esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Test Results
### Structural Stress  
There are three couples of strain gauges close to each opening, to investigate the stress state and influence range under different buried depths. As shown in Figure 7, the three couples of strain gauges were located at the hance of the semi-split ring, the first adjacent ring, and the second adjacent ring, denoted as R0, R1, and R2, respectively. For instance, the semi-split ring and the second adjacent ring of the small circular opening are denoted as SC-Op-R0 and SC-Op-R2.  
The results of the inner and outer stress are plotted in Figure 13, where the positive stress values represent tensile stress and the negative values represent compressive stresses.  
It can be found that the stresses at the inner surface are compressive, and increase with the buried depth. The inner stresses at the semi-split rings (R0) are much greater than those at the adjacent rings (R1, R2), which demonstrates that significant stress concentrations occurred at the semi-split rings. The inner compressive stresses at the semi-split ring of the big circular opening are 31.7 MPa and 41.9 MPa under the buried depths of 1.5 D and 2.0 D, respectively, which exceed the design compressive stress of C60 concrete of 27.5 MPa, meaning that the concrete segment may collapse. The inner compressive stress at the semi-split ring of the small circular opening is 32.3 MPa, also exceeding the limit strength. Thus, in practical engineering, higher-strength materials or other reinforcement measures should be adopted for segments around the openings to avoid the risk of structural damage or even instability due to stress concentration.  
For the stresses of the outer surface, the variations are complex. For the adjacent rings (R1, R2) of the three openings, the stresses at the outer surface are positive, demonstrating the tensile state, and the tensile stresses increased slightly with the increase of the buried  
Figure 13: Variations of the structural stress.  
depth. For the semi-split rings (R0), on one hand, the tensile stresses may increase due to the increase of the buried depth; on the other hand, the concentration due to the opening may increase the compressive stresses, so the outer stresses do not vary linearly with the buried depth. However, it should be noted that the absolute values of the outer stress are much smaller than the inner stresses, so more attention should be paid to the inner stress.
# Sustainable Approach of Generating Water and Energy:
## 3 Mathematical Model
### Solution Algorithm  
The proposed hybrid system was evaluated using typical Riyadh, Saudi Arabia weather conditions. Figure 5 depicts in detail the solution procedures utilized in this study. The first step in the simulation is to estimate the hourly electricity and hot water production from the PVT. In this case, the electricity production obtained from the PVT is dispatched to the house for immediate use or sold to the grid if the electricity production is more than house electric demand. The hot water stream is supplied from the storage tank to the DCMD unit, and the portable clean water is sent to residents. The rejected cold water comes from the DCMD is sent back to the chiller to be used for a regenerative purpose. The end goal of this simulation is to optimize the hybrid PVT size by minimizing the annual operating cost and maximizing water productivity. In this case, the proposed hybrid system was optimized using MATLAB built-in function gamultiobj to minimize the total operating cost expressed in Equation (15) and maximize the water yields from the DCMD.  
The inputs parameters used to solve the mathematical model is illustrated in Table 3. The flow rate to the DCMD and hot and cold-water temperature varies from maximum and minimum values. Similarly, the number of PVT panels connected in serious and parallel configuration ranges from 5 to 25. These values have a tremendous impact on the levelized cost of water \(\frac{5}{m^{3}}\). Thus, these variables were optimized to minimize the total operating cost. The ambient conditions implemented in this study are presented in Figure 6.  
Figure 5: Algorithm diagram for solution.  
\begin{table}
\begin{tabular}{c} Varies depend on \\ weather conditions \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 3: Parameters used in the simulation.  
Figure 6: Weather condition for the selected location showing Global horizontal irradiance vs. ambient temperature.
## 3 Results and Discussion
### Methylene Blue Removal
#### 3.2.1 Particle Size  
Particle size is known to be one of the most important determinants in adsorption mechanisms [38]. Thus, the MB removal of the biosorbents was tested for five particle size ranges (<106 \(\upmu\)m, 106-212 \(\upmu\)m, 212-300 \(\upmu\)m, 300-425 \(\upmu\)m, 425-600 \(\upmu\)m), apart from LD, which was only available at <212 \(\upmu\)m. Figure 2 shows the effects of particle sizes of biosorbents (RS, WW, LD, HH, and CH) on the removal efficiency and adsorption capacity of MB. For all samples, increasing particle size had a significant impact on MB removal efficiency (Figure 2). MB removal efficiency was reduced from ~97 wt.% for CH at <106 \(\upmu\)m particle size to 66 wt.% at 425-600 \(\upmu\)m particle size. Increasing particle size had the least impact on WW adsorption efficiency (25 wt.% to 18 wt.%), but WW also had the lowest adsorption efficiency of all the samples. This decreased adsorption capacity with increasing particle size can be attributed to the decrease in available surface area with increasing particle sizes [88]. The larger particles cause the more internal diffusion resistance, which limits the mobility of MB molecules into the internal surface of the particles [89]. The results of the particle size effects on MB removal efficiencies showed that a particle size below 106 \(\upmu\)m should be used for all subsequent experiments to ensure maximum MB uptake.
