esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
Mesenchymal stem cells (MSCs), which are known to contribute to tissue regeneration and repair [1] are normally present in a dormant state in almost all organs [2]. Upon the release of cytokines by a stimulus such as inflammation or tissue injury, MSCs begin to proliferate and migrate to where cytokines are being released [3]. However, proliferation and migration of MSCs take time, and a lack of MSCs at required sites may impede tissue healing. Although some methods utilize the regenerative properties of stem cells for therapy [4], in terms of cell delivery methods, a gold standard has not been established and further study is needed. For current cell therapies, MSC administration routes include intra-arterial/intravenous, intraperitoneal, and direct administration into tissues and organs. These methods can roughly be classified into two groups: systemic administration and local administration. Systemic administration is a well-documented method [5; 6] whereby MSCs are applied in the cardiovascular system and reach the damaged area through circulating blood. However, fewer than 10% of administered MSCs accumulate at the site of damage, with many cells becoming captured in the lungs [7; 8; 9; 10].  
Local administration of MSCs to the injury site has advantages, such as a rapid and localized reaction [11]. However, it involves risks such as cells inducing apoptosis when administered at high density [12], or bleeding and secondary damage caused by the administration itself. For these reasons, treatment outcomes are not always stable and the ideal administration method has not yet been established for MSC-based therapies.  
Therefore, we focused on the potential of MSCs to autonomously accumulate in damaged tissue [13; 14; 15]. Our hypothesis is that subcutaneous administration of a mixture of scaffold and MSCs into the body, such as under the easily accessible back skin, will result in MSCs autonomously migrating from the scaffold to the site where cytokines are produced and enhancing tissue repair [16]. Collagen gel, which has high biocompatibility and is easily applied in the clinic, was used as a scaffold to administer a sufficient number of stem cells in this study. We investigated the migration of delivered stem cells and the healing of a tooth extraction site, where both soft and hard tissue healing are involved.
## 3 Navigation
### Fusion Method
#### 3.2.2 Vision + IMU Integration  
Cameras have the advantage of providing an extensive amount of information while having a low weight, limited power consumption, low cost, and reasonable size. However, vision methods have their shortcomings, such as illumination change and distortion because of fast movement. Inertial sensors offer good signals with a high rate of measurement during fast motions, but they are sensitive to accumulated drift resulting from double integration during position estimation. In contrast, visual sensors provide precise ego-motion estimation with a low rate in the long term, but they suffer from blurred features under fast and unpredicted motions. The aim of inertial and vision sensor integration is to overcome these fundamental limitations of vision-only tracking and IMU-only tracking using the complementary properties of both methods.  
The two main approaches are IMU-based and vision-based approaches. The IMU-based approach derives from the INS method, and the error model is used to describe the error of the position, velocity, and acceleration; in addition, the relationship between 3D and 2D video image is taken as the measurement data of the position to deduce the IMU drift error [19]. A nonlinear estimation method, such as EKF or UKF (see Sections 4.2.2 and 4.2.3, respectively, for details) is used to obtain the estimated position [67; 68].  
The vision-based approach is shown in Figure 5. Natural landmarks, such as corridors, edges, doors, wall, and ceiling lights, are objects or features that are part of the environment. The choice of features is vital because it will determine the complexity of the feature description, detection, and matching. The attitude (roll, pitch, and yaw speed) is available as part of the image metadata. This facilitates acceleration of the localization algorithms, either by providing an initial guess of the attitude, or in closed form as direct contribution to the localization. The first part, shown by black arrows in the figure, depicts that the attitude extracted from the IMU helps the camera catch the more accurate feature points, which can result in the higher performance of the trajectory estimation [51; 52]. The methods of feature detection include Integral Channel Image Patches (ICIMGP) [69], Scale Invariant Feature Transform (SIFT) [70], Features from Accelerated Segment Test (FAST) [71] and Speed Up Robust Features (SURF) [72]. The blue arrows in Figure 5 denote the position information of position obtained by the IMU being fused with that obtained by the camera to improve the accuracy of the trajectory estimation. Estimation of the fused weights of these two types of information was discussed by [PERSON] et al. [73].
## 3 Basic Assumptions and Network Communication Model
### Network Communication Model  
Wireless communication can occur over various transmission mediums. To better understand the proposed routing techniques, the free space model and multipath channel radio propagation models are considered in this article. Using either of the mentioned propagation models depends upon the distance between the receiver and transmitter. If the distance between the receiver and transmitter is greater than or equal to a certain threshold distance (\(d_{thresh}\)) multipath propagation model is adopted; otherwise, the free space model is assimilated.  
