esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methods
### Angle for SBTS  
As described earlier, the PC was the linear combination of the variables and the first PC was the linear axis that held the largest variance. The direction of the first PC depended on a given data set, and the degree of its fluctuations/variations was less affected by the robust aspect of PCA [16]. This aspect was considered as one of the main advantages of the PC approach. The angle of the first PC was used to measure the SBTS of a group of the spatiotemporal speed data. The angle between the first PC and the standard vector, \(v_{s}\) = [1, 1, 1,...], at a time \(t\), \(Ang_{t}\) was calculated by the following equation, where \(w_{1}\) is the first PC.  
\[Ang_{t}=Arccos\left(\frac{w_{1}\cdot v_{s}}{\left|w_{1}\right|\cdot\left|v_{s} \right|}\right) \tag{4}\][PERSON] et al. [19] used this angle for making real-time crash predictions. They have extracted eigenvectors from traffic speed, occupancy, and flow data. The eigenvectors are independent of each other, and it was possible to avoid the multicollinearity between the loop data.  
In order to illustrate the proposed concept for identifying SBTSs, a simulated numerical example has been employed. It was assumed that there were eight consecutive stationary detectors. The gaps between the detectors were identical with the length of 1.67 km. The first downstream detector did not experience the traffic congestion because the activation point of the bottleneck was the second upstream detector. From second to sixth downstream detectors experienced two types of shockwaves, i.e., backward-forming and forward-recovery. The wave speed of the backward-forming was 20 km/h, and the wave speed of the forward-recovery was 10 km/h. The time period of a day was from 2 p.m. to 10 p.m., and the bottleneck was activated around 4 p.m. and deactivated at around 8 p.m. The speed of the free flow state was 80 km/h, and the congested state was 15 km/h. The speed data were combined with the noise of the standard normal distribution with a standard deviation of 8 km/h. The \(Ang_{t}\) calculated with the speed data and the speed contour-plot is shown in Figure 1. The change of the \(Ang_{t}\) is shown clearly according to the evolution of traffic congestion. The \(Ang_{t}\) can be used to divide the SBTS of the study area and also used as an independent variable for the imputation model.  
Figure 1: Speed contour and \(Ang_{t}\) plots for the numerical example.
# In Vivo Evaluation of Decellularized Human Tooth Scaffold for Dental Tissue Regeneration
## 3 Results
### Gene Expression in Recellularized PDLSCs  
qPCR was performed to assess the expression of genetic markers in recellularized PDLSCs. There was a significant difference in the expression levels of CP23, Col I, Col XII, OC, and alkaline phosphatase (ALP) between the PDLSC and control group. In particular, gene expression upregulation was observed in OC (4.59 \(\pm\) 3.97-fold) and CP23 (8.46 \(\pm\) 3.87-fold). However, there was no significant difference in the expression levels of periostin (POSTN) (Figure 3).  
Figure 3: Evaluation of gene expression level by relative fold changes for genes encoding _CP23, Col XII, POSTN, OC, ALP_, and _Col I_ in dHPDL transplants. The expression levels of _CP23, Col XII, OC, ALP_, and _Col I_ differed significantly between the two groups (* \(p\) < 0.05). The expression of _POSTN_ did not differ significantly between the two groups (_p_ > 0.05). Abbreviations: C, control group; P, PDLSC group, _CP23_, cement-derived protein 23; _Col XII_, collagen type XII; _POSTN_, periostin; OC, osteoclast; _ALP_, alkaline phosphatase; _Col I_, collagen type I; dHPDL, decellularized human periodontal ligament.  
Figure 2: Immunohistochemical staining of PDLSCs recellularization on dHPDL transplants. dHPDL transplants were immunostained with (**A**,**D**) anti-human Col XII, (**B**,**E**) anti-human CP23, and (**C**,**F**) anti-human OC antibodies. The red arrows indicate examples of positively immunostained cells. Scale bars: 20 \(\upmu\)m. Abbreviations: PDLSCs, periodontal ligament stem cells; dHPDL, decellularized human periodontal ligament; Col XII, Collagen type XII; CP23, cement-derived protein 23; OC, osteoclast.  
