esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Four Time Function Models and Parametric Analysis
#### 2.2.3 Richards Time Function Model  
The Richards model is primarily used to describe the impact of various limiting factors on crop growth processes [51; 52]. [PERSON] et al. [19] applied this model to dynamically predict surface movement in mining areas, achieving favorable results. Combining the Richards model provides an expression for dynamic surface subsidence prediction as follows:  
\[W(t)=W_{m}*(1-ae^{-bt})^{1/c} \tag{3}\]  
where \(a\) is a parameter related to the initial value of surface deformation; \(b\) is a parameter describing the speed of surface subsidence; and \(c\) is a curve shape control parameter.  
Analyzing the impact of parameter \(a\) on the predictive model, with \(W_{m}=-3560\) mm, \(b=0.05\), and \(c=3\times 10^{-5}\), as shown in Figure 7, an increase in parameter \(a\) leads to an extension of the initial phase of surface subsidence, while the active and decline phases shorten. The time required to reach the maximum surface subsidence speed and acceleration increases. However, the influence of parameter \(a\) on the trends and peak values of the subsidence curve, velocity, and acceleration is not very noticeable, and the overall shape of the curves remains largely unchanged.  
Figure 6: (**a**) Effect of parameter \(b\) on surface subsidence; (**b**) effect of parameter \(b\) on surface subsidence velocity; (**c**) effect of parameter \(b\) on surface subsidence acceleration.  
Figure 7: (**a**) Effect of parameter \(a\) on surface subsidence; (**b**) effect of parameter \(b\) on surface subsidence velocity; (**c**) effect of parameter \(b\) on surface subsidence acceleration.  
Regarding parameter \(b\), with \(W_{m}=-3560\) mm, \(a=5\times 10^{-3}\), and \(c=3\times 10^{-5}\), Figure 8 illustrates that as parameter \(b\) increases, the initial phase and active phase of surface subsidence shorten, while the decline phase lengthens. The three curves become steeper. The maximum values of subsidence velocity and acceleration increase, and the time needed to reach the maximum subsidence velocity and acceleration decreases.  
As for parameter \(c\), with \(W_{m}=-3560\) mm, \(a=5\times 10^{-3}\), and \(b=0.05\), Figure 9 demonstrates that parameter \(c\) has the opposite effect to parameter \(a\). An increase in parameter \(c\) leads to a shortening of the initial phase of surface subsidence, a slight increase in the active and decline phases, and a reduction in the time required to reach the peak values of subsidence velocity and acceleration. However, in general, the shapes of the three curves remain relatively unchanged, and the effect of the parameter \(c\) on the trends and peak values of the subsidence curves, subsidence velocity, and subsidence acceleration curves are not significant.
## 2 Proposed Architecture
### RSP16T Cell
#### 2.1.2 Write Mode  
To write the state Q = 0, BL/BLB are driven to VSS and VDD, respectively, with WL set to VDD and WWL to VSS. This opens connections for each node to the bit lines, pulling them to the opposite values of their stored states. Once node values surpass the threshold voltage difference necessary to flip states, the transistors managing pull-up and pull-down paths toggle, finalizing the write operation.
## 2 Experimental Procedure and Data Reduction
### Experimental System and Procedure
Each set contained six calibrated thermocouples with an accuracy of \(\pm\)0.2 \({}^{\circ}\)C.  
The change law of total heat transfer for the radiators was revealed by testing the temperature differences of the cooling water in the radiators. Small adjustable-speed fans were placed in the upper and lower air ducts to simulate ambient crosswind. During data collection, each measured value was read at least three times. Then, the arithmetic mean of the recorded data for a certain working condition was used to calculate the other parameters. This experiment was completed under atmospheric pressure and room-temperature conditions (15-25 \({}^{\circ}\)C). The air-cooled radiators in a crossflow of air were studied. One of the radiators is shown in Figure 5; they consisted of finned tube heat exchangers with double rows of tubes, a single tube-pass, and plain fins. Heat exchangers only tested samples of the air-cooling tower used in the power plant. The tubes and fins were constructed from copper and aluminum, respectively. The fins measured 143 \(\times\) 125 \(\times\) 44 mm, and fin thickness and space were 0.5 mm and 2.5 mm, respectively. Six sets of  
Figure 4: Picture of the wind deflector. Figure 3: Sketch of the rotating wind deflector system. 1—Wind deflector; 2—Slewing bearing; 3—Driving gear; 4—Driving motor. radiators were installed at the bottom of the cooling tower in this experiment, with 10 tubes inside each set of radiators. The installation configuration of the experimental system is shown in Figure 6.  
Suitable data acquisition systems and data collectors were used to gather and save the experimental data. The transient temperature of water and air, declines in water pressure, and water flow meter signals were measured and recorded using a real-time hybrid recorder. The testing parts were covered by thick foam insulation to reduce heat loss. All measurement instruments were adjusted to verify the testing methods and results before the experiment was officially initiated. According to uncertainty estimation methods [29], the maximum experimental uncertainty for the temperature drop and heat transfer rate of water was 1.41% and 2.06%, respectively.
# 185-215 GHz CMOS Frequency Doubler with a Single Row Staggered Distribution Layout Design
## 2 Circuit Design
### 110 GHz Balun and Driver Amplifier (DA)  
Providing equally distributed in-phase and inverted-phase RF signals, rat race balun is a popular microwave passive component. It is used in many RF circuits [31]. Physical dimensions [32], frequency bandwidth [33], and insertion loss [34] are key indicators of balun's performance.  
Figure 1: Diagram of the designed module of driver amplifier and frequency doubler.  
In this design, the rat race balun, which converts the signal from a single end to a differential form, was made of thick copper layers at the top of the chip. In this way, the insertion loss could be effectively reduced.  
As shown in Figure 2, the rat-race balun consists of three \(\lambda/4\) transmission lines and a \(\frac{3}{4}\lambda\) transmission line. The performance of the rat-race balun with respect to the magnitude and phase imbalance was simulated, and the simulated results are shown in Figure 3. As shown in Figure 3, the \(\mathrm{S_{21}}\) and \(\mathrm{S_{31}}\) were about \(-4.4\) dB and \(-4.8\) dB, respectively, at \(110\) GHz for the rat-race balun.  
The design of the drive amplifier was similar to the design of the low-power amplifier. However, the signal amplified by the drive amplifier should be ensured to be as distortion-free as possible.  
According to Figure 1, the circuit used to amplify the power level of the 110 GHz signal was a six-stage DA. The DA was designed as a pseudo-difference structure. This structure is characterized by the fact that the capacitance \(\mathrm{C_{gd}}\) between the gate and drain of the transistor can be compensated by the capacitance of the cross-coupling. As shown in Figure 4, the final stage of DA consisted of four NMOS transistors. The input signal at this stage was uniformly distributed to feed the gates of the NMOS transistors. The drains of the transistors were connected at the center, while the source was directly connected to the ground.  
Figure 3: Simulated S-parameter of rat-race balun.  
Figure 2: Schematic of the designed rat-race balun.  
The output power versus the frequency of the six-stage DA was simulated with an input power level of \(-10\) dBm, and the simulated results are shown in Figure 5. According to Figure 5, the DA gain became saturated at the 110 GHz frequency point. The saturation output power at 110 GHz was 11.5 dBm. Figure 5 shows that if the input power level of the DA was no less than \(-10\) dBm, the power transmitted from the DA end to the frequency doubler could be up to 11.5 dBm.
## 4 Discussion
On the other hand, it might be possible to control the height of the arm movement at landing through instruction, such as \"arms above the shoulder at landing.\" No studies justify that the height of the COM\({}_{\text{wb}}\) should be the same at takeoff and landing. At least, the current instruction focusing on lower limb posture cannot prevent the potential \"cheating\" that can be accomplished by lowering arms as much as possible at landing. Previous studies have shown that arm swing improved jump height [30], but the improvement might be somewhat overestimated when the jump height was calculated from flight time. Therefore, the flight time method cannot be recommended for a vertical jump with arm swing, especially when compared with others, such as at tryouts. From the contour curve (Figure 5), jump height did not greatly affect the overestimation. For example, if the jump height is \(0.20\) m and the COM\({}_{\text{wb}}\) height is \(0.04\) m lower at landing compared to takeoff, then the jump height from flight time is overestimated by \(0.0195\) m. if the jump height changes to \(0.60\) m, the overestimation from the same difference in the COM\({}_{\text{wb}}\) height is \(0.0198\) m. The relationship between the difference in the COM\({}_{\text{wb}}\) height and the overestimation of jump height is in a ratio of almost two to one. Many studies have considered force platforms as the \"gold standard\" to evaluate jump height [22; 31; 32], but this confuses the instrumentation with the calculation method. We can calculate jump height by two methods using force platforms: (1) vertical velocity at takeoff, and (2) the time in the air [8; 9]. To clarify the validity and reliability of the simple methodology to calculate jump height from flight time, force platforms are considered the gold standard to calculate flight time because the method contained is valid under certain conditions, as described above. The jump height from vertical velocity at takeoff is the true gold standard for jump height measurement. Calculating jump height from flight time is still useful for coaches who want to measure changes in an individual resulting from their training program because of its low cost, simplicity, and ease of implementation. Recently, many commercial devices are have been developed to measure jump height from flight time, such as an iPhone app [33] and inertial measurement unit [24]. Other methods have also been in development, such as linear position transducers, but these showed overestimation by \(7.0\) cm compared to the jump height from flight time [34]. In this study, we confirmed that the flight time method has high intra-participant reliability and no proportional bias, though there is a fixed bias. Researchers and coaches are usually interested in comparing jump height before and after training. If the same device is used for both pre- and post-tests, it is useful.
## 4 Discussion
Interestingly, the antibacterial activity of nLEAP-2 and eLEAP-2 increased at low temperatures as compare to the nLEAP-1 and eLEAP-1, this may indicate that these peptides are adapted to low temperatures with structural modification or that there was a conformational change in the bacterial membrane due to the lower temperature, leading to higher susceptibility to the peptide. Such as piscidin-like antimicrobial peptide, Chionodracine isolated from the _Chionodracohamatus_ an icefish species, showed significant antibacterial activity against Antarctic psychrophilic bacteria strains _Psyclrobracet_ sp. _TAD_ (MIC = 10 \(\upmu\)M) and _TA144_ (MIC = 15 \(\upmu\)M) at 15 \({}^{\circ}\)C, the Gram-positive _B. cereus_ (MIC = 5 \(\upmu\)M) at 25 \({}^{\circ}\)C, and the Gram-negative _E. coli_ (MIC = 5 \(\upmu\)M) at 25 \({}^{\circ}\)C. Nevertheless, when the activity of the peptide was tested against _E. coli_ and _B. cereus_ at 37 \({}^{\circ}\)C, the MIC levels increased to 20 \(\upmu\)M and 10 \(\upmu\)M, respectively [51]. The peptides with reduced number of disulfide bonds may show significant antibacterial activity. Such as the \"rocket\" analogue-N2 derived from the marine peptide NZ17074 (N1). It is characterized by two disulfide bonds and showed the strongest antimicrobial activity against Gram-negative bacteria with MIC values ranging from 0.25 to 1 mg/mL against _E. coli_, _Salmonella_, and _Pseudomonas_ strains compared to other analogues [69]. The reduction to 4 cysteine (nLEAP-2 and eLEAP-2) in the Antarctic notothenioid LEAPs would increase the flexibility of the hairpin structure. In Antarctic and non-Antarctic notothenioid species the structure-function comparisons of lactate dehydrogenase A4 orthologs (A4-LDHs) showed that the amino acid substitution leads to increase in molecular flexibility in small areas of the cold-adapted A4-LDHs that affect the mobility of adjacent active sites resulting in modulation of the enzyme's catalytic rate (Kcat) and substrate-binding affinity (Km) [70]. In the 4 cys LEAPs of Antarctic fish, the most distinct substitutions are the cysteine residues that lead to the reduction of intramolecular disulfide bonds, and this would likely increase structural flexibility and its functional ability.
# Path Planning of Underground Robots via Improved A* and Dynamic Window Approach
## 4 Experiment and Analysis
### Improved A* Algorithm Experiment
#### 4.1.3 Adaptive Curve Smoothing Experiment  
In Section 2.4, a method of adaptively selecting the order and smoothing factor of B-spline curve smoothing according to the characteristics of the global path planning route is introduced. After many experiments, the maximum offset is set to 3.0, and the weights are respectively \(w_{L}=0.1\), \(w_{\rm C}=0.4\), \(w_{\rm F}=0.4\), and \(w_{R}=0.1\). Adaptive selection of \(k\) is 3, and \(s\) is 10.91. The experiment results are shown in Figure 12.  
Figure 11: The A* graph after improving the heuristic function.  
Figure 12: Adaptive smoothing of the A* algorithm on the Zijin map.  
In the figure, the blue line represents the original unsmoothed path, while the red line shows the path after adaptive B-spline smoothing. It can be seen from the figure that the smoothed path shows the characteristics of smooth and non-obtrusive inflection points, effectively reducing the number of inflection points increased by the heuristic function adjustment, and successfully crossing unnecessary continuous inflection points. On the whole, the smoothed path basically fits the original path, while ensuring no contact with obstacles.  
In view of the above three improvement measures, the specific evaluation indicators after the gradual implementation of each stage are summarized in the following Table 2.  
According to the data in Table 2, it can be seen that the improved A* algorithm shows a series of significant advantages after introducing the diagonal anti-collision detection mechanism, integrating the roadway width into the heuristic function, and adopting the adaptive B-spline curve smoothing method. Specifically, the number of extended nodes decreased by 43.79%, the calculation time decreased by 16.28%, and the number of inflection points decreased by 80.70%. The intuitive display of Figure 12 and the detailed data of Table 2 verify the effectiveness and superiority of the proposed improved algorithm.
###### Abstract
Therefore, the authors emphasized that facility managers need proper tools to manage scarce resources. However, the proposed FM systems in the literature or available commercial systems are either query-based or present limited data analytic opportunities or limited data availability depending on lack of average values (rule-based systems) or a specific area such as energy and maintenance or need for an external analysis solution such as Python, R, or Weka in terms of particular data sources (such as BIM) [15; 17; 20; 21; 22; 23; 24; 25; 26; 27]. Therefore, data-driven decisions cannot be performed during FM. [PERSON] et al. [3] stated that bigger datasets are created during the building project lifecycle. However, value from these data sources is hidden. Authors expressed that BI&A can be used to analyze or predict project KPIs. In parallel to the study of [PERSON] et al., [PERSON] et al. [28] reported that data analytics could be used in analyzing relationships and impacts of FM key performance indicators (KPIs). Moreover, [PERSON] et al. [29] and [PERSON] et al. [17] stated that available performance metrics consider only one aspect of performance and undermine new technology and progress in data analytics. Within this context, data analytic activities present a promising feature to discover more detailed facility performance and new KPIs for FM. Although there are some studies that use individual BI&A solutions for FM problems and their usage in the assessment of building performance in the literature, there is no comprehensive study that combines all necessary FM KPIs. A few studies focused only on determination and ranking of FM KPIs for healthcare facilities. However, these studies do not consider the power of data analytics. Therefore, although high-tech systems are used in healthcare FM, discovery of new KPIs is limited in these studies. Additionally, lack of determination of data analytic activities in FM induces hidden value in or non-measurement of data and depends on non-availability data. That is why performance benchmarking has been limited to crude metrics. As a result of non-data-driven decisions, FM results in cost inefficiencies, inadequacy, unsuitability of facilities for future needs, and non-contribution to the organization's mission [17; 30]. Therefore, this study aims to:(i) identify and determine FM KPIs for the management of healthcare facilities throughout their service life; and (ii) prioritize FM KPIs for the Turkish healthcare industry. Thus, the contribution of the study is to reveal measurable FM KPIs to enable detailed performance analysis and strategic decision-making in healthcare facilities. With an empirical investigation, the study contributes to the practice with the identification of FM KPIs that help increase awareness of facility managers in terms of data analytic activities in FM and identification of requirements or expectations of healthcare facility managers.
# 3-D SRIM Simulation of Focused Ion Beam
## 2 Theory on Ion-Solid Interaction  
A series of physics-chemistry actions will occur when high-energy ion beam bombarding the sample as shown in Figure 1. Sputtering depth and radial range are discussed as they affect the precision of FIB sputtering greatly. If non-redeposition of the sputtered atoms, same material removal rate and Gaussian distribution incident beam are assumed during the bombardment [29], the sputtering depth can be expressed in Equation (1).  
\[\Delta Z_{ij}=\Sigma_{k=1}^{n_{1}}\Sigma_{l=1}^{n_{2}}\frac{\phi(x_{k},y_{l}) }{\eta}f_{x_{k},y_{l}}(x_{i},y_{j})S(\theta_{x_{i},y_{j}})t_{x_{k},y_{l}} \Delta x_{k}\Delta y_{l}, \tag{1}\]  
where \(\Delta Z_{ij}\) is the sputtering depth at point \((x_{i},y_{j})\), \(\phi(x_{k},y_{l})\) is the ion flux with the unit of cm\({}^{-2}\) s\({}^{-1}\), \(\eta\) is the atomic density of the target with the unit of atom\(\cdot\)cm\({}^{-3}\), \(f_{x_{k},y_{l}}(x_{i},y_{j})\) is the ion beam current density function for the Gaussian distribution, \(S(\theta_{x_{i},y_{j}})\) is the angle-dependent sputtering yield, \(\theta_{x_{i},y_{j}}\) is the incident angle, \(t_{x_{k},y_{l}}\) represents the dwell time, \((x_{i},y_{j})\) is the midpoint of each pixel \((x_{k},y_{l})\), and \(i=1\dots n_{1},j=1\dots n_{2}\), \(n_{1}\), \(n_{2}\) are the number of square pixels along \(x\) and \(y\) axes. Under the Gaussian distribution, the ion beam current density function will satisfy the equation as follows [30]:  
\[f_{x_{k},y_{l}}(x_{i},y_{j})=\left(\frac{I}{\sqrt{2\pi}\sigma}\right)^{2} \exp\left(-\frac{(x_{k}-x_{i})^{2}+(y_{l}-y_{j})^{2}}{2\sigma^{2}}\right), \tag{2}\]  
where I is the total ion beam current, \(i,j\), \(n_{1}\) and \(n_{2}\) have the same meanings as above, \(x_{k}\) and \(y_{l}\) are independent of each other and the standard deviation \(\sigma\) represents dispersions of \(x_{k}\) and \(y_{l}\) upon \(x_{i}\) and \(y_{j}\).  
According to Equation (1), the sputtering depth depends on the dwell time, the incident angle, the beam current distribution of the incident beam, and the characteristics of the sample. As for the radial range, it depends on the sputtering depth, original ion position, speed and so forth. It is necessary to determine the beam current distribution of the incident beam before simulating ion-solid interaction.
## 2 Physical Layer-Based Full-Duplex Underwater Acoustic Communication Systems
### Comparison of Different FD-Physical Layer-Based UWAC Systems
#### 2.5.2 Bit Error Rate  
The AUA-CC [40] has lower BER values of \(10^{-3}\) and \(10^{-4}\) when using a time-invariant channel. However, due to its delay independent characteristics, the A-DTWR [19] performs significantly better on the time variant channel. Moreover, as fading becomes quicker, the system in [19] experiences more ICI, resulting in a \(10^{-1}\) fold increase in BER. TRDC [22] is a novel automated cancelation scheme that combines time reversal (TR) and soft-decision feedback turbo equalization (SDFE) to remove SI and ISI, resulting in a \(10^{-3}\) improvement in BER. Due to the presence of significant noise, the ML-SC-IBFD [23] system achieves a higher BER of \(10^{-1}\) at lower SNR. However, as the SNR increases, the ML-SC-IBFD reaches a lower BER of \(10^{-3}\). Furthermore, A-IBFD [24] uses the Over-Parameterization based Recursive Least Squares algorithm (OPRLS) to estimate the nonlinear SI channel, but the BER gain increases by \(10^{-1}\) as the duration of non-overlapping increases up to 35 ms.
# VIIRS Day/Night Band--Correcting Striping and Nonuniformity over a Very Large Dynamic Range
## 3 Results  
### Survey of DNB Imagery  
Nonuniformities, including striping and discontinuities at aggregation zone boundaries have been observed in all DNB SDR calibrated radiances. The effects are generally stronger nearer to the edges of the swath, but can be seen in many cases nearer to the center. Daytime, twilight and nighttime scenes will be considered individually. Desert scenes, and particularly the Sahara Desert, provide relatively uniform targets to evaluate non-uniformities, and some of these scenes are used in the following cases. Polar ice scenes also provide relatively uniform targets to consider. However, even for less uniform scenes, strong non-uniformities are sometimes observed, and these non-uniformities make interpretation of imagery difficult. We include some of these more variant scenes as a way of illustrating the challenges faced by operational users of DNB imagery.  
All examples shown here depict radiance values from the DNB SDR, and have not been projected onto a mapping grid. The along-track direction is shown vertically and the in-scan direction is shown horizontally. The reason for this is to preserve the relationship between pixels and detectors in the along-track direction and the aggregation zones in the horizontal direction. Map projections rotate the image and so these relations become less apparent. The radiance range present in the scene is given in the caption.