esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Layout and More PEX and EM Simulation Results
### EM Simulated Results vs. PEX RCC
Here, stability factors K and Mu are defined in [19] as  
\[\text{K}=\frac{1-|S_{11}|^{2}-|S_{22}|^{2}+|\Delta|^{2}}{2|S_{12}S_{21}|} \tag{5}\]  
\[\text{Mu}=\frac{1-|S_{11}|^{2}}{|S_{22}-\Delta S_{11}^{*}|+|S_{12}S_{21}|} \tag{6}\]  
where \(S_{11}^{*}\) is the complex conjugate of \(S_{11}\) and \(|\Delta|=|S_{11}S_{22}-S_{12}S_{21}|\). On the contrary, EM simulations show that at frequencies below 36 MHz, the LNA is only conditionally stable and could potentially oscillate at low frequencies. However, even though Mu is less than 1 in EM simulations, it is extremely close to 1 (only \(\sim\)2 \(\times\) 10\({}^{-6}\) less than 1), which means that shunting a large resistor at the output may offset the negative resistance and stabilize the circuit. As expected, when an 8.2 k\(\Omega\) shunt resistor (\(R_{2}\) in Figure 3a) to ground was added at the LNA load, the instability disappeared at low frequencies (<100 MHz), as in Figure 13. The shunt resistor has negligible effects on the performance of the LNA, with \(\sim\)0.03 dB drop in gain and virtually no change in noise figure and \(S_{11}\). This is primarily a result of the large shunt resistance value; for noise figure and \(S_{11}\), the good isolation (maximum \(S_{12}=-42.2\) dB from 100 MHz to 100 GHz) in two-stage amplifiers makes these two parameters even less sensitive to the change in the LNA's load.
Wind-Induced Phenomena in Long-Span Cable-Supported Bridges: A Comparative Review of Wind Tunnel Tests and Computational Fluid Dynamics Modelling  
[PERSON]  
1 School of Civil Engineering, University College Dublin, Belfield, Dublin 4, Ireland; [PERSON][PERSON]  
2 School of Mechanical and Materials Engineering, University College Dublin, Belfield, Dublin 4, Ireland; [EMAIL_ADDRESS]  
[PERSON]  
1 School of Civil Engineering, University College Dublin, Belfield, Dublin 4, Ireland; [PERSON]###### Abstract  
Engineers, architects, planners and designers must carefully consider the effects of wind in their work. Due to their slender and flexible nature, long-span bridges can often experience vibrations due to the wind, and so the careful analysis of wind effects is paramount. Traditionally, wind tunnel tests have been the preferred method of conducting bridge wind analysis. In recent times, owing to improved computational power, computational fluid dynamics simulations are coming to the fore as viable means of analysing wind effects on bridges. The focus of this paper is on long-span cable-supported bridges. Wind issues in long-span cable-supported bridges can include flutter, vortex-induced vibrations and rain-wind-induced vibrations. This paper presents a state-of-the-art review of research on the use of wind tunnel tests and computational fluid dynamics modelling of these wind issues on long-span bridges.  
long-span bridge; cable-supported bridge; aerodynamics; wind tunnel test; computational fluid dynamics; flutter; vortex-induced vibration; rain-wind-induced vibration +
Footnote †: journal: Applied Sciences  
power and modelling capacity, Computational Fluid Dynamics (CFD) modelling is coming to the fore as a complement to and potential replacement for wind tunnel testing. There are several disadvantages to the use of wind tunnel testing, namely the scaling issues, and the presence of physics sensors that can disrupt the flow--neither of which are concerns with CFD modelling. However, the results of CFD simulations can come with errors caused by computational codes and numerical schemes, and uncertainties brought by the assumptions made in the model. Additionally, the accuracy of CFD simulations is strongly related to the computational capacity.  
The main focus of this paper is to review the state-of-the-art experimental wind tunnel and numerical CFD methods applied in the area of long-span cable bridges. Merits and existing challenges of both methods will be discussed. In addition, focus is given to the three key concerns around wind effects in long-span cable bridges which will be discussed, namely: flutter, vortex-induced vibrations (VIVs) and rain-wind-induced vibrations.
