esa-sceva/satcom-chunk-collection huggingface dataset:
# D-Limonene: Promising and Sustainable Natural Bioactive Compound
## 4 Antimicrobial Mechanism of D-Limonene  
### Antibacterial and Antifungal Mechanism of D-Limonene  
#### 4.1.1 Damage to Cell Membranes and Cell Walls  
The most intuitive and obvious effect of D-limonene on microorganisms is the disruption of cell membranes and cell walls (Figure 2). In several experiments, scanning electron microscopy (SEM) and transmission electron microscopy (TEM) showed that the overall shape, cytoplasmic homogeneity, and cell wall thickness of bacterial and fungal cells were significantly changed after D-limonene treatment. In addition, it includes increased cell membrane permeability, altered membrane potential, and decreased heat resistance; leakage of intracellular substances, such as proteins, lipids, and nucleic acids, was also observed and even cell lysis at high concentrations, which may be due to the lipophilic and hydrophobic properties of D-limonene that promote lipid solubilization in the plasma membrane [70, 77, 79, 86, 110, 111, 112, 113, 114]. However, unlike bacteria, D-limonene affects ergosterol and \(\beta\)-1,3-glucan, which are unique to fungi. Ergosterol in fungal cell membranes is re  
Figure 1: The formation of biofilm.  
sponsible for maintaining cell function and integrity and a decrease in its level can lead to cell death [115]; in one study, D-limonene without cosolvent had no effect on the ergosterol content in yeast cell membranes [116] but, in another study, D-limonene-Tween 80 cosolvent could reduce the ergosterol content in the _Saccharomyces cerevisiae_ cell membrane, which may be due to the fact that Tween 80 improved D-limonene permeability [117]. Interestingly, 107 mg/L D-limonene ceased the growth of yeast but D-limonene-Tween 80 cosolvent did not cause a growth disturbance for reasons that are not clear [116]. In addition, preventing biofilm formation by inhibiting the quorum sensing (QS) system and EP system is also one of the properties of D-limonene in inhibiting microbial growth, which will be described in the biofilm section.
## 4 Space-Qualified RF Generator Techniques
### SSB Mixing
It can be concluded that this design, although a valid approach for RF generation in space applications, has many drawbacks compared to the other discussed RF generation techniques.
## 3 Results  
### Microstructure  
In Figure 8, the microstructures after the respective heat treatments are presented. The microstructure after the STA heat treatment is much finer and shows a smaller quantity of large \(\alpha_{\text{p}}\), but more of fine \(\alpha_{\text{s}}\), compared to the as-built microstructure. This can be seen by comparing Figure 8a,b to Figure 1. Due to the solution step below the \(\beta\)-transus temperature (T\({}_{\beta}\)), a large number of \(\alpha\) nucleation sites are being introduced, and therefore a refining of the precipitations takes place. Furthermore, it can be seen that \(\alpha_{\text{p}}\) precipitate either along grain boundaries or grow almost perpendicularly away from the grain boundary into the grains. In Figure 8, very fine precipitated \(\alpha_{\text{s}}\) can be seen. These precipitations are nucleated during the quenching, possibly at the metastable \(\omega\) phase [40; 41], and can grow and rearrange during the aging step [29]. If these \(\alpha_{\text{s}}\) precipitates are held for long times at high temperatures they form triangles [42], which appear to be superior for a high mechanical strength. The mechanical properties are shown in Table 2. Due to the fine precipitations, dislocations can be accumulated conveniently; moreover, hard precipitations show a different deformation behaviour