esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Reactivity of Biocarbon Briquettes  
The reactivity was measured for biocarbon briquettes from selected recipes (R10, R18 and R24) and the results are compared to fossil carbon briquettes (R0, CBB and FC), as shown in Figure 6. FC exhibited the lowest weight loss (\(\sim\)10%) and consequently, the lowest reactivity after gasification with CO\({}_{2}\) at 1100 \({}^{\circ}\)C for 1 h, while R18 demonstrated the highest weight loss (\(\sim\)54%) among the tested briquettes, which can be attributed to the higher content of biocarbon (74% charcoal). The gasification trend of CBB, FC and R0 were similar and the weight loss was in the order of CBB \(>\) R0 \(>\) FC. The lowest weight loss (\(\sim\)31%) of biocarbon briquettes was revealed by R10, which can be attributed to the lower content of biocarbon (25% Charcoal 2). It can be concluded that the biocarbon content in the briquettes is the predominant factor affecting the reactivity of the briquettes. All biocarbon samples showed a weight loss of \(\sim\)8-12% at 250 \({}^{\circ}\)C and \(\sim\)15-30% at -800 \({}^{\circ}\)C, which can be attributed to the decomposition of molasses, dehydration of lime and cement and the devolatilization of charcoal. At temperatures between 800 and 1100 \({}^{\circ}\)C, the gasification took place in all types of briquettes with different rates according to the reactivity of embedded carbon. The effect of volatiles and dehydration can be neglected at 1100 \({}^{\circ}\)C and the change in weight can be only attributed to the gasification of fixed carbon with surrounding CO\({}_{2}\) gas.  
To ensure that the gasification rate is not related to the differences in starting carbon content in the sample, normalization was conducted to neutralize the effect of carbon content. Equations (1)  
Figure 4: Average cold compression strength of developed biocarbon briquettes R0–R27.  
Figure 5: Average splitting tensile strength (STS) of developed biocarbon briquettes R0–R27.  
and (2) are used to calculate the apparent gasification rate (R\({}_{Apparent}\)) and the normalized gasification rate (R\({}_{Normalized}\)), respectively. Figure 7 shows the normalized gasification rate in fossil and biocarbon biquettes. In general, the reactivity trend can be classified into three sets; low-reactive biquettes (FC, R0, CBB), middle-reactive biquettes (R10) and high-reactive biquettes (R18 and R24). The relatively lower apparent gasification rate of R24 compared to R18 can be attributed to the effect of silica suspension, which reduces the reactivity of carbon, as was reported elsewhere [15].  
\[\text{R}_{Apparent}=\frac{\Delta\text{W}_{t}}{\text{W}_{0}\times\Delta t} \tag{1}\]  
\[\text{R}_{Normalized}=\frac{\text{R}_{Apparent}}{\text{X}_{Fixed~{}C}} \tag{2}\]  
where \(R_{Apparent}\): apparent gasification rate (%/min); \(\Delta\text{W}_{t}\): weight change of sample at time t (mg); \(\text{W}_{0}\): initial weight of sample (mg); \(R_{Normalized}\): normalized gasification rate (1/min); \(\text{X}_{Fixed~{}C}\): total content of fixed carbon in the recipe (%).
## 4 Results  
This section begins by presenting the performance of single-constellation Galileo, single-constellation GPS, and combined Galileo/GPS PPP time transfer in static mode. Subsequently, it investigates and analyzes the performance of single-constellation Galileo, single-constellation GPS, and combined Galileo/GPS PPP time transfer in kinematic mode.
# Effects of Resonant Electromagnetic Fields on Biofilm Formation in _Pseudomonas aeruginosa_
## 3 Results
### Biofilm Formation Is Consistently Inhibited at a Specific Frequency  
PAO1 bacteria in 96 well microtiter plates were exposed for 20 h on six different occasions (series) to an oscillating field of 3.9 Hz (130 nanoT AC overlayed on a 10 microT DC/static field). As a control, PAO1 bacteria in 96-well microtiter plates were left unexposed inside the shielded chamber for 20 h (labelled 'Zero').  
Figure 4 illustrates that a reduction of bacterial biofilm formation was consistently observed when PAO1 cultures were exposed to an oscillating field of 3.9 Hz. An average of 27% reduction was observed over six experimental series ranging from 16% to 41% reduction (see Figure 4). Statistical significance was calculated using a \(t\)-test. \(p\)-values ranged from \(p\) < 2 \(\times\) 10\({}^{-8}\) to \(p\) < 5 \(\times\) 10\({}^{-26}\), showing significant changes in bacterial biofilm formation after exposure to the oscillating field versus control. Since each microtiter plate consists of 96 technical replicates, the degrees of freedom are significant, and thus strengthen the calculated \(p\)-values.  
Figure 3: Effect of ambient versus shielded conditions on Biofilm Adhesion Units (ratios of OD590/OD600) and normalized to control condition “Zero/Shielded (value at 100)”. Data points and standard deviations shown for five experimental series (A–R) under two conditions: “Ambient/room”, “Zero/shielded”. Y-axis show normalized biofilm ratios relative to the “Zero/shielded” condition (value 100).  
Since planktonic growth was not affected (see Figure 2), the exposure of PAO1 bacteria to a combination of a static (DC) magnetic field of 10 microTesla overlaid with a 3.9 Hz oscillating (AC) magnetic field of 130 nanoTesla may affect the ability of the tested cells of _P. aeruginosa_ to adhere and thus develop infectious biofilms.
## 3 The Dynamic Reliability Evaluation and Critical Component Identification Method for Power System
### Load Loss Calculation Model  
In this paper, the reliability levels of power systems are characterised by the expected energy not supplied (EENS).  
\[EENS=8760*\sum_{i\in S}\alpha_{i}\cdot E_{i}\cdot P_{i} \tag{33}\]  
where \(P_{i}\) is the probability of the system under state \(i\); \(S\) is a set of critical states; \(E_{i}\) represents the load loss of the system under state \(i\). And,  
\[\alpha_{i}=\left\{\begin{array}{cc}1,&\text{E}_{i}\textgreater 0\\ 0,&\text{E}_{i}\leq 0\end{array}\right. \tag{34}\]  
When a power system outage occurs, the optimal power flow (OPF) model is used to schedule to minimise as much as possible the load shedding of the power system under the constraints of power balance and line capacity. The reliability levels of the power system are computed using the quantity of load shedding during a system outage and the probability of its occurrence.  
Due to the large number of states that must be calculated during the reliability evaluation of a power generation and transmission system, the optimal power flow model based on the DC power flow is frequently used to reduce the number of calculations. It can be expressed mathematically as Equations (35)-(40).  
\[\min\sum_{i\in ND}C_{i} \tag{35}\]  
Subject to  
\[T(S)=A(s)(PG-PD+C) \tag{36}\]  
\[\sum_{i\in NG}PG_{i}+\sum_{i\in ND}C_{i}=\sum_{i\in ND}PD_{i} \tag{37}\]  
\[PG_{i}^{\min}\leq PG_{i}\leq PG_{i}^{\max}(i\in NG) \tag{38}\]  
\[0\leq C_{i}\leq PD_{i}(i\in ND) \tag{39}\]  
Figure 2: The two-dimensional truncation strategy.  
\[|T_{k}(S)|\leq T_{k}^{\max}(k\in L) \tag{40}\]  
where \(T(s)\) is the line active power flow under state S; \(A(s)\) is the correlation matrix between the line active power vector and the node injection power under state \(S\); \(PG\) is the output active power of the generator set; \(PD\) is the load power of the load bus; \(C\) represents the load loss of load buses; and \(PG_{i}\), \(PD_{i}\), \(C_{i}\), and \(T_{k}(s)\) are the elements in \(PG\), \(PD\), \(C\), and \(T(s)\), respectively. \(PG_{i}^{\min}\), \(PG_{i}^{\max}\), and \(T_{k}^{\max}\) are the maximum values of \(PG_{i}\) and \(T_{k}(s)\), respectively. \(NG\), \(ND\), and \(L\) are the set of power bus, load bus, and branch, respectively.
## 3 Test Results
### Visual Observation and Failure Modes
#### 3.1.2 Shear Performance Testing  
The shear test parameters encompass the shear span ratio, the thickness of the composite material grid, and a comparison between using a composite material grid or steel at the bottom.  
For this test, four sets of composite material grid concrete beams were used. The bottom grid strips were 9 mm thick, and the side grid strips were 3 mm and 5 mm thick. The side grid strips' role in shear resistance was thoroughly investigated. Two HRB400 steel bars with an 18 mm diameter were utilized at the bottom of the remaining two sets of composite material grid concrete beams.  
During the preparation of the composite material grid framework, the dimensions of the framework were 150 mm \(\times\) 320 mm \(\times\) 1250 mm, and the binding process was the same as the one used in the bending test for the beam. In the case of the HBGCS-3 and HBGCS-5 beams, two HRB400 steel bars were used at the bottom to replace the composite material grid strips.  
Strain gauges were affixed at two distinct positions to monitor the tensile strain of the bottom composite material grid strips/steel bars and the strain of the side grid strips during the shear test.  
As shown in Figure 7, for BGCS-3-a and BGCS-3-b, the test beam shear span ratios were 0.8 and 1.16, respectively, and the side grid thickness was 3 mm. A vertical crack with a height of 35.2 mm developed at the base of the test beam's pure bending section when the test load reached 83 kN. When the load reached 193.3 kN, the concrete on the test beam's upper and lower surfaces was crushed, producing a loud breaking sound of the BFRP grating strips, and the test beam experienced shear compression failure. At the same time, the test beam produced a tearing sound of BFRP and concrete spalling appeared on the surface.  
The test beams for BGCS-5-a and BGCS-5-b had shear span ratios of 0.8 and 1.16, respectively, and the side grid sheet thickness was 5 mm. The test beam's pure bending section experienced its first vertical crack, measuring 36 mm in height, when the test force hit 79.1 kN. The BFRP grating began to rip at a higher volume when the force reached 270 kN, and the test beam sheared.  
The side grid sheet thicknesses for HBGCS-3 and HBGCS-5 were 3 mm and 5 mm, respectively, and the shear span ratio was 1.16. The composite grid was replaced at the  
Figure 6: Experimental phenomenon: (**a**) BGC-5-a; (**b**) BGC-3-a; (**c**) BGC-1-a; (**d**) grid break; (**e**) grid damage; (**f**) concrete damage.  
bottom with two HRB400 steel bars. Since steel bars were used at the bottom end of the test beam instead of a composite grid, the test beam clearly yielded during the loading phase. The load value on the hydraulic testing equipment started to drop when the 220 kN peak load was attained. The two test beams failed by barometric failure when the load reached 110 kN and the concrete at the support fell.
## 2 Preparation
### Data
#### 2.2.5 Meteorological Data  
We produced the national daily raster images with 500 m spatial resolution by interpolating the meteorological recordings from 753 meteorological observation stations using the Inverse Distance Weighting (IDW) method from 2009 to 2012. The total daily solar radiation was computed by summing up the observed solar duration for every hour during the sun course of a day as Equation (2) [39]:  
\[S=1370/10^{6}\cos\theta\times day\ length\times 3600\times(0.1545+0.5489T/day \ length) \tag{2}\]  
where \(S\) is the total daily solar radiation (MJ/m\({}^{2}\)/day); \(\theta\) is the solar zenith angle (degree) for a specific hour during the sun course of a day; \(day\)_length_ is the daily possible maximum solar duration (hour); \(T\) is the observed solar duration (hour). In the process, we also quantitatively described the effect of increasing altitude on temperature by assuming that temperature will decrease by 6\({}^{\circ}\) for each elevation increase of 1000 m. Then, five meteorological raster layers were generated including daily precipitation (mm), daily maximum temperature (Celsius), daily minimum temperature (Celsius), daily relative humidity (%), and daily solar duration (hour). Finally, corresponding meteorological map layers for our study area were masked based on the boundary vector layer and resampled down to 30 m spatial resolution with the IDW method.
## 3 System Model Design
### Forwarding Messages by the Feedback Mechanism  
As mentioned in the previous sections, each node in the network first gathers enough and accurate information about the state of the network, and then begins to calculate the transmission preference with the destination through the fuzzy evaluation of mobile and social similarities. If there exists a higher similarity between a pair of nodes, there will be a higher probability of successful data transmission between them. Therefore, we proposed that the source node, or message carriers, would seek the future two-hop nodes that possess a higher transmission preference with the destination, rather than their own, as the suitable relay nodes. In the data transmission process of opportunistic social networks, this efficient method is called the feedback mechanism.  
Ordinarily, traditional routing algorithms in opportunistic social networks select a suitable relay node only from the neighbors, but the feedback mechanism in our proposed algorithm pays attention  
Figure 4: The controlling result of three membership functions.  
to not only the neighbor nodes, but also the second-hop nodes. As shown in Figure 4, the source node \(A\) needs to send a message to the destination \(N\). After the warming-up phase, each node obtains detailed information about the state of the network and computes the transmission preference value using the destination \(N\). After that, the source node \(A\) starts to collect and compare the transmission preference values from its future two-hop nodes. The source node forwards messages to the future two-hop nodes, which have a higher transmission preference with the destination than its own. In Figure 5, the nodes \(B\), \(C\), and \(D\) are neighbors of the source node \(A\); the nodes \(E\), \(F\), and \(G\) are neighbors of the node \(B\). Because \(T(E,N)>T(A,N)\) and \(T(B,N)>T(A,N)\), the source node \(A\) only sends messages to the neighbor node \(B\), and the node \(B\) then forwards messages to the second hop \(E\). Finally, data packages will be delivered to the destination node \(N\) based on the feedback mechanism.
# The Opto-Electronic Functional Devices Based on Three-Dimensional Lead Halide Perovskites
## 3 The LHP Based Functional Devices
### Modulators
reported a CsPbBr\({}_{3}\) QDs/silicon hybrid THz modulator, which operated in 0.23-0.35 THz and was driven by a 450 nm laser [114]. Under 2 W/cm\({}^{2}\) pump light, a 45.5% MD and 2.5 MHz modulation frequency is achieved. Although the MD is less than that for the MAPbI\({}_{3}\)/silicon device, CsPbBr\({}_{3}\) QDs degrades in a slower rate than MAPbI\({}_{3}\) and possibly leads to a longer lifespan. To achieve a multiband amplitude modulation, [PERSON] et al. theoretically proposed a LHP QDs/metamaterial THz modulator [116]. The metamaterial unit was composed of a split-ring resonator (SRR) on the top and concentric double rings on the bottom. The LHP QDs were deliberately filled in the split gap of the SRR. With the presence of the split-ring, three resonant frequencies existed, leading to very low transmission (nearly goes to zero) at these frequencies. When the pump light was on, however, the conductivity of the LHP varied, and the split-ring changed into a metallic one. This consequently results in the alteration of resonant states and THz transmission modulation (at 0.42 THz, 0.83 THz, 1.18 THz, and 1.62 THz). The modulation speed was estimated to be larger than 3 MHz.
###### Abstract  
The purpose of this study was to measure and correlate the fitness and trueness of a 3-unit fixed dental prosthesis (FDP) fabricated using two digital workflows. The 3-unit FDPs were fabricated using two digital workflows (\(N=15\)). The digital workflows were divided into chairside (closed type) and in-lab (open type) groups. The scanning, computer-aided design (CAD), and computer-aided manufacturing (CAM) processes were conducted with 3 shape E1 scanner, exocad CAD software, and DDS EZIS HM, respectively, in the in-lab group; and with CEREC omnicam intraoral scanner, CEREC CAD software, and CEREC MC XL, respectively, in the chairside group. The fitness of the fabricated 3-unit FDPs was evaluated by scanning the silicone replica of the cement space and analyzing the thickness of the silicone replica in the three-dimensional (3D) inspection software (Geomagic control X). The trueness of the milling unit was analyzed by 3D analysis of the CAD reference model, which is the design file of the 3-unit FDP, and the CAD test model, which is the scanned file of the 3-unit FDP. In the statistical analysis, comparison of the two groups was conducted by Mann-Whitney U test, and the correlation between the fitness and trueness was conducted by Pearson correlation test (\(\alpha=0.05\)). The marginal and internal fit were significantly lower in the in-lab group at all measurement positions (\(p<0.001\)). The trueness of the milling unit was significantly higher in the in-lab group compared to the chairside group (\(p<0.001\)). There was a positive correlation between the trueness and internal fit (correlation coefficient = 0.621) in the in-lab group (\(p=0.013\)). The use of appropriate equipment in an in-lab (open type) digital workflow enables a better fabrication of 3-unit FDPs than a chairside (closed type) digital workflow, and poor trueness on the inner surface of the crown adversely affects the internal fit.  
marginal and internal fit; trueness; dental CAD-CAM; dental ceramic; dentistry
# Intelligent Fault Diagnosis of Unbalanced Samples Using Optimized Generative Adversarial Network
## 3 Results
### Case I: CWRU Dataset  
#### 3.1.1 Data Description  
The case data are from the CWRU dataset [38] generally employed for fault diagnosis. The experiment platform includes electronic control equipment, torque sensors, power meters, and an electric motor. The test bed evaluated the bearings that support the motor. The fault state of the bearing was measured by electrical discharge machining (EDM) technology. For the experiment, the fault data for the drive end bearing were chosen with a speed of 1730 rpm and a sample frequency of 12 kHz. There are nine failure classes including ball fault, inner ring fault, and outer ring fault with 0.007 inches, 0.014 inches, and 0.021 inches, respectively. With normal conditions, ten classes of fault labels are tested.  
In order to ensure that each sample has at least one period of vibration data, samples are defined at 600 data points apart. For the purpose of diagnosing faults, the 1D vibration data were converted into time-frequency images. The preprocessed results of wavelet transform in the CWRU dataset are shown in Figure 7.  
The CWRU dataset was implemented to simulate the environment of sample imbalance with three imbalance ratios, including IR = 4:1, 8:1, and 16:1. Data preprocessing is used for transforming 1D raw signals into feature images. Table 1 shows the sample sizes for different imbalance ratios (IRs) with various classes for Case I. Ten experiments were repeated for each training and test cycle.
## 3 Results
### COX-2 Interactions  
The interaction parameters of the selected terpenes with COX-2, compared to AA, are summarized in Table 2.  
AA exhibits the lowest binding energy (\(-7.07\) kcal/mol), indicating strong interaction with COX-2. Among the analyzed terpenes, camphor has the lowest binding energy (\(-6.48\) kcal/mol), followed by menthol (\(-6.42\) kcal/mol). The lowest Ki value is also observed for AA (\(6.55\)\(\upmu\)M), which is expected given its natural role in the COX-2 enzymatic reactions. Among the terpenes, camphor (\(17.84\)\(\upmu\)M) and menthol (\(19.80\)\(\upmu\)M) exhibit the lowest Ki values, identifying them as potential COX-2 inhibitors. Figure 1: The comparative 3D visualization of the binding interactions between the selected terpenes, arachidonic acid (AA), and the COX-1 enzyme. The observed high RMSD values (>50 A) for all analyzed molecules indicate significant spatial reorientation upon binding. The highest RMSD values are recorded for \(\beta\)-pinene (67.77 A) and menthol (67.75 A), suggesting substantial conformational changes during interaction with the enzyme. Among all studied terpenes, the highest torsional energy is observed for linalool (+1.49 kcal/mol) and AA (+4.47 kcal/mol), indicating greater molecular flexibility. In contrast, \(\alpha\)-pinene and \(\beta\)-pinene exhibit zero torsional energy (+0.00 kcal/mol), suggesting conformational stability upon binding. The lowest unbound energy is recorded for AA (\(-1.18\) kcal/mol), consistent with its high binding affinity. The results indicate that, among all compounds studied, AA exhibits the highest affinity for COX-2, consistent with its role as the enzyme's natural substrate. Among the terpenes, camphor and menthol demonstrate the most favorable parameters, identifying them as potential COX-2 inhibitors. In contrast, linalool and \(\alpha\)-pinene show comparatively weaker interactions, likely due to their higher torsional energy and conformational flexibility. For comparison, previously published docking studies report a mean binding energy of \(-10.78\) kcal/mol for selective COX-2 inhibitors and \(-6.69\) kcal/mol for nonselective NSAIDs, as determined using AutoDock 4 [60]. In this study, camphor (\(-6.48\) kcal/mol) and menthol (\(-6.42\) kcal/mol) show similar binding energies to the nonselective group, but remain significantly weaker than those observed for selective inhibitors. A 3D visualization of the interactions between the terpenes, AA, and the COX-2 enzyme is presented in Figure 2. Ligands are shown in the ball-and-stick representation (light brown), and the enzyme is displayed as a cartoon structure (cyan). Hydrogen bonds and relevant interactions are indicated with dashed lines where applicable. The positioning of each ligand relative to key catalytic residues illustrates potential binding modes and differences in spatial orientation.
# Improving Human Ground Control Performance in Unmanned Aerial Systems
###### Abstract
In particular, we adopted a MultiTouch wall display to design a human-machine interface for a GCS (Ground Control Station) of a fleet of drones, conceived to allow collaboration among multiple operators. The working conditions of the control room operators are characterized by high cognitive workload in terms of user's attention and decision processes both in daily routine and extraordinary/unexpected circumstances. Therefore, the concept of situation awareness acquires a collective meaning, shifting the focus from the SA of the individual operator to that of the entire team of decision makers, resulting in a multidimensional feature that researchers call Team Situation Awareness (TSA) [8; 9; 10]. Maximizing TSA has been our major interface design challenge. The study we present is meant to test the hypothesis that shared displays may improve team performance [11; 12]. To do this, in the experiment the displays are manipulated within a simulation of a mission, and the performance assessed in routine operating conditions and in the event of unexpected events. The results show that the shared displays do not universally improve team performance, but they help maintain performance in off-nominal situations. Hence, it can be assumed that in those situations the operators will deliver better performance, relying on a higher level of situation awareness. The paper is organized as follows. Section 2 illustrates some related work on the Ground Control Station interfaces, on the team situation awareness and the shared displays and team performances. Section 3 illustrates the system requirements that may impact the interface design choices, the description of the GCS interface prototype, and the system architecture. Section 4 discusses the study carried out to evaluate the team performance and situation awareness of the use of the prototype on a shared screen, and the related results. Section 5 concludes the document.