# Variable Speed Limit and Ramp Metering for Mixed Traffic Flows: A Review and Open Questions
## 3 Research and Implementation
### Merging Control Approaches in Mixed Traffic Flows
#### 3.5.2 Car-Following and Trajectory-Planning-Based Merging Control Methods  
Authors in [95] developed a vehicle trajectory planning method for CAV coordination at on-ramps, formulating the planning tasks of the ramp vehicle and the mainline vehicle as two related distributed optimal problems. One simulation scenario with only three CAVswas used. Results show that all the three facilitating vehicles entering through on-ramp were able to develop a suitable gap under the influence of their respective leading vehicles whose speeds are constantly changing. All three merging vehicles could maneuver to the desired merge locations with the desired merge-in speeds.  
The traffic flow strategy with vehicles using an optimized look-ahead cruise control method for merging control was proposed in [97]. A balance was achieved between reducing the energy demand of traffic flow and simultaneously reducing the queue length in the controlled lanes. It was found that the proposed method has several advantages, such as simple implementation, handling system nonlinearities, and considering parameter dependencies [97].  
In [99], the authors used the Cooperative IDM (CIDM) to examine the system performance under different AV penetration rates for motorway merging control. A proposed CIDM-based controller determines the acceleration and deceleration rate of AVs in response to the actions of surrounding vehicles to improve road capacity and stability. The results imply that CIDM-based AVs can eliminate or alleviate speed oscillations on the motorway. The results show that with increasing AV penetration rate and under different safe time gaps in the IDM with values 0.4, 0.6, 0.8, 1, and 1.2 s, the speed oscillations were reduced by 46.1%, 53.9%, 54.8%, 69.7%, and 72.3%, respectively. For the safe time gaps of 0.4, 0.6, and 0.8 s, \(ATT\) was reduced by 15.4%, 12.3%, and 12%, respectively, while for the other cases, \(ATT\) was increased by 3.9% and 5.12%, respectively.
## 6 Proposed Method
### System Model  
In this research work, the system model consists of a base station (BS) and the sensors (N) which maintains uniform distribution based random deployment in the coverage area. As so to enhance the network connectivity the network is filled with huge number of sensor nodes and are deployed inside the coverage area. The subsections and the hidden details of the network are described as follows.  
* The network is totally static which includes the BS and the sensor nodes.
* At the initial stage, all nodes have equal initial energy.
* The BS has no energy limitations its computation energy extremely high.
* According to the coverage area and localization primary cluster head (PCH) and secondary cluster head (SCH) are chosen periodically.
* According to the transmission distance the energy is optimized by the nodes.
* In order to reduce the energy consumption, sleep and wake node concept is initiated in the network.
* Both the cluster heads are multi weighted which it maintains the variable energy level.
* The nodes have the capability to send their address details to its neighbor nodes in the network.
* The BS and PCH are placed within the transmission range in the network.  
In general, one hop communication greatly affects the energy. So, p-jump is a better way. Here, the multi weight clustering will be discussed in this paper that uses load balancing in clusters in order to reduce the power consumption and to increase the energy efficiency of WSN.
## 2 Biologically Active Polysaccharides--Structure and Function  
In the recent decades, natural polysaccharides isolated from bacteria, algae, fungi, and higher plants have attracted attention in the fields of nutrition and medicine [28]. The reasons include their broad spectrum of biological and pharmacological activity combined with low toxicity and rare negative side effects [29]. Many of them have been used for hundreds of years in traditional medicine. Further, some of the natural polysaccharides are already used as auxiliary substances, drug carriers, blood substitutes, and pharmacologically active compounds [30; 31; 32].  
The polysaccharides showing immunomodulatory and anti-tumor properties are of particular interest [4]. Biological activity of polysaccharides results from direct inhibition of the neoplastic cells survival (cytotoxic activity) and from indirect mechanisms involving the activation of the immune system (immunostimulating activity). Detailed characterization of the structure of polysaccharides showing immunomodulatory effect is challenging; however, the most frequently mentioned parameters include molecular weight, monosaccharide composition, type of glycosidic bond, degree of cross-linking, conformation of the molecule, and modification in the structure of the molecule (carboxymethylation, sulfonation, aminopropylation, hydroxyethylation, phosphorylation, element incorporation into the molecule) [13; 33; 34; 35; 36; 37]. The higher the molecular weight of a polysaccharide, the greater its chance of effective binding to a receptor or binding protein, which triggers the immune response or tumor cell apoptosis [38]. According to the literature data, the polymers of glucose and mannose show the highest immunomodulatory activity [39], which can bind to the receptors specific to these polysaccharides [40].  