than the surrounding matrix. Hence, a microstructure that leads to a high mechanical strength and low elongations was achieved. When comparing the STA microstructure in Figure 8a,b to the BASCA microstructure in Figure 8c,d, it can be seen that the precipitations are shaped differently. During the annealing step of the BASCA heat treatment, the as-built microstructure was completely dissolved and in the subsequent slow cooling \(\alpha_{\text{p}}\) was precipitated. Due to the long times at high temperatures (>600 \({}^{\circ}\)C), the quantity of large \(\alpha_{\text{p}}\) is very high. When the samples were held at 600 \({}^{\circ}\)C at the aging step \(\alpha_{\text{s}}\) can be precipitated inside the grains, and both (\(\alpha_{\text{p}}\) as well as \(\alpha_{\text{s}}\)) can grow into large precipitations.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Material State** & **YS in MPa** & **UTS in MPa** & **Elongation at Break in \%** \\ \hline As-built\({}_{\text{con.}}\) & \(860\pm 0\) & \(887\pm 71\) & \(21.90\pm 2.9\) \\ As-built\({}_{\text{in situ}}\) & \(820\pm 3\) & \(856\pm 5\) & \(1.1\pm 0.2\) \\ STA\({}_{\text{con.}}\) & \(1360\pm 14\) & \(1460\pm 21\) & \(4.2\pm 1.3\) \\ STA\({}_{\text{in situ}}\) & \(1166\pm 42\) & \(1212\pm 41\) & \(1.21\pm 0.1\) \\ BASCA\({}_{\text{con.}}\) & \(888\pm 15\) & \(971\pm 13\) &
# Concurrent Transmission Based on Distributed Scheduling for Underwater Acoustic Networks
## 3 Concurrent Transmission Based on Distributed Scheduling
### Collisions  
Although the calculation of the sending sequence and time of the source nodes can ensure that the data packets sent by a source node will not overlap with other packets at the corresponding destination node if the source nodes know the correct information about the source and destination node pairs in the current transmission cycle, collisions will still occur when a node loses one or more CTS packets. Assume that there are \(M\) destination nodes in the current transmission cycle and each destination node selects a time in \([0,T_{0}]\) to send its CTS packet uniformly and independently. Then for a source node \(s_{i}\), the arrival time of the CTS packets from the destination node \(d_{j}\) will follow a uniform distribution in \(\left[D_{d_{j}s_{i}},T_{0}+D_{d_{j}s_{i}}\right]\), i.e., \(T_{d_{j}s_{i}}^{cs}\sim\bigcup\left(D_{d_{j}s_{i}},T_{0}+D_{d_{j}s_{i}}\right)\), where \(T_{d_{j}s_{i}}^{cs}=T_{j}^{cls}+D_{d_{j}s_{i}}\) denotes the arrival time of the CTS packets from \(d_{j}\) to \(s_{i}\); \(T_{j}^{cls}\) and \(D_{d_{j}s_{i}}\) are the sending time of the CTS packets from \(d_{j}\) and the propagation delay from \(d_{j}\) to \(s_{i}\), respectively; and U(\(a\), \(b\)) denotes the uniform distribution. An approximate lower bound of the no-collision probability at \(s_{i}\) is given in Appendix A as  
\[P(no\;\text{collision}\;\text{at}\;s_{i})>1-M(M-1)\left(\frac{L^{cls}}{T_{0}}- \frac{1}{2}\left(\frac{L^{cls}}{T_{0}}\right)^{2}\right) \tag{7}\]  
Equation (7) shows that the collision probability at \(s_{i}\) is proportional to the second-order polynomial of \(M\) and become less when \(L^{cls}/T_{0}\) decreases.  
When one or more CTS packets are missing, a source node will miscalculate its sending time table, which will cause collisions in the current transmission cycle. Let \(T_{i-err}^{data}\) denote the miscalculated sending time of \(s_{i}\). Then, the data packet from \(s_{i}\) will conflict with the packet from \(s_{j}\) when:  
\[T_{i,err}^{data}>T_{j}^{data}+D_{s_{j}d_{i}}-D_{s_{j}d_{i}}-L^{data} \tag{8}\]  
or:  
\[T_{i,err}^{data}<T_{j}^{data}+L^{data}+D_{s_{j}d_{j}}-D_{s_{j}d_{j}}. \tag{9}\]  
This miscalculation only affects the current transmission cycle and will not accumulate.