Each node consumes its battery energy for transmission and reception of data. A node can transmit a message of the size of \(r\)-bits, and energy consumed for transmitting, \((E_{tr})\) Joules, is calculated as per the Equation (1). Figure 1 shows the radio model considered in this research article that is the same as the one mentioned in [16]. Figure 1 emphasizes different components where energy is consumed for transmission and reception of data.  
\[E_{tr}(r,d)=\begin{cases}r*E_{ele}+r*\varepsilon_{fs}*d^{2}&\text{if }d<d_{thresh}\\ r*E_{ele}+r*\varepsilon_{mp}*d^{4}&\text{if }d\geq d_{thresh}\end{cases} \tag{1}\]  
Similarly, to receive a message of size \(r\)-bits, a node consumes \((E_{rc})\) Joules of energy and it is calculates as per Equation (2).  
\[E_{rc}(r,d)=r*E_{ele} \tag{2}\]  
where,  
* \(E_{ele}\) is energy dissipated due to operation of hardware and selectronic components associated with a node. The typical operations on a circuit board are modulation and demodulation of signals, encoding and decoding, signal filtering, etc.
* \((r*\varepsilon_{fs}*d^{2})\) and \((r*\varepsilon_{mp}*d^{4})\) indicate the amplifier energy.
###### Abstract  
We demonstrated a straightforward approach to generate red and near-infrared laser emissions by a Raman-assisted four wave mixing (FWM) process in a nonlinear Yb-doped fiber amplifier, delivering 342 fs pulses of 241 nJ at 864 nm, 834 fs pulses of 21 nJ at 751 nm, and 1.9 ps pulses of 3.8 uJ at 1030 nm. A pair of gratings was employed as the pre-compressor to promote the intensity of the fundamental wave in the main amplifier. Multiple wavelengths from 751 to 1273 nm resulted due to cascaded-Raman-assisted FWM. The pre-compression also underlay the achievement of 25.1, 701, and 2000 kW peak power for the red (751 nm), near-infrared (864 nm), and fundamental (1030 nm) components respectively, which restrained the gain narrowing effect during the amplification. It finally led to shorter pulse duration under increased power.  
four-wave mixing; femtosecond fiber laser; fiber amplifier +
Footnote †: journal: Applied Sciences
# Effect of Corrosion on the Hysteretic Behavior of Steel Reinforcing Bars and Corroded RC Columns
## 2 Experimental Procedure
### Corrosion Damage Estimation
#### 2.4.1 Mass Loss (Gravimetric Measurements)  
After the removal, all specimens were cleaned with a bristle brush following the ASTM G1-72 standard [25] to remove corrosion oxides and debris. Afterwards, the corrosion damage of the steel bars, reduced to their corroded length, was estimated in terms of mass loss according to the following equation:  
\[\eta\ =\frac{\mathrm{M_{ref}-M_{cor}}}{\mathrm{M_{ref}}}\cdot 100\ (\%), \tag{1}\]  
Figure 4: Cyclic horizontal displacement history.  
Figure 5: Test set-up of the experimental procedure.  
where \(\eta\) accounts for the percentage mass loss and M\({}_{\text{ref}}\) and M\({}_{\text{cor}}\) stand for the mass of the non-corroded (reference) and the corroded specimen, respectively.