## 3 Proposed Method
### Causal Network Learning
#### 3.2.2 Adjacent Local Causal Network Construction  
To identify the factors that have a key impact on the target, it is natural to search the adjacent local causal network of the target. The Markov blanket [41,43] is the most typical. For the convenience of description, the following definition is given first:  
**Definition 1**.: _Causal Operation Criterion--For causal variables \(A\) and \(B\), it is assumed that the experimenter can manipulate variable \(A\) by setting its value to \(a_{e}\), denoted as \(do(A=a_{e})\). If the experimenter observes that \(P(B|do(A=a_{e}))\
eq P\Big{(}B|do\Big{(}A=a_{f}\Big{)}\Big{)}\) for some \(e\) and \(f\) (within the time window)\(dt\), it indicates that \(A\) is the cause of \(B\) (within)\(dt\)._  
**Definition 2**.: _Direct and Indirect Cause--If \(A\) is the cause of \(B\) according to Definition 1, then \(A\) is an indirect cause of \(B\) with respect to a set \(\mathcal{C}\), if and only if some assignment of \(A\) to \(C-\{A,B\}\) (by operation) is not a cause of \(B\). Otherwise, \(A\) is a direct cause of \(B\)._  
According to the above definitions, for the target variable \(t\), some variables in the global causal network are its direct causes and others are its indirect causes. Combined with the Markov blanket, the adjacent local causal network of the target variable \(t\) can be constructed as follows:  
Step 1: Choose the target variable \(t\) and obtain its Markov blanket \(\mathcal{J}\).  
Step 2: Determine the order \(k(k\geq 2)\) of the adjacent local causal network.  
Step 3: Starting from the target variable \(t\), search its direct and indirect causes within \(k\) steps. Among them, the causal pathway length between the target and its direct cause is defined as 1.  
Step 4: Take the target variable \(t\) and its direct and indirect causes found in Step 3 as nodes, along with the edges between them, to build the adjacent local causal network.
## 4 Results
### Correlation Analysis
#### 4.3.2 Pre-Test and Post-Test Correlation  
As shown in Table 5, the pre-test and post-test stress scale data exhibit high correlations with statistical significance. In the Pearson correlation analysis of the same subscales in Stage 1 and Stage 2, STAI-S (r = 0.770), STAI-T (r = 0.948), and the total STAI score (r = 0.916) demonstrated strong correlations, indicating that changes in state anxiety (STAI-S), trait anxiety (STAI-T), and total scores were highly consistent. Similarly, children's state anxiety (STAI-C S, r = 0.776) and trait anxiety (STAI-C T, r = 0.934) also showed strong correlations, suggesting that children's state and trait anxiety exhibited consistent patterns of change before and after the intervention.  
Notably, the highest correlations were observed between STAI-T and STAI-C T, at 0.948 and 0.934, respectively, indicating that post-intervention changes in trait anxiety were remarkably stable and closely aligned with pre-intervention measurements. In contrast, the correlations for state anxiety (STAI-S and STAI-C S) were relatively lower, possibly suggesting that state anxiety is more susceptible to short-term intervention effects, leading to less consistent changes between pre-test and post-test measurements.  
Additionally, the statistical significance of the pre-test and post-test correlations further validates the reliability and consistency of the experimental data, demonstrating that the research design captured psychological state changes before and after the intervention, with minimal influence from random noise.
# Offline-Online Change Detection for Sentinel-1 InSAR Time Series
## 2 Methods  
### InSAR Processing and Time Series Analysis  
We downloaded 257 Sentinel-1 single look complex (SLC) images from descending track 81 acquired between January 2015 and May 2020 over the Hatfield Moors test site in the UK (Longitude, latitude: \(-1.01,53.58\)), and performed initial InSAR processing by forming interferograms with every 2 consecutive pairs of images using the Interferometric synthetic aperture radar Scientific Computing Environment (ISCE) software [26, 27]. To improve the signal-to-noise ratio we multilooked each image by 9 pixels in range and 3 pixels in azimuth giving pixel sizes of approximately 50 m.  