# Navigating the Maps: Euclidean vs. Road Network Distances in Spatial Queries
## 3 Methodology
### Road Network Search
#### 3.5.1 K-Nearest Neighbors Search in Road Network Distance  
The process of obtaining results through the kNN search using the road network distance method closely aligns with the Euclidean distance method. However, a key difference lies in the approach. While the Euclidean distance method calculates the direct distance between a query point and points of interest using a spatial database, the road network distance method acquires distances by utilizing the Open Source Routing Machine (OSRM) API through Python code. This API extracts the shortest route, and the resulting distances are then stored in a specified database table.  
When conducting kNN queries using the road network distance method, data are sorted in ascending order based on the distances in the osrm_dist column. Following this, the results are constrained to the first 20 data points, thereby aligning with the range of k values from 1 to 20. This process reflects the Euclidean search method, where a corresponding SQL query is employed, as shown in Listing 4.  
```
1 SELECT*
2 FROMhospital_q1
3 ORDERBYosm_dist
4 LIMIT20;
```  
Listing 4: SQL statement for retrieving kNN results for the road network distance method.  
In this context, osrm_dist symbolizes the column containing distances measured along the shortest routes on genuine road networks between the query point and POIs. The constraint to the initial 20 data points is influenced by the range of k values, thereby providing a comprehensive set of potential nearest neighbors for the given query.
## 4 Results and Discussion
### Variable Slip Inversion  
Assuming uniform slip on fault planes is a simplification since the heterogeneity in fault displacement and rake is observed on all scales of fault [44]. The fault slip is variable. Therefore, based on the results of the nonlinear inversion, we extend the fault length and width to 60 km and 30 km, then divide it into the multiple patches of 1.5 km by 1.5 km, thus having 800 fault patches. Based on the Okada model, the Green functions were calculated, which contains the predicted LOS and three direction changes at each location in the displacement data set produced by unit slip on each fault patch (linear inversion). Thus having Equation (4),  
\[\left[\begin{array}{c}\mathbf{u}\\ 0\\ 0\end{array}\right]=\left[\begin{array}{cc}G_{1}&G_{2}\\ \kappa\cdot\mathbf{L}_{1}&0\\ 0&\kappa\cdot\mathbf{L}_{2}\end{array}\right]\left[\begin{array}{c}\mathbf{s}_{1}\\ \mathbf{s}_{2}\end{array}\right]+\epsilon \tag{5}\]  
where \(\mathbf{u}\) is the observed deformation; \(G_{1}\) and \(G_{2}\) are the Green's function relating the surface deformation to the strike-slip and dip-slip, respectively; \(\mathbf{L}_{1}\) and \(\mathbf{L}_{2}\) are the Laplace smoothing constraint matrix of strike-slip and dip-slip, respectively; \(\kappa\) is the smoothing factor; \(\mathbf{s}_{1}\) and \(\mathbf{s}_{2}\) are strike-slip and dip-slip; \(\epsilon\) is the error of observation and model building.  
The Laplacian operator was used to smooth the slip solution, and its effect was controlled by the smoothing factor \(\kappa\), which also controls a constraint of zero on the side and lower edges of the fault plane [44]. We fixed the rake at the value derived from the uniform slip model, and use the Fast Non-Negative Least Square (FNNLS) algorithm [45] to solve Equation (4). The amount of slip \(\mathbf{S}=(\mathbf{s}_{1}{}^{2}+\mathbf{s}_{2}{}^{2})^{1/2}\). Figure 5a,b shows the main fault slip distribution based on the 3D and LOS deformation data sets. Figure 5a shows that the slip distributes at depths between 12 and 18 km, the peak slip with value of 6.53 m appears at depth of 14.5 km; Figure 5b shows that the slip distributes at depths between 12 and 18 km too. After comparing, we can find that the slip distribution of Figure 5a is more concentrated, more reasonable and having finer details than Figure 5b, thus the constraining by the 3D deformation data sets is more successful than the constraining by the LOS deformation data sets.  
