esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### C3G Uregulates the Expression of Prdm16 Gene Both In Vitro and In Vivo  
As the expression level of the BAT-specific gene _Ucp1_ was increased by C3G treatment and this process was not dependent on the activation of the _Ucp1_ promoter, we speculated the existence of other regulators as target genes of C3G, such as transcription factors. _Prdm16_ determines the brown fat-like program and thermogenesis in both brown and white adipose tissues [19; 20; 21; 22], so we consequently sought to determine whether this thermogenesis-related gene participates in this process, and found that treatment with C3G (20 uM) resulted in a 4.42-fold increase in _Prdm16_ mRNA expression in C3H10T1/2 cells, compared to the control group. Interestingly, the expression levels of _Prdm16_ followed the same trend as that of _Ucp1_ after treatment with various concentrations of C3G (Figure 2A) (\(p<0.01\)). Moreover, the promotional effect of C3G on _Prdm16_ was consistent with that in previous reports [4; 17]. Similarly, in light of the animal experiments in this study, it was ascertained that C3G treatment can significantly increase the expression of _Prdm16_ and _PPARa_ genes in the BAT of mice. In the db/db mouse model and the DIO model mice, the expressions of _Prdm16_ and _PPARa_ genes in C3G-treated mice increased by 7.21 times, 10.16 times, 3.91 times, and 4.58 times (\(p<0.05\)), respectively, compared with the control group (Figure 2B,C). These results are consistent with our previous research results [4; 17],  
Figure 1: C3G was able to up-regulate the expression of _Ucp1_ gene both in vitro and in vivo. (**A**) Effects of C3G at different concentrations on _Ucp1_ gene expression in C3H10T1/2 cells. (**B,C**) C3G up-regulated the expression of _Ucp1_ gene both in db/db mice model (**B**) and mice with DIO model (**C**) (\(n=6\)–8). (**D**) Electrophoretic map of cloned Ucp1, M: [PERSON] 200 Bpladder; Lane 1–3: Ucp1 amplification products (**E**) Electrophoresis diagram of pGL3-Basic enzyme digestion product, M: DL15000 marker; Lane 1–3: pGL3-Basic double enzyme digestion product.(**F**) PCR electrophoresis diagram of pGL3-Ucp1 bacterial solution; M: [PERSON] 200 Bpladder; Lane 1–6: PCR products from the picked six single-colony bacterial solutions.(**G**) Comparative diagram of sequencing results of PGL3-Ucp1 (**H**) Activation of _Ucp1_ promoter by C3G with different concentrations. Values represent means \(\pm\) SD. Error bars represent SD; mean values with asterisk or different letters are significantly different (* means \(p<0.05\); *** means \(p<0.01\)).  
indicating clearly that C3G treatment increased the expression of _Prdm16_ gene, both in vitro and in vivo.
# Regeneration Performance of Activated Carbon for Desulfurization
## 2 Materials and Methods
### SO\({}_{2}\) Adsorption Experiment  
The experimental device is shown in Figure 1. A single factor experiment was used to investigate the effects of activated carbon dosage, oxygen content, and flue gas flow rate on activated carbon adsorption, as shown in Table 1. A certain amount of adsorbents were placed in a 15 \(\times\) 150 mm U- shaped tube and heated in a water bath at 60 \({}^{\circ}\)C, under a mixed gas flow (the consistent mixed gas contained 2000 ppm SO\({}_{2}\), a certain amount of O\({}_{2}\), and was balanced by N\({}_{2}\). The amount of SO\({}_{2}\) in the exhaust gas was analyzed by a portable flue gas analyzer (KANE9506, England KANE). The desulfurization efficiency of the adsorbent AC calculated, which determined the suitable adsorption conditions. The desulfurization efficiency can be evaluated by:  
\[\eta=\frac{\mathrm{C_{i}\ -C_{o}\times 100\%}}{\mathrm{C_{i}}} \tag{4}\]  
where \(\eta\) is the desulfurization efficiency, Ci represents the SO\({}_{2}\) inlet concentration, and Co the SO\({}_{2}\) outlet concentration (ppm).
## 4 Results
### Search Outcome and Study Selection  
From the initial 337 studies identified through our systematic search in WoS and Scopus, 204 duplicates were eliminated (Figure 3). Following rigorous screening, inclusion, and exclusion criteria, 35 papers were determined to fit our review parameters.  