Additionally, polysaccharides with a predominance of \(\beta\)-(1-6) glycosidic bonds show weaker activity than those with predominant \(\beta\)-(1-3) bonds in the main chain [41; 42]. Both too low and too high a degree of branching of the molecule results in a decrease of the activity of the polysaccharide [43]. A greater number of side chains are associated with a tighter conformation of the helix, which makes polysaccharides difficult for the receptors to recognize [44]. Moreover, the degree of branching of the most biologically active \(\beta\)-(1-3)glucans is 0.20-0.33 [45]. Furthermore, the conformation of polysaccharides is related to the spatial arrangement of the atoms that determine the shape of the chain. The triple-helix conformation provides strong immunostimulatory effects of \(\beta\)-D-glucans by promoting the release of TNF-\(\alpha\) by macrophage cells [46]. It must be admitted that the structure-activity relationships proposed by the above-cited authors work well only for a fairly homogeneous group of \(\beta\)-D-glucans, but not for all currently known immunoactive polysaccharides.  
The section below describes the biological activity of the polysaccharides isolated from various natural sources.
## 2 2 DOF PI-P Controller Design Using FRIT
The ideal response is obtained by multiplying fictitious reference signal \(\left(\widetilde{r}\left(q,k\right)\right)\) with reference model \(M_{1}(z)\), which is then the error signal \(\widetilde{e}\left(k\right)\), which can be calculated using:  
\[\widetilde{e}\left(k\right)=y_{0}\left(k\right)-M_{1}\left(z\right)\widetilde{r }\left(q,k\right), \tag{10}\]  
where \(M_{1}(z)\) is a given reference model for the step response in a discrete time, as shown in Equation (11), and:  
\[M_{1}\left(z\right) =z\left(M_{1}\left(s\right)\right) \tag{11}\] \[M_{1}\left(s\right) =\frac{{\omega_{1}}^{2}}{\left(s+\omega_{1}\right)^{2}} \tag{12}\]  
where \(M_{1}(s)\) is the step reference model for the step response in a continuous time and \(\omega_{1}\) is the natural frequency (rad/s). Using the error signal Equation (10), the performance index is minimized using:  
\[J\left(q\right)=\sum_{k=1}^{N}\widetilde{e}\left(k\right)^{2} \tag{13}\]  
PSO is used for FRIT optimization [11], and the Scilab program is used to program FRIT. Scilab is an open source software for scientific computation that includes hundreds of general purpose and specialized functions like MATLAB [25]. PSO is an optimization method that is based on swarm intelligence, i.e., the type of flock movement behavior that birds and fish use to find the best paths to their food. The flock of birds and the school of fish are considered as particles that are assumed to have two characteristics: position and velocity [26].
# Optical Channel Selection Avoiding DIPP in DSB-RFoF Fronthaul Interface
## 3 Calculation Results
### Optical Channel Selection Algorithm
The main limitation here is the operating range of the optical system, where \(f_{0}\in\langle f_{\text{Omin}},f_{\text{Omax}}\rangle\), including the ROADM nodal devices and optical fibers belonging to the selected optical path. During the calculations, it was assumed that in the DWDM system we do not exceed the narrowest single-modality range of optical fibers with non-zero chromatic dispersion shifted. The speed of tuning or switching the laser is decisive in terms of the final speed of selecting and setting up an optical channel. If no suitable optical channel is detected (Figure 12, Table 5), the process of selecting a new optical path must take place. This is related to the need to run a routing procedure, which was also included in the algorithm (Figure 19). Figure 19: Optical channel selection algorithm based on DIPP-CIR calculations at two decision thresholds. An EON or flexible DWDM network, e.g., an algorithm including routing, modulation level, and spectrum allocation (RMLSA) techniques [52; 53; 54], can be equipped with an additional computational-decision-making component based on the algorithm shown in Figure 19. This operation not only ensures precise selection of the optical path parameters in the case of a signal in the RFoF format, but can significantly improve the classical transmission in the on-off keying (OOK) format because fast streams also experience the DIPP phenomenon at the location of the photodetector, which lowers the sensitivity of the direct receiver.