We processed the resulting 509 small baseline interferogram stack using the Miami INsar Time-series software in PYthon (MintPy) [28, 29] to produce a geocoded line-of-sight displacement time series for every pixel in the dataset. The average spatial coherence history of the interferograms shows a clear annual signal with generally poor coherence during the summer months (Figure 2). This is probably due to decorrelation from atmospheric signals, surface water in the peat and actively growing vegetation, which incoherently scatters the radar waves. We therefore removed interferograms with an average coherence less than 0.45, unless it was required to maintain network connectivity, which resulted in the removal of 85 interferograms. We applied corrections for atmospheric noise using ERA5 reanalysis weather model data according to the method of [PERSON] et al. [30], and used the SRTM 30m digital elevation model [31] to estimate the topographic error. Additionally we remove a ramp from each interferogram to remove any remaining long wavelength trends. Mintpy does not apply any spatial filters but uses snaphu connected components as a guide for the time series inversion [28, 32]. We estimate and correct unwrapping errors in the interferograms using the bridging and phase closure method functionality in MintPy [28].  
Figure 1: Location of the Hatfield Moors test site. The main figure is the land cover classification for the area using Sentinel-2 satellite imagery [22], which shows the study site is mostly peatbog surrounded by agricultural fields. The south and western edges of the site are bounded by lakes and ponds.
## 2 Materials and Methods
### Mathematical Representation of the Spectrum Sensing Scenario  
The spectrum sensing scenario that we consider can be mathematically represented as:  
\[\mathbf{y}=\begin{cases}\mathbf{w}&\text{if }H_{0}\\ \mathbf{x}\cdot e^{i(2\pi f_{\Delta}n+\phi)}+\mathbf{w}&\text{if }H_{1}\end{cases} \tag{1}\]where \(\mathbf{y}\in\mathbb{C}^{N}\) represents the vector of received samples; \(\mathbf{x}\in\mathbb{C}^{N}\) represents the vector of transmitted signal samples; \(f_{\Delta}\) represents the carrier frequency offset (CFO) expressed in terms of data rate; \(n\) represents the zero-based array index; \(\phi\) represents the phase offset in radians; \(\mathbf{w}\in\mathbb{C}^{N}\) represents the vector of independent and identically distributed additive white Gaussian noise (AWGN) samples, where \(\mathbf{w}\) follows a complex Gaussian distribution \(\mathbb{C}\mathcal{N}(\mathbf{0},\sigma^{2}\mathbf{I})\); \(H_{0}\) denotes the null hypothesis indicating the vacancy of the channel; and, finally, \(H_{1}\) denotes the alternative hypothesis representing channel occupancy.
# Six Sigma-Based Frequency Response Analysis for Power Transformer Winding Deformation
## 3 Results--Verify Phase
### Case 2
#### 3.2.2 Case 2: Analysis of 1000-10,000 Frequency Range (Hz)  
This range is critical in identifying clamping pressure loss, bulk winding movement, and mechanical displacement, as highlighted in Table 7. Deviation in this region often indicates winding instability, which could lead to mechanical stress and insulation damage.  
Figure 12a reveals a slight downward deviation in the measured FRA response compared to the baseline, suggesting possible bulk winding movement. However, the UCL and LCL boundaries remain intact, preventing immediate fault classification.  
A closer look at Figure 12b indicates that the \(C_{p}\) and \(C_{pk}\) values range between 1.33 and 1.50, classifying the transformer as being in an acceptable yet monitored condition. As per Table 4, this range implies that trends should be closely observed for any further degradation. The \(\overline{R}\)-chart also confirms a gradual increase in variability, reinforcing the hypothesis of incipient clamping pressure loss. Regular FRA testing is recommended to track any further shifts in the winding structure.  
Remarks: It should be noted that the transformer is exhibiting progressive winding displacement and potential clamping pressure loss, which requires scheduled inspection before these mechanical instabilities worsen.