Histological and Immunohistochemical Analysis of DPSC Recellularization after In Vivo Transplantation  
DPSCs were shown to be repopulated on dHDPs. This result was confirmed after nine weeks using the same method that was used to confirm the result for PDLs. In the DPSCs group, the repopulated cells were observed through HE and MT staining and their human origin was confirmed through HN staining (Figure 4). Unlike the control group, in the DPSC group, the newly formed hard tissues were observed through HE staining (Figure 4A,D). In the DPSC group, mineralized matrices were poorly organized and featured intermingled matrices with different alignments and embedded cells with large cytoplasmic spaces and nuclei (Figure 4D). Immunohistochemical staining showed the presence of anti-human DSP, OC, VEGF, and CD34. The DPSC group was stained while the control group was not (Figure 5).  
Figure 4: Histological characteristics of recellularized dHDP transplants with DPSCs. **(A,D)** HE staining. (**B,E**) MT staining. (**C,F**) HN staining. HE, MT, and HN staining showed that dHDP transplants were recellularized. Recellularized cells are indicated by red arrows. Scale bars: 20 \(\upmu\)m. Abbreviations: dHDP, decellularized human dental pulp; DPSCs, dental pulp stem cells; HE, hematoxylin and eosin; MT, Masson’s trichrome; HN, human nuclei.
## 4 Game Theoretic Client Selection Model
### Nash Game Formulation  
Each client \(i\) is a player in the game defined by:  
* Strategy set: \(a_{i}^{t}\in\{0,1\}\)
* Utility function: \[U_{i}(a_{i}^{t},a_{-i}^{t})=a_{i}^{t}\cdot(\omega_{A}A_{i}^{t}+\omega_{B}B_{i}^ {t}+\omega_{P}P_{i}^{t})-a_{i}^{t}\cdot(\lambda_{B}\Delta B_{i}^{t}+\lambda_{P} \Delta P_{i}^{t})\] (7) where
* \(a_{-i}^{t}\) represents the action profile of all clients other than \(i\).
* \(\omega_{A}\), \(\omega_{B}\), and \(\omega_{P}\in[0,1]\) are time-varying reward weights that satisfy the following condition: \[\omega_{A}+\omega_{B}+\omega_{P}=1.\] (8) These weights are dynamically updated at each training round based on the real-time state of each client. They reflect three key aspects: the client's contribution to model accuracy (\(\omega_{A}\)), battery constraints (\(\omega_{B}\)), and privacy sensitivity (\(\omega_{P}\)). The adjustment of these weights is grounded in both theoretical reasoning Formulas (4)-(6) and empirical observations. In practice, involving a client in training improves global model accuracy, but at the cost of energy consumption and potential privacy risks. Therefore, to balance this trade-off, raw utility scores are computed from current client metrics: recent local model accuracy, normalized battery level, and a predefined privacy score. These scores are then normalized to ensure the resulting weights sum to one. This dynamic weighting allows the system to prioritize clients that provide high utility while considering their limitations. For example, clients with low battery or high privacy sensitivity receive higher \(\omega_{B}\) or \(\omega_{P}\), which lowers their chance of being selected. In contrast, clients contributing significantly to accuracy are favored through increased \(\omega_{A}\). This adaptive mechanism ensures fairness, improves training efficiency, and allows the strategy to perform well under diverse and changing edge environments.
* \(\lambda_{B}\), \(\lambda_{P}\) are penalty weights for battery and privacy degradation, respectively.
* \(\Delta B_{i}^{t}\) and \(\Delta P_{i}^{t}\) denote the expected battery drop and privacy loss. Each client aims to maximize their utility: \[a_{i}^{t*}=\arg\max_{a_{i}^{t}\in\{0,1\}}U_{i}(a_{i}^{t},a_{-i}^{t})\] (9) A NE is achieved when the following conditions are met: \[U_{i}(a_{i}^{t*},a_{-i}^{t*})\geq U_{i}(a_{i}^{t},a_{-i}^{t*})\quad\forall a _{i}^{t}\in\{0,1\},\forall i\] (10)
## 4 Biological Activities of _Passiflora caerulea_ L.
### Anticancer Activity  
Cancer is considered one of the most significant problems of the 21 st century. Medicinal plants could be used as an anticancer remedy due to their complex biological activities. _P. caerulea_ has been recently investigated for its anticancer properties. Research indicates that various species within the _Passiflora_ genus exhibit cytotoxic and antitumor activities, highlighting the genus's diverse bioactivity [46].  
The results of the study by [PERSON] and [PERSON] demonstrate the significant activity of the extract of _P. caerulea_ against breast cancer (MCF-7) cells [23]. MTT assay results showed that the crude methanolic extract of the plant possessed considerable activity against breast cancer (MCF-7) cells. The dose-dependent cellular viability was observed, and it was found to be IC\({}_{50}\) = 50.22 \(\upmu\)g/mL. These results necessitate deeper investigations into the pharmacological and therapeutic usefulness of extracts from _P. caerulea_. [PERSON] et al. compared the inhibitory activity of three _Passiflora_ species--_P. alata_, _P. incarnata_ and _P. caerulea_--against human acute lymphoblastic leukemia CCRF-CEM cells [24]. The data showed inhibitory effects for the _P. alata_ and _P. incarnata_ extracts. However, the extractsfrom _P. caerulea_ did not show any activity, despite the qualitative phytochemical similarities between the profiles obtained from _P. incarnata_ and _P. caerulea_. A study by [PERSON] et al. has explored the cytotoxic effects of _P. caerulea_ leaf extracts combined with titanium oxide nanoparticles (TiO2 NPs) on cancer cell lines such as A549, U937 and HeLa [47]. The combination showed a greater decrease in cell viability than the individual treatments, indicating a synergistic effect.  
While the plant _P. caerulea_ has shown promise in preliminary studies, further research is necessary to fully understand its anticancer potential and to identify the specific compounds responsible for its bioactivity.
## 4 Experimental Validation  
The normal-incidence sound absorption coefficient of the HPSET with melamine foam is measured by an impedance tube (B&K 4206, see Figure 4) based on the transfer function method. The measurements are manipulated according to the ISO 10534-2 standard [31], and the experiment set up is shown in Figure 4. The distance between the two microphones is 50 mm and the measured frequency range is from 0 to 1600 Hz. The inner diameter of the impedance tube is 100 mm. The room temperature is 17.5 \({}^{\circ}\)C, the atmosphere pressure is \(1.01\times 10^{5}\) Pa, and the relative humidity is 66.4%. The hollow perforated spherical structure used in the experiments is made of plastic, and the extended tubes are made of copper. Parameters for the HPSETs are shown in Table 2. Thickness of hollow perforated spherical structure and extended tubes is 0.5 mm. The thickness of the foam is 50 mm, and the average densities of the HPSET with foam are 47.1 kg/m\({}^{3}\).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Parameters** & \(d_{0}\) **(mm)** & \(t\) **(mm)** & \(N\) & **2R (mm)** \\ \hline HPSET\({}_{1}\) & 4.9 & 10 & 2 & 65 \\ HPSET\({}_{2}\) & 4.9 & 10 & 5 & 65 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameters of the tested samples of HPSETs.  
Figure 3: Comparison of the normal incidence sound absorption of composite absorber with different parameters. (**a**) The resistivity of melamine foam; (**b**) The diameter of extended tubes; (**c**) The length of extended tubes; (**d**) The number of extended tubes.  
Figure 5 shows the comparison of the experimental result and the calculation result for the sound absorption coefficient of the melamine foam. The calculation result uses the inverse characterization techniques described in Section 2.1 (inversion method-based parameters are listed in Table 1). It is shown that the calculation result is highly consistent with the actual experimental result, which verifies the reliability of this inversion characterization method.  
