esa-sceva/satcom-chunk-collection huggingface dataset:
# Organizational Culture: The Key to Improving Service Management in Industry 4.0
## 4 Results
### Research Hypotheses Validation and Discussion  
To ensure rigor, this study used the bootstrap method (bootstrapping) to test the research hypotheses. Bootstrapping uses the observed sample to estimate the parent and then re-samples the estimated parent to implement statistical inference. In most cases, the approximation provided by bootstrapping is more precise than the commonly used limit approximation and works well for small data sets [116]. Its advantage lies in its ability to correct for the limitation that the study model does not conform to the normal distribution, to obtain more precise estimates [117]. As suggested by [PERSON], [PERSON], and [PERSON] [106],  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**HTMT** & **Incentives** & \begin{tabular}{c} **Organizational** \\ **Commitment** \\ \end{tabular} & \begin{tabular}{c} **Organizational** \\ **Culture** \\ \end{tabular} & **Leadership** & \begin{tabular}{c} **Service** \\ **Management** \\ \end{tabular} \\ \hline Incentives & - & - & - & - & - \\ \hline Organizational Commitment & 0.838 & - & - & - & - \\ \hline \begin{tabular}{c} Organizational Culture \\ \end{tabular} & 0.727 & 0.652 & 0.788 & - & - \\ \hline \begin{tabular}{c} Leadership \\ \end{tabular} & 0.858 & 0.893 & 0.844 & 0.679 & - \\ \hline \begin{tabular}{c} **Fornell–Larcker Criterion** \\ \end{tabular} & **Incentives** & \begin{tabular}{c} **Organizational** \\ **Commitment** \\ \end{tabular} & \begin{tabular}{c} **Organizational** \\ **Culture** \\ \end{tabular} & **Leadership** & \begin{tabular}{c} **Service** \\ **Management** \\ \end{tabular} \\ \hline Incentives & 0.834 & - & - & - & - \\ \hline \begin{tabular}{c} Organizational Commitment \\ \end{tabular} & 0.760 & 0.820 & - & - & - \\ \hline \begin{tabular}{c} Organizational Culture \\ \end{tabular} & 0.770 & 0.725 & 0.745 & - & - \\ \hline \
\begin{tabular}{c} Leadership \\ \end{tabular} & 0.680 & 0.596 & 0.746 & 0.838 & - \\ \hline
\begin{tabular}{c} Service Management \\ \end{tabular} & 0.783 & 0.797 & 0.762 & 0.618 & 0.804 \\ \hline \hline \end{tabular}
\end{table}
Table 4: HTMT and Cornell–Larker Criterion.  
The results of this study show that organizational culture is the most important factor affecting service management, followed by leadership. In the past, service management was handled by leaders or service supervisors, so leadership would influence the success or failure of service management; however, for the cooperative type of Industry 4.0 networking, the decision-making process would be dispersed between people and machines through the data and information circulating among the enterprise modules, and the influence of organizational culture would be highlighted instead. Therefore, how to motivate employees and how they treat the management level of the system will still be factors that affect service management.
## 2 Monitoring Protocols and Methods
### Regulatory Aspects
#### 2.7.1 Regulations Governing Baseline Monitoring  
A set of gas species (CO\({}_{2}\), O\({}_{2}\), CH\({}_{4}\), \({}^{4}\)He and \({}^{222}\)Rn for soil concentrations, CO\({}_{2}\) and CH\({}_{4}\) for soil flux) was regularly monitored at the 36 monitoring points. These data were used as input for defining seasonal changes in the soils overlying the detrital rock formations that originated from the dismantling of the Pyrenees forefront.
## 2 Preparation and Methodology  
### Fabrication of Mechanochromic Sensor  
The fabrication process for the mechanochromic sensor is illustrated in Figure 1. A 780 nm monolayer of polystyrene particles (Bangs Laboratories, Inc., Fishers, IN, USA) was assembled using a spin-coating method, as previously reported [15]. Subsequently, it was etched in an oxygen-reactive ion chamber (RIE) to reduce its mean diameter. A 200 nm (Cr) film was then deposited on the substrate using an e-beam evaporator. After peeling off the nanoparticles using adhesive tape, a master mold with concave nanostructures was obtained for further replication on a polydimethylsiloxane (PDMS) substrate. In this study, a PDMS substrate with convex nanostructures (nanobumps) on its surface served as a mechanochromic sensor.  