## 3 Results and Discussion  
### Structure Description  
**Cu(AZPY)-MOF** has previously been reported on [47], but we newly synthesized it via a solvothermal reaction (see the Experimental section) to investigate its antifungal activities. The structure of **Cu(AZPY)-MOF** is similar to [Cu\({}_{2}\)(Glu)\({}_{2}\)(bpe)] [40]. Glutarates bridge paddle-wheel Cu\({}_{2}\) dinuclear units to form 2D layers, and these layers are connected by AZPY to form a 3D framework (Figure S1) while bpe was used for connecting 2D sheets for 3D [Cu\({}_{2}\)(Glu)\({}_{2}\)(bpe)]. The morphology of **Cu(AZPY)-MOF** was investigated by scanning electron microscopy (SEM), as shown in Figure 1. The number of solvent water molecules was estimated from thermogravimetric analysis (TGA) (Figure S2). The weight loss of 8.26% was attributed to three solvent water molecules (calc. 8.63%), and **Cu(AZPY)-MOF** is formulated as [Cu\({}_{2}\)(C\({}_{5}\)H\({}_{6}\)O\({}_{4}\))\({}_{2}\)(C\({}_{10}\)H\({}_{8}\)N\({}_{4}\))].3(H\({}_{2}\)O). **Cu(AZPY)-MOF** is stable up to 271\({}^{\circ}\), and it is decomposed with a weight loss of 49.51%, which may attributed to glutarates (calc. 41.6%) [39]. The purity of the as-prepared **Cu(AZPY)-MOF** was confirmed by powder X-ray diffraction (PXRD), as shown in Figure 2. The intensities of diffraction planes of (2 0 0), (3 1 0), (2 2 0), (3 1 -1) and (6 0 1) at 20 = 7.22, 12.769, 15.392, 16.099 and 24.776\({}^{\circ}\), respectively, were maintained in those of samples after 1 day in H\({}_{2}\)O and PBS as well as the as-prepared sample. This result indicates that **Cu(AZPY)-MOF** is stable in water and PBS solution.  
In addition, MOF degradation tests were performed in H\({}_{2}\)O and PBS to confirm the stability of **Cu(AZPY)-MOF**. The amount of Cu\({}^{\text{II}}\) ions released from **Cu(AZPY)-MOF** was measured by inductively coupled plasma mass spectrometry (ICP-MS) after 1 day and 4 days (Figure S3). The amount of Cu\({}^{\text{II}}\) ions released from **Cu(AZPY)-MOF** after 1 day was 2.34 \(\times\) 10\({}^{-4}\) mg/mL, which is negligible compared to the 99% inactivating property of 0.5 mg/mL.
# Thin-Walled CFST Columns for Enhancing Seismic Collapse Performance of High-Rise Steel Frames
## 3 Seismic Performance of the Thin-Walled SMRFs  
### Building Models  
Extensive high-rise steel buildings have been constructed since 1965, especially during the construction-boom period of high-rises in the 1980s. CFST columns were initially employed in high-rise steel buildings in Japan [2]. Figure 4 shows a tendency of the various structural systems for high-rises based on 300 building cases higher than 100 meters constructed from 1966 to 1998 in Japan. The CFST system shows a stably ascending tendency since 1981. In addition, the HSS columns in the high-rise buildings higher than 80 meters usually filled with concrete but this was not accounted as CFST buildings. Thus, the realistic number of existing high-rise CFST buildings should be higher than what is presented in the statistical database as provided in Figure 4.  
Generic existing high-rise steel moment-resisting frames with 20, 30 and 40 story levels are selected as the prototypes, as shown in Figure 5. The damping factor of the building models is simply assumed as an identical value of 2% in order to eliminate the influence of the varying damping factors on the seismic responses of structures. The slab composite action for beam components, the P-\(\Delta\) effect and strong-column weak-beam are considered in the structural design. The high-rise building models are seismically designed according to current Japanese seismic design standards [27]. The design base shear coefficient is obtained based on the standard shear coefficient of the seismic demand of buildings, the seismic intensity of areas, and the types of ground soils. According to the statistical database on high-rises of steel and CFST buildings, steel materials of SM490 and SN490 are the most frequently used in existing steel office buildings, therefore the steel material SM490 and SN490 with the nominal yield strength of \(\sigma_{y}=320\) MPa and the ultimate tensile strength of \(\sigma_{u}=490\) MPa are used in the modelling of high-rise building models in this paper. Besides, the concrete with the compressive strength of 60 MPa is used in the composite slabs and CFST columns. Design procedures include, the allowable stress method and time-history dynamic analyses subjected to moderate rare earthquakes controlled by peak ground velocity (PGV). The input ground motions for the dynamic design include Art-Hachi as a synthetic wave, El-Centro-NS, Hachinohe-NS and Tafe-EW as observed waves. Besides, CHB009-NS and C-San-EW waves are long-period ground motions with the intensity of a maximum capacity earthquake (Level 2).