## 2 Calorimetric Analysis, Aging and Compatibility Studies on Industrial-Grade Fatty Acid Mixture
### Compatibility between the PCM and Its Capsule  
The second part of this aging study aims to conclude whether the encapsulation significantly alters the thermo-physical properties of the SA-PA mixture. In other words, it is important to know if contact between the polyolefin mixture (polyethylene and polypropylene) constituting the spherical capsules (nodules) and the SA-PA mixture induces significant differences in the latter's storage capacities. Compatibility tests were carried out in the same ovens as the aging tests, i.e., at a fixed temperature, and the time and temperature constraints were also similar. Nodule pieces were sliced and then inserted into the flasks and completely immersed in the PCM. As many flasks as the number of samples needed for analysis were prepared. At the end of the experiment, the flasks were removed from the oven, photographed, and weighed. A sample of PCM was taken for analysis, then the nodule piece was removed from the bottle and photographed. A thorough study of the condition of the nodule would require additional investigations, such as tensile tests, to analyze the evolution of the material's resistance over time according to the working temperature. However, as such analysis was not covered in the frame of this project, only visual and mass observations of the nodule were carried out.  
Figure 3 shows an example of the evolution of the visual appearance of the nodule pieces inserted within the flasks containing the SA-PA mixture. Photographs were taken of nodule pieces from flasks placed at 85 \({}^{\circ}\)C, the highest temperature stress, and removed after 7 days and then 28 days.  
The two nodule pieces showed no visual changes, and therefore do not appear to have been degraded by contact with stearic acid over time. Their irregular shape is due to the fact that they were cut from a spherical nodule, and not to their undergoing plastic deformation due to exposure to high temperatures.  
Figure 4 shows the evolution of the latent heat of fusion and onset temperature of the SA-PA mixture as a function of storage time and temperature.  
The same behavior was observed whether the SA-PA mixture was or was not in contact with a sample of the nodule; that is, the latent heat was stable over the experiment time whatever the temperature, and a decrease in the onset temperature was recorded at 85 \({}^{\circ}\)C.  
No compatibility issue was detected between the SA-PA and the encapsulation material. However, a compatibility study on pure stearic and palmitic acid and various plastics, including polypropylene, underlined the plastic swelling issue, possibly leading to its mechanical resistance degradation [25]. Some caution should then be taken when conducing the experimental campaign on the prototype, monitoring the nodules' integrity.  
Figure 3: Nodule pieces after 7 days (**left**) and 28 days (**right**) in contact with the SA–PA mixture at 85 \({}^{\circ}\)C.
# Soil Moisture Monitoring at Kilometer Scale: Assimilation of Sentinel-1 Products in ISBA
## 5 Discussion  
### Does S1 SSM Perform Better than Other SSM Products?  
The results in Table S1 are consistent with similar studies comparing satellite-derived products with ground-based measurements (e.g., [63; 64; 65; 66; 67]). Table 3 summarizes the S1 SSM validation results of this study and compares them with results from [PERSON] et al. [66] and [PERSON] et al. [67]. The latter results include Advanced Scatterometer (ASCAT), Soil Moisture Active and Passive (SMAP), Soil Moisture and Ocean Salinity (SMOS), and SMOS INRAE-CESBIO (SMOS-IC) products.  
The station-averaged R and ubRMSD values we obtained over Southern France, 0.60 and 0.062 m\({}^{3}\) m\({}^{-3}\), respectively, are consistent with the results of [PERSON] et al. [66] for S1 SSM products at 1 km resolution over this area (0.59 and 0.056 m\({}^{3}\) m\({}^{-3}\), respectively). A key finding of [PERSON] et al. [66] is that the values of these scores are not much affected by spatial resolution, for spatial resolutions ranging from 1 km to 25 km. For the Salamanca region, the S1 SSM R score was not as good, R = 0.48, but the accuracy was similar, with a ubRMSD value of 0.053 m\({}^{3}\) m\({}^{-3}\). The lower score may be due to the fact that some REMEDHUS stations are irrigated from time to time, as reported by ISMN ([[https://ismn.earth/en/news/](https://ismn.earth/en/news/)]([https://ismn.earth/en/news/](https://ismn.earth/en/news/)), accessed on 30 August 2023), or that irrigation is dominant in the 1 km \(\times\) 1 km grid cell while absent over the station. A temporal discontinuity in the in situ SSM was observed during the summer of 2018, triggering a decrease in SSM values. Visual inspection of the Google map image covering the Las Brozas station and of the soil moisture observations from this station showed that surface runoff from a nearby irrigated field can occur. [PERSON] et al. [67] used only selected REMEDHUS stations.  