The measured and calculated sound absorption coefficient curves of HPSET with melamine foam are shown in Figure 6. Good agreement is observed between the measurement and calculation. The HPSET combined with a porous foam in a limited thickness of 65 mm reaches a good sound absorption property in the frequency range from 200 to 1600 Hz. The thickness of the porous material is only 1/28 of the sound wavelength, which realizes the purpose of controlling the large wavelength with thin layer materials.  
Figure 4: Experiment set up.  
Figure 5: Sound absorption coefficient of the melamine foam. The red solid line and blue dashed line represent the calculated results by inversion method and directly measured results, respectively.
# A Brief Review on the Recent Experimental Advances in Thermal Rectification at the Nanoscale
## 1 Introduction
The fourth one is the asymmetric scattering of ballistic phonons, which is easier to realize experimentally compared to the idealized one-dimensional chain approaches. For example, for a thin film with pyramidal inclusions (or some other asymmetric nanostructures), phonons emitted from the top contact are more likely to be transmitted, while phonons emitted from the bottom contact are more likely to be backscattered. This demonstrates the asymmetry of heat conduction in the devices [15]. Here, we only mentioned several important physical mechanisms for nanomaterials, while a large number of MD simulations have been published, especially for the two-dimensional materials. By designing asymmetric thickness [16], width [17; 18; 19], strain [20; 21], and defect [22; 23] graphene structures in the simulation, thermal rectification could be realized. [PERSON] and [PERSON] conducted a very complete and significant review of experimental demonstrations for thermal diodes, switches, and regulators, which covers the fields of heat conduction, convection, and radiation. After organizing and summarizing a wide variety of physical mechanisms, this review discusses the potential applications for the switchable and nonlinear heat transfer [24]. On the other hand, experimental studies are quite scarce for thermal rectification at the nanoscale due to the challenge of manufacturing asymmetric nanostructures. Nevertheless, rapidly developing nanotechnology has enabled recent experimental advances to be made and provided valuable measurement data for MD simulation and theoretical analysis. The new nanotechnology and experimental skills discussed in this paper also provide clear guidance for the future development of efficient thermal rectifiers at the nanoscale.
# Zero-TCE: Zero Reference Tri-Curve Enhancement for Low-Light Images
## 4 Experiments and Results
### Quantitative Evaluation
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Learning** & **Datasets** & **LOLv1** & \
\begin{tabular}{c} **LOLv2-** \\ **Real** \\ \end{tabular} &
\begin{tabular}{c} **LOLv2-** \\ **Synthetic** \\ \end{tabular} & **DICM** & **MEF** & **NPE** & **ExDark** & **Avg** \\ \hline \multirow{2}{*}{Conventional} & MSRCR & 19.405 & 29.207 & 20.043 & 16.015 & 19.812 & 24.268 & 18.547 & 21.042 \\  & Dong & 15.136 & 25.908 & 8.807 & 8.657 & 7.960 & 11.529 & 12.426 & 12.918 \\ \hline \multirow{2}{*}{Supervised} & KinD++ & 18.626 & 18.182 & 16.501 & 11.911 & 18.099 & 13.090 & 20.745 & 16.736 \\  & RetinexNet & 22.129 & 32.463 & 12.252 & 12.965 & 18.099 & 10.926 & 13.314 & 17.450 \\  & Retinexformer & 11.338 & 6.802 & 9.214 & 5.153 & 12.159 & 7.164 & 12.655 & 9.212 \\ \hline \multirow{2}{*}{Unsupervised} & EnlightGAN & 9.729 & 20.586 & 9.611 & 6.031 & 8.688 & 12.653 & 13.993 & 11.613 \\  & Zero-DCE++ & 7.926 & 23.957 & 11.247 & 6.784 & 8.172 & 12.640 & 13.325 & 12.293 \\ \cline{1-1}  & CLIP-LIT & 13.343 & 26.386 & 11.024 & 9.565 & 8.714 & 11.614 & 13.146 & 13.399 \\ \cline{1-1}  & NeRCo & 10.436 & 9.303 & 9.593 & 22.084 & 15.479 & 21.965 & 25.287 & 16.307 \\ \cline{1-1}  & Colie & 10.933 & 25.036 & 11.317 & 7.962 & 12.753 & 13.702 & 15.599 & 13.900 \\ \cline{1-1}  & Ours & 9.230 & 21.418 & 9.023 & 6.287 & 6.532 & 11.057 & 12.340 & 10.841 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Quantitative comparison results of (LOLv1, LOLv2-real, LOLv2-synthetic, DICM, MEF, NPE, and ExDark) datasets using the PIQE metric. A lower value of the PIQE metric indicates better performance. The first, second, and third best scores are highlighted with red, blue, and green colors, respectively.