# 3-D Printed Fabry-Perot Resonator Antenna with Paraboloid-Shape Superstrate for Wide Gain Bandwidth
## 3 Design of the Proposed FPRA with Wide Gain Bandwidth  
### Configuration of the Proposed FPRA  
As shown in Figure 2, the proposed FPRA is mainly composed of a metallic grating superstrate with a paraboloidal shape, a ground plane, and an open-ended feeding circular waveguide acting as its excitation antenna. The FPRA is fed from a WR-159 standard rectangular waveguide, and a transition is connected between the circular and the rectangular waveguides. The circular superstrate diameter and thickness are \(L\) = 200 mm and thickness \(t\) = 2 mm. The width of the metallic strip and the spacing distance between two adjacent metallic strips are \(W\) = 5 mm and \(P\) = 20 mm, respectively. The heights of the cavity and the paraboloid-shape superstrate are \(H_{c}\) and \(H_{p}\), respectively. The radius and insertion length of the feeding circular waveguide are \(R_{f}\) = 14.5 mm and \(H_{f}\) = 5 mm, respectively. The working frequency of the proposed FPRA is 6 GHz, and thus its diameter \(L\) of 200 mm corresponds to an electrical dimension of \(4\lambda\), where \(\lambda\) is the wavelength at 6 GHz in the free space.  
Figure 1: Schematic view of the ray tracing model for Fabry–Pérot resonator antenna.  
Figure 2: \(Cont.\)
## 3 Proposed Channel-Quality Aware Query Tree Algorithm  
This study proposes the Channel-quality Aware Query Tree algorithm (CAQT) to minimize the effect of the rapidly changing channel quality on estimating number of the tags. The CAQT is based on query tree protocol and can be characterized by three features:  
1. it estimates PER in each slot including the collision resolution process in order to improve the accuracy of the tag estimation in the rapidly changing channel quality environment;
2. when a reader receives an unreadable message from tags, it then adopts the most efficient strategy, which is to ask the tag to retransmit it or to split the collide tags, after evaluating the efficiency of regarding this event caused by channel error and that of treating it as a collision event;  
Figure 5: The mechanism of ERE-ABS.  
the number of the group which it splits is based on the estimated number of tags collide in current slot in order to accelerate the tag identification process.
## 3 Materials and Methods
### Method for the Generation of High Temporal Resolution Time Series
#### 3.3.2 Integration of Cloud-Affected Landsat Scenes  
[PERSON] _et al._[3] recommended using a large number of temporally well-distributed input Landsat data in ESTARFM in order to rely on high-resolution observations that are as close as possible to the prediction dates. High-resolution images that are temporally too far from the prediction dates are expected to decrease the quality of the output, particularly if input scenes are from another vegetative season as compared to the prediction date [3]. However, previous ESTARFM studies either restricted the input data to clear-sky high-resolution images or, when integrating partially cloud-covered scenes, these approaches produce gaps in the resulting synthetic images [5; 8]. This substantially hampers the utility of the ESTARFM algorithm in regions of high cloud cover. In this study, an automated gap-filling approach was implemented to make best use of available, even partly cloud-covered Landsat images, without producing gaps in the output of ESTARFM. The gaps are filled with ESTARFM predictions (Figure 4) based on the other modifications introduced in the framework and described as follows.  
In a first step of this gap filling, the first target scene with cloud gaps to be filled is selected. The shoulder scene combination (SSC), _i.e._, one Landsat image before and one after the target scene, fulfilling two conditions is identified: (a) the temporal distance between these shoulder scenes has to be as small as possible; and (b) an ESTARFM prediction derived from this SSC has to fill at least 50% of the gaps in the target scene. The selected SSC is used to generate an ESTARFM prediction and fill the target scene. If after that the residual gaps make up less than one percent, the next cloud-affected scene is processed. If the gaps are still larger than one percent, the first step is repeated based on the remaining SSC candidates. If after this second iteration the residual gaps in the target scene is still above one percent, the SSC is selected that can fill the residual gaps so that the target scene is at least 99% gap free. This way, a maximum of three synthetic scenes have to be generated to fill one target scene and the filling is completed in a reasonable amount of time.  
Figure 4: Workflow of the automatic filling of cloud-affected Landsat scenes. The filling is based on ESTARFM predictions for the gaps in the target scene. Shoulder scene combinations (SSC) are automatically selected after a certain ruleset and then used as input for ESTARFM. A maximum of three synthetic scenes are generated this way to fill one target scene.