Since there was a lack of scientific studies conducted explicitly to predict and optimize the TCO of CWPs, we considered various relevant studies on the topic. As illustrated in Figure 4, the articles included through systematic review can be broadly categorized into three domains: chiller predictive modeling, control, and maintenance [1, 20, 30, 48, 49, 50, 57, 110, 114, 117, 119, 127], life cycle cost (LCC) of chiller [3, 5, 6, 11, 52, 98, 101], and HVAC or building design [46, 115, 116, 118, 120, 121, 123, 124, 125, 126]. The articles cover calculating or forecasting life cycle or ownership costs for HVAC systems or CWPs, employing AI-based strategies for cost prediction, and analyzing the impact of various chiller parameters on the TCO, among other related aspects.
# On Coding and Decoding Reconfigurable Radiation Pattern Modulation Symbols
## 2 Materials and Methods
### Symbol Selection
The number of orthogonal symbols given when \(\gamma\) and \(\gamma^{\prime}\) are swept is depicted in Figure 2(b); from the 256 combinations, 72 are unique. Increasing the threshold reduces the number of transmitted bits per period; however, the probability of error is diminished for noisy channels. ```
Result: A set of orthogonal symbols that minimize the BER is defined. Calculate the total number of possible radiation patterns \(RP\) for an \(N_{T}\) antennas array. A matrix \(M\) of dimensions \(RP\ \times\ RP\) is defined. The Euclidean distance \(M_{i,j}=\sqrt{\sum_{k=1}^{P+\varphi}\left(A{F_{i}}(\theta_{k},\phi_{k})-A{F_{j }}(\theta_{k},\phi_{k})\right)^{2}}\) between each radiation pattern pair is calculated. The mean distance \(m\) is calculated for \(M\). The matrix is normalized by \(M^{\prime}=M\times\frac{1}{m}\). A minimum normalized threshold distance \(\gamma\) between symbols is chosen. First threshold (\(\gamma\)): for\(i=1\):\(RP\)do if\(M^{\prime}_{i,j}<\gamma\)then \(M^{\prime}_{i,j}=0\)else \(M^{\prime}_{i,j}=1\). end end end  end if  Second threshold (\(\gamma^{\prime}\)) definition : \(\gamma^{\prime}=\frac{RP}{4}\ (usually)\) for\(i=1:RP\)do \(M^{\prime}_{i}=\sum_{j=1}^{RP}M_{i,j}\) if\(M^{\prime}_{i}>\gamma^{\prime}\)then \(i\) symbols can be transmitted. end if  end for  Remove redundant symbols if required. ```  
**Algorithm 1**Defining orthogonality for reconfigurable radiation patterns.
# Towards Collaborative and Dynamic Spectrum Sharing via Interpretation of Spectrum Access Policies
## 5 Policies
### Planner
#### 6.4.1 Transmission Opportunities
* Apply the disallowed rule intersections to the allowed list, effectively carving out their hyperspaces from the allowed hyperspace list wherever an overlap is found. * Depending on the shapes to be carved out, this last step may result in multiple output hyperspaces for each input-allowed hyperspace. Depending on the expected size of the active policy in terms of the number of rules, and given the simplification assumption introduced above, whereby geographical areas specified in the rules and the queries are rectangular, one can improve the algorithmic efficiency by indexing hyper-rectangles on each of their dimensions using Interval Trees. For that purpose, our implementation uses an augmented AVL implementation of a balanced binary search tree, which guarantees a worst-case complexity for the needed insertions and intersection lookups as \(\mathcal{O}(\log n)\). Once the opportunities for transmission are determined as stated above, one can simply derive a few useful functions, for example, get the allowed frequency ranges: given a multi-criteria transmission query that bounds the operational space--time interval and communication parameters--the algorithm returns an ordered list of disjoint frequency ranges that a radio may use to communicate with its peers. The processing follows the steps shown below. * Evaluate the set of opportunities answering the query. * Index the opportunities on frequency intervals using an interval tree. * To ensure resulting intervals are disjointed, for each opportunity candidate for insertion in the tree, split the candidate into complements to readily inserted elements and insert those complements in lieu of the candidate. * Traverse the tree in-order while merging adjacent intervals to create the ordered list of allowed frequency ranges. We have also implemented a SPARQL-based version of searching for opportunities that is based on a rather complex SPARQL query associated with a couple of registered functions to determine whether hyper-rectangles intersect and what their intersection is.
