esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Theoretical Background  
The photonic crystal periodic arrangement of PSM-SA plays a crucial role in controlling light propagation, and its saturable absorption effect is the key mechanism for transforming continuous light into pulsed light. Once the light intensity exceeds a specific threshold, the material's linear absorption capacity becomes saturated, resulting in the saturable absorption effect. This effect is a fundamental factor for achieving efficient pulse generation. By designing the PSM-SA structure, light is confined within specific modes and pathways, effectively preventing its dispersion within the PBG [30], thereby significantly limiting and enhancing light absorption within a particular frequency range. According to [PERSON]'s law [31; 32], the periodicity or the refractive index contrast of the PSM-SA can lead to a shift in the PBG [33]:  
\[\lambda_{max}=\frac{2}{m}\ d\ \sqrt{n_{eff}^{2}-sin^{2}\theta} \tag{1}\]where \(\lambda_{max}\) is the wavelength of the maximum reflection (photonic band gap) peak, d is the lattice constant, m is the diffraction order, \(n_{eff}\) is the effective refractive index and \(\theta\) is the angle of incidence of the light for the photonic crystals. The effective refractive index can be determined by utilizing the Maxwell Garnett effective medium approximation [34].  
\[n_{eff} = n_{air}\ \frac{2(1-f)n_{air}\ +(1+2f)n_{mat}}{2(2+f)n_{air}\ +(1-f)n_{mat}} \tag{2}\]  
where \(n_{mat}\) is the dielectric constant of the material, \(n_{air}\) is the dielectric constant of air, and \(f\) is the filling factor.  
The Bragg-Snell's law states that different sizes of PSM correspond to different wavelengths of light. The \(\lambda_{max}\) is the maximum reflection peak that forms the photonic band gap, and the optimum working wavelength can be adjusted according to the formula by changing the periodicity or refractive index contrast through any external stimulus. This correlation is further confirmed by the transmission spectrum of polystyrene microspheres, which aligns with the theoretical concept [15]. Notably, microspheres with a size of approximately 500 nm exhibit the highest reflection (or scattering) rate for 1550 nm light, suggesting the presence of a directional band gap.  
In the experiments, it is crucial to consider the potential impact of incident light on the refractive index of PS and the high reflective Au mirror [35]. Additionally, the presence of the Au mirror on the surface can induce local plasmon resonance effects [36], which enhance the surface magnetic field [37] and result in a redshift of the wavelength [38; 39]. Considering these factors, PSMs with a size of less than 500 nm were chosen to prepare PSM-SA devices for a 1550 nm pulse laser.
## 2 Results and Discussion
### Antioxidant Activity of Scepe-A, Scepe-B and Scepe-C
#### 2.4.2 HO\(\bullet\) Scavenging Activity  
HO\(\bullet\) is highly reactive and consequently short-lived and can damage virtually all types of macromolecules, including carbohydrates, nucleic acids, lipids, and proteins [5]. The HO\(\bullet\) scavenging activity of SCPE-A, SCPE-B and SCPE-C was dose-dependent at the test concentrations, as shown in Figure 6B. The EC\({}_{50}\) values of SCPE-A, SCPE-B and SCPE-C were 0.28, 0.21, and 0.15 mg/mL, respectively, and SCPE-C exhibited the highest HO\(\bullet\) scavenging activity. The EC\({}_{50}\) of SCPE-C was lower than that of PYFNK (0.24 mg/mL), LDK (0.17 mg/mL) [19; 20], Leu-Gly-Leu-Asn-Gly-Asp-Asp-Val-Asn (LGLNGDDVN) (0.687 mg/mL) [34], PSYV (2.64 mg/mL) [28], HFGDPFH (0.50 mg/mL) [32], Phe-Pro-Glu-Leu-Leu-Ile (FPELLI) (0.57 mg/mL) and Val-Phe-Ala-Ala-Leu (VFAAL) (0.31 mg/mL) [4], as well as that of Tyr-Pro-Pro-Ala-Lys (YPPAK) (0.228 mg/mL) [23], Pro-Ser-Lys-Tyr-Glu-Pro-Phe-Val (PSKYEPFV) (2.86 mg/mL) [35], PSYFK (2.283 mg/mL), GFGPL (1.612 mg/mL), VGGRP (2.055 mg/mL) [30], Tyr-Leu-Gly-Ala-Lys (YLGAK) (scavenging rate: 45.14% at 0.5 mg/mL), Gly-Gly-Leu-Glu-Pro-Ile-Asn-Phe-Gln (GGLEPINFO) (scavenging rate: 41.07% at 0.5 