esa-sceva/satcom-chunk-collection huggingface dataset:
# Designing Hydrogel-Based Bone-On-Chips for Personalized Medicine
## 4 Hydrogel-Based Bone-On-Chips
### Culture Chamber Designs  
The complexity of culture chamber designs in BOCs is intrinsically related to the composition of the tissue. Indeed multi-cellular and multi-tissue interaction can be modelled within each device. In general, single channeldevices are preferred for cell migration studies, where cells can be tracked over time within the hydrogel while applying mechanical [84] or chemical stimuli [85, 86, 87]. Microposts between channels facilitate the identification ofthe region of interests while taking multiple images over time and support the hydrogel stability via surface tension [87; 88]. Moreover, microposts separate culture chamber, thus confining cells and hydrogels in compartments where different culturing conditions can be applied [89].  
The definition of compartments in organ-on-chips is one of the main advantage of microengineered technologies over traditional culture techniques. By means of computer aided design (CAD), it is possible to tune the geometry of each single channel and the way multiple channels communicate with each other [90]. An example of effective culture chamber design in BOC is the one proposed by [PERSON] et al., who studied cancer cell extravasation in bone tissue by creating channels for osteocytes and a lumen. In the osteocyte channel, cells could be selectively stimulated with oscillatory fluid flow, while the lumen channel consisted of a cylindrical hydrogel structure coated with endothelial cells and seeded with breast cancer cells. Moreover, the distance between the osteocyte and the lumen channel was 300 \(\upmu\)m, to recapitulate the physiological distance between osteocytes and effectors cells [91]. Similarly, a bone-marrow-on-chip was recently developed based on two channels separated by a porous membrane. The two channels consisted of a hematopoietic and a vascular compartment. The in vivo functionality of the vascular lumen was replicated by feeding the whole chip exclusively via perfusion through the vascular compartment that was covered with endothelial cells [16]. For the microfabrication details of organ-on-chip devices comprising multiple cell types embedded in hydrogels, we recommend reading the protocol developed by [PERSON] et al. [82], [PERSON] et al. [92] and [PERSON] et al. [93].  
Besides using channels as culture chambers, microengineered system can induce chemical gradients within the culture environment. In addition, microfluidic devices facilitate the application of independent fluid flows for different channels. Thus, a system of two serpentine channels perfused by different cell culture media induced an osteo/chondrogenic gradient within a single hydrogel. In this way, the same hydrogel-based environment induced a differentiation gradient for MSCs mimicking the interface between bone and cartilage [94].
# Removal of Volatile Phenols From Wine Using Crosslinked Cyclodextrin Polymers
## 3 Materials and Methods
### Four-Phase HS-SPME GC-MS Analysis  
A recently developed four-phase HS-SPME GC-MS method was used (with minor modification) to quantify residual volatile phenol levels [35]. An ampoule containing 0.2 mL of model wine solution was spiked with the isotopically labelled standard (\(d_{3}\)-4-methylguaiacol, at 2 mg/L) and inserted into a 20 mL headspace sampling vial (Sigma Aldrich, Castle Hill, NSW, Australia), containing 0.2 mL of the sample to be analyzed. The vial was then incubated for 5 min at 35 \({}^{\circ}\)C. The fiber extraction of headspace aroma compounds occurred over 15 min without agitation. Samples were then analyzed using an Agilent 6890 GC-MS system coupled to a 5973 mass selective detector (Santa Clara, CA, USA), fitted with a Gerstel MPS autosampler (Mulheim, Germany). The column used for GC separation was a 60 m DB-Wax column with 0.25 mm internal diameter and 0.25 \(\upmu\)m film thickness (Agilent J&W, Folsom, CA, USA). Helium (BOC Gas, Adelaide, SA, Australia) was used as the carrier gas at a constant flow of 1.5 mL/min. The inlet temperature was set at 240 \({}^{\circ}\)C. The oven temperature started at 40 \({}^{\circ}\)C for 5 min, then increased to 250 \({}^{\circ}\)C at 3 \({}^{\circ}\)C/min and was held at 250 \({}^{\circ}\)C for 5 min, to give a total run time of 80 min. Selected Ion Monitoring (SIM) mode was used to record the mass spectra of target ions. The ions monitored in SIM mode were: _m/z_ 109, 124 for guaiacol; _m/z_ 123, _138_ for 4-methylguaiacol; _m/z_ 126, _141_ for \(d_{3}\)-4-methylguaiacol; _m/z_ 122, 137, _152_ for 4-ethylguaiacol; and _m/z_ 77, _122_ for 4-ethylphenol; with italicised ions used for quantitation. Volatile phenol concentrations are reported as relative peak areas (RPA), i.e., as the ratio of the peak area of the analyte relative to the peak area of the normalising standard (\(d_{3}\)-4-methylguaiacol).