Since the color on the sensor is generated by the interaction between the incident light and the periodic nanobumps, its color is determined by the periodicity of the nanobumps if the angle of incident light is fixed. When the sensor is stretched to certain strain (\(\varepsilon\)), the initial lattice constant (d\({}_{0}\)) shifts to d\({}^{\prime}=\) d\({}_{0}(1+\varepsilon)\)[16]. The diffracted wavelength of the undeformed sensor (\(\lambda_{0}\)) is determined by the incident angle (\(\theta_{1}\)) and diffracted angle (viewing angle, \(\theta_{2}\)). Therefore, the diffracted wavelength shift (\(\lambda^{\prime}\)) due to deformation is given by:  
\[\lambda^{\prime}=\lambda_{0}(1+\varepsilon) \tag{1}\]  
Figure 2 shows the reflection spectra of the sensor as a function of wavelength to depict its color variation for different strain levels. This result was obtained under conditions where the incident angle (\(\theta_{1}\)) and the viewing angle (\(\theta_{2}\)) were set to 40\({}^{\circ}\) and 0\({}^{\circ}\), respectively. The initial color of the sensor was blue. As the strain increased, the peak of wavelength  
Figure 1: Fabrication and structure of mechanochromic sensor, where RIE, Cr, and PDMS stand for oxygen-reactive ion chamber, chromium, and polydimethylsiloxane, respectively.  
spectrum shifted to a higher region of wavelength (Figure 2a). At strains of 0%, 10%, 20%, 30%, 40%, and 50%, the sensor displayed blue, light blue, green, yellow, orange, and red colors, respectively. It was noted that the reflection intensity became weaker with increasing the strain. This might be because tensile deformation caused wrinkles on the surface of the sensor [7] and contraction of the nanostructure in height due to the Poisson effect [17].  
As indicated in the above equation, the wavelength changed linearly with the strain. The measured wavelengths corresponding to various strain levels were accurately predicted by Equation (1) (gray arrow in Figure 2b).
# Enhancing Phishing Email Detection through Ensemble Learning and Undersampling
###### Abstract  
In real-world scenarios, the number of phishing and benign emails is usually imbalanced, leading to traditional machine learning or deep learning algorithms being biased towards benign emails and misclassifying phishing emails. Few studies take measures to address the imbalance between them, which significantly threatens people's financial and information security. To mitigate the impact of imbalance on the model and enhance the detection performance of phishing emails, this paper proposes two new algorithms with undersampling: the Fisher-Markov-based phishing ensemble detection (FMPED) method and the Fisher-Markov-Markov-based phishing ensemble detection (FMMPED) method. The algorithms first remove benign emails in overlapping areas, then undersample the remaining benign emails, and finally, combine the retained benign emails with phishing emails into a new training set, using ensemble learning algorithms for training and classification. Experimental results have demonstrated that the proposed algorithms outperform other machine learning and deep learning algorithms, achieving an F1-score of 0.9945, an accuracy of 0.9945, an AUC of 0.9828, and a G-mean of 0.9827.  
phishing emails detection; imbalance; undersampling Article
## 3 Ambient Parameter Monitoring and Sensor Technology
### Ethylene  
The gaseous plant hormone ethylene enhances metabolic processes in many fruit and vegetables, but especially stimulates ripening in climacter fruits [23]. Already extremely low concentrations can affect sensitive products [23; 50]. Detrimental effects include accelerated senescence [23], loss of chlorophyll and yellowing in green-colored vegetables [16; 22; 23], accumulation of bitter compounds in carrots and parsnips [16; 22; 51] and excessive flesh softening, e.g., in kiwifruit and watermelons [51; 52]. Unfavorable conditions can arise when multiple commodities are stored together, since the presence of ethylene producing products can lead to quality decline in sensitive ones [18]. Even when ethylene is intentionally added for controlled ripening and de-greening purposes, the concentrations should be monitored and controlled carefully [53]. Therefore, real-time monitoring of ethylene concentrations would be highly valuable during transport and storage of climacter fruits and products that are sensitive to exposure. Several technological approaches exist; however, challenges regarding specificity, selectivity, price, size and stability in a harsh measurement environment remain [50; 51]. Until now, ethylene sensors with sufficient accuracy are too costly for an application in IoT devices, but promising progress is being made, particularly in the field of chemiresistors, chemicapacitors and NDIR spectroscopy [6; 50]. Another possibility for further cost reduction could be the development of \"threshold\" sensors that only transmit data if the ethylene concentration reaches a certain level, dependent on the respective product [50].
