esa-sceva/satcom-chunk-collection huggingface dataset:
# The Effect of LEDs on Biomass and Phycobiliproteins Production in Thermotolerant _Oscillatoria_ sp.
###### Abstract  
This study evaluates the role of different LED lights (white, blue/red), intensity (\(\mu\)mol m\({}^{-2}\) s\({}^{-1}\)), and photoperiod in the production of biomass and phycocyanin-C, allophycocyanin and phycovery-thrin (C-PC, APC, and PE respectively) from a novel thermotolerant strain of _Oscillatoria_ sp. Results show that a mixture of white with blue/red LEDs can effectively double the biomass concentration up to 1.3 g/L, while the concentration of the selected phycobiliproteins increased proportionally to biomass. Results also indicate that high light intensities (>120 \(\mu\)mol m\({}^{-2}\) s\({}^{-1}\)) can diminish the final concentration of C-PC, APC, and PE, significantly reducing the overall biomass produced. Finally, the photoperiod analysis showed that longer light exposure times (18:6 h) improved both biomass and phycobiliproteins concentration. These results demonstrate that the application of LEDs to produce a novel strain of _Oscillatoria_ sp can double the biomass concentration, and the photoperiod regulation can eventually enhance the final concentration of specific phycobiliproteins such as APC and PE.  
light:dark cycle; light intensity; light quality; C-PC; photosynthesis +
Footnote †: journal:
# Magneto-Acoustic Imaging in Biology
## 4 Maet
### New Directions in Magneto-Acoustic Tomography  
[PERSON] and his students examined two novel ideas related to MAET. [PERSON] recognized that ultrasound in the presence of a magnetic field created a unique signature in anisotropic tissue [54]. As in traditional MAET, if the ultrasonic vibration propagates in the \(z\) direction and the magnetic field is in the \(x\) direction, then the Lorentz force produces a current in the \(y\) direction. If, however, the tissue's electrical conductivity is anisotropic, and the fibers are aligned at an angle to the \(y\)-axis, then the Lorentz force and the current it causes will not point in the same direction. The anisotropy rotates the current away from the \(y\)-axis, producing a component in the \(z\) direction. Because the ultrasonic wave propagates in the \(z\) direction, this creates oscillating regions of positive and negative charge and voltage that travel in sync with the wave (Figure 7). In principle, this zebra stripe-like voltage distribution could be observed and would provide information about the anisotropy. Unfortunately, it would probably need to be detected using electrodes within the tissue, instead of electrodes placed around and outside the sample, and therefore may not be practical for noninvasive studies.  
[PERSON] explored MAET theoretically, but he applied a continuous ultrasonic source rather than a pulsed one [55]. This modification sacrifices one of the main advantages associated with pulsed ultrasound imaging: the delay of a pulse response maps to the distance from the ultrasound detector. The continuous wave instead creates a sinusoidally distributed current source. If, in addition, the conductivity is divided into a background homogeneous part and a spatially varying part, [PERSON] and [PERSON] showed that the heterogenous part produces a current dipole that creates an electric signal at a distant electrode. They then proposed that measurements on a sample be repeated with ultrasonic waves of different wavelengths and in many directions, and proved that such a procedure would map out the two-dimensional Fourier transform of the heterogeneous part of the conductivity distribution in the spatial frequency domain. Once such an experiment was complete, an inverse Fourier transform would yield the two-dimensional conductivity distribution. \"Tomography\" is the \"T\" in MAT-MI and MAET, but of all the conductivity imaging techniques [PERSON]'s algorithm is the most closely related to true tomography as used in an x-ray computed tomography scan or in MRI. The idea of employing plane waves propagating in a variety of directions during MAET has been extended by several groups [56; 57; 58; 59]. [PERSON] et al. employed a phased array transducer to provide steerability of the ultrasonic wave [60].