###### Abstract
Numerous investigators have discovered that the photochemical reflectance index (PRI), defined as the normalized difference index utilizing narrow band reflectance at 531 and 570 nm [29], often exhibits a negative connection with NPQ, which might describe the epoxidation status of xanthophyll pigments [30]. The variations in PRI can accurately represent the changes in NPQ generated by the xanthophyll cycle in response to the external environment because of sluggish adjustments in the pigment pools at diurnal timescales. Under the premise that some NPQ information is provided by the PRI, it makes sense to study the influence of PRI on SIF-based GPP estimation. [PERSON] et al. [31] found that the PRI is sensitive to the soil water content and can be used to improve SIF-based GPP estimation. [PERSON] et al. [27] used MODIS PRI, OCO-2 SIF observations, and contemporaneous flux data from EC locations to evaluate the value of PRI for SIF-based GPP estimation. Using tower-based observations, [PERSON] et al. [32] improved the carbon flux estimates in deciduous and evergreen forests by combing the normalized difference vegetation index (NDVI), the PRI, and the quantum yield of SIF. In addition, at seasonal timescales, changes in the structural factors (such as the leaf area index) and foliar pigment concentration can also contribute to the differences in PRI [33]. Some studies have reported that the seasonal-corrected PRI can be a good indicator of the dynamics of photosynthetic status under various environmental conditions and can be used to improve GPP estimation [13; 29; 34]. By eliminating the effects of foliar pigments and the LAI, the highly temporally resolved ground-based PRI can be an indicator of light use efficiency in various environmental conditions [28; 29; 34; 35]. In addition, [PERSON] et al. [34] used the delta PRI (\(\Delta\)PRI) derived from the midday PRI (xanthophyll de-epoxidation state) and the early morning PRI (xanthophyll epoxidationstate) and found that the highly temporally and spatially resolved \(\Delta\)PRI could be used to track plants' responses to changing environmental conditions. Although the combination of SIF and PRI can quantify GPP based on ground, airborne, or satellite observations [27; 31; 32; 36], the potential impacts of weather conditions on GPP estimation using 'corrected' SIF and the 'corrected' PRI have yet to be investigated with tower-based measurements. In this study, we examined the impacts of weather conditions on GPP estimation using \(SIF_{TOT\_FULL\_PSII}\) (the broadband photosystem-level SIF for photosystem II) and \(\Delta\)PRI (formed of the early morning and midday PRI) and explored the usefulness of combining weather conditions to improve soybean GPP estimation. The specific issues addressed in detail included the following:  
1. How successful is GPP estimation using SIF\({}_{TOT\_FULL\_PSII}\) and \(\Delta\)PRI? 2. How do the weather conditions affect GPP estimation using SIF\({}_{TOT\_FULL\_PSII}\) and \(\Delta\)PRI? 3. Is it possible to improve GPP estimation accuracy using SIF\({}_{TOT\_FULL\_PSII}\) and \(\Delta\)PRI by considering the weather conditions?