# Cryogenic System for FTIR Analysis of Hydrocarbon Fuels at Low Temperature and Atmospheric Pressure
## 2 Materials and Methods
### Spectrometer Configuration: Experimental Design and Modification
Warming the samples is carried out by using a power heater and its cables attached to the bar. The cryogenic capillary system is linked to the previously mentioned Dewar vessel using a hermetically sealed fastening mechanism (Figure 3). This setup is designed to deliver and maintain the cooling of samples at atmospheric pressure within the low-temperature cell, using the continuous circulation of liquid and gaseous nitrogen at 80 K through copper capillaries. The system includes a Dewar vessel (vacuum flask) (Figure 3, item 1), a pressure gauge (Figure 3, item 2), a sealing fastening unit (Figure 3, item 3), a copper capillary (Figure 3, item 4), a check valve (Figure 3, item 5) to prevent excessive pressure buildup inside the flask, and electrical contacts for clamps (Figure 3, item 6) to establish an electrical connection and supply voltage from the heater. Pre-Experiment Checklist:  
1. Dewar sealed; check that valve is working; pressure \(\geq\) 6 psi (0.4 atm). 2. Heater powers contact element; nitrogen circulates via thermal expansion. 3. No stray electrical connections, avoiding short circuits. 4. Thermocouple is functional and connected to PID controller. 5. Capillaries are insulated for minimal heat loss and stable cooling. Figure 3: Cryogenic capillary system and Dewar vessel (vacuum flask). “1—vacuum flask, 2—pressure gauge, 3—a sealing fastening unit, 4—copper capillary, 5—check valve, 6—electrical contacts for clamps”. It is essential to determine the precise sample temperature while recording a spectrum. For this purpose, we use an E-type thermocouple (composed of chromel and constantan), which is connected to the cryogenic temperature PID controller. The experiment begins by connecting the cryogenic capillary system to the sample cell and placing it into the diffuse reflection attachment within the FTIR spectrometer. When the sample temperature reaches its lowest value and the PID controller displays 80 K, we collect the Fourier-transform infrared spectra. After that, the heating phase begins by adjusting the heater power connected to the sample bar by the heating cables. During the warming process, spectra are recorded at specific temperatures by balancing the cold nitrogen supply with heater power. After the experiment, the obtained spectra are analyzed, and the findings are summarized.
# Adaptive Traffic Signal Control Method Based on Offline Reinforcement Learning
## 5 Experiments
### Comparison Methods  
The experimental results are compared with several mainstream traffic signal control methods, including traditional methods and reinforcement learning approaches:  
Traditional Traffic Signal Control Methods:  
* Fixed-Time Method [1]: a traditional method where traffic signals operate according to a predefined sequence and schedule, without considering real-time traffic conditions.
* Max Pressure [2]: a method that adjusts phase sequences by calculating and minimizing the \"pressure\" at intersections to optimize traffic flow and maximize throughput across the network.
* Efficient-MP [22]: an enhanced version of the max pressure method that improves efficiency by calculating traffic pressure more precisely, considering actual traffic demand and capacity at signalized intersections.  
Reinforcement Learning Methods:  
* Colight [8]: this method uses a graph attention network to facilitate communication between traffic signals, achieving network-wide cooperation. It learns dynamic communication and models nearby intersections without requiring explicit indexing.
* AttentionLight [9]: this method introduces a self-attention mechanism to capture phase correlations by reevaluating queue lengths, without relying on human expertise in the competition between traffic signal phases.
* DynamicLight [10]: a two-stage RL framework that first uses reinforcement learning to determine the optimal traffic signal phase and then employs traditional methods to determine phase duration.
* DataLight [12]: A data-driven offline method that optimizes traffic signal control by leveraging effective state representations and reward functions. It uses historical data to improve the efficiency of traffic management systems without requiring real-time environment interactions.
# Characterization of Slag Cement Mortar Containing Nonthermally Treated Dried Red Mud
###### Abstract  
In this study, a method was suggested to produce dried powder from red mud (RM) sludge with 40%-60% water content without heating. The RM sludge is discharged from the Bayer process, which is used to produce alumina from bauxite ores. Nonthermally treated RM (NTRM) powder was produced by mixing RM sludge (50%), paper sludge ash (PSA, 35%), and high-calcium fly ash (HCFA, 15%). The physicochemical properties of NTRM were investigated by analyzing its water content, X-ray fluorescence spectra, X-ray diffraction patterns, and particle size. Moreover, to examine the applicability of NTRM as a construction material, slag cement mortar in which 20 wt% of the binder was replaced with NTRM was produced, and the compressive strength, porosity, and water absorption rate of the mortar were evaluated. Results indicated that NTRM of acceptable quality was produced when the water content in RM sludge decreased and CaO contained in PSA and HCFA reacted with moisture and formed portlandite. The NTRM-mixed mortar requires further examination in terms of durability because of the increased capillary voids and high water absorption rate, but its compressive strength is sufficient to enable its use in sidewalks, bike roads, and parking lots.  
red mud; bauxite; slag cement; paper sludge ash; high-calcium fly ash Article
# Estimation of Inter-System Biases between BDS-3/GPS/Galileo and Its Application in RTK Positioning
## 2 RTK Positioning Models  
In this section, the function models of LCM RTK and TCM RTK together with ISB estimation are derived after the introduction of the GNSS observations. Then, the stochastic model of both the RTK and ISB model is demonstrated.