Figure 6 indicates that fault has weak dextral slip motion and strongly thrust motion. Most of the slip distribute in south area of epicenter. The depth of the fault top-left corner is 10.8 km. The city Sarpol-e-Zahab locates at the fault boundary almost, that's why it suffe  
Figure 5: The main fault slip distribution. (**a,b**) are obtained by variable slip inversion based on the 3D deformation data sets and the line-of-sight (LOS) deformation data sets, respectively.  
and destructions. The geologic structure background mentioned in Section 2 accounts for these characteristics of fault slip, precisely.
## 3 Discussion and Conclusions  
In conclusion, we implemented an easy, reliable, and precise frequency detection method using a DDS. Basically, it is known that high quality factor can be achieved by using the side-band close to the resonance frequency. Thus, in this experiment, we found that the appropriate frequency shift having the highest signal-to-noise ratio was 2 kHz by producing precise modulation frequencies. Especially in the TGS measurements conducted here, the SNR is roughly 2.6 times better than the  
Figure 4: Applied optimal frequency to hard cantilever. EFM images of lead zirconate titanate (PZT) and triglycine surfaces (TGS) in a 10 \(\upmu\)m \(\times\) 10 \(\upmu\)m scan size at a high resonance frequency (\(\omega_{o}\): 314 kHz). (**a**) Topographic image of PZT (inset: the external field distribution), (**b**) typical AM-EFM image of PZT (\(\omega_{e}\): 2 kHz), (**c**) FM–EFM image of PZT at optimized side-band frequency (\(\omega_{o}\) - \(\omega_{e}\): 312 kHz), (**d**) line profile of the PZT surface; (**e**) Topographic image of TGS, (**f**) typical AM-EFM image of TGS (\(\omega_{e}\): 2 kHz), (**g**) FM–EFM image of TGS at optimized side-band frequency (\(\omega_{0}\) - \(\omega_{e}\): 312 kHz) and (**h**) line profile of the TGS surface.  
Figure 3: Applied optimal frequency using EFM set-up. Images of triglycine surface (TGS) in 10 \(\upmu\)m \(\times\) 10 \(\upmu\)m scan size. (**a**) The topographic image; (**b**) AM mode image and line profile (\(\omega_{e}\): 2 kHz, SNR: 6.5735); (**c**) FM–EFM image and line profile (\(\omega_{o}\) - \(\omega_{e}\): 64.7 kHz, SNR: 8.8943); (**d**) FM–EFM image and line profile (\(\omega_{o}\) - 2\(\omega_{e}\): 62.7 kHz, SNR: 5.1456); FM: frequency modulation; AM: amplitude modulation.  
lowest SNR. Additionally, the results with a stiff cantilever, which is less affected by the humidity on the sample [19], also indicate that the DDS approach is feasible at high frequencies in the FM mode. Ultimately, the EFM images at the optimized side-band frequency are 1.57 to 2 times better than the conventional EFM images. We expect that the findings of our research will help to inspire an accurate and reliable methodology that can be used to analyze the electric characteristics of semiconductor and bio-molecular films.  
This work was supported by Korea Research Institute of Standards and Science (KRISS) under the project \"Development of core measurement technologies for the next generation of nanodevices\" (Grant No. 17011063) and Korea Basic Science Institute (KBSI) (Grant No. D37613).  
[PERSON] and [PERSON] conceived and designed the experiments; [PERSON], [PERSON], and [PERSON] performed the experiments; [PERSON], [PERSON], [PERSON], and [PERSON] analyzed the data; [PERSON] wrote the paper. All authors read and approved the final manuscript.  
The authors declare no conflict of interest.