# Strengthening Design of RC Columns with Direct Fastening Steel Jackets
## 3 Design Procedure
### Estimating ALR of Strengthened RC Columns  
The confined concrete divisions are shown in Figure 8a. Confined concrete 1 is confined by the erected steel jacket. Confined concrete 2 is confined by the stirrup. Confined concrete 3 is confined by the stirrup and erected steel jacket. Because the area of confined concrete 1 and confined concrete 2 is relatively smaller than that of confined concrete 3, and these two areas are close, the confined concrete divisions can be simplified in the design calculation. The simplified concrete divisions are shown in Figure 8b, in which the confined effect of confined concrete 2 is imposed on confined concrete 1. Thus, the initial three varieties of confined concrete can be equivalent to one confined concrete that is confined by a stirrup and steel jacket. For the confined concrete in Figure 8b, the imposed equivalent passive confinement stress is \(f_{l}=f_{est}+f_{ed}\), which can be determined according to (3). Using the confined concrete strength model in (5), the confined concrete strength (\(f_{cc}\)) can be determined. Thus, the axial load capacity is given as:  
\[N_{c,stren}=\frac{A_{cc}f_{cc}^{\prime}+A_{c0}f_{c}^{\prime}}{\gamma_{c}}+ \frac{(4m-4)A_{l}f_{yl}+4P_{p,critical}}{\gamma_{s}} \tag{30}\]  
where \(A_{cc}\) is the area of confined concrete in Figure 8b; and \(A_{c0}\) is the area of the unconfined concrete in Figure 8b.  
The confined concrete area \(A_{cc}\) and unconfined area \(A_{c0}\) are respectively given as:  
\[A_{cc}=d_{c}^{2}-4\frac{d_{l}^{2}}{6}-(4m-4)A_{l} \tag{31}\]  
\[A_{c0}=4\frac{d_{l}^{2}}{6} \tag{32}\]  
where \(d_{l}\) is the edge-to-edge horizontal distance of the connections, as shown in Figure 8b.  
The ALR is given as:  
\[\text{ALR}=\frac{N_{0}}{N_{c,stren}} \tag{33}\]  
According to the recommendation in EN 1998-1:2004 [34], the ALR must necessarily be less than 0.65.
## 4 Simulation and Analysis
It can also be seen that, although the observation of the study [46] can quickly observe the external disturbance, there is still a small inconsistency in the observation results, which does not completely achieve the accurate estimation of the external disturbance. On the contrary, the observation results of the FDOCAM control algorithm, whether in the initial stage of the simulation, or the disturbance of a sudden change, the FDO designed in this paper does not appear to be a sudden change, and there is no overshooting phenomenon, and the observation error can be stabilized near the zero domain without chattering; although the algorithm's disturbance observation error is exponentially convergent, the speed of convergence is not worse than that of the study [46], and in Figure 9,c, the convergence speed of the FDOCAM is more rapid. Figure 8: Anti-saturation auxiliary system compensation states change results with disturbance observer. (**a**) denotes the amount of compensation state \(\lambda_{1}\). (**b**) denotes the amount of compensation state \(\lambda_{2}\).
## 2 SE-TransUNet Water Seepage Detection Model
### Encoder of Water Seepage Recognition Model  
The encoder component of the model primarily integrates ResNet50 and Vision Transformer (ViT). ResNet50, a classical residual network, mitigates the vanishing gradient problem in deep network training through residual blocks, enabling effective training with increased convolutional layer depth. Tunnel leakage scenarios exhibit complex and diverse features that require deep networks to hierarchically extract low-level to high-level features. The residual architecture of ResNet50 ensures effective deep feature learning, providing high-quality foundational feature inputs for subsequent modules (e.g., ViT).  
Upon image input, features are initially processed through ResNet50's first three convolutional layers for progressive local feature extraction, followed by deep feature extraction via the Vision Transformer module. This establishes an encoding paradigm where ResNet captures multi-scale local features while ViT extracts global semantic representations, as depicted in the left structure of Figure 1.  
ResNet50 Submodule Analysis  
In the leakage identification encoder, ResNet50 is divided into four submodules (encoder1-encoder4). Taking a 512 \(\times\) 512-pixel RGB image as an input example, the output feature dimensions, and channel counts of each submodule are specified in Table 1.