# Brillouin Frequency Shift Sensing Technology Used in Railway Strain and Temperature Measurement
## 2 Measurement Theory  
### Brillouin Back-Scattering in Silica Single-Mode Fiber  
When incident light is transmitted through an optical fiber, the light passes through the silica molecules and other doping particles in the optical fiber. If the radius of these particles is smaller than the wavelength of the incident light, light scatters in all spatial directions. Back-scattering light specifically refers to scattered light in the opposite direction to the incident.  
There are three types of scattering: Rayleigh scattering, Raman scattering, and Brillouin scattering. The frequency spectrum in Figure 1[2] shows the scattering and frequency band that correspond to 1550 nm incident light.  
Brillouin scattering refers specifically to inelastic scattered light, and it is mainly caused when phonons (acoustic phonons) collide with incident light and generate scattering with a lower power and different frequencies. From this phenomenon, we can gather the distance of an event in the fiber by counting the time difference between the incident light and back-scattered light. In the meantime, by measuring the frequency difference of the back-scattered light and incident light, we can obtain the values of the external strain and temperature that affected the fiber.
# Influence of Source Shape on Semi-Airborne Transient Electromagnetic Surveys
## 5 Conclusions  
Our experiments demonstrate that the transmitting source shape significantly affects observational data. Since our theoretical model is simpler than actual geological models, and field transmitting sources have diverse spatial distributions, the impact of the source spatial distribution on observational data is even more complex in practice. As the source shape primarily causes distortion in early-time response data in small offset regions, to ensure effective SATEM detection, we recommend primarily using late-time data from large offset regions. However, late-time data from large offset regions has a very small signal amplitude and is highly susceptible to background noise interference. Early-time response data from small offset regions has a larger signal amplitude and is less affected by background noise. Therefore, considering these factors comprehensively, when conducting SATEM work in the field, the shape of the transmitting source should be recorded in detail to ensure that early-time data from small offset regions remains usable, thereby enabling the effective detection of three-dimensional subsurface electrical structures across a wide range--from small to large offset regions and from shallow to deep earth.  
Conceptualization and methodology, L.L. and J.Z.; software, J.X. and W.L.; formal analysis, W.L. and J.Z.; writing--original draft preparation, L.L. and J.Z.; writing--review and editing, L.L., W.L. and J.Z.; visualization, J.X. and W.L.; project administration and funding acquisition, J.Z. All authors have read and agreed to the published version of the manuscript.  
Not applicable.  
All data and files for running the simulations are available at [[https://github.com/liziyuan1/Paper_data](https://github.com/liziyuan1/Paper_data)]([https://github.com/liziyuan1/Paper_data](https://github.com/liziyuan1/Paper_data)), accessed on 1 April 2025.  
The authors would like to thank the Gansu Provincial Coal Geological Exploration Institute for equipment support.  
The authors declare no conflicts of interest.
## 6 Simulations and Testbed Experiments
### Simulation Results
#### 6.2.4 Packet Delay  
Figure 13 compares the packet delay between the three schemes. Figure 13(a) represents the relationship between the average delay and the nodes' hop. And Figure 13(b) illustrates the relationship between the average delay and the number of descendants of nodes. Each point in Figure 13(a) and Figure 13(b) is the mean value of 20 simulation experiments in each group. Figure 13(c) illustrates the average delay in each round. Each point in Figure 13(c) is the point of the first experiment in each group. From Figure 13(a), we can obtain that the average packet delay rises up when the nodes' hops increases. And the average delay of O-CDSA rises up much quickly while the average delay of T-CDSA is close to that of the non-scheduling scheme. From Figure 13(b), the average packet delay of the three schemes tends to fall down when the number of descendants of nodes increases. This is more distinct in the O-CDSA. From Figure 13(c), we can get that the average delay of the O-CDSA at each round is always longer than that of others, but will reduce to a lower level for the running rounds.
