esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
Volatile Composition of Potted (Infested) Lychee Plants and Field-Collected (Non-Infested) Lychee Tissues  
A total of 58 compounds were identified from potted lychee plants, accounting for 98.05% to 99.73% of total volatiles: 44 terpenoids, 2 esters, 1 alcohol, 1 ketone, 4 alkanes, and 6 aldehydes (Table 1). In lychee leaves with different LEM infestation levels (A1 to A5), monoterene hydrocarbons (MHs), sesquiterpene hydrocarbons (SHs), aldehydes and alkanes were the major group of volatile components (Figure 2). Among the volatile terpenoids in samples analyzed, sabinene (1.92% to 11.67%), limonee (3.81% to 9.94%), \(\beta\)-caryophyllene (5.21% to 10.34%), _ar_-curemene (10.25% to 38.54%) were the most abundant compounds, followed by \(\alpha\)-copaene (1.08% to 2.82%) and \(\alpha\)-zingiberene (0.24% to 2.03%). The aliphatic aldehydes were most characterized by higher relative amounts of nonanal (18.65% to 51.52%) and decanal (2.28% to 7.76%). Nonanal was more abundant in stage A1 (43.52%), followed by 24.06% in stage A2, 29.31% in stage A3, and 18.65% in stage A4. Nonanal concentration was higher in control plants (A5, 51.52%), and A1 stage plants, than in plants under higher levels of infestation (A2, A3, A4). A similar pattern was observed for decanal, 7.76% in control plants and 6.74% in stage A1. (\(E\))-2-decanal (1.58%), undecanal (0.30%), and (\(E\))-2-undecanal (0.12%) were higher in stage A1 than in other stages. Similar patterns were also observed with alkanes. Hexadecane (2.21%) and heptadecane (5.38%) were found in higher concentrations in the initial infestation stage than in the later infestation stages. The concentration of pentadecane was higher in early infestation stages (0.82% to 1.03%) than in progressed (0.21%) and over-exploitation stages (0.37%). Figure 2: Abundance (%) of the chemical classes in volatiles from potted (infested) lychee plants and from field-collected (non-infested) lychee tissues. MH- monoterpene hydrocarbons, OM- oxygenated monoterenes, SH- sesquiterpene hydrocarbons, alcohols, aldehydes, alkanes, esters, and ketones. Initial infestation (A1), intermediate infestation (A2), heavy infestation (A3), overexploitation (A4), and healthy, uninfested plant (A5). Floral buds (B1), open flowers (B2), leaf buds (B3), and new leaf shoots (B4).
# Scalability of Mach Number Effects on Noise Emitted by Side-by-Side Propellers
###### Abstract  
This paper presents a numerical investigation of noise radiated by two side-by-side propellers, suitable for Distributed-Electric-Propulsion concepts. The focus is on the assessment of the variation of the effects of blade tip Mach number on the radiated noise for variations of the direction of rotation, hub relative position, and the relative phase angle between the propeller blades. The aerodynamic analysis is performed through a potential-flow-based boundary integral formulation, which is able to model severe body-wake interactions.The noise field is evaluated through a boundary-integral formulation for the solution of the Flowcs Williams and Hawkings equation. The numerical investigation shows that: the blade tip Mach number strongly affects the magnitude and directivity of the radiated noise; the increase of the tip-clearance increases the spatial frequency of the noise directivity at the two analyzed tip Mach numbers for both co-rotating and counter-rotating configurations; for counter-rotating propellers, the relative phase angle between the propeller blades provides a decrease of the averaged emitted noise, regardless the tip Mach number. One of the main results achieved is the scalability with the blade tip Mach number of the influence on the emitted noise of the considered design parameters.  
a +
Footnote †: journal: Applied
# Electrocatalysis for the Oxygen Evolution Reaction in Acidic Media: Progress and Challenges
## 3 Tailoring Strategies for Effective OER Electrