esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Performance Experiment Results
#### 3.3.2 \(T_{\mathit{differ}}\) Variation  
\(T_{l}\) was influenced by the combined action of the cold radiation source and basic refrigeration units (Figure 9). \(T_{\mathit{differ}}\) was used as an index to evaluate the cooling capacity of the cold radiation source. \(T_{\mathit{radiation}}\) was set to \(-40.0\)\({}^{\circ}\)C, and \(T_{\mathit{differ}}\) was \(-4.4\), \(-5.2\), and \(-5.5\)\({}^{\circ}\)C, relative to \(T_{\mathit{basic}}\) at \(-5.0\), \(0\), and \(5\)\({}^{\circ}\)C. Furthermore, \(T_{\mathit{radiation}}\) was \(-20.0\)\({}^{\circ}\)C, \(T_{\mathit{differ}}\) was \(-3.5\), \(-3.6\), and \(-3.7\)\({}^{\circ}\)C, respectively. For \(T_{\mathit{radiation}}\) at \(0\)\({}^{\circ}\)C, \(T_{\mathit{differ}}\) was \(-1.0\), \(-1.1\), and \(-0.8\)\({}^{\circ}\)C, respectively. These results showed that different \(T_{\mathit{basic}}\) had little influence on the cooling ability of the cold radiation source. \(V_{\mathit{cool-leaf}}\) was similar to that of the natural environment under the individual effect of the cold radiation source within the first one hour. Following those results, \(T_{l}\) slowly decreased.
# Integrating Knowledge Graphs into Distribution Grid Decision Support Systems +
## 6 Analysis of Outages
### Monte Carlo Simulation  
To show how GridKG can be used for a more complex analysis, we present a simple technique for estimating the number of customers affected by outages. This approach uses Monte Carlo simulation to construct the distributions of affected customers.  
For a target number of outages \(N\) in a given grid cell \(P\), \(N\) outage locations are randomly selected. This process is repeated \(S\) times. The outage locations are linked to protective devices, such as breakers, switches, and fuses. The number of affected customers is determined based on available data which only allows for a rudimentary, illustrative analysis.  
The locations of the outages in the cell are selected based on a probability distribution calculated for each protective device based on its involvement in previous (historical) outages stored in GridKG. If, historically, a specific location and piece of equipment was more prone to experience power outages, it has a higher probability of being selected in the simulation.  
For this example, to show the potential of using GridKG, an arbitrary service area is selected and divided into a grid of 10 \(\times\) 10 km cells, as shown in Figure 6. The locations of historical power outages are depicted with black dots. The impact of power outage severity is simulated for the blue grid cell.
# Pointing Accuracy of an Operational Polarimetric Weather Radar
## 9 Examples from the Radar Network
From Figure 14 we conclude that the azimuthal variation of the azimuth pointing bias can be attributed to the drive train of the radar system and the absolute encoder. Figure 13: Mean a zimuth (upper panel) and elevation (lower panel) pointing bias based on full diurnal cycles of solar boxscans at 4 radar sites: Boostedt (BOO, 27 June 2018, Neuheilenbach (NHB, 24 July 2018), Hannover (HNR, 24 July 2018) and Flechtdorf (FLD, 27 June 2018). About 90 bias estimates are available for each radar site. An important aspect is that the azimuthal variation of pointing bias is different for each system. This is attributed to a combination of the mechanical uncertainty of the manufacturing process of bull gears and the mechanical uncertainty of the absolute encoder. If we want to correct for this azimuthal dependence of pointing accuracy, this azimuthal dependence has to be determined for each individual system. So far we do not plan to apply such a correction. As a first step we plan to optimize the angle acquisition with an improved mechanical design of the absolute encoder. The bias in elevation and its elevation dependence in relation to the same angle acquisition that is used in azimuth is not as crucial since only a smaller angle range is required. This relates to the operational data acquisition between 0.5\({}^{\circ}\) and 25\({}^{\circ}\) elevation.
# Effects of Movement for High Time-Bandwidths in Batched Pulse Compression Range-Doppler Radar
## 6 Discussion of Findings and Consideration for Processing Approaches
### Summary  
All of the compensation methods shown in Section 6.3 try to address primarily a pure uniform rectangular extent that results from a constant effective velocity with varying computational effort. This is practically limited and it will not compensate actual target return fluctuations and possible deviations for example introduced by motion (Section 6.4). Even if a slight improvement was possible, the summarised influences constrain the maximal realized and achieved coherent integration gain \(G_{\text{int,achieved}}\) even below the theoretical maximum of \(G_{\text{int,desired}}=T_{\text{int}}\,B_{\text{eff}}\) (7) from Equations (10) and (4) to  
\[G_{\text{int,achieved}}\leq G_{\text{int,compensated}}\leq G_{\text{int,desired }}\leq T_{\text{cov}}\,B_{\text{eff}}\leq T_{\text{cov}}\,f_{\text{s}}. \tag{32}\]  
The upper bounded consideration in Equation (32) is important for an in-depth performance evaluation by that the products \(B_{\text{eff}}\,T_{\text{int}}\) and \(T_{\text{cov}}\,f_{\text{s}}\) are practically limited. The question how much coherent integration gain can be realized and if a target hit also leads to a better detection and established track depends on many of the described factors. A remaining extension either caused by the resolved target dimension, multipaths or due to straddling losses overlayed by target fluctuations within the bandwidths \(B_{\text{eff}}/B_{\text{occ}}\) (Section 5.5) and strong non-stationary clutter will still raise particular challenges. Any concept will need a careful evaluation of the influences present. The possibility to cope with hardware implications like drifts, oscillator stability and phase noise are equally important. These aspects will determine the final detection and localisation capability. However, if the target was 'visible' with less coherent gain close to the detection threshold in individual channels, it could be processed non coherently with lower coherent integration gain. This might avoid some of the implications shown.
###### Abstract
If not attenuated, these effects culminate in cell death [24; 25], as also reported in soybean plants grown under saline conditions [5]. Cellular ROS homeostasis is modulated by their synthesis, mainly through the NADPH oxidase complex, and their scavenging through the antioxidant machinery with glutathione and ascorbate acting as major antioxidants [26]. Soybean plants exposed to salt stress exhibit a rapid increase in the activities of both superoxide dismutase (SOD) and catalase (CAT) [22] and major changes in the activity of the enzymes participating in the ascorbate-glutathione redox system [5]. An enhanced antioxidant system in response to salinity is, thus, a major component in dealing with stress [5]. Among the modulators of ROS and antioxidant responses is nitric oxide (NO) [27]. Overproduction of NO during several stress conditions such as hypoxia or water deficit augments ROS and depresses the activities of several antioxidant enzymes, thus triggering cellular and tissue damage which compromise plant survival [28; 29; 30]. Nitric oxide also modulates the Na\({}^{+}\)/K\({}^{+}\) homeostasis during salinity stress through regulation of the inward rectifying K\({}^{+}\) channel POTASSIUM TRANSPORTER 1 (AKT1), albeit with contrasting results [31; 32]. In these studies, cellular NO levels were modulated using pharmacological applications, i.e., NO donors or scavengers, which pose some challenges when interpreting the results due to uniformity of applications and duration of the effect of treatment [33]. The same authors acknowledged that the mechanisms of NO action in salinity stress could be better analyzed using a transgenic approach. Modulating NO can be achieved using transgenic plants with different levels of phytoglobins (Pgbs); plant proteins known for their NO scavenging properties [34]. Induced at the onset of abiotic and biotic stress, Pgbs have been shown to play a protective role by attenuating oxidative stress through cell- and tissue-specific suppression of NO [28; 30; 35; 36]. Overexpression of the soybean _GmPgb1_ mitigated the growth inhibition of waterlogging [37]and drought [38] through reduction of NO, ROS, and oxidative stress. To further our knowledge on the function of Pgb1 as a stress attenuator, we examined the behavior of previously characterized soybean plants overexpressing or downregulating _GmPgb1_[37] to Na2 SO4 stress. It is hypothesized that during salt stress, overexpression of _GmPgb1_ attenuates the depression of several morphophysiological parameters through limitation of ROS-induced oxidative damage and Na+ accumulation. To address this hypothesis, we measured biomass, water content, leaf damage, and K+ and Na+ concentrations, as well as photosynthetic gas exchange parameters and the enzymes related to oxidative stress known to be influenced by salinity.
## 2 Materials and Methods
### Derivation of Field-Scale \(V_{cmax,0}^{25}\) Measurement  
This study employed a classical optimization algorithm, the differential evolution (DE) algorithm [52], to retrieve the daily \(V_{cmax,0}^{25}\). The differential evolution algorithm contains three steps, including mutation, crossover, and selection. Detailed information about DE algorithm were displayed in the Supplementary Material Section S7. The differential evolution algorithm was implemented using the lmfit python package [53], with the mean square error (MSE) chosen as the objective function. Integrating the DE algorithm with BEPS, called BEPS-DE framework, was employed to derive the field-scale \(V_{cmax,0}^{25}\) under the assumption of constant \(V_{cmax,0}^{25}\) for 7 days. Only daytime GPP observation (when incoming shortwave radiation is larger than 0) were used to fit \(V_{cmax,0}^{25}\) because the BEPS model sets high-time GPP as 0 while night-time GPP observation from the EC system are not 0.
# Simulating and Evaluating a Real-World ElasticSearch System Using the RECAP DES Simulator
## 3 Use Case: Opening.io  
Established in 2015 in Dublin, Ireland, Opening.io was named as one of Europe's 20 super AI, SaaS and enterprise start-ups in 2018. Opening.io leverages machine intelligence on top of existing large-scale recruitment processes to accelerate and inform recruitment decision-making and job-role matching. Most resumes are structured following a logical pattern, with individual coherent sections covering particular distinct snapshots (delimited according to time and/or activity) and are typically sorted by importance. Most highlight various skills, abilities and major experience gained over time. The core of Opening.io's processes relies on an analysis of these relationships--both observable as well as latent--in jobs, candidates and interactions data, with either unstructured information (raw job descriptions and resume text) or pre-existing relations (employment history, internal categorisation). Opening.io's underlying system performs a number of functions including parsing and analysing resumes, information extraction, matching and ranking candidates in relation to jobs, candidate skills inference and recommendations, CV summaries, salary recommendations and intelligence around talent pools and the human capital ecosystem at large. In the process of distilling knowledge, vast amounts of information are being indexed. The ES cluster is a central component within the wider Opening.io system and can suffer from relatively heavy traffic due to batch ingestion processes (jobs/ candidates imports) and high numbers of search queries performed in parallel. When such scenarios emerge, Opening.io observes traffic spikes in the order of 300 req/s (writes, ingestion) and 100 req/s (reads, queries).  
In this work, we model and simulate the ES cluster within the Opening.io system. In particular, we simulate high-traffic scenarios resulting from search queries (read, queries).
# Delay-Sensitive Multi-Sensor Routing Scheduling Method for Underground IoT in Mines
## 6 Conclusions  
This paper studied the real-time data transmission problem of the IoT for underground mining safety. The current work was analyzed, and it was demonstrated that existing approaches cannot effectively solve the real-time data transmission problem of IoT in underground mines. Addressing the limitations of current approaches, this paper analyzed the tunnel structure of IoT in mines and proposed a hybrid network topology that combines wireless and wired transmission. A multi-sensor, multi-base-station real-time routing scheduling problem was defined, and it was proven to be NP-hard. Three different algorithms were proposed, and experiments were designed to analyze the delay performance of these algorithms under varying node sizes and packet numbers. At the same time, the computational complexity of the three algorithms was analyzed by evaluating their convergence speeds. The method proposed in this paper can be applied to the real-time routing scheduling of multi-sensors in IoT systems in mining environments and provides a feasible solution with relatively better constraints before the deadline.  
In practical applications, due to the complex structure of mines, the reliability of wireless signals is difficult to ensure. Additionally, since the algorithm proposed in this paper requires good clock synchronization between base stations, but devices in underground mines find it challenging to maintain synchronization through low-performance crystal oscillators, these issues must be addressed. In practical applications, a polling method should be used to ensure clock synchronization, and algorithms like CSMA should be appropriately considered to ensure the reliability of data transmission from sensors to base stations.  
The main direction for future research is to improve the execution efficiency of the algorithm and analyze the optimization algorithm design approach for specific scenarios, such as dynamic programming. The goal is to propose an optimized scheduling algorithm with pseudo-polynomial time complexity.  