Figure 10: Year-to-year evolution from 2018 to 2019 over the Alés sub-domain of the mean differences in July between (**a**–**c**) analyzed and open-loop WG5 and (**e**–**g**) LAI, when (**a**,**e**) S1 SSM was assimilated alone, (**b**,**f**) PROBA-V LAI was assimilated alone, and (**c**,**g**) SSM and LAI were jointly assimilated, compared with (**d**) the spatial distribution of irrigation from [PERSON] et al. [51]. The red (blue) color indicates that the analysis added (removed) water to the root zone and increased (decreased) LAI more in 2019 than in 2018 in response to the assimilation.
# Collaborative Facilitation and Collaborative Inhibition in Virtual Environments
## 3 Results
### Effect of Experimental Conditions and Type of Word List on Subjects' Performance  
In order to examine the influence of the experimental conditions (individual, nominal and collaborative condition) and the type of word list (abstract word list--the List A versus concrete word list--the List B) on the subjects' performance, a MANCOVA was carried out. As displayed in Table 3, the general model was significant on the dependent variables, showing an effect of the experimental condition for the studied words, an effect of the type of list for the non-studied words, and an interaction effect (experimental condition * type of list) for non-studied words.  
With regard to the experimental condition affecting the studied words, we found a statistical difference between the collaborative condition and the other modalities (nominal and individual condition). These latter modalities were not significantly different between them. The performance of the collaborative condition appeared to be the highest, showing a greater number of studied words than the other conditions (Collaborative: \(M=8.29\); Nominal: \(M=7.61\); Individual: \(M=7.56\)). Concerning the non-studied words, individual, nominal and collaborative conditions showed no difference between them, revealing the same average number of errors (Nominal: \(M=2.22\); Individual: \(M=2.19\); Collaborative: \(M=2.22\)).  
Regarding the type of list, results showed that the list referring to abstract words (feelings--List A: \(M=2.60\)) elicited significantly more non-studied words than the list concerning concrete words (musical instruments--the List B: \(M=1.83\)), revealing a greater difficulty. It is interesting to note that List A (\(M=7.57\)) showed a non-significant trend in the collection of fewer studied words than List B (\(M=8.07\)).  
The interaction effect (experimental condition * type of list) emphasizes these results. The difference in difficulty between the List A (feelings) and the List B (musical instruments) decreases when the effect is matched with the experimental condition. Particularly, the experimental condition mitigates the effect of the list type on the non-studied words. The difference in difficulty between the two lists decreases from collaborative condition (List A: \(M=2.99\); List B: \(M=1.69\)) to individual condition (List A: \(M=2.54\); List B: \(M=1.60\)) and to nominal condition (List A: \(M=2.25\); List B: \(M=2.21\)). Therefore, we may assume that the difficulty of the list is affected by the experimental condition, and mainly a greater effect of the difficulty of the List A (feelings) is revealed in the collaborative condition.
# Railway Vehicle Wheel Flat Detection with Multiple Records Using Spectral Kurtosis Analysis
## 3 Methodology for Flat Detection  
### Definition of Kurtosis  
The shape and density of the signal distribution can be visualized by moments about the mean [56]. The mean of a set of data \(\overline{x}_{m}\), is calculated through the following equation:  
\[\overline{x}_{m}=\frac{1}{N}\sum_{j=1}^{N}x_{i} \tag{3}\]  
in which \(x_{i}\) is the recorded data sample and \(N\) is the number of samples. Therefore, the \(k\)-th central moment is calculated as:  
\[\mu_{k}=\frac{1}{N}\sum_{j=1}^{N}\left(x_{j}-\overline{x}\right)^{k} \tag{4}\]  
where \(\overline{x}\) is the true mean. Kurtosis is a non-dimensional quantity that measures the relative intensity of the signal compared to a Gaussian distribution. Signals with impulsive-type responses lead to high kurtosis values (\(K\)). It is defined as the fourth central moment (\(\mu_{4}\)) divided by the square of variance \(\mu_{2}^{2}\) as follow [57]:  
\[K=\frac{\mu_{4}}{\mu_{2}^{2}} \tag{5}\]  
For signals containing impulse features, the kurtosis value can be high. Therefore, high amounts of kurtosis indicate that the data include spikes [58]. Since the defect in the wheel causes a spike in the signal, one way to detect the defect in the wheel is to calculate the kurtosis for various frequency bands rather than the original signal.