# L-Band Erbium-Doped Fiber Optimization and Transmission Investigation +
## 3 Transmission Investigation and Discussion  
### L-Band Transmission Investigation Setup  
An experimental investigation was conducted on the few-mode transmission system applied with the designed FM-EDFA. The scheme of the transmission system is shown in Figure 9. A tunable laser is selected as the source. The continuous wave (CW) laser output is first adjusted in power using a variable optical attenuator (VOA) and then regulated in polarization via a polarization controller (PC). Subsequently, the laser is modulated through a Mach-Zehnder modulator (MZM, Fujitsu, 12.5 GHz bandwidth, Kanagawa, Japan) driven by the output signal from a pulse pattern generator (PPG, Anritsu, MP1763, Atsugi, Japan). A pseudo-random bit sequence (PRBS) with a type of 2\({}^{\prime}\)7-1 and a bit rate of 9.953 Gbps is selected as the data stream. The first MSPL converts the fundamental mode to the specific mode with low insertion losses. The modulated signal then transmits through a 5 km long passive transmission fiber. The RI profile and the fiber profile under the microscope are shown in Figure 10. The transmission fiber has the same RI profile as the MSPL, ensuring minimal mode degradation at the connection between the MSPL and transmission fiber. The signal enters the designed FM-EDFA with an input power of \(-20\) dBm. The output port of the FM-EDFA is connected to the second MSPL, which converts the signal mode to fundamental mode and directs the signal to the specific port. Then, the signal passes through an optical band-pass filter consisting of an optical circulator (OC) and a fiber grating to suppress the amplified spontaneous emission (ASE). The transmission distance is relatively short (5 km); thus, the dispersion effect is not pronounced. The optical signal is detected by a photodetector (PD, Thorlabs, 20 GHz bandwidth, Newton, NJ, USA). The resulting voltage signal is amplified by an electric amplifier (EA, Anritsu, 10 GHz bandwidth) and improved by a clock and data recovery (CDR, OC-192). Meanwhile, the CDR extracts a clock from the signal stream and provides it for the error detector (ED, Anritsu, MP1764C). Finally, the signal and the recovered clock are fed to the ED to calculate and display the bit error rate (BER).  
Figure 9: Schematic of the transmission verification system. (VOA: variable optical attenuator, PC: polarization controller, MZM: Mach–Zehnder modulator, MSPL: mode selective photonic lantern, FM-WDM: few-mode wavelength division multiplexer, OC: optical circulator, PD: photodetector, EA: electric amplifier, CDR: clock and data recovery, BERT: bit error rate tester, PPG: pulse pattern generator, ED: error detector).
# Development of Modified Gas Composition for Atmosphere Packaging of Sliced Apple Chips
## 1 Introduction
The most important textural parameter of dried apples is hardness which is associated with moisture content [18]. Optimizing process parameters is crucial for achieving a satisfactory final product quality. The modern food market imposes increasingly stringent requirements regarding food products' quality, shelf life, and safety. In response to these demands, innovative packaging technologies are being developed to extend product shelf life while maintaining high sensory and nutritional values. One effective method for extending the shelf life of food is modified atmosphere packaging (MAP), which involves replacing air with a specifically designed gas mixture suited to the product [25; 26; 27]. Modified atmosphere packaging helps maintain product quality and additionally extends its shelf life. The usage of MAP packaging to control changes in fruits and vegetables during storage (ripening, browning, etc.) [28]. It also reduces the respiration rate. The following gases are currently in use: O\({}_{2}\), CO\({}_{2}\) and N\({}_{2}\). Creating a suitable gas mixture for a specific product is essential [27; 29; 30]. There is little research taking into consideration the MAP gas composition used for apple storage [31]. Therefore, our research aimed to develop and optimize the gas composition in modified atmosphere packaging of apple dry slices to extend their shelf life without deterioration of quality. To our knowledge, there is little research investigating the physico-chemical changes during storage in terms of dried apple slices in modified atmosphere packaging where different concentrations of oxygen and carbon dioxide have been used. Since this study aims to determine the optimal gas composition for storing dried apple slices, the results could be beneficial for scientific and industrial entities, as well as consumers who seek high-quality products with an extended shelf life, without the need for artificial preservatives.