## 2 Methodology
### Numerical Method
#### 2.2.1 One-Dimensional Plasma Fluid Model (1D PFM)
The instantaneous voltage of the externally applied power source is assigned on the powered electrode and the potential of the grounded electrode is kept at zero as the boundary conditions for solving the Poisson equation. The rate constants of electron-impact reactions and electron transport properties are calculated before the simulation by using the Boltzmann equation solver (i.e., Bolsig+ [25]) as look-up tables. The mobilities of ions are adopted from the literature [16; 26] with diffusivities evaluated from the [PERSON] relation. The diffusivities of neutral species are evaluated from the binary diffusion theory [27] with relative parameters that can be found in [28]. The reactions considered in the discharge chemistry are listed in Table 1. The charged species considered include electrons, He\({}^{+}\), He\({}_{2}\)\({}^{+}\), N\({}_{2}\)\({}^{+}\), and N\({}_{4}\)\({}^{+}\), to model the conduction currents properly through ionization reactions, association reactions, and recombination reactions. The excited species of helium including He\({}_{\text{m}}\) and He\({}_{2}\)\({}^{+}\) play essential roles for introducing ionization pairs through Penning ionization. The excited species of nitrogen (i.e., \(N_{2}\)\(\left(A^{3}\sum_{i}^{+}\right)\), \(N_{2}\)\(\left(B^{3}\Pi_{S}\right)\), \(N_{2}\)\(\left(C^{3}\Pi_{u}\right)\), and \(N_{2}\)\(\left(a^{\prime 1}\sum_{u}^{-}\right)\)) are considered to account for the energy released from exothermic reactions. Interactions among helium and nitrogen related species are also included to mimic the realistic discharge chemistry. The discharge heating source is obtained from the integration of the power released by different heating mechanisms during the cycle as the cyclic average heating source. From the 1D PFM, the cyclic average heating source of the discharge is calculated as:  
\[S_{CA-DFM}=\frac{1}{t_{cycle}}\int_{t_{cycle,start}}^{t_{cycle,end}}\left\{\sum_{ k=1}^{N_{B}}\left[3\frac{m_{e}}{M_{k}}n_{e}k_{B}v_{m,k}\left(T_{e}-T_{S}\right) \right]+\sum_{i=1}^{N_{i}}(q_{i}\Gamma_{i}\cdot E)+\sum_{l=xn}R_{l}\Delta E_{ l}\right\}dt \tag{6}\]  
where the subscript \"\(CA\)\" represents cyclic average quantity calculated, \(t_{cycle}\) is the period of one cycle, \(N_{i}\) is the number of ion species, \(R_{l}\) is the reaction rate of the \(l\)th reaction, \(\Delta E\) is the energy released from the reaction. The first term of the integrand considers the energy released from elastic collisions. The second term of the integrand considers the ion Joule heating under the assumption that energy acquired by ions from the electric field is transferred thoroughly to background gases due to efficient collisions among ions and molecules of background gases. The last term of the integrand considers the energy released from exothermic reactions listed in Table 1.
## 3 Numerical Study
### Effect of Distance between Impact and Void Position  
When the impact location is far from the void, the void existence is not distinctly recognised. Accordingly, in this subsection, different impact positions were analysed. The distance between the impact location and the void centre was defined as \(Dx\), which was varied from 0 to 6 m for the different void size conditions. Figure 20 shows the dynamic stiffness \(K_{\text{d}}\) of the responses. The average values of the dynamic stiffness were calculated in the range of 20-100 Hz due to the steady amplitudes.  
For condition S0, \(K_{\text{d}}\) is not affected by \(Dx\). When \(Dx\) changes from 0 to 1 m, in the range of void size, \(K_{\text{d}}\) for condition S1 decreases slightly, while for conditions S2 and S3 increase slightly.  
For conditions S1, S2, and S3, when \(Dx\) changes from 1 to 3 m, \(K_{\text{d}}\) increases significantly. That is, the dynamic stiffness changes quickly when the impact point moves through the void edge. When \(Dx\) changes from 3 to 6 m, \(K_{\text{d}}\) increases slowly. The dynamic stiffness for void conditions becomes almost the same as that of the non-void condition when \(Dx\) reaches to 6 m.
# Precision and Reliability of a Dynamometer for Trunk Extension Strength and Steadiness Assessment
## 2 Materials and Methods
### Data Processing  
Data processing was performed using Matlab(r) software R2024b (The MathWorks Inc., Natick, MA, USA). During MVC, the peak force, considered the maximum force point during this evaluation, was manually extracted using the Delsys system and normalized to body mass. No additional signal processing, such as smoothing or filtering, was applied. Likewise, submaximal force values were derived directly from the raw data without further processing. During SMVC, the variables mean force and force steadiness were derived from a 20 s duration. For the analysis of mean force and steadiness, the central five seconds of the task were considered for data extraction. Mean force values were expressed as percentages relative to the MVC peak using the following formula: [(SMVC mean/MVC peak) \(\times\) 100]. Force steadiness was calculated by determining the coefficient of variation (CV) using the formula: [(standard deviation of mean force/mean force) \(\times\) 100].