Writing-original draft, Y.Z.; validation, M.L.; writing--review and editing, A.T. All authors have read and agreed to the published version of the manuscript.  
Figure 11: Comparison of completion rates when Station is 10 and Package is 250.  
**Funding:** The development and application demonstration of the comprehensive service platform for health maintenance and tour in Dujiangyan, Chengdu Science and Technology Bureau, 2020-YF09-00010-SN.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** All experimental data are presented in the paper.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
# Novel ZIF-67@Bentonite (ZIF-67@BNT) Nanocomposite for Aqueous Methyl Orange Removal
## 3 Results and Discussion  
### ZIF-67@BNT Characterization  
#### 3.1.1 X-Ray Diffraction Analysis  
The X-ray diffraction (XRD) patterns of the ZIF-67@BNT nanocomposite, ZIF-67, and BNT are shown in Figure 5A. The diffraction pattern of BNT (shown in blue) shows some characteristic peaks related to the clay mineral structure. Major reflections seen at 20: 6.1\({}^{\circ}\) (001), 19.8\({}^{\circ}\) (110), 26.7\({}^{\circ}\) (020), 35.0\({}^{\circ}\) (220), 54.9\({}^{\circ}\) (240), and 68.1\({}^{\circ}\) (330) correspond to different planes of crystals existing in BNT [41] The basal reflection determined close to 6.1\({}^{\circ}\) (001) is an indicator of the montmorillonite interlayer spacing, which is a fundamental structural property of BNT. Some peaks established near 20-30\({}^{\circ}\) correspond to quartz impurities predominating in natural BNT, while their broad character signifies an amorphous phase of BNT owing to its hydrated layered silicate structure. The calculated crystallite size of BNT is 2810.19 nm; therefore, it shows structural features in relatively large particle dimensions and a less defined crystalline nature than ZIF-67.  
The X-ray diffraction (XRD) pattern of ZIF-67 (red pattern) can be assigned to characteristic peaks at 20 = 7.3\({}^{\circ}\), 10.4\({}^{\circ}\), 12.7\({}^{\circ}\), 14.7\({}^{\circ}\), 16.4\({}^{\circ}\), 18.1\({}^{\circ}\), 22.2\({}^{\circ}\), 24.5\({}^{\circ}\), 29.4\({}^{\circ}\), and 33.5\({}^{\circ}\), typical of the crystalline metal-organic framework (MOF) structure of ZIF-67 [42]. They are assigned as (011), (002), (013), (112), (222), (233), (044), (134), (244), and (235) crystal planes, respectively [43]. The calculated crystallite size of ZIF-67 and ZIF-67@BNT is 44.48 and 79.28 nm, respectively, with the ZIF-67@BNT nanocomposite being significantly larger than ZIF-67 alone, suggesting a structural reorganization upon composite formation. The decline in crystallinity in the composite might potentially point to surface changes and flaws, which would improve the adsorption capacity by raising the number of accessible active sites [44]. Furthermore, BNT in the composite is projected to increase the general structural stability of ZIF-67, hence avoiding its collapse in aqueous surroundings [45].  
With all factors taken into account, the XRD study verifies the effective synthesis of the ZIF-67@BNT composite by maintaining the main structural characteristics of ZIF-67 and including modifications caused by interactions with BNT. The noted rise in crystallite size points to BNT's stabilizing action for ZIF-67, hence enabling better textural qualities. Moreover, the retention of unique ZIF-67 crystal planes in the composite shows that its crystalline structure stays intact; on the other hand, the addition of BNT brings structural changes advantageous for adsorption uses. The minimal shifting and widening of BNT's peaks point to exfoliation or intercalation, which improves ZIF-67 dispersion in the composite [46].
# Flexible Shear and Normal Force Sensor Using only One Layer of Polyvinylidene Fluoride Film
## 2 Fabrication  
The fabrication process of the sensor is shown in Figure 1a-c. First, two custom-made molds were fabricated with a 3D printer (ProJet HD3500 Plus, 3D Systems, Inc.) in ultra-high-definition (UHD) mode using Visilet M3 Crystal and Visilet S300 as the build and support materials, respectively. Then, the molds were coated with trichlorosilane (Sigma Aldrich, Inc.) to easily separate the PDMS from these molds [36]. PDMS (SYLGARD 184 A/B, Dow Corning Corp.) was molded using the custom-made molds and cured at 55 \({}^{\circ}\)C for 4 h to fabricate the bottom and top PDMS (Figure 1a). Therein, the base/agent weight ratio of the bottom PDMS was 25:1 for the adhesion surface and that of the top PDMS was 5:1. As shown in Figure 1b, the upper surface electrode of the silver-coated PVDF film (thickness: 28 \(\upmu\)m; Measurement Specialties, Inc.) with an area of \(6\times 23\) mm\({}^{2}\) was patterned by a laser marking machine (Cat-Fs20 Mini, Marc Co., Ltd.). Finally, the patterned PVDF layer and flexible printed circuit board (FPCB, 4 MITECH Co., Ltd.) layer were stacked up on the bottom PDMS and, afterwards, the top PDMS was used to cover them (Figure 1c). Figure 1d shows a photograph of the shear and normal force sensor with a \(2\times 2\) sensor array. The module of this sensor consisted of three piezoelectric sensors (namely, P1, P2, and P3 in Figure 1e). From the side view, there were two isosceles trapezoid bumps in the bottom PDMS. The length of an upper base and the legs of the trapezoid was 3 mm, and the angle between a bottom base and a leg was 45\({}^{\circ}\) (Figure 1f). For measuring the sensor outputs, the FPCB with electrode pattern as shown in Figure 1g was utilized by connecting it to the top surface electrode, and the bottom surface electrode was connected to an electric wire.  
Figure 1: Schematic diagram showing the fabrication process of the shear and normal force sensor. (**a**) Formation of the top and bottom PDMS. (**b**) Electrode patterning of a silver-coated PVDF film. (**c**) Assembly of the PDMS, PVDF film, and FPCB. (**d**) Photograph of the fabricated sensor array with (**e**) the sensor numbers. (**f**) Schematic of a cross-section view of the sensor. The length unit is millimeters (mm). (**g**) Electrode pattern of the FPCB.
# Comparative Analysis of Different Model-Based Controllers Using Active Vehicle Suspension System
## 2 Mathematical Model of Quarter-Car Active Suspension System  
### Mathematical Model  
The quarter-car model with active suspension is mostly considered by experts as it holds a simple design. So, it can effectively be expanded to full vehicle model. In Figure 1, quarter-car model is given which is subjected to uneven road surface excitation. In this model, the mass supported by suspension refers to the sprung mass, while wheel and shaft are included in the unsprung mass. Mathematical modeling helps in representing the state-space model of system. The mathematical expression for the quarter-car are obtained by taking the sum of forces acting in the vertical direction on the sprung and unsprung masses. The numerical values of parameters of quarter-car suspension model is presented in Table 1.  
This model has dynamic characteristics that helps in understanding the system behavior by explaining the correlation between input and output of the system.  
Figure 2 represents two-degree of freedom (DOF) quarter-car model, where \(X_{c}\) and \(X_{wh}\) represent vertical displacement of sprung and unsprung masses. Furthermore, \(F(t)\) represents control input and \(X_{g}\) is the road surface input of the system.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model Parameters** & **Symbol Representation** & **Values** & **Standard Units** \\ \hline \(d_{wh}\) & Damping Unsprung Coefficient & 1000 & \(\mathrm{Ns/m}\) \\ \(d_{c}\) & Damping Sprung Coefficient & 18,000 & \(\mathrm{Ns/m}\) \\ \(C_{wh}\) & Stiffness Value of Wheel & 16,182 & \(\mathrm{N/m}\) \\ \(C_{c}\) & Stiffness Value of Suspension Spring & 190,000 & \(\mathrm{N/m}\) \\ \(M_{c}\) & Sprung Mass or Vehicle Body Mass & 290 & \(\mathrm{Kg}\) \\ \(M_{wh}\) & Unsprung Mass or Suspension Mass & 60 & \(\mathrm{Kg}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: List of parameters of quarter-car model.  
Figure 1: Active suspension system [29].  
Figure 2: Suspension models of quarter-car (**a**) Passive suspension system (**b**) Active suspension system.