# A Dual-Beam Leaky-Wave Antenna Based on Squarely Modulated Reactance Surface
###### Abstract  
In this paper, a novel dual-beam leaky-wave antenna (LWA) based on squarely modulated reactance surface (SquMRS) is proposed. The equivalent transmission lines model is utilized to characterize the field distributions of surface wave guided by the SquMRS. The calculated dispersion characteristics of SquMRS are verified by the simulated results, and it is demonstrated that SquMRS exhibits a more flexible control of phase constant and attenuation constant compared with traditional sinusoidally modulated reactance surface (SinMRS), which means SquMRS has a great potential for near-field focusing and far-field beam shaping. On this basis, a versatile method, based on a superposition of individual modulation patterns, was used to generated two beams with almost identical gain at 8.5 GHz. The measured results show that the gains are 10 dBi and 8.2 dBi at \(\theta_{1}=-30^{\circ}\) and \(\theta_{2}=18^{\circ}\), respectively, and the radiation efficiency is 83%, which shows good agreement with the simulated results.  
leaky-wave antenna (LWA); squarely modulated reactance surface (SquMRS); surface wave +
Footnote †: journal: Applied Sciences  
_Article_  
A Dual-Beam Leaky-Wave Antenna Based on  
Squarely Modulated Reactance Surface
## 3 Results and Discussion
### Processing Overhead  
The system's processing time was analyzed to examine the processing overhead. The system took an average of 3 min to perform all the steps, namely, preprocessing, segmentation of the brain image, and colorization. A processing time of below 3 min was considered negligible for providing a colorized image for diagnosis.
## 3 Results
### CFD Simulation Results
#### 3.1.2 Velocity Fields under Different Welding Positions  
The velocity fields for the flat welding position and the climbing welding position are shown in Figures 8 and 9, respectively, when the welding processes have reached the quasi-steady state.  
For the flat welding position, there exist high velocities downwards at the central part of the weld pool, which indicates the molten metal flows appreciably in thickness direction under the joint action of surface tension, gravity and arc pressure. Obviously, such a flow pattern is favorable to the increase in weld depth, while too significant downward fluid flow may result in defects such as excessive weld sagging and burn-through holes, and therefore should be avoided.  
Compared to the flat welding, the fluid flow in the sheet thickness direction is weakened when welding with the climbing position, while the fluid flow afterwards (in -x direction), or in the lateral direction, is reinforced under the action of gravity. The velocity is the maximum in the lateral direction afterwards, indicating the fluid flow is the more significant in this direction than that in the sheet thickness direction for the welding conditions studied. Such a flow pattern leads to a longer and wider top surface of weld pool and shift the weld pool slightly behind the flat case, as shown in the preceding figures.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Positions of Measurement**} & \multicolumn{2}{c}{**Climbing Welding (Start end)**} & \multicolumn{2}{c}{**Flat Welding (Finish end)**} \\ \cline{2-5}  & **Length, mm** & **Width, mm** & **Length, mm** & **Width, mm** \\ \hline Top surface & 12.3 & 10.1 & 10.6 & 9.3 \\ Bottom surface & 9.9 & 8.5 & 9.5 & 8.4 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Dimensions of weld pools numerically predicted for flat and climbing welding positions.  
Figure 8: _Cont._Figure 8: Velocity fields in the weld pool for flat welding (unit: m/s): (a) whole model; and (b) magnified melt pool region.  
Figure 9: _Cont._
# Intelligent Reflective Surfaces for Wireless Networks:
## 5 Practical Challenges, Open Issues and Future Research Lines
### Practical Challenges
#### 5.1.1 Channel State Acquisition  
The IRSs constitute a large array of reflective passive elements, i.e., a passive communication element. The enhancements provided by IRS-aided systems relies on the proper configuration of the IRS phase-shift matrix according to the knowledge of the channel conditions. The channel state acquisition, by considering an IRS as a totally passive element, is usually performed at the communication ends, i.e., the transmitter or the receiver. This leads to degradation in the accuracy of channel estimation, and high computation and power requirements when considering large IRSs. A practical and efficient channel estimation strategy is still one of the key issues to enable IRS technologies. A possible solution may be the use of low-power sensors --which can be powered by energy harvested modules--embedded through the IRS.