# GNSS-R Observations of Marine Plastic Litter in a Water Flume: An Experimental Study
## 2 Materials and Methods
### GNSS-R-Like System
#### 2.1.3 Antennas Specifications  
All the antennas were commercially available microstrip patch antennas. The transmission antennas for 30\({}^{\circ}\) and 60\({}^{\circ}\) elevation and azimuth angles, as well as the up-looking receiving antenna were RHCP matched at L1 and L5 GPS bands, with an axial ratio of \(\sim\)2 dB, which were manufactured by Matterwaves Antenna Technology [9]. The 45\({}^{\circ}\) elevation and azimuth angles antennas as well as the down-looking antenna were L1 RHCP/LHCP dual-polarization antennas designed and manufactured in-house [10]. The main specifications are summarized in Table 1.
# Radiation-Balanced Lasers: History, Status, Potential
## 4 Development of Radiation-Balanced Lasers
### Athermal Disk Lasers
On the right, the mounting of the 5%-doped Yb\({}^{3+}\):YAG disk of 0.5 mm thick and 4 mm \(\times\) 5 mm cross section is shown where it is glued onto two bare fibres, which are in turn supported by a glass slide to reduce the heat load [36]. Gaussian radial intensity distribution of the modes. Beam shaping can be used to mitigate this temperature difference. Recently, Yb\({}^{3+}\):YLF and Yb\({}^{3+}\):LLF laser disks have been investigated in external multipass pumping schemes (Figure 12) [37; 38]. Compared to intracavity pumping, external multipass pumping offers higher control on pump spot size and mode-matching conditions. LLF is an isomorph of YLF. They have very similar thermo-optics properties. The Yb\({}^{3+}\):YLF and Yb\({}^{3+}\):LLF laser disks with the Yb\({}^{3+}\) concentration of 10%, 1 mm thick, and 5 mm diameter were pumped at the wavelength 1020 nm corresponding to the lowest energy transition between ground and excited manifolds of Yb\({}^{3+}\) ions. This wavelength is convenient for the system cooling. Laser emission was observed around 1050 nm. The linear laser cavity is constituted by mirrors M1 and M3, both with 10 cm radius of curvature (Figure 12a). A hole in the center of the M2 mirror allows propagation of the laser mode. The input laser beam was focused near the focal point of M1 and M2, and thus collimated after reflection of M2 and focused again after reflection on M1. A tilted window at Brewster's angle was used to shift the beam towards the center of M2. After several roundtrips between M1 and M2, the pump is eventually scattered out by the sidewalls of the window. The sample was placed at the middle point of M1 and M2. Radiation-balanced operation at about 1 W output power was achieved with the Yb\({}^{3+}\):YLF disk. Radiation-balanced laser operation was observed at about 200 mW output power with the Yb\({}^{3+}\):LLF laser disk (Figure 12b). It was shown that large transversal thermal gradients are expected in beam area for high-power (kW) radiation-balanced laser operations that require mitigation techniques. Figure 11: The thermal images of the laser disk; (**a**) is the unpumped laser disk at room temperature. The red lines represent the outline of the disk; (**b**) is the laser pumped disk. (**c**) The line-integrated time-evolution of the temperature change along the white dashed line in (**a**). The VECSEL cavity is unblocked at 1 min [36].
## 3 Material Properties and Test Program
### Geosynthetics  
Three geosynthetic products (geotextile, geogrid, and geocell) were used in this research project to evaluate their relative performance under the conditions presented herein. The geosynthetics used were obtained from Geoplas and their technical properties are  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **Subgrade Layer** & **Subbase/Base Course Layer** \\ \hline Max. dry unit weight \(\gamma_{\text{kmax}}\) (kN/m\({}^{3}\)) & 15.6 & 21.30 \\ Opt. moisture content \(\omega_{\text{opt}}\) (\%) & 21.0 & 4.80 \\ Specific gravity Gs (-) & 25.0 & - \\ Liquid limit LL(\%) & 44.1 & - \\ Plastic limit PL (\%) & 34.0 & - \\ Plasticity index PI (\%) & 10.1 & - \\ Classification (USCS) & CI & GW \\ Water absorption (\%) & - & 0.60 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The properties of subgrade and subbase/base course material.  
Figure 3: The variation of CBR with water content for subgrade soil.  
given in Table 2. Corresponding photos are provided in Figure 4. Geosynthetic tensile strength is a prevalent method employed to evaluate the load-bearing capacity of geosynthetics, particularly their strengths, which typically align with the material's machine and cross-machine directions.