## 3 Results  
### Flood Risk Baseline Map Derived from the Baseline Model  
#### 3.1.1 Analysis of Historical Precipitation and Inundation Scenarios  
Historical precipitation reveals that almost the entire PLEEZ area experienced at least 3 days of heavy rainfall during the annual flood season in the study period, with some localized regions even experiencing 3 days of heavy rainstorms (Figure 4). RMAX3 correlated well with the severity of the actual flood disasters. For instance, in 2017 and 2020, which witnessed more significant flooding than other years, the RMAX3 values were notably higher. Conversely, RA and RF failed to reflect the hazard extreme in the 2020 megaflood (illustrated in Figures S1 and S2 in the Supplementary Materials, and also see other supplementary figures and tables). Given the high linear correlation of over 0.80 between the multiyear average of these two factors and RMAX3, we determined RMAX3 to be the effective climatic hazard indicator for measuring rainfall intensity. In fact, continuous rainstorms had the greatest impact on flood formation. Among the maximum 1- to 7-day rainfall factors, RMAX3 best reflected flood damage [81]. During the 2020 megaflood, the northern region suffered continuous severe rainstorms (Figure 4), with precipitation exceeding 300 mm over an area of 2992 km\({}^{2}\), more than twice the extent seen in 2017 (Figure S3). The average RMAX3 of the grids in the entire region was 193.8 mm. The fact that both RA and RF were smaller in 2020 than in 2017, while RMAX3 in 2020 was larger than in 2017, further reveals that the 2020 storms were more concentrated and violent, indicating the extreme nature of the hazard.  
We traced the annual flood evolution and extracted the maximum range of inundation that could cause damage (Figures S4 and S5). The two major flood events shown in Figure 5 occurred in 2020 and 2017. The progressive inundation of the lakeshore area shown in Figure 5a,d, from the saucer-like lake shore to the surrounding lands, illustrates the flooding process from the lake rising stage to the flooding stage. In the flood receding stage, the inundated areas tended to be larger and scattered on the south side of the main lake and along the rivers (Figure 5b,e), where cultivated land is widely spread. Combined with the inundation area (Figures 5c,f and S5, Table S2), it can also be confirmed that the 2020 flood was more extreme due to wider spatial impact compared with other years. Ultimately, the historical flood inundation frequency (Figure 6b), as a critical indicator reflecting flood hazard, was obtained by superimposing the inundation areas for each year in Figure S5.
## 2 Pump-Probe Time-Resolved SFX Experiments
The structural changes in PSII induced by two-flash illumination were observed at a resolution of 2.35 A while using the setup for TR-SFX at SACLA [8]. In this study, PSII microcrystals that were grown by a batch method were mixed with grease and injected by the high-viscosity sample injector. A hydroxyethyl cellulose matrix served for the TR-SFX of nitric oxide reductase (P450 nor) with a caged substrate to capture the intermediate structures of the enzyme [9]. As such, this result defined the first successful experiment of TR-SFX while using a caged compound that releases a substrate by ultraviolet pump illumination at 308 nm. Recently, not only the optical-fiber-based setup, but also different setups for the pump-probe experiments, have been developed. For instance, the femtosecond laser system [27] can be introduced into the pump-probe setup without optical fibers to trigger ultrafast reactions. For anaerobic samples, the DAPHNIS setup allows for sample injection in the helium chamber and illumination by a pump-laser pulse from one direction without using optical fibers. Additionally, a droplet injector generating pulsed droplets can be used for sample injection in pump-probe TR-SFX with smaller sample consumption than those with a liquid jet [28]. Figure 2: Jet speed measurements using high-speed imaging. (**a**) The photograph shows lipidic cubic phase (LCP) extrusion using the high-viscosity sample injector [21]. (**b**) Time course of LCP extrusion speed. Bacterihodopsin crystals embedded in the monoolein-based LCP were used for high-speed camera testing. The sample stream was recorded at 2000 flames per second using a high-speed camera (FASTCAM SA-Z). Three data points from each period of high-speed camera video (0.5 s of recording every 60 s) were plotted. The horizontal dotted line indicates that the target extrusion speed set via the high-performance liquid chromatography (HPLC) pump. Experimental condition: The sample, which was prepared according to the previous method [7], was extruded at a flow rate of 2.8 uL/min as setting value of the HPLC pump from the nozzle with an inner diameter of 75 μm using the high-viscosity injector [21]. A nitrogen sheath gas supplied from the outer nozzle of the injector was set to a flow rate of 0.7–1.0 L/min.
