to capture complex interactions between the targeted vehicles, the fiber-optic cable geometry, and the subsurface response to the traffic dynamics--a capability absent in existing seismic-focused synthetic DAS frameworks [13; 14]. The primary objective of this study is to introduce and validate a methodology for generating synthetic strain-rate signals simulating DAS measurements, derived from the outputs of transport microsimulation models. Our secondary objectives are to demonstrate the framework's utility by training ML models to perform critical tasks. The selected example applications target training (U-Net) models to enhance spatial resolution beyond hardware limitations (doubling the spatial resolution by reducing the effective gauge length from 1.034 m to 0.517 m), isolating directional traffic flows even for dense traffic situations and simulating the effects of alternative cable layouts. This approach aims to provide valuable tools for both the DAS and transport engineering communities to develop robust detection algorithms, optimize sensor deployments, and advance traffic analysis capabilities. Controlling dataset creation allows for various congestion levels under different cable layouts and complex traffic scenarios, ultimately contributing to advancements in traffic analysis and smart city initiatives utilizing DAS technology. The methodology's novelty lies in three key aspects:  
* Transport-physics coupling: Direct mapping of microsimulated traffic and vehicle parameters (mass, wheelbase, driving behavior, acceleration, and speed profiles) to strain-rate signals through modeling. This reduces the label data acquisition costs for kilometer-scale networks while producing high numbers of labeled datasets for any targeted scenario across the whole transport network. * Cable layout-aware synthesis: Explicit incorporation of real-world cable deployment factors like burial depth variations and non-linear routing. This enables the analysis of existing and future cable layouts. * ML-ready datasets: Generation of paired synthetic measurements (features + labels) for supervised learning tasks, including directional filtering and resolution enhancement. This paper is structured as follows: Section 2 details our synthetic signal generation pipeline, covering transport modeling and physics-based simulation. Section 3 outlines the machine learning implementation, and Section 4 presents the results, evaluating model performance on synthetic data and application to real DAS measurements. Section 5 addresses limitations and future directions for physics-informed DAS processing, while Section 6 summarizes the work and significance of the contribution. The appendices provide details on the Flamant-Boussinesq approximation (Appendix A) and the U-Net architecture (Appendix B).
## 3 Results and Discussion
### TQ Content Estimation
#### 3.2.1 Specification of Used Methods  
The FTIR spectrum of TQ showed strong absorption bands and shoulders in the 1700-1600 cm\({}^{-1}\) region that could be assigned to carbonyl stretching, and weaker bands corresponded to aliphatic (\(\sim\)2967 cm\({}^{-1}\)) and vinylic (\(\sim\)3040 cm\({}^{-1}\)) C-H stretching [33; 34]. Moreover, TQ contains chromophores that absorb in the UV range, with a prominent peak (\(\lambda_{\rm max}\)) at about 254-257 nm (Figure S1, inset), which can be used for estimating its quantity [35]. The most accurate methods of quantitative analysis are undoubtedly chromatographic methods. In this study, we therefore utilized high-performance liquid chromatography (HPLC) to confirm the possible observed changes in TQ content in BCSO detected by the used spectroscopy techniques. HPLC analyses (monitored at 254 nm) of the oil samples after extraction with methanol revealed a peak with a retention time at 10.5 min, which corresponds to TQ (Figure S1). The peak was identified by comparing the retention time and UV spectrum with a TQ standard and with literature data [14].  
The obtained FTIR spectra of BCSO, Figure 2, have demonstrated the existence of a variety of sharp, strong peaks belonging mainly to the triglycerides [36; 37]. Some of the weaker peaks can, in turn, be attributed to the presence of a known bioactive component like TQ, dithymoquinone, or thymol, the presence of which in BCSO was confirmed, among others, by FTIR spectroscopy [19].  
In the frequency region, at the FTIR spectra characteristic to TQ, an absorption band was observed with a maximum at 1659 cm\({}^{-1}\) (inset at Figure 2) belonging to the C=O stretching, which is supported by the values reported for TQ [19; 33]. The changes in the intensity of this band were used to track variations in the TQ content in the heating oil and, together with spectrophotometric and HPLC results, were shown in Figure 4.  
Figure 3: The spectral changes in 3009/2854 peak ratio (circle—right scale) and at 987 cm\({}^{-1}\) (rectangle—left scale) during heating of BCSO open and closed samples. Error bars show the variations in three determinations in terms of standard deviation.  
The quantification of TQ in BCSO was established on the base of the UV spectra, by measurements of the absorption intensity at \(\lambda_{\text{max}}\) = 254 nm, using the Beer-Lambert Law and a standard curve with the correlation coefficient of 0.9965. The amount of TQ from HPLC chromatograms (retention time 10.5 min) was calculated based on a standard curve with the correlation coefficient of 0.9976.
## 2 Molecules, Properties, and Sources
### Ulvan
Despite the complexity of the ulvan molecule itself, other factors contribute to the variabilities related to the number of _Ulvu_ species such as seasonal period of harvesting, eco-physiological variation, extraction techniques [63; 67]. The correct identification of _Ulva_ species presents considerable difficulty/confusion because there are many species in nature, and they present intermediate forms with flexibility of the morphology (phenotypic changes under different growth conditions), in growth and metabolite accumulation [39; 60]. Thus, it is necessary to identify them on the basis of their morphological characteristics and molecular analysis [71]. An interesting feature of the polymer is the ability of gel formation with low intrinsic viscosity in aqueous solution, and the mechanism appears unique among polysaccharide hydrogel [38]. The ultrastructure analysis revealed a hydrophobic and aggregative behavior of ulvan gel that forms bead-like (\"raspberry-like\") aggregates interconnected by fiber-like constituents. How ulvan is present in the seaweed cell walls is still an open question [61]. Ulvan exhibits many direct biological activities, including antioxidant, immunomodulatory and immunostaining, cytotoxic and antitumor, anti-inflammatory, anticoagulant, antihypertensive, antiviral, antibacterial and tissue engineering properties as reviewed by [PERSON] et al. [67], [PERSON] et al. [72] and [PERSON] et al. [70]. Furthermore, the polymer is an elicitor or priming agent with the capacity to modulate cellular signalling in metabolic pathways suitable for plant protection [73; 74]. The small, non-sulphated oligomers are able to induce oxidative burst in dicot cells [53]. The detailed knowledge of ulvan structures provides the basis for its modification and for the relationship between its chemical structure and biological activity [53]. However, its complex structure remains a challenging aspect of ulvan applications. Future studies should be focused on the structure-activity relationship to obtain more active and well-defined ulvans, resulting in the second generation of molecules [49]. In fact, ulvan is a valuable bio-ingredient and it guides the market to further exploitation for future applications [72]. A pivotal strategy will be to have ulvan as a commercially available source. Currently, at least three companies in the carbohydrate chemistry field provide ulvan polysaccharide as a laboratory reagent for research and innovation development. The emerging demand of the market is indeed the industrial ulvan production, available in high quantity and with a certified quality [66]. However, the standardization of processes and big scale producers are still missing. The sum of these characteristics makes ulvan a unique molecule with a broad number of biological and biomedical applications. In fact, there are different possibilities to include ulvan for the preparation of biomaterials or hybrid biomaterials: membrane, particles, hydrogels, films, scaffolds, nanofibers, stabilizer emulsifier, adsorbents [67; 75; 76].
## 3 System Model and Description
### Network Configuration and Data Transmission
#### 3.4.2 BFS-SPF-Three with Opportunistic Routing
```
1:Call the procedure to find potential-forwarder
2:for node i \(\epsilon\) Nodes do
3:for node n \(\epsilon\) Neighbor-table do
4:if\(|\) Neighbor (n) \(|\geq 1\) then
5:if\(|\) Neighbor (Neighbor (n)) \(|\geq\)1 then
6:ifn == Cross node then
7: Potential node forwarder = n
8: Go to step 32
9:endif
10:endif
11:if\(|\) Cross node == 0 \(|\) then
12:for node i \(\epsilon\) Not void nodes do
13:if\(|\) Neighbor (Neighbor (n)) \(|\geq\)1 then
14: [Distance, Path]= \(BFS-SPF\) (Adjacency matrix, Start ID, Final ID);
15: Save the complete shortest path
16:if\(|\) Neighbor (Neighbor (n)) \(|\geq 1\) then
17: Potential node forwarder = Path (2)
18:endif
19:endif
20:endif
21:endfor
22:endfor
23: Now data transmission starts from here
24: Node i starts receiving data packets
25:ifi is a cross node then
26: Multi-cast the data packets
27:else
28: Unicast the data packets
29:endif
30:Output: (\(Minimum\)\(EC\), \(Affordable\)\(PRR\), \(Affordable\)\(delay\), \(Minimum\)\(active\)\(nodes\))
## 4 Discussion
When cells are exposed to various stresses, such as hypoxia and DNA damage, p53 can initiate the exogenous apoptotic pathway by inducing the expression of FAS and FASL, and it can also interact with members of the Bcl-2 family to promote the expression of bax, which ultimately altersthe permeability of the mitochondrial membrane, activates the caspase effect in the cytoplasm, and induces cells to enter the mitochondria-mediated apoptotic pathway (i.e., the endogenous apoptotic pathway) [47]. In addition, caspase-7 is a response gene of p53. Increased expression of the p53 transcription factor allows the caspase-7 gene to be further transcribed and activated, with its protein expression showing a time-dependent increase with increasing p53 expression [48]. Under certain conditions, the expression of p21 is closely related to the apoptotic precursor protein bax and can function to enhance the expression of bax. WST can induce apoptosis by increasing the expression levels of both p53 and p21. NF-kB is an important negative regulator of tumor cell apoptosis and is involved in important processes, such as the immune response, the inflammatory response, and apoptosis. Activated NF-kB can interact with other inflammatory signaling pathways and promote the expression of inflammatory factors, adhesion molecules, and inflammation-related enzymes. Upon induction of multiple factors, NF-kB is released from 1 kB and transferred to the nucleus where it binds to thekB binding site of the target gene promoter region [49]. Cox-2 has an inflammatory effect, and its overexpression is an important factor in the transition from inflammation to the occurrence of malignant tumors [50]. After treatment with WST, the mRNA levels of NF-kB and Cox-2 in liver cancer cells were significantly reduced, increasing their anti-inflammatory activity. In addition, on the basis of previous related experiments, we found the optimal concentrations range of WST extracts in the anti-oxidant (0.2, 0.6, 1.0 mg/mL), anti-mutation (1.25, 2.5 mg/plate) and anti-cancer (40, 100, 160 mg/mL) research project, respectively, and obtained good biological activity. However, we have not further studied the Western blot and the structural analysis of specific compounds, and we will further improve them in future experiments.