# Improvement of the Transglycosylation Efficiency of a
## 3 Results and Discussion
### Regioslectivity in Transglycosylation
Figure 6: BEH amide chromatography of a representative sample in positive mode (**A**–**D**) and negative mode (**E**–**H**). Extracted ion chromatograms of LNT isomers (blue, \(m/z\) 708 [M\(+\)H]\({}^{+}\), \(m/z\) 706 [M\(-\)H]\({}^{-}\)) and lactose (red, \(m/z\) 365 [M\(+\)Na]\({}^{+}\) and \(m/z\) 683 [2M\(+\)H]\({}^{+}\), \(m/z\) 341 [M\(-\)H]\({}^{-}\) and \(m/z\) 683 [2M\(-\)H]\({}^{-}\), in a sample (**A,B,E,F**) and a blank reaction added without enzyme (**C,D,G,H**). Figure 7: Retention time comparison of isolated isomers before and after GH35 \(\beta\)-galactosidase digestion. (**A–C**): Extracted ion chromatograms of 2\({}^{\prime}\)LNT2 (2\({}^{\prime}\)GlcNAc-Lac; (**A**), 14.6 min, blue box), 4\({}^{\prime}\)LNT2 + 3\({}^{\prime}\)LNT2 (4\({}^{\prime}\)GlcNAc-Lac + 3\({}^{\prime}\)GlcNAc-Lac; (**B**), 14.1 and 18.7 min, purple and red box), and 6\({}^{\prime}\)LNT2 (6\({}^{\prime}\)GlcNAc-Lac; (**C**), 16.2 green box) chemically synthesized standards. (**D–K**): extracted ion chromatograms of LNT isomers (blue chromatograms) in the isolated fractions and corresponding LNT2 isomers (red chromatograms) following GH35 \(\beta\)-galactosidase digestion. The \(\beta\)-galactosidase treatment of the last isomer did not result in any LNT2-like structure (Figure 7G), although the isolated fraction clearly contained an LNT-like structure (Figure 7F). It was previously reported that enzymatic transglycosylation can yield products with 1,1 linkages, i.e. a linkage between two reducing ends, both for GH20 \(\beta\)-\(N\)-acetylhexosaminidaseases with the formation of \(\beta\)-Gal-1,4-\(\beta\)-Glc-1,1-\(\beta\)-GlcNAcAc (Lac-1,1-GlcNAcAc; Figure S2) [18] as well as for \(\beta\)-galactosidase-catalyzed formation of galactod oligosaccharidesadides (GOS) used for infant nutrition such as \(\beta\)-Gal-1,1-\(\beta\)-Glc [54]. To investigate the presence of 1,1-linked compounds in the LnbB reaction mixture, selected reactions were reduced by NaBH\({}_{4}\). The comparison of the extracted ion chromatograms of native LNT isomers (\(m/z\) 706) with those of the reduced reactions (expected LNT alidtiols \(m/z\) 708), it was evident that one compound (15.5 min in Figure 8) could not be reduced, whereas all other compounds were reduced, now appearing as \(m/z\) 708 (Figure 8). It can be assumed this compound lacks the reducing end and is most likely LNB-1,1-Lac.
## 3 Results and Discussion
### Analysis of Volatile Compounds  
The chemical composition of volatile compounds in propolis residue, performed by HS-SPME-GC-MS, is reported in Table 3 and Figure 1. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Total Phenolic** & **Total Flavonoid** & **Antioxidant** \\
**Content** & **Content** & **Activity (DPPH)** \\
**(TPC) mg GAE/kg** & **(TFC) mg** & **mg TE/kg DW** \\
**DW** & **RE/kg DW** & \\ \hline Propolis & 16,726.67 \(\pm\) 36.07 \({}^{\text{a}}\) & 6802.96 \(\pm\) 32.40 \({}^{\text{b}}\) & 2921.61 \(\pm\) 24.04 \({}^{\text{c}}\) \\ \hline TPC was expressed as gallic acid equivalent per kg dry material (mg GAE/kg DW), TFC was expressed as mg of ruin equivalent per kg dry material (mg RE/kg DW), antioxidant activity (DPPH method) was expressed as mg Trolox equivalent per kg dry material (mg TE/kg DW). Different letters indicate significant differences by ANOVA test (\(p<0.05\)). & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Total phenolic and flavonoid contents and antioxidant activity determined by UV-Vis spectrophotometric analysis in propolis residue. A total of 33 compounds have been identified, mainly terpenoids (68.41%), hydrocarbons (13.42%), acetals (5.27%), esters (4.78%), flavonoids (4.64%), alkaloids (1.60%), aldehydes (0.38%), and organic acids (0.28%). Among the terpenoids, monoterpenes turn out to be the predominant ones (42.63%), mainly represented by \(\alpha\)-pinene (20.09%), followed by the sesquiterpenes (21.83%), mainly represented by caryophyllene (9.76%). Ternpenes, including sesquiterpenes and monoterpene hydrocarbons, are also identified as some of the major volatile compounds in propolisis (50; 51; 52). These two compounds are the most abundant in the propolisis residue, followed by limonene (7.60%), copaene (6.93%), \(\beta\)-pinene (6.79%), \(\alpha\)-cymene (6.34%), and 3-carene (6.20%). Many of these compounds, particularly monoterpenes such as \(\alpha\)-pinene, limonene, and \(\beta\)-pinene, are widely recognized for their aromatic, antimicrobial, and antioxidant properties. \(\alpha\)-Pinene, for example, has been used in the fragrance industry due to its pine-like scent, while limonene is employed as a flavoring agent in food and beverages and as a natural solvent in green cleaning products (53). Additionally, caryophyllene and copaene exhibit anti-inflammatory and antifungal activity, making them valuable for pharmaceutical and cosmetic applications (54). These properties highlight the potential of propolisis residue volatiles for use in natural flavoring, fragrance formulations, and functional product development. These data agree
# Using Stochastic Ray Tracing to Simulate a Dense Time Series of Gross Primary Productivity
###### Abstract  
Eddy-covariance carbon dioxide flux measurement is an established method to estimate primary productivity at the forest stand level (typically 10 ha). To validate eddy-covariance estimates, researchers rely on extensive time-series analysis and an assessment of flux contributions made by various ecosystem components at spatial scales much finer than the eddy-covariance footprint. Scaling these contributions to the stand level requires a consideration of the heterogeneity in the canopy radiation field. This paper presents a stochastic ray tracing approach to predict the probabilities of light absorption from over a thousand hemispherical directions by thousands of individual scene elements. Once a look-up table of absorption probabilities is computed, dynamic illumination conditions can be simulated in a computationally realistic time, from which stand-level gross primary productivity can be obtained by integrating photosynthetic assimilation over the scene. We demonstrate the method by inverting a leaf-level photosynthesis model with eddy-covariance and meteorological data. Optimized leaf photosynthesis parameters and canopy structure were able to explain 75% of variation in eddy-covariance gross primary productivity estimates, and commonly used parameters, including photosynthetic capacity and quantum yield, fell within reported ranges. Remaining challenges are discussed including the need to address the distribution of radiation within shoots and needles.  
laser scanning; canopy structure; gross primary productivity; eddy covariance; data fusion +
Footnote †: journal: Remote Sensing
## 2 Materials and Methods
### Magnetoinductive Directional Filters
#### 2.3.3 Frequency Tuning  
So far, the filter centre frequency has been determined by \(\omega_{0}\), which also determines the operation of the MI waveguides. It would clearly be beneficial to introduce tuning within the MI passband. Figure 3c shows the result of tuning the resonance of the coupling resonators to different values, namely, (a) \(\omega^{\prime}_{0}=0.9\omega_{0}\), (b) \(\omega^{\prime}_{0}=0.95\omega_{0}\), and (c) \(\omega^{\prime}_{0}=1.1\omega_{0}\), assuming lossless resonators and lossless MI terminations. Although the best performance is obtained when \(\omega^{\prime}_{0}=\omega_{0}\), tuning around \(\omega_{0}\) is feasible for the variations in \(\omega^{\prime}_{0}\) shown.  
Figure 3: MI directional filter performance for \(\kappa=0.6\) and \(\kappa_{c}=0.3\): (a) scattering parameters for lossless and lossy (\(Q=200\) ) resonators with MI terminations (1) and real narrowband terminations (2); (b) variation of \(Q_{bp}\) with \(\kappa\) and \(\kappa_{c}\) for a lossless filter with MI terminations (1); (c) scattering parameters for lossless resonators with MI terminations (1) for \(\omega^{\prime}_{0}/\omega_{0}=0.9,\,1.0,\,\text{and}\,1.1\).  
From the above, we conclude that the network shown in Figure 2a with lossless terminations possesses all the properties of a directional filter except that it has null reflectivity only within the MI passband. \(S_{11}\) is zero at the resonant frequency and very small in its vicinity. Lossy systems and systems with narrowband terminations have similar properties, the main difference being a reduction in the magnetoinductive bandwidth.