## 4 Materials and Methods  
The contribution of our paper is a deep learning-based computer vision model to semantically infer the magnitude of damage to individual buildings after natural disaster from pre- and post-disaster satellite images. This model fits into a larger pipeline that allows us to identify buildings from satellite images, infer the damage magnitude, and display the results for all buildings within the satellite image. This pipeline is composed of our best performing model, which we will call the classification model, a segmentation model that is used for identifying each building within the satellite images, and some image-based utility functions to adjust the inputs and outputs of the classification and segmentation models for each step in the pipeline appropriately.
# Feature-Oriented CBCT Self-Calibration Parameter Estimator for Arbitrary Trajectories: FORCAST-EST
###### Abstract  
Background: For the reconstruction of Cone-Beam CT volumes, the exact position of each projection is needed; however, in some situations, this information is missing. Purpose: The development of a self-calibration algorithm for arbitrary CBCT trajectories that does not need initial positions. Methods: Projections are simulated in a spherical grid around the center of rotation. Through using feature detection and matching, an acquired projection is compared to each simulated image in this grid. The position with the most matched features was used as a starting point for a fine calibration with a state-of-the-art algorithm. Evaluation: This approach is compared with the calibration of nearly correct starting positions when using FORCASTER and CMA-ES minimization with a normalized gradient information (NGI) objective function. The comparison metrics were the normalized root mean squared error, structural similarity index, and the dice coefficient, which were evaluated on the segmentation of a metal object. Results: The parameter estimation for a regular Cone-Beam CT with a 496 projection took 1:26 h with the following metric values: NRMSE = 0.0669; SSIM = 0.992; NGI = 0.75; and Dice = 0.96. FORCASTER with parameter estimation took 3:28 h with the following metrics: NRMSE = 0.0190; SSIM = 0.999; NGI = 0.92; and Dice = 0.99. CMA-ES with parameter estimation took 5:39 h with the following metrics: NRMSE = 0.0037; SSIM = 1.0; NGI = 0.98; and Dice = 1.0. Conclusions: The proposed algorithm can determine the parameters of the projection orientations for arbitrary trajectories with enough accuracy to reconstruct a 3D volume with low errors.  
CBCT; cone-beam CT; computed tomography; calibration +
Footnote †: journal: Applied Sciences
## 3 Results and Discussion
### Thermogravimetric Analysis  
According to the TGA data (Figure 3 and Table 3) a single weight-loss step was observed for all samples between 300 and 500 \({}^{\circ}\)C. The thermal stability of the 25% and 30% glass fiber composites is similar to that of the composite with treated ash powder and glass fiber where the temperature at the maximal rate of decomposition only varies by a maximum of 1 \({}^{\circ}\)C. The lowest thermal stability is associated with PP-25G-E, with a difference of almost 24 \({}^{\circ}\)C compared to PP-25G and PP-30G. A study by [PERSON] et al. [40] noted that the addition of glass fiber in neat polypropylene can lead to an increase in thermal stability of polypropylene composites due to possible changes in the PP crystalline structure and the improved transfer of heat characteristics of glass fiber. It can be seen from Table 3 that E starts to decompose and decomposes at the maximum rate at temperatures 12 \({}^{\circ}\)C lower than PP-25G. Added to PP-25G, E interacts through the ethylene-butylene blocks with PP and affects its crystallization process, having a strong nucleation effect. This behavior would be due to an under-cooling effect, which is reflected in the decrease in Tonset. The addition of treated ash powder in the final composite (PP-25G-C-E) leads to an increase in thermal stability compared to PP-25G-E and a smaller difference (16 \({}^{\circ}\)C) between this composite and the glass fiber ones (PP-25G and PP-30G). The decomposition temperature of C far exceeds that of PP-25G, and as pointed out from EDX analysis and  
Figure 2: FTIR spectra of PP-25G, PP-25G-C, PP-25G-E and PP-25G-C-E composites.  