## 3 Results
### Enamel Surface Micro-Hardness  
The treatment microhardness measurements were made to verify that the most remineralized surface would be the hardest. The mean micro-hardness values of the enamel surfaces are shown in Figure 1. The fluoride group had the highest micro-hardness after remineralization with 329.8 VH (\(p<0.05\)). Groups A and D were significantly different (Control, Laser). Tri-calcium phosphate and tri-calcium phosphate + LLL showed no differences. Figure 2 shows indentation marks.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Calcium** & **A** & **B** & **C** & **D** & **E** & **F** \\ \hline A & - & & & & & \\ B & 0.018271 & - & & & & \\ C & 0.000000 * & 0.000000 * & - & & & \\ D & 0.399090 & 0.009474 & 0.000002 * & - & & \\ E & 0.000000 * & 0.000035 * & 0.000000 * & 0.000000 * & - & \\ F & 0.003380 & 0.000001 * & 0.013938 & 0.007088 & 0.000000 & - \\ \hline
**Phosphorus** & **A** & **B** & **C** & **D** & **E** & **F** \\ \hline A & - & & & & & \\ B & 0.000000 * & - & & & & \\ C & 0.000000 * & 0.199111 & - & & & \\ D & 0.444079 & 0.000000 * & 0.000000 * & - & & \\ E & 0.000000 * & 0.076646 & 0.279880 & 0.000000 * & - & \\ F & 0.000001 * & 0.019918 & 0.113015 & 0.000002 * & 0.265181 & - \\ \hline \hline \end{tabular}  
* * Adjusted \(p\)-value for significance is 0.001667. One-way analysis of variance by ranks (Kruskal-Wallis Test). A = Control, B = Fluoride (Durapath varamish), C = Tricalcium phosphate (Clinpro White varish), D = LLL (IRS10 (Quantum)), E = Fluoride + LLL, F = Tricalcium phosphate + LLL.  
\end{table}
Table 2: Analysis of calcium and phosphorus ion content in solution after the use of remineralizing agents.  
Figure 1: Mean microhardness values between groups (\(p<0.05\)).  
Spearman correlation determined a relationship between decreased mineralization in the remineralizing solution and the microhardness of the enamel surface (Table 3).
_Article_  
Experimental Investigation on Soft Galloping and Hard Galloping of Triangular Prisms  
[PERSON], [PERSON], [PERSON],*[PERSON], [PERSON] and [PERSON]  
State Key Laboratory of Hydraulic Engineering Simulation and Safety, Tianjin University, Tianjin 300072, China; [EMAIL_ADDRESS] (J.L.); [EMAIL_ADDRESS] (X.Y.); [EMAIL_ADDRESS] (J.Z.); [EMAIL_ADDRESS] (Q.R.) Department of Civil Engineering, Case Western Reserve University, Cleveland, OH 44106, USA; [EMAIL_ADDRESS] Correspondence: [EMAIL_ADDRESS]; Tel.: +86-22-2740-1127  
Academic Editor: [PERSON]  
Received: 15 November 2016; Accepted: 13 February 2017; Published: 17 February 2017  
**Abstract:** The studies currently on soft galloping (SG) and hard galloping (HG) are scarce. In this study, SG and HG of spring-mounted triangular prisms in a water channel are investigated experimentally. A power take-off system (PTO), a spring system, additional weights, and different triangular prisms were used to achieve the variations in damping coefficient \(c\), system stiffness \(K\), oscillation mass \(m\) and section aspect ratios \(\alpha\), respectively. The present paper proves that the VIV (vortex-induced vibration) lower branch can be observed in the SG response. In SG response, VIV branches are incomplete while the galloping branch is complete, and galloping can be self-initiated only in the self-excited region. On the contrary, in HG response, VIV branches are complete, the galloping branch is incomplete, and galloping can only be initiated by external excitation at a velocity exceeding the critical velocity. As \(c\) and \(m\) increase, or \(K\) and \(\alpha\) decrease, the oscillation mode of a triangular prism gradually transitions from SG to CG (critical galloping), and continues to HG. The amplitude in VIV branch is the main reason causing the onset of galloping in SG response. A critical damping coefficient \(c_{c}\), which is dependent on \(m\), \(K\) and \(\alpha\), is proposed to predict the occurrences of SG, CG and HG. When \(c<c_{c}\), SG occurs; when \(c>c_{c}\), HG occurs; when \(c=c_{c}\), CG occurs.  
**Keywords:** triangular prism; flow-induced motion; damping; stiffness; mass; section aspect ratio
## 5 Particle Swarm Optimization Algorithm
### Fitness Function  
According to the optimization principle of the particle swarm algorithm, this paper transforms the ADRC parameter optimization problem into a problem of finding a set of controller parameters within the feasible range of the parameters to minimize the fitness function value. The fitness function is used to judge the pros and cons of the iterative process of the algorithm. A reasonable choice of the fitness function is the prerequisite for the particle swarm algorithm to find the optimal value.  