# A Low-Complexity Pilot-Based Frequency-Domain Channel Estimation for ICI Mitigation in OFDM Systems
## 2 System Model
### Link Simulator
These IQ symbols are used by the channel estimator block, which selects the sub-carriers with reference signals to perform channel estimation. Once the channel estimates are obtained and interpolated, the equalizer block performs equalization of IQ data symbols that are collected by the sub-carrier de-mapper block. The channel estimation and equalization can follow the classical 1 tap ZF approach described in Section 2.1 or it can follow our proposed approach detailed in Sections 3 and 4. Finally, equalized symbols are detected by the DeMod block, which obtains a detected TB. This detected TB is compared with the transmitted TB to compute the BER.
_Appl. Sci._**2015**, \(5\), 485-515; doi:10.3390/app5030485  
_Applied sciences_  
**ISSN 2076-3417**  
www.mdpi.com/journal/applsci  
_Review_  
**Fundamentals of Highly Non-Degenerate Cascaded Four-Wave Mixing**  
**[PERSON] \({}^{1,*}\) and [PERSON] \({}^{2}\)**  
\({}^{1}\) Departamento de Optica, Facultad de Ciencias Fisicas, Universidad Complutense de Madrid,  
Avda. Complutense s/n, Madrid 28040, Spain  
\({}^{2}\) Departamento de Fisica e Astronomia e IFIMUP-IN, Faculdade de Ciencias, Universidade do Porto,  
R. do Campo Alegre 687, Porto 4169-007, Portugal; E-Mail: [EMAIL_ADDRESS]  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +34-91-394-4508; Fax: +34-91-394-4683.  
Academic Editor: [PERSON]  
_Received: 16 July 2015 / Accepted: 20 August 2015 / Published: 7 September 2015_  
**Abstract:** By crossing two intense ultrashort laser pulses with different colors in a transparent medium, like a simple piece of glass, a fan of multicolored broadband light pulses can be simultaneously generated. These newly generated pulses are emitted in several well-defined directions and can cover a broad spectral range, from the infrared to the ultraviolet and beyond. This beautiful phenomenon, first observed and described 15 years ago, is due to highly-nondegenerate cascaded four-wave mixing (cascaded FWM, or CFWM). Here, we present a review of our work on the generation and measurement of multicolored light pulses based on third-order nonlinearities in transparent solids, from the discovery and first demonstration of highly-nondegenerate CFWM, to the coherent synthesis of single-cycle pulses by superposition of the multicolored light pulses produced by CFWM. We will also present the development and main results of a dedicated 2.5-D nonlinear propagation model, _i.e._, with propagation occurring along a two-dimensional plane while assuming cylindrically symmetric pump beam profiles, capable of adequately describing noncollinear FWM and CFWM processes. A new method for the generation of femtosecond pulses in the deep-ultraviolet (DUV) based on FWM and CFWM will also be described. These experimental and theoretical results show that highly-nondegenerate third-order nonlinear optical processes are formally well understood and provide broader bandwidths than other nonlinear optical processes for the generation of ultrashort light pulses with wavelengths extending from the near-infrared to the deep-ultraviolet, which have many applications in science and technology.  