## 4 Simulation Results
In the conventional AFD method, \(\mathit{THD}_{i}\) increases to 5.53% as the _cf_ value increases from 1% to 5%. Both the AFDPF method and the proposed method show low \(\mathit{THD}_{i}\), which is 3.18%, because the initial value _cf_ 1% is only inserted when the grid is connected. For AFD method, islanding is not prevented because DG source and load power were balanced. For AFDPF method, islanding is also not prevented, because the frequency deviation is not enough by using low \(K_{p}\) gain 0.02. In case of the proposed method, islanding is prevented with the same \(K_{p}\) gain of AFDPF method, because this method does not depend on the frequency trip settings. Figure 8: Anti-islanding test results when active frequency drift method (_cf_ = 5%) is used. Figure 9: Anti-islanding test results when active frequency drift method with positive feedback is used.
# RS-YOLOX: A High-Precision Detector for Object Detection in Satellite Remote Sensing Images
## 5 Discussion
### About the Neck Network  
The neck network is usually used to achieve feature fusion. Researchers have tried to improve the feature fusion of the neck network to improve the performance of the  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & **Evaluation** & **Improved Faster** & **YOLT** & **YOLOv3** & **YOLOv4** & **YOLOv5** & **YOLOX** & **RS-YOLOX** \\  & **Metrics** & **R-CNN [21]** & [23] & [17] & [18] & [19,24] & [20] & **(Ours)** \\ \hline \multirow{4}{*}{DOTA-v1.5} & mAP\({}_{50}\) (\%) & 72.51 & 72.06 & 77.05 & 86.23 & 88.56 & 90.12 & **92.83** \\  & mAP\({}_{50\text{-}95}\) (\%) & 50.26 & 51.27 & 58.32 & 69.01 & 71.38 & 73.57 & **78.49** \\  & Latency (ms) & 263.29 & 25.68 & 19.11 & 16.13 & **11.87** & 13.66 & 17.86 \\  & P(M) & 60.42 & 50.54 & 61.60 & 64.02 & **7.05** & 8.94 & 14.38 \\ \hline \multirow{4}{*}{TGRS-HRSD} & mAP\({}_{50\text{-}95}\) (\%) & 74.29 & 73.19 & 74.38 & 85.63 & 85.87 & 86.01 & **88.04** \\  & mAP\({}_{50\text{-}95}\) (\%) & 53.23 & 54.17 & 55.26 & 64.58 & 63.33 & 65.12 & **68.26** \\ \cline{1-1}  & Latency (ms) & 260.78 & 23.96 & 18.12 & 15.02 & **9.24** & 11.34 & 15.23 \\ \cline{1-1}  & P(M) & 60.42 & 50.54 & 61.60 & 64.2 & **7.50** & 8.94 & 14.38 \\ \hline \multirow{4}{*}{RSOD} & mAP\({}_{50\text{-}95}\) (\%) & 80.79 & 79.28 & 81.36 & 90.23 & 90.97 & 92.01 & **93.07** \\ \cline{1-1}  & mAP\({}_{50\text{-}95}\) (\%) & 62.12 & 61.33 & 64.57 & 76.12 & 75.34 & 76.27 & **78.56** \\ \cline{1-1}  & Latency (ms) & 265.35 & 28.72 & 21.79 & 17.56 & **11.88** & 14.86 & 18.29 \\ \cline{1-1}  & P(M) & 60.42 & 50.54 & 61.60 & 64.2 & **7.50** & 8.94 & 14.38 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Performance comparison of advanced object detection models on aerial remote sensing images.  
model. [PERSON] et al. [21] used the feature pyramid network (FPN) in Faster RCNN to achieve multi-scale feature fusion, and used channel attention in the FPN structure to achieve high accuracy in detecting tailing ponds in remote sensing images. [PERSON] et al. [54] introduced the ASFF structure in the FPN of YOLOv3, which strengthened the feature fusion of FPN, and achieved good detection results.  
Besides FPN, PAFPN used by YOLOX is also a commonly used neck network, so we tried to connect the ASFF structure after the neck network. The results are shown in Table 3, and the accuracy of the model has improved significantly. However, a disadvantage of ASFF is also very obvious. It increases the amount of parameters and calculation of the model.