from previous determined chemical oxidic composition [12], C consists mostly of Si. It is possible that the silica particles strongly restricted the thermal decomposition of PP. Similar to a study by [PERSON] et al. [41], good dispersion of silica particles between PP chains caused by the addition of compatibilizing and dispersion agents may also lead to an enhancement of thermal stability. In our case, there is a strong interaction between the surface-treated ash, the compatibilizing agent and the PP matrix through propylene, -OH and ester groups from poly(propylene glycol adipate). The XRD results presented in Section 3.1 proved an increase in the crystallinity and orientation indices as well as the crystallite size, which is reflected in an increase in thermal stability. A previous study by [PERSON] et al. [12] also showed similar improvements in thermal stability for recycled PP with the introduction of ash powder. This behavior is also confirmed by the data in Table 2, where the temperature at which the samples lose 5% of their weight has small variations between PP-25/30G, PP-25G-C and PP-25G-C-E (5-8 \({}^{\circ}\)C) and large variations between PP-25/30G and PP-25G-E (29-32 \({}^{\circ}\)C).
## 1 Introduction
Furthermore, the VLC system is different from the fiber system and the performance of the PS needs to be verified. The optimal SE of PS-256 QAM is investigated in the entropy loading discrete multi-tone system [25]. This study is carried out in a multi-carrier modulation system, which will inevitably suffer from high peak to average power ratio (PAPR). Additionally, the optimal QAM order of PS at different SNR is not revealed. In the VLC system, there is little research on the selection of the PS QAM order and SE, which can meet the specific NGMI threshold with high AIR. In this paper, we proposed a novel PS QAM order and SE selection scheme that can realize a high AIR at the lowest computation complexity, and meet the NGMI threshold. According to the channel SNR and the proposed schemes, the SE can be adjusted to meet the NGMI threshold. The AIR loss to the highest AIR is approximately 0.1 bpQs, while the computation complexity is the lowest among other possible combinations of SE and QAM order. The simulation results show that the NGMI of the PS signal fluctuates only around the threshold of \(\pm\)0.004. Experimental results proved that by just subtracting 0.1 bpQs SE, the system can work above the NGMI threshold at all the \(\mathrm{V_{PP}}\), and the fluctuation range of the experimental result is only between the threshold of \(\pm\)0.005. The following sections are organized as follows: the principle of the GMI, NGMI, and signal distribution in this paper are shown in Section 2. In Section 3, the experimental setup is introduced. Then, the system performance from PS QAM order 32 to 512 is measured in Section 4. In Section 5, we will draw the conclusion.
###### Abstract  
The reconstruction of optical properties for opaque mediums is highly desired for medical, atmosphere and aerosol applications. However, the modeling and reconstruction process is highly related with multiple scattering phenomena, which elevates both the complexity and computational costs for such efforts. This work introduces a time-based Markov chain method, which uses a sparse transition matrix to represent the likelihood for a photon to transit in the turbid media. The accuracy of the time-based Markov chain model was verified against the forwarding calculations of the scattering-based Markov chain model and Monte Carlo simulations. Then, reconstruction was performed with backscattered photon angular distributions. Based on the characteristics of the sparse transition matrix, the optical properties (droplet diameters) could be obtained layer by layer with transmitted photon distributions at different time durations. It is shown that the time-based Markov chain model can reconstruct the optical properties of a turbid slab with satisfactory accuracy and lower computational costs.  
multiple scattering; optical properties reconstruction; Markov chain; time-based method +
Footnote †: journal: Medical and Applied Sciences