## 3 HDELC Algorithm
### Classifier Selection and Ensemble Models
#### 3.2.1 Classifier-Selection Process
Mutual Information (MI) [30], as a statistical measure of the amount of information shared between two variables, can capture the nonlinear relationship between variables, and is scale-independent, and helps in feature selection. Therefore, the feature subspace similarity \(\theta(\omega),\omega_{h}\) is defined using the mutual information of two subspaces \(\omega\), \(\omega_{h}\) with feature display centers \(U^{j}=\left\{\mu_{1}^{j},\mu_{2}^{j},\ldots,\mu_{kj}^{j}\right\}\) and \(U^{h}=\left\{\mu_{1}^{h},\mu_{2}^{h},\ldots,\mu_{kh}^{h}\right\}\):  
\[\theta(\omega_{j},\omega_{h})=\sum_{\mu_{i}^{j}\in\omega_{j}}\sum_{\mu_{b}^{j }\in\omega_{h}}p(\mu_{i}^{j},\mu_{b}^{h})\log(\frac{p(\mu_{i}^{j},\mu_{b}^{h}) }{p(\mu_{i}^{j})p(\mu_{b}^{h})}) \tag{23}\]  
where \(p(\mu_{i}^{j},\mu_{b}^{h})\) is the joint probability distribution of \(\omega_{j}\) and \(\omega_{h}\), and \(p(\mu_{t}^{j})\) and \(p(\mu_{b}^{h})\) are the marginal probability distributions of \(\omega_{j}\) and \(\omega_{h}\), respectively. The loss function is set to ensure that the classifiers in the ensemble system can guarantee high levels of accuracy and improve the diversity of classifiers in the ensemble system. This approach not only integrates accuracy and diversity but also ensures the consistency of the loss function throughout the training process, making the loss values comparable between different training stages and \(\theta\) values. Only one hyperparameter \(\theta\) needs to be adjusted, which reduces the number of hyperparameters that need to be optimized for model tuning and simplifies the complexity of the model. In other words, high-dimensional classifier integration algorithms based on the loss function \(\Delta_{i}(\varphi)\) tend to select classifiers that improve model performance and increase the diversity of models in the ensemble system.
# Ant Colony Optimization Task Scheduling Algorithm for SWIM Based on Load Balancing
## 4 Ant Colony Optimization Algorithm Based on Load Balancing
### Ant Colony Optimization Task Scheduling Algorithm Rule
\[\Delta\tau_{ij}=\sum_{k=1}^{m}\Delta\tau_{ij}^{k} \tag{7}\]  
\[\Delta\tau_{ij}^{k}=Performance(s_{j})*(1-LS_{j})=[\omega_{1}*Mip(s_{j})+\omega_ {2}*Bandwith(s_{j})]*(1-LS_{j}) \tag{8}\]  
The heuristic function \(\eta_{ij}\) in ant colony algorithm can be expressed by Equation (9). \[\eta_{ij}=1/et_{ij} \tag{9}\]  
In order to maximize the utility, we must also consider the completion time factor of all tasks and try to minimize the task completion time. So, the expected completion time \(et_{ij}\) is used to calculate the heuristic function of the ant colony algorithm. The smaller the expected completion time, the larger the value of \(\eta_{ij}\) and the higher the value of \(P_{ij}^{k}(t)\). Thus, the degree of expectation that an ant will transfer from task \(t_{i}\) to node \(s_{j}\) is improved. The pheromone update calculation in Equation (6) and heuristic function calculation in Equation (9) are substituted into Equation (1) to obtain the probability value that task \(t_{i}\) is scheduled to resource node \(s_{j}\) after the optimization of the ant colony algorithm. The computational complexity of the improved ACO algorithm presented in this paper is expressed as \(O(NC_{\max}*n^{3})\), where \(NC_{\max}\) is the number of cycles and \(n\) is the total number of tasks.