# Q-Learning Empowered Highly Dynamic and Latency-Aware Routing Algorithm for Flying Ad-Hoc Networks
## 6 Conclusions  
This paper proposes a novel routing algorithm, QEHLR, to enhance the routing stability of highly dynamic FANETs. Simulation results demonstrate that the improved routing algorithm QEHLR has a packet delivery rate approximately 12% higher than AODV, approximately 22% higher than OLSR, approximately 20% higher than DSDV, approximately 8% higher than PARROT, and approximately 25% higher than B.A.T. Mobile, thus exhibiting the highest packet delivery rate. Furthermore, the Q-learning based algorithm offers a utility advantage by incorporating delay and mobility into the calculation of the discount in the Q-learning function. This adaptive learning method enables better prediction of the network status through long-term vision and interaction with the topology of dynamic networks. Therefore, the algorithm is a promising choice for providing QoS-integrated services in FANETs, particularly for users who prioritize stable transmission and a high packet delivery ratio. Additionally, the computational efficiency of the algorithm is greatly improved, making it more suitable for highly dynamic FANETs scenarios. The routing principle can be easily extended to various mobile networks and may be an attractive option for networks with high dynamic characteristics. The algorithm in this paper does indeed have computational overhead issues due to the real-time storage and maintenance of Q-tables for each node. This makes the protocol proposed in this paper more computationally complex compared to conventional routing protocols. Therefore, the protocol presented in this paper is only suitable for smaller-scale networks. The operation of the routing mechanism in the case of a fully expanded node scale has not been considered in this paper. However, the routing protocol for large-scale UAV network clustering is one of the incomplete research directions in the field of FANETs routing protocol. Therefore, in the future, this work will complete the design of a large-scale intelligent FANETs routing protocol under the premise of limited computing resources.  
Conceptualization, Q.X.; methodology, Q.X., J.Y. and Y.Y.; investigation, X.T., J.S.; resources, J.S. and Y.C.; writing--original draft preparation, Q.X.; writing--review and editing, Q.X. and Y.C.; supervision, Y.C.; project administration, G.L.; funding acquisition, Y.C. All authors have read and agreed to the published version of the manuscript.  
The work described in this paper is funded by the National Natural Science Foundation of China (No. 52072408). The authors gratefully acknowledge the funding.  
Not applicable.  
The authors declare no conflict of interest.
## 3 Proposed Methodology
### Classification
#### 3.6.6 Simple Recurrent Neural Network  
To process sequential data, such as time series or text, a simple recurrent neural network (RNN) is a type of neural network architecture [81]. It has a feedback loop that enables information to endure over time and can deal with inputs of varying length. A basic drawback of a simple RNN is the vanishing gradient problem [82], which makes it difficult for it to recognize long-term dependencies in sequences. Due to this problem, more sophisticated RNN variants, such as long short-term memory (LSTM) [83] and gated recurrent unit (GRU) [84], were created in order to solve the vanishing gradient issue and enhance performance on sequential tasks.
## 2 Materials and Methods
### Definition of Weather Data  
For evaluating the impact of climate change by using our proposed IFC-based thermal comfort analysis, we use TMYx weather (2007-2021) for Cologne Bonn, as this matches with the German construction setup from TEASER templates. On this weather data, we applied the open-source morphing tool Future Weather Generator [56] to generate future weather predictions for 2050 (i.e., median months from 2036 to 2065) and 2080 (i.e., median months from 2066 to 2095) [56] based on the up-to-date CMIP6 Global Climate Model EC-Earth3 [66]. For simplicity, the years of the future weather scenarios are denoted in the following as 2050 and 2080. For further evaluation of the proposed methods, we choose the IPCC Scenario SSP5-8.5, following the recommendation of [PERSON] et al. [54] to use extreme weather data for analysis of thermal resilience. We choose the worst-case scenario to underline the potential of thermal comfort analysis during design stage, keeping in mind that all weather data only represent predictions of future scenarios. By using the morphing tool by [PERSON] et al. [56], we address the criticism that much research still relies on the outdated climate model CMIP3 [55]. However, for a detailed analysis of climate change's impact on thermal comfort, CMIP6-weather data should be used with specific predictions that match the actual design purpose (e.g., extreme weather summer periods for determination of overheating risks), which has not been taken into account within our proposed demonstration case.  
Figure 3 displays the outdoor temperature for the selected weather scenarios per month. The monthly median temperatures in the SSP5-8.5 scenarios are for 2050 up to 4.37 \({}^{\circ}\)C higher than the TMYx baseline and 6.4 \({}^{\circ}\)C higher for the corresponding 2080 weather data. Since the weather data was transformed to the future weather scenario using morphing, the general characteristics (e.g., amount of outliers per month) are approximately similar to the baseline weather. As already criticized by [PERSON] et al. [54], the variability of current weather data is preserved through the morphing methodology, such that changes in the variability (or even developed phenomena like heat islands) are not represented in the morphed data.  
Our proposed study focuses on climate trends morphed for local weather conditions. The analysis of urban microclimate, heat islands, and hazards is part of further research and can easily be built upon our proposed IFC-based thermal comfort framework.  
To evaluate the use of the IFC-based thermal comfort, we use the SSP5-8.5 weather data for 2080 to further analyze the results. As we do not have specific hot summer design weather data available for the latest IPCC scenario, the 2080 data are the most extreme data that are available for our current comfort analysis.