# Damping Estimation of an Eight-Story Steel Building Equipped with Oil Dampers
###### Abstract  
The damping estimation of an eight-story steel building equipped with oil dampers is examined, carried out by adopting a proposed framework, which consists of an enhanced strain-energy method and an improved direct method for model updating. The building is located at Tohoku Institute of Technology and is equipped with a structural monitoring system that measures its seismic response, including floor acceleration and displacement and force of oil dampers. The enhanced strain-energy method is first developed and employed to assess the supplemental damping and stiffness provided by oil dampers, herein quantified in the form of equivalent damping ratios and natural frequencies. Then, modal characteristics extracted from the earthquake measurements are modified accordingly and utilized for the building model updating, in which mass and stiffness matrices are corrected by the improved direct method. The updated model accurately reproduces the target modal data, especially measured mode participation factors, and is further used for the building response predictions. Through prediction validations, the precision of the modified modal parameters is verified. Finally, a large earthquake event is chosen to demonstrate the effectiveness of the proposed framework for the damping estimation of the investigated building.  
equivalent damping ratios; strain-energy method; direct updating method; oil dampers; earthquake measurements +
Footnote †: journal: Applied Sciences
## 3 Magnetic Characterization Instruments
### Hall Effect Magnetometers  
Another category of magnetometers is the Hall effect magnetometers. These devices present a high level of noise but can characterize the nanoparticles with accuracy, and the distance between the sensor and the target is small. In addition, they have a small dead time, less than 1 \(\upmu\)s, as stated in [38], which presents a novel CMOS Hall effect sensor chip for magnetorelaxometry measurements with a dead zone of only 64 ns, in comparison to SQUID and Fluxgate magnetometers, which have dead zone times greater than 300 \(\upmu\)s. Obtaining a small dead zone time provides the sensor the ability to characterize a great variety of nanoparticles. In [60], a novel Hall effect magnetometer is proposed, with various sensitivities, which can characterize both bulk materials and samples inside liquids. In [88], a Hall magnetometer with two stages is proposed for the sensitivity of the detection to be increased. In [89], a planar Hall effect magnetometer is studied for maghemite nanoparticle characterization. In [90], a modular Hall magnetometer is under for detecting the properties of iron oxide, nickel, and cobalt ferrite nanoparticles. In [91], iron oxide nanoparticles are also characterized using Hall magnetometers. Finally, in [92], a novel 3D Hall effect magnetometer is proposed for magnetic measurement purposes.
# Channel Model for Estimating Received Power Variations at a Mobile Terminal in a Cellular Network
## 3 Experimental Measurements Conducted in a Cellular Network
### Statistical Inference and Data Variability Assessment  
To analyze the behavior of the received signal strength indicator (RSSI) across different measurement scenarios, a statistical analysis was performed. Descriptive statistics were first computed to summarize the central tendency and dispersion of the data. Specifically, the arithmetic mean was used to characterize the typical signal level observed in each zone or sector, while the standard deviation quantified the signal variability around that mean, providing insight into fluctuations due to user mobility, antenna alignment, and propagation effects [2; 14; 15; 16].  
To evaluate whether the observed differences between measurement groups (e.g., inter- and intra-handover zones or different base station sectors) were statistically significant, an inferential statistical framework was applied. A one-way analysis of variance (ANOVA) was conducted to compare the group means and determine if any statistically significant variation existed across conditions. The ANOVA test computes the F-statistic by assessing the ratio between the variance among group means (between-group variability) and the variance within groups (within-group variability). A significance level of \(\alpha=0.05\) was used as the decision threshold.  
Figure 11: Model of a three-sector base station.  
When the ANOVA indicated significant differences, post hoc pairwise comparisons were conducted using [PERSON]'s Honestly Significant Difference (HSD) test [14,17].  
This method constructs simultaneous confidence intervals for all pairwise mean differences, accounting for multiple comparisons and controlling the family-wise error rate. [PERSON]'s test helped isolate which specific groups (e.g., particular sector pairs or handover zones) exhibited statistically significant differences in the average RSSI, thus refining the understanding of signal dynamics during mobility and cell transitions.  
These analyses were implemented in RStudio (version 2023.06.2+561), using the built-in aov() function to perform the analysis of variance (ANOVA) and the TukeyHSD() function for post hoc testing. This statistical pipeline enabled the extraction of meaningful insights into the spatial and procedural factors that influence signal reception in mobile networks, thereby supporting the development of data-driven optimization strategies for handover and coverage planning.