**Keywords:** nondegenerate cascaded four-wave mixing; multicolored femtosecond pulse generation; two-octave spanning spectra; few- and single-cycle coherent pulse synthesis; broadband deep-ultraviolet (DUV) and vacuum-ultraviolet (VUV) femtosecond pulses; femtosecond DUV pulse characterization
# Mining Sequential Patterns with VC-Dimension and Rademacher Complexity
## 5 Sampling-Based Algorithm for Frequent Sequential Pattern Mining
: _Given \(\varepsilon,\delta\in(0,1)\), let \(S\) be a random sample of size \(m\) sequential transactions taken independently at random with replacement from the dataset \(\mathcal{D}\) such that \(\sup_{p\in\mathbb{U}}|f_{\mathcal{D}}(p)-f_{S}(p)|\leq\varepsilon/2\) with probability \(\geq 1-\delta\). Then, given \(\theta\in(0,1]\), the set \(FSP(S,\theta+\varepsilon/2)\) is a FPF \(\varepsilon\)-approximation to \(FSP(\mathcal{D},\theta)\) with probability \(\geq 1-\delta\)._  
Proof. : Suppose that \(\sup_{p\in\mathbb{U}}|f_{\mathcal{D}}(p)-f_{S}(p)|\leq\varepsilon/2\). In such a scenario, we have that for all sequential patterns \(p\in\mathcal{D}\), it results \(f_{S}(p)\in[f_{\mathcal{D}}(p)-\varepsilon/2,f_{\mathcal{D}}(p)+\varepsilon/2]\). This also holds for the sequential patterns in \(\mathcal{F}=FSP(S,\theta+\varepsilon/2)\). Therefore, the set \(\mathcal{F}\) satisfies Property 3 from Definition 2. It also means that for all \(p^{*}\
otin FSP(\mathcal{D},\theta)\), \(f_{S}(p^{*})<\theta+\varepsilon/2\), so such \(p^{*}\
otin\mathcal{F}\) and \(\mathcal{F}\) also satisfies Property 1. Now, let \(p^{\prime}\) be a sequential pattern such that \(f_{\mathcal{D}}(p^{\prime})\geq\theta+\varepsilon\). Then, \(f_{S}(p^{\prime})\geq\theta+\varepsilon/2\), that is \(p^{\prime}\in\mathcal{F}\), which allows us to conclude that \(\mathcal{F}\) also has Property 2 from Definition 2. Since we know that \(\sup_{p\in\mathbb{U}}|f_{\mathcal{D}}(p)-f_{S}(p)|\leq\varepsilon/2\) with probability at least \(1-\delta\), then the set \(\mathcal{F}\) is a FPF \(\varepsilon\)-approximation to \(FSP(\mathcal{D},\theta)\) with probability at least \(1-\delta\), which concludes the proof. ```
Data: Dataset \(\mathcal{D}\); \(\varepsilon,\delta\in(0,1)\); \(\theta\in(0,1)\). Result: Set \(\mathcal{C}\) that is an \(\varepsilon\)-approximation (resp. a FPF \(\varepsilon\)-approximation) to \(FSP(\mathcal{D},\theta)\) with probability \(\geq 1-\delta\). 1\(m\leftarrow\) ComputeSampleSize(\(\mathcal{D},\varepsilon,\delta\));
2\(S\leftarrow\) sample of \(m\) transactions taken independently at random with replacement from \(\mathcal{D}\);
3\(\mathcal{C}\gets FSP(S,\theta-\varepsilon/2)\); \(/^{*}\) resp. \(\theta+\varepsilon/2\) to obtain a FPF \(\varepsilon\)-approximation */ return\(\mathcal{C}\);
```  
**Algorithm 6**Sampling-Based Algorithm for Frequent Sequential Pattern Mining. As explained above, the sample size \(m\) can be computed with Algorithm 2 that uses an efficient upper bound on the VC-dimension of sequential patterns. Then, the sample is generated taking \(m\) transactions uniformly and independently at random, with replacement, from \(\mathcal{D}\). Finally, the mining of the sample \(S\) can be performed with any efficient algorithm for the exact mining of frequent sequential patterns. Figure 2 depicts a block diagram representing the relations between the algorithms presented in this work.