mg/mL) [36], Asn-Gly-Leu-Glu-Gly-Leu-Lys (NGLEGLK) (0.313 mg/mL), Asn-Ala-Asp-Phe-Gly-Leu-Asn-Gly-Leu-Glu-Gly-Leu-Ala (NADPGLNGLNGLEGLA) (0.612 mg/mL) [32], FIMGPY (3.04), GPAGDY (3.92 mg/mL) and IVAGPQ (5.03 mg/mL) [11] from the protein hydrolysates of scalloped hammerhead muscle, conger eel, weatherfish loach, mussel sauce, Chinese cherry seeds, blue mussel, grass carp, egg white, giant squid and skate (_R. porosa_) cartilage. The three isolated peptides, especially SCPE-C, revealed good HO\(\bullet\) scavenging activity, which demonstrated that it could serve as a scavenger to reduce or eliminate the damage induced by HO\(\bullet\) in foods and biological systems.  
Figure 6: DPPH\(\bullet\) (A); HO\(\bullet\) (B); ABTS\({}^{\bullet}\)\(\bullet\) (C); and O\({}_{2}^{\
eg}\)\(\bullet\) (D) scavenging activities of SCPE-A, SCPE-B and SCPE-C. All data are presented as the mean \(\pm\) SD of triplicate results. DPPH\(\bullet\): 2,2-diphenyl-1-picrylhydrazyl radicals; ABTS\({}^{\bullet}\): 2, 2\({}^{\prime}\)-azino-bis-3-ethylbenzothazoline-6-sulfonic acid radicals; O\({}_{2}^{-}\)\(\bullet\): superoxide anion radicals.
## 3 Results
### Post-Questionnaire for Patients  
The three patients reported that the most used features of the technology system were the exercises and the calendar to view the scheduled exercises and appointments. The training to learn to use the system was sufficient for the patients, and support was mainly provided by the therapists. Two patients reported having trained more with the system, and one reported having approximately trained the same amount. Similar to this was the patients' motivation, where two stated that the system motivated them to train more, and one patient estimated his motivation to be as high as that without the system.  
When asked about the quality of the therapy with the system compared to conventional (face-to-face) therapy, all patients stated that they perceived the quality of the therapy via the tablet as worse compared to face-to-face. The reason for this is the need for more efficiency and supervision during video calls. The system was rated positively as an addition to conventional therapy. Integrability in daily life was rated good by all patients as there was no effort to use the system. The advantages for the patients of the combined therapy concept (face-to-face, video calls, asynchronous training) were the possibility to contact therapists and have supervision, less traveling, and more motivation through different exercises. Patients who also used the Pablo reported an increased motivation through its integration into therapy and training.  
In general, the three patients were very satisfied with the telerehabilitation concept. Two of them experienced good therapy progress with improvements in different areas, and one patient reported no therapy progress.
# A Review on Data-Driven Condition Monitoring of Industrial Equipment
## 2 Data-Driven Condition Monitoring of Industrial Equipment
### Data Processing Methods
#### 2.1.1 The Fourier Transform Approach
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**References** & **Application** & \multicolumn{2}{c}{**Type of Equipment**} & **Signal** & **Fault Type** \\ \hline
[28] & Feature extraction & Rolling & Vibration & \\  & extraction & bearing & signal & Ball, inner race, and outer race faults \\
[29] & Fault diagnosis & Rolling & Vibration & \\  & diagnosis & bearing & signal & Roller, inner race, and outer race faults \\
[33] & Feature extraction & Rolling & Vibration & 9 kinds of bearings with various faults, i.e., inner race \\  & extraction & bearing & signal & fault, ball fault and outer race fault with \\  & & & & 3 diameters status \\
[34] & Feature extraction & Rolling & Vibration & \\  & extraction & bearing & signal & Inner race, outer race, cage, and ball faults \\  & Fault diagnosis & Rolling & Vibration & \\  & diagnosis & bearing & signal & Ball, inner race, and outer race faults \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Bearing condition monitoring using Fourier transform.
# Link-State Aware Hybrid Routing in the Terrestrial-Satellite Integrated Network +
## 3 System Model
### Space-Time Topology Model
The link delay of graph \(G(t)\) represents the sum of data propagation delay \(T_{p}(v_{i}^{t},v_{j}^{t})\), queuing delay \(T_{q}(v_{i}^{t},v_{j}^{t})\), and transmission delay \(T_{w}(v_{i}^{t},v_{j}^{t})\) in a time interval, which can be expressed as:  
\[ \begin{split} T(v_{i}^{t},v_{j}^{t})&=T_{p}(v_{i}^{t },v_{j}^{t})+T_{q}(v_{i}^{t},v_{j}^{t})+T_{w}(v_{i}^{t},v_{j}^{t})\\ &=\frac{d(v_{i}^{t},v_{j}^{t})}{v_{p}}+\frac{\sum\limits_{k}d_{i} ^{t}(k)}{r(v_{i}^{t},v_{j}^{t})}+\frac{\sum\limits_{k}\delta(v_{i}^{t},v_{j}^{ t})}{r(v_{i}^{t},v_{j}^{t})},\end{split}  \tag{1}\]  
where \(d(v_{i}^{t},v_{j}^{t})\) is the distance of link \((v_{i}^{t},v_{j}^{t})\). \(v_{p}\) denotes the transmission speed of radio signal, \(r(v_{i}^{t},v_{j}^{t})\) denotes the capacity of link \((v_{i}^{t},v_{j}^{t})\) (in bit/s), \(q_{i}^{t}(k)\) is the data size in queue  
Figure 2: Space–time topology model. of \(v_{i}\) for \(UT_{k}\) data during the \(t\)-th time interval (in bit), \(\vartheta(v_{i}^{t},v_{j}^{t})\) is the flow on link \((v_{i}^{t},v_{j}^{t})\) (in bit).
## 2 State of the Art: Tolerancing Information Sharing throughout the Product Life Cycle  
### The Role of Tolerancing Information  
The main goal of all product development activities is the provision of products that live up to the customer requirements. In this context, tolerancing for design or functional tolerancing can be defined as the set of all design activities that aim at translating such functional (customer) requirements (FR) into geometrical specifications of the single parts, which is relevant even at the early design stages [3,4]. More particularly, this involves tolerance specification, i.e., identifying required tolerance types on function-relevant features as well as suitable datum features, tolerance allocation (i.e., defining suitable values for these tolerance types), and tolerance analysis (i.e., the evaluation if the chosen tolerance types and values ensure the functional requirements) [5]. As a result, these geometrical specifications are typically annotated during design and presented as symbols and numerical quantities on technical drawings or on three-dimensional solid models and represented per semantic model-based definition (MBD) of product and manufacturing information (PMI) [6]. In this regard, the concept of tolerancing evolved from the need for the unambiguous communication between the various actors in geometrical variations management. However, this communication is not unidirectional from design to manufacturing and inspection activities. However, as tolerances link the design intent and the product function with the manufacturing capability and measurement accuracy [2], they serve as a multi-directional connection and are means to overcome the disruption of the different activities in geometrical variations management and to reintegrate them by knowledge and information sharing [7]. Consequently, tolerances affect nearly every aspect of the product origination (see Figure 1) and they are required by many actors for different purposes, e.g., for tolerance transfer, tolerance synthesis, tolerance verification and performance correlation [8]. In this regard, tolerances are a critical link between design, manufacturing, inspection and assembly as they have distinct effects on the product function and quality as well as on the manufacturing, inspection and assembly costs.  
Figure 1: The ubiquitous role of tolerances and different types of tolerancing information. FR: functional (customer) requirements.
### Organization of the Article  
This review is organized into seven sections, and the structure and organization of this paper are shown in Figure 2. The rest of this paper is organized as follows. Section 2 provides the models and some architectures of UAV networks in some specific applications. Routing protocols for UAVs are discussed in Section 3. Section 4 presents the communication in UAV networks. Control algorithms are provided in Section 5 to support UAV groups and UAV swarms operating in different sensor fields. Section 6 provides AI-based approaches to support UAVs to operate more efficiently, thereby addressing core challenges in UAV networks that traditional methods have limitations, such as real-time multi-sensor data processing, adaptive routing in dynamic environments, and multi-task swarm control for coordinated operations among UAVs. Finally, conclusions and future work are presented in Section 7.
## 2 Results
### Changes in NMDA Receptors Subunits and NOS Activities Related to Chronic SD (Experiment III)
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Components of** & **Young Adult Controls** & **Old Controls** & **ANOVA: p** \\
**NMDA—NO Pathway** & **(_n = 32_)** & **(_n = 32_)** & \\ \hline NR1 expression & 1.034 \(\pm\) 0.008 & 0.971 \(\pm\) 0.004 & \textless{}0.001 *** \\ NR2A expression & 1.026 \(\pm\) 0.004 & 0.988 \(\pm\) 0.009 & \textless{}0.001 *** \\ NR2B expression & 1.000 \(\pm\) 0.005 & 0.969 \(\pm\) 0.005 & \textless{}0.001 *** \\ nNOS activity in the R side & 423.3 \(\pm\) 26.2 & 313.6 \(\pm\) 29.3 & \textless{}0.007 ** \\ nNOS activity in the L side & 361.6 \(\pm\) 22.0 & 294.9 \(\pm\) 28.8 & \textless{}0.071 \\ eNOS activity in the R side & 155.9 \(\pm\) 17.0 & 97.2 \(\pm\) 11.1 & \textless{}0.005 ** \\ eNOS activity in the L side & 172.8 \(\pm\) 18.1 & 109.5 \(\pm\) 13.4 & \textless{}0.007 ** \\ iNOS activity in the R side & 9.2 \(\pm\) 2.2 & 8.5 \(\pm\) 1.5 & \textless{}0.800 \\ iNOS activity in the L side & 9.5 \(\pm\) 2.2 & 6.0 \(\pm\) 2.3 & \textless{}0.272 \\ \hline \hline \end{tabular} Results are presented as means \(\pm\) SEM. Differences were calculated from data of young adult and old controls presented in Figures 1–6. Statistical significance (ANOVA) was calculated with respect to young adult controls (\(\textless{}p<0.050\), **\(p<0.010\), **\(p<0.001\), **\(p<0.001\)**). Results of ANOVA with repeated measures: nNOS: aging—F(1,62) = 62.3, \(p=0.015\), laterality—F(1,62) = 8.87, \(p=0.004\), interaction—F(1,62) = 2.54, \(p=0.116\), eNOS: aging—F(1,62) = 9.23, \(p=0.004\), laterality—F(1,62) = 3.79, \(p=0.056\), interaction—F(1,62) = 0.09, \(p=0.761\), iNOS: aging—F(1,62) = 0.99, \(p=0.325\), laterality—F(1,62) = 0.30, \(p=0.589\), interaction—F(1,62) = 0.51, \(p=0.477\).  
\end{table} 
Table 2: Age-dependent changes in NMDA receptor—NO system. Figure 7: Comparison of activities of NO synthases in the right and left parietal cortex of young adult and old rats exposed to chronic sleep deprivation (experiment III). Summary histogram of activities of NOS isoforms (**A–C**) expressed as nmoles/30 min/mg of proteins. Results are presented as means \(\pm\) SEM. Statistical significance (Student’s \(t\)-test) was calculated with respect to young adult controls (\(\textless{}p<0.05\), **\(p<0.01\)**). RH—right hemisphere, LH—left hemisphere, nNOS—neuronal NOS, eNOS—endothelial NOS, iNOS—inducible NOS, SD—sleep deprivation, YCTRL—young adult controls (\(n=12\)), YSD—young adult rats exposed to chronic SD (\(n=12\)), OCTRL—old controls (\(n=12\)), OSD—old rats exposed to chronic SD (\(n=12\)).
## 3 The New Algorithm for General Stable Marriage
Finally suppose both \(m\) and \(w\) are married, the wife of \(m\) is \(w^{\prime}\), and the husband of \(w\) is \(m^{\prime}\). As pair \(mw\) is blocking, \(m\) strictly prefers \(w\) to \(w^{\prime}\), so \(m\) also proposed to \(w\) and she rejected him. If at the time of this rejection \(w\) was not flighty, then she got finally a husband not worse than \(m\). If she was flighty that time, then she remained on the list of \(m\), so \(m\) proposed to her again before \(w^{\prime}\). In all cases we came to a contradiction. \(\Box\)  
**Lemma 3**: _There is no short augmenting path._  
**Proof. ** Suppose \(m^{\prime}wmw^{\prime}\) is a short augmenting path (see Figure 1). The algorithm finished, so \(m^{\prime}\) is an old bachelor and \(w^{\prime}\) is a maiden, consequently \(m\) never proposed to her, so \(m\) is a lad and \(\mathrm{pri}(m,w^{\prime})\leq\mathrm{pri}(m,w)\). First we claim that \(\mathrm{pri}(m,w^{\prime})<\mathrm{pri}(m,w)\). If \(w\) and \(w^{\prime}\) have the same priority at \(m\) then when \(m\) proposed to \(w\), she was a maiden, so \(m^{\prime}\) did not propose to her before, and at this moment (after getting the proposal from \(m\)) she became flighty. However surely \(w\) could not remain flighty till the end, as she got some other proposals (e.g., at least two from \(m^{\prime}\)), thus she rejected \(m\) at some point. At this point \(m\) preferred \(w^{\prime}\) to \(w\), so he proposed to \(w^{\prime}\) before proposing again to \(w\), a contradiction. Next we claim that \(\mathrm{pri}(w,m^{\prime})<\mathrm{pri}(w,m)\). First observe that \(w\) got at least three proposals (one from \(m\) and two from \(m^{\prime}\)), so by Lemma 1 she kept the best proposal (not counting the first one). Consequently, the proposal from bachelor \(m^{\prime}\) is not better for her than the finally kept proposal from lad \(m\). The two claims together contradict to the fact that \(M_{\mathrm{opt}}\) is a stable matching. \(\Box\)  
These lemmas together with the next section give:  
**Theorem 3**: _The new algorithm always gives a stable matching in linear time for the general problem, and is \(3/2\)-approximating._
# Hashtag Recommendation Methods for Twitter and Sina Weibo: A Review
## 2 Methodology  
Based on this objective, we seek answers to the following research questions:  
* **RQ1**: Has the number of research papers regarding hashtag recommendation for Twitter and Sina Weibo been increasing in the last decade?
* **RQ2**: What are the hashtag recommendation methods used in Twitter and Sina Weibo?
* **RQ3**: What are the techniques used in the hashtag recommendation methods?* **RQ4**: What are the characteristics of the dataset used in each paper related to the research topic?
* **RQ5**: What are the future directions for hashtag recommendation methods for Twitter and Sina Weibo?  
To address these research questions, we adopt the guidelines of Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) [31], which is an established methodological protocol for systematic reviews. The subsection below explains the eligibility criteria, information sources and searches, paper selection and results.
# Linewidth Measurement of a Narrow-Linewidth Laser: Principles, Methods, and Systems
## 2 Basic Principles
### Linewidth Calculation Method Based on Frequency Noise
#### 2.5.2 Optical Coherent Reception Method Based on Delayed Self-Homodyne/Self-Heterodyne  
There are self-homodyne/self-heterodyne optical coherent reception methods based on delayed self-homodyne difference linewidth measurements [73]. Compared with the MI-type delayed self-homodyne method mentioned earlier [74; 75], the optical coherent receiving method introduces an intradyne coherent receiver (ICR) with a 90\({}^{\circ}\) mixer at the position of the beam combiner, as shown in Figure 5c. The function of a coherent receiver is to coherently demodulate the signal light and local oscillator light, recover the I/Q component of the laser differential phase noise, obtain the phase modulation noise power spectrum in a digital signal processing (DSP) system, and calculate the integral linewidth and Lorentz linewidth [76; 77; 78].
# From Vine to Wine: Coloured Phenolics as Fingerprints
## 3 Results
### Multivariate Analysis
#### 3.3.2 Linear Discriminant Analysis  
Table 4 presents the results of a Linear Discriminant Analysis (LDA) performed to classify the Canary red wines analysed based on several grouping variables such as grape  
Figure 5: Projections of anthocyanin compounds on the factor analysis in space defined by the first two factors. * Anthocyanin compounds abbreviations—D_3_glc: Delphinidin-3-glucoside; C_3_glc: Cyanidin-3-glucoside; Pt_3_glc: Petunidin-3-glucose; Pe_3_glc: Peonidin-3-glucose; M_3_glc: Malvidin-3-glucose or oenin; C_6_ac_3_glc: Cyanidin-(6-acetyl)-3-glucose; Pt_6_ac_3_glc: Petunidin-(6-acetyl)-3-glucose; Pe_6_ac_3_glc: Peonidin-(6-acetyl)-3-glucose; M_6_ac_3_glc: Malvidin-(6-acetyl)-3-glucose; Pe_6_co_3_glc: Peonidin-(6-coumaroyl)-3-glucose; M_6_co_3_glc: Malvidin-(6-coumaroyl)-3-glucose; Glu/oenin: Glucosides without oenin; Glu: Glucosides with oenin; Acet: Acetylated Anthocyanius; Cou: Coumaroyled Anthocyanius; Tot: Total Anthocyanins. cultivar, island of precedence, Tenerife DO, and wine aging. The results showed variable levels of correct classification, before and after cross-validation. Additionally, the table shows the number of discriminant functions, as well as the main anthocyanins associated with these functions, which were selected from the stepwise analysis, along with the most correlated anthocyanins influencing each function. After application of LDA to differentiate red wines according to grape cultivar using the directly determined and calculated anthocyanins, a correct classification of 75.6% was achieved, which dropped to 64.1% after cross-validation. Stepwise LDA resulted in a lower accuracy (55.6%, 50.7% after cross-validation), with Acet, M-3-glc, D-3-glc, and C-6-ac-3-glc identified as more relevant to the differentiation of red wines according to grape cultivars. LDA applied for classifying the red wines according to the island of production showed a rate of 75.9% correct classification (69.2% after cross-validation) using all anthocyanin compounds considered. The stepwise LDA had lower correct classification (71.8%, 56.7% after cross-validation), with M-3-glc and Pt-3-glc, Tot and Glc, emerging as the most relevant variables to the differentiation according to the production island of red wines. For red wines included under the Tenerife DO, the accuracy was lower than for other classification variables, with 66.7% correct classification (57.9% after cross-validation) using all variables. Stepwise LDA had a lower correct classification (43.9%, 40.5% after cross-validation), D-3-glc and Acet being the anthocyanins more important for separation. Wine aging yielded the highest correct classification of red wines, achieving 86.3% correct classification (83.4% after cross-validation) using all variables. When stepwise LDA was applied, the correct classification decreased to 82.9% (80.3% after cross validation). Selected LDA variables such as Cou, Glc, and Pt-6-ac-3-glc were the more relevant variables related to aging chemical transformations.
## 4 Results and Discussion
### Analysis of Metrics for the Implementation of V2G with Power Smoothing Methods
#### 4.4.4 Ripple Index  
The ripple index (see Figure 15), which measures the signal variation relative to the mean, experienced a reduction with smoothing methods. Specifically, the V2 GSmooth method proved to be effective in providing smoother and more stable energy generation.