esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Material and Methods  
### HEK293 Cell Patch-Clamp Recordings  
The whole-cell patch-clamp technique was used in the electrophysiology recordings of HEK293 cells to obtain the activation, desensitization, and deactivation rates of the expressed AMPARs. Using [PERSON]'s instrument; the integrated patch amplifier (IPA, Novato, CA 94949, USA), the whole-cell recordings were optimized, and data acquisition was analyzed using Igor Pro7 (Wave Metrics, Inc. Lake Oswego, OR 97036, USA). SutterPatch Software v. 1.1.1 (Sutter Instruments, Novato, CA 94949, USA) was used to replicate the cells' membrane currents; hence, the sampling frequency was set at 10 kHz, while the low-pass filter was set to 2 kHz. Borosilicate glass was used to fabricate the patch electrodes with a resistance of 2-4 M\(\Omega\). The extracellular solution contained (values are in mM): 150 NaCl, 2.8 KCl, 0.5 MgCl\({}_{2}\), 2 CaCl\({}_{2}\), 10 HEPES adjusted to pH 7.4 with NaOH. Upon succeeding a gigaseal with an AMPAR expressed HEK293 cell, it was interchangeably exposed to three different solutions; the first contained the glutamate alone, the second contained glutamate + Citronellol while the last tube contained glutamate + Gerniol. The cell was supplied with these solutions one at a time, exchanged every 500 ms, from a double barrel glass (theta tube) that was also constantly supplying the cell with a solution of 110 CsF, 30 CsCl, 4 NaCl, 0.5 CaCl\({}_{2}\), 10 Trypsin EDTA solution B (0.25%), EDTA (0.05%), 10 HEPES, adjusted to pH 7.2 with CsOH (values are in mM), from the other barrel. The theta tube was mounted on a high-speed piezo solution switcher (AutoMate Scientific, Inc. Berkeley, CA 94710, USA) to mimic the speed of naturally-induced AMPAR currents. The control were the baseline currents produced from supplying the cell with glutamate alone, tested and recorded before and after each exposure of the cell to the oils. AMPAR-current deactivation and desensitization were fitted with two exponentials, and the weighted tau (\(\tau\)w) was calculated as \(\tau\)w = (\(\tau\)f \(\times\) af) + (\(\tau\)s \(\times\) as), where (af) and (as) are the relative amplitudes of the fast (\(\tau\)f) and slow (\(\tau\)s) exponential component. Hence, the currents were evoked by the application of 3 mM glutamate for desensitization while for deactivation, the cells were administered 1 mM of glutamate for 500 ms. To compare the current before and after the administration of the EOs, the currents (I) at the steady-state was normalized to the current obtained with agonist alone (I\({}_{0}\)). The same concept follows for the currents obtained from separately and individually treating the cell with the selected EOs. Inhibition was calculated as a percentage of the difference in current amplitude from the pulse before to antagonist application and the second pulse after the current stabilization post-antagonist application. Significance compared with AMPAR expressed alone or with AMPAR+ compound of interest; \(p\)-value (one-way ANOVA): * \(<\) 0.05, ** \(<\) 0.01, *** \(<\) 0.001, ns: not significant.
## 2 Materials and Methods
### Volatile Components  
The volatiles were isolated via solid-phase microextraction using a 2 cm Divinylbenzene/Carboxen/Polydimethylsiloxane (DVB/CAR/PDMS) fiber with a film thickness of 50/30 \(\mathrm{\SIUnitSymbolMicro m}\) (Supelco, Bellefonte, PA, USA). The content and composition of volatile compounds were determined by gas chromatography/mass spectrometry (GC/MS) according to our previously published method [42] with some minor modifications. A total of 15 mg of the internal standard (0.15% solution of 4-methyl-2-pentanol in freshly refined sunflower oil) was weighed and transferred to a 20 mL vial with a silicone septum along with 10 g of VOO and then placed in a heating block at 40 \({}^{\circ}\)C with a magnetic stirrer (Pierce Reactin-Therm Heating/Stirring Module, Artisan, Champaign, IL, USA). After 10 min sample conditioning, the fiber was exposed for 30 min and then immediately desorbed in the injector of the gas chromatograph in splitless mode at 260 \({}^{\circ}\)C for 1 min. The samples were analyzed using an Agilent Technologies 8890 GC system coupled with an Agilent Technologies 7000D TQ mass detector. Volatile compounds were separated on an HP-5 column (30 m \(\times\) 0.25 mm \(\times\) 0.25 \(\mathrm{\SIUnitSymbolMicro m}\); Agilent Technologies, Santa Clara, CA, USA). The oven temperature was maintained at an initial temperature of 30 \({}^{\circ}\)C for 3 min and then increased to 150 \({}^{\circ}\)C at a rate of 5 \({}^{\circ}\)C/min, followed by an increase to 250 \({}^{\circ}\)C at 20 \({}^{\circ}\)C/min. This final temperature was maintained for a further 5 min. The temperature of the ion source was 250 \({}^{\circ}\)C and that of the transfer line was 260 \({}^{\circ}\)C. Helium was used as the carrier gas at a constant flow rate of 1.5 mL/min. The mass spectra were recorded in the mass range 50-550. The volatile compounds were identified by comparing their mass spectra with the records in the NIST mass spectra library. For additional conformation, the retention index values calculated after analyzing the n-alkane mixture (C8-C20) under the same chromatographic conditions were compared with the previously reported values. The quantification of the volatile compounds was carried out using the internal standard mentioned above.
# Space Weather Effects on Satellites
## 2 SW Drivers and Their Effects on Spacecraft Technology  
In this report we focus on the effects experienced by satellites during SW events [46], either in the IP space or in the terrestrial magnetosphere and atmosphere. The SW effects vary depending on the local environment surrounding the spacecraft. Several locations are used for satellites orbiting the Earth. Stationary positioning at the Lagrange points is common for many astronomical and solar-dedicated missions (e.g., on the Sun-Earth line, at about 1.5 million km, either always facing the Sun at L1 or hiding in the Earths' shadow at L2). In contrast, the other common orbits are geostationary (GEO), medium Earth orbit (MEO), low Earth orbit (LEO), and polar and Sun-synchronous orbits ([[https://www.esa.int/Enabling_Support/Space_Transportation/Types_of_orbits](https://www.esa.int/Enabling_Support/Space_Transportation/Types_of_orbits)]([https://www.esa.int/Enabling_Support/Space_Transportation/Types_of_orbits](https://www.esa.int/Enabling_Support/Space_Transportation/Types_of_orbits)), accessed on 17 August 2023). Each of them experiences a different space and terrestrial environment, or a combination of both. Thus, the spacecraft will be subject to different plasma, particle, and EM influences which require different sets of observations, models, and forecasting tools. Below, we compose a concise review on the SW effects according to the essence of the driver ([[https://www.swpc.noaa.gov/noaa-scales-explanation](https://www.swpc.noaa.gov/noaa-scales-explanation)]([https://www.swpc.noaa.gov/noaa-scales-explanation](https://www.swpc.noaa.gov/noaa-scales-explanation)), accessed on 17 August 2023) in terms of light, magnetized plasma, or energetic particles, e.g., [2; 8; 45] and the references therein.
## 1 Introduction  
The dermis is the connective layer of the skin which connects the outermost epidermis layer with subcutaneous tissues. Although it is not directly exposed to environmental harmful effects, the penetration ability of ultraviolet (UV) rays causes several complications in the dermis layer upon long time exposure [1, 2]. UVA is one of the light rays that are emitted by the sun together with UVB and UVC. UVB has the most detrimental and broad harmful effects and affects mainly the top layer of skin, causing sunburn, redness and cancer [3, 4]. UVA irradiation can penetrate deep layers of skin and it is the main cause of premature skin aging and wrinkling [5]. UVA exposure increases the generation of reactive species in the dermis layer of the skin [6]. The UVA-mediated skin aging, also known as photoaging, arises due to the deleterious effects of UVA on the collagen and elastin backbone of the extracellular matrix (ECM) via matrix metalloproteinases (MMPs), natural ECM degraders [7, 8], UVA-induced generation of reactive species stimulates the expression and enzymatic activity of MMPs, especially MMP-1, -2, and -9 which in turn causes the rapid digestion of collagen [9]. MMPs are crucially involved in the remodeling of ECM, migration of cells, and photoaging [10, 11]. This cascade of stimulation leads to the damaged dermal ECM, which is the reason for the formation of wrinkles, and prematurely aged skin [12]. Like its UVB counterpart, UVA exposure is also closely linked with the activation of mitogen-activated protein kinase (MAPK) pathways [13]. MAPKs take part in MMP production and the cleavage of collagen in ECM [14, 15]. Furthermore, the collagen synthesis pathways such as TGF\(\beta\)/Smad are negatively affected by UVA exposure resulting in loss of elasticity in the skin [16].  
Considering the role of MMPs in the harmful effects of UV irradiation, diseases and complications such as tumor metastasis, inflammation, atherosclerosis and cardiovascular diseases, numerous natural MMP inhibitors have been studied and developed [17, 18]. A considerable portion of these MMP inhibitors was derived from phytochemicals such as polyphenols, flavonoids, coumarins, caffeic acids and their derivatives [19, 20, 21]. The (2'S)-columbianetin is a coumarin with several bioactivities including UVB-protective effects in keratinocytes [22]. In this context, the current study aimed to investigate the protective effects of (2'S)-columbianetin against UVA induced damages in dermal fibroblasts, cells that are responsible for connective tissue formation which is a crucial step of the skin recovery. Therefore, as a part of the ongoing effect to develop cosmeceuticals against photoaging, (2'S)-columbianetin (Figure 1) was isolated from _Corydalis heterocarpa_, a salt marsh plant from Korea, and studied for its ability to revert detrimental effects of UVA on collagen and MMP mechanisms.
# Multi-Hop Routing Protocols for Oil Pipeline Leak Detection Systems
## 4 Conclusions--Future Work  
This paper discussed wireless communication solutions and multi-hop routing as solutions for pipeline networks' leak detection systems. The main objectives of the survey presented in this study are to review the existing multi-hop systems and routing protocols and define the limitations, like the network energy and the volume of data traffic. We studied how the various network parameters, which are important for WSN multi-hop networks, are deployed along oil pipelines.  
In future work, we will focus on investigating energy-efficient, reliable routing protocols for the oil pipeline use cases, including throughput, energy, and reliability performance indicators. It would be desirable to have sensors spaced quite close to each other to detect with high accuracy inconsistencies caused by various events such as leaks, fire, loss of pressure, corrosion, impact, etc. Such dense sensor deployment may lead to the generation of excessive measurement traffic leading to congestion, packet collisions, interference, and loss of communication for various nodes within the networks. Additionally, a dense network using multi-hop connections (either flat or hierarchical) may lead to heavy-duty operation of devices that will have to relay the packets of their neighbors towards the destination gateway, affecting their energy autonomy and operational efficiency. There are multiple approaches to addressing such challenges. For example, we could space the sensors further apart and reduce the network node density, or we could schedule specific devices to relay the generated measurements. We must also consider other criteria, such as delay criticality of data and the \"coherence time\" of the different measurement types to ensure that the network optimizes its operation according to the transported information. This tradeoff will be investigated in future work that will expand on the presented methodology.  
Figure 11: Energy consumption for simple sensors in different topologies.  
**Author Contributions:** Conceptualization, H.A. and N.D.; methodology, H.A. and N.D.; validation, N.D., M.F. and H.B.; investigation, H.A.; writing--original draft preparation, H.A.; writing--review and editing, N.D., M.F. and S.A.; visualization, H.A.; supervision, M.F. and N.D.; project administration, G.A.; All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Big-Delay Estimation for Speech Separation in Assisted Living Environments
## 6 Discussion and Future Work
Although this approach increases the computation time in MATLAB due to the localization of spectral energies across \(M=1000\), discrete azimuth levels, it offers the advantage of reducing interference in the recovered utterance compared to Elevato-DUET, but this comes at the cost of a significant increase in MATLAB computation time. Elevato-DUET is computationally efficient compared to Elevato-AdRess. One of the key motivations behind the interest in Elevato-AdRess is that its computational time can be reduced, since it can be computed in parallel, making it suitable for real-time implementation. Computing the WDO measure in Equation (33) and the PSR measure in Equation (40), for Elevato-AdRess kind of masking is a challenge. This is because AdRess-based masking uses a brute-force method of explicitly localizing the non-overlapping spectral energies of a speech utterance on the frequency-azimuth plane. It is not the matrix-mask, \(\mathbf{M}[k,\tau]\in\{1,0\}\), in the traditional sense as that of DUET. Elevato-AdRess and Elevato-DUET perform well in noiseless anechoic scenarios. Future work will focus on investigating how the quality of separation in Elevato-DUET deteriorates with an increasing presence of overlapping TF bins in the mixture, by increasing the number of speakers in the AL environment. The Elevato-AdRess and Elevato-DUET algorithms demonstrate improvements in intelligibility by effectively addressing the phase wraparound issue. However, aggressive source separation may introduce audible artifacts and spectral distortions. We will explore adaptive post-processing techniques to minimize these distortions, thereby enhancing perceived speech quality. For example, in the context of music source separation, once the AdRess algorithm has been applied to determine the stems in the original mixture--individual component tracks of a mixed audio recording--some of the mixture is added back into the demixed stems to improve the perceptual quality of the resulting sources.
## 3 Routing
### Routing in CRSN: Rising Issues
#### 3.2.4 Spectrum Decision/Coordination  
The spectrum decision of a node greatly affects the process of routing and can indefinitely initiate route rediscovery processes if not properly coordinated. Hence, routing nodes must coordinate their spectrum decision, especially for real-time applications.
## 2 Theoretical Basis
### The GGI Model
Equations (2) and (3) can now be written as follows:  
\[T_{\Omega}=\sum_{k=1}^{n}\left(\rho_{k}G\sum_{i=1}^{m_{k}}\int_{z_{2i}}^{z_{2} }\int_{y_{1}}^{y_{2}}\int_{x_{2}}^{x_{2}}\frac{1}{l_{i}}dx_{i}dy_{i}dz_{i}\right) \tag{5}\]  
\[T_{\kappa}=\sum_{j=1}^{s}\left(\delta_{j}G\int_{z_{1}}^{z_{2}}\int_{y_{1}}^{y _{2}}\int_{x_{1}}^{x_{2}}\frac{1}{l_{j}}dx_{j}dy_{j}dz_{j}\right) \tag{6}\]where \(\rho_{k}\) is the searched constant density of zone \(k\); \(n\) is the number of constant density zones of DEM; \(m_{k}\) is the number of rectangular prisms of DEM in zone \(k\); \(x_{i1}\), \(x_{i2}\), \(y_{i1}\), \(y_{j2}\), \(z_{i1}\), \(z_{i2}\) are the coordinates defining the rectangular prism \(i\) of DEM; \(l_{i}=\sqrt{\left(x_{i}-X_{P}\right)^{2}+\left(y_{i}-Y\right)^{2}+\left(z_{i} -Z_{P}\right)^{2}}\); \(s\) is the number of rectangular prisms of the \(\kappa\) volume; \(\delta_{j}\) is the searched density of rectangular prism \(j\); \(x_{j1},x_{j2},y_{j1},y_{j2},z_{j1},z_{j2}\) are the coordinates defining rectangular prism \(j\) and \(l_{j}=\sqrt{\left(x_{j}-X_{P}\right)^{2}+\left(y_{j}-Y\right)^{2}+\left(z_{j }-Z_{P}\right)^{2}}\). In calculations a certain reference density model is usually used. When the determined density model is written as, \(\mathbf{\tau}^{\mathrm{T}}=\left[\mathbf{\rho}^{\mathrm{T}},\ \mathbf{\varsigma}^{ \mathrm{T}}\right]=\left[\rho_{1},\ldots,\rho_{n},\delta_{1},\ldots,\delta_{s}\right]\) after adopting a reference model in the form \(\mathbf{\tau}_{0}^{\mathrm{T}}=\left[\rho_{0}^{\mathrm{T}},\ \mathbf{\xi}_{0}^{ \mathrm{T}}\right]=\left[\rho_{1}^{0},\ldots,\rho_{n}^{0},\delta_{1}^{0},\ldots,\delta_{s}^{0}\right]\), the sought vector of density differences is: \(\mathbf{d\tau}^{\mathrm{T}}=\mathbf{\tau}-\mathbf{\tau}_{0}\). The model parameters (vector \(\mathbf{d\tau}^{\mathrm{T}}\) and the coefficients \(a_{1},\ldots,a_{5}\) of the polynomial that defines potential \(T_{E}\)), are determined by the least-squares method. It should also be mentioned that calculations can be performed with or without (version use in the article) a global geopotential model.
## 5 Experimentation and Results  
### Dataset-Panel  
The panel training set has four panel sizes, 0.4 m \(\times\) 0.6 m, 0.5 m \(\times\) 0.6 m, 0.6 m \(\times\) 0.6 m, and 0.7 m \(\times\) 0.6 m, and its thickness is 2 mm. The number of panels for training and validation is 320, for which there are 80 samples for each panel size and 20 samples for testing. The testing samples were build based on the four panel sizes which are used for training the data, but they have a different curvature.  
The panel has an arbitrary shape with morphing. All panels have a different curvature and depth by moving the panel nodes in the panel normal direction randomly using four morphing handles on a panel center area, as shown in a Figure 7. The maximum height of the morphed panel node is 10 mm.  
Figure 8 shows the natural frequency and mode shape of different sizes of flat panels and morphed panels. The flat panel's first natural frequency of each size of the panels is 115 Hz, 84 Hz, 68 Hz, and 60 Hz, respectively, in a clamped boundary condition, and the natural frequency of the panel decreases as the panel size increases. The morphed panel's first natural frequency of four panel sizes is 124-194 Hz, 93-148 Hz, 84-130 Hz, and 71-113 Hz, respectively.  
The morphed panel natural frequency and mode shape in Figure 8 are samples from the training set. The panel mode shape is a major factor which decides the panel radiation efficiency [17]. Morphed panel natural frequency generally increased and the mode shape changed because of the increased effective thickness and curvature caused by morphing. In the flat panel, depending on the frequency, the generated mode shape follows a certain  
Figure 6: Convolutional neural network for predicting panel–cavity coupled structure-borne noise. Numbers in each layer are the convolution filter sizes and the number of outputs.  
Figure 7: Panel samples which morphed with 4 handles in a panel center area for panel dataset.  
rule according to the aspect ratio of the panel. But in the morphed panel, some mode shapes cannot be seen in the flat panel. This phenomenon is intensified when the effective thickness due to curvature is large compared to the panel size. As shown in Figure 8, it is more possible for the 0.4 m \(\times\) 0.6 m-sized panel to have an abnormal mode shape than the 0.7 m \(\times\) 0.6 m-sized panel.
# Error Source Analysis and Correction of GF-3 Polarimetric Data
## 3 Influence Analysis of Error Source  
Based on the above full-link error model of GF-3 polarization observation, this section details all error sources and their calibration methods. Additionally, the influence of every error source on GF-3 polarization observation is quantitatively analyzed, and the main error sources which have to be corrected are pointed out.
# Multibeam Wideband Transmit Beamforming Using 2D Sparse FIR Trapezoidal Filters
## 4 Full-Wave Electromagnetic Simulation Results
### Multibeam Beamforming
#### 4.1.2 Three Independent Signals Transmission in Three DODs  
As an example of three-direction beamforming with three independent signals, consider the case where \(\theta_{1}=-45^{\circ}\), \(\theta_{2}=0^{\circ}\), and \(\theta_{3}=45^{\circ}\), with corresponding \(\alpha_{1}=-35.26^{\circ}\), \(\alpha_{2}=0^{\circ}\), and \(\alpha_{3}=35.26^{\circ}\), respectively. The magnitude frequency response \(|H(e^{\psi_{\alpha_{1}}},e^{\psi_{\alpha_{2}}})|\) of the sum of 2-D FIR filters of order \(14\times 64\), with nonsparse and with sparse impulse responses, is shown in Figure 8c and Figure 8d, respectively. Values for the other parameters are \(\mu=0.05\) and \(\epsilon_{th}=0.006998\) for \(\text{DOD}=\pm 45^{\circ}\) filters and \(\mu=0.15\) and \(\epsilon_{th}=0.006094\) for \(\text{DOD}=0^{\circ}\) filter. The wideband beam patterns produced by the nonsparse and sparse filters are shown in Figure 7g and Figure 7h, respectively. Moreover, Table 1 provides a comparison of wideband beam pattern and DSP complexity between the proposed 2D sparse and nonsparse FIR filter-based transmit beamformers. The results clearly indicate the minimal effect of sparsification of the filter impulse response towards the performance of the filter with regard to beamforming, saving more than 86% in DSP complexity.
## 4 Discussion and Conclusions  
### Algorithm Discussion  
The A* algorithm commonly employs the distance function as its heuristic function, with popular choices including Manhattan distance and Euclidean distance [42; 43; 44]. The distance heuristic function is widely used in road network path search, providing relatively reliable and effective shortest path planning for travelers. However, the shortest path is far from satisfying the needs of travelers for public transportation networks, and the optimal  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Plan** & **Transfer Number** & **Walk Distance (m)** & **Cost (CNY)** & **Time (min)** \\ \hline Plan 1 & 2 & 100 & 12 & 80 \\ Plan 2 & 2 & 1500 & 7 & 120 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Plan Comparison for path analysis of Case 2.  
Figure 12: Query result for Plan 2 in Case 2.  
path searched according to the traditional A* algorithm is ineffective in most cases. For example, the algorithm is likely to allow travelers to constantly change the type of ride or ride to meet the requirements of the shortest distance, and the number of transfers may reach dozens of times. It is not in line with the objective reality of the situation, in which the traveler is more concerned about the number of transfers, travel time, travel costs, and walking distance. At present, with the continuous development of the city, people's travel options are greater and greater, and travelers often complete their trips in a mixed network composed of road networks and public transportation networks. The distance heuristic function of the traditional A* algorithm can no longer solve the modern urban travel path planning problem.  
In this study, the heuristic function of the traditional A* algorithm is optimized and improved, and a multivariate heuristic function is proposed by adding a multivariate constraint function into the original distance function. Factors concerning travelers such as the number of transfers, travel time, and travel cost, which are correlated, are also integrated into the heuristic function as a constraint function. By prioritizing the control of each evaluation criterion, the different demands of travelers in restricted and unrestricted networks are realized.