In this paper, according to the step response of the system, the effects of the fitness function designed by integral square error (ISE), integral time square error (ITSE), integral absolute error (IAE), integral time absolute error (ITAE) and the combination of them on the performance of the system controller are compared through experiments. Since the rising phase of ADRC is mainly affected by the TD part, based on the IAE, the designed fitness function is defined as the cumulative sum of errors between the response value and the expected value of the system during the period from the rise time to the end of the sampling period. Compared with other fitness functions, the fitness function designed in this paper makes the controller have better performance. The mathematical expression of fitness function is as follow:  
\[J=\int_{t_{r}}^{T}|e(t)|dt \tag{16}\]  
where \(t_{r}\) is the rising time of the step response of the system, and \(T\) is the sampling period of the system.
## 2 Materials and Methods
### Data Analyses  
To assess the effect of salinity on the maximum specific growth rate and photosynthetic efficiency of _Spirulina_ sp., a Generalized Linear Model (GLM) was fitted using a Gaussian error distribution and an identity link function. The model included salinity treatment as a categorical predictor. An analysis of deviance was conducted to evaluate the overall significance of the predictor by comparing the full model against the null model.  
Following a significant effect of salinity treatment, a post hoc pairwise comparison was performed using [PERSON]'s Honest Significant Difference (HSD) test to determine which salinity levels significantly differed from each other. The _glht_ function from the _multcomp_ R package [22] was used to carry out the multiple comparisons, with adjusted \(p\)-values obtained using the single-step method to control for Type I error across all contrasts.  
Principal Component Analysis (PCA) was employed to investigate the changes in size structure across salinity treatments. The counts in each size class were combined and transformed into relative proportions to standardize the data per treatment. We used PCA to reduce the dimensionality of the relative abundance after centering and scaling while employing a correlation matrix to identify patterns in size structure. We focused on thefirst two principal components to detect significant patterns while using size class loadings to understand which size categories influenced these observed patterns.  
The analysis of size structure under salinity influence used a permutational multivariate analysis of variance (PERMANOVA) with the _adonis2_ function from the _vegan_ R package [23]. The analysis used a Bray-Curtis dissimilarity matrix, which was created from square root-transformed size class densities. The analysis examined salinity as a fixed effect through 999 permutations of a reduced model.  
All statistical analyses were conducted in R version 4.5.0 [24], and the significance was assessed at an alpha level of 0.05.
# A 60-GHz Ultra-Thin and Flexible Metasurface for Frequency-Selective Wireless Applications
## 3 Results and Discussion
### Measured Results of the 60-GHz Metasurface  
Figure 10a,b presents the measured results of the designed metasurface screen-printed on Melinex and Kapton flexible films, respectively. Fabrication accuracy, as well as the precision of the results, are determined by the reproducibility of results, thus multiple samples have been taken randomly from a long printed pattern. Three samples taken from various locations and variable distances apart from each of the screen-printed metasurface film have been tested in the quasi-optic bench to validate the reliability and precision of the measurements. The plots show that both of the metasurfaces cover 60 GHz band and the achieved bandwidth is above 5 GHz while considering a strict estimation of \(-20\) dB as a reference level. Though it is also observed that the resonant dip shows a slight offset towards the lower frequency range. This frequency shift towards the left side is mainly due to fabrication tolerances or dilation of substrate films due to heat exposure while heat sintering. However, the overall performance of the metasurface is not affected by this resonant shift as the operating bandwidth fully covers the 60 GHz band.  
Figure 9: Testing of the designed metasurface by using the quasi-optic bench for the measurement of transmission at 60 GHz (**a**) sample mounted in the test bench, (**b**) complete quasi-optic bench.  
As the reliable performance can be ensured on the bandwidth by taking -10 dB reference, in this case, it has been observed that the range of 50-67 GHz has been covered in all the measured results of both of the metasurfaces. The measured bandwidth of 17 GHz has been accomplished from the proposed design which is significantly wider than that reported in [23] (i.e., 10 GHz bandwidth in the range 57.5-67.5 GHz), and more evenly extended on the left and right side of the resonant frequency of 60 GHz. Also, the proposed bandwidth of multiple samples remains conserved as the measured plots converge. The results have shown that when the antenna beams are oriented perpendicular to the designed FSS metasurface, the transmission is blocked in the bandwidth of 50-67 GHz, which implies that the signal propagates on the surface and this can be deployed as a low-loss wireless guiding medium. The proposed structure can be used as wallpapers on walls and windows to enhance the signal transmission for this highly attenuated 60-GHz band and allows a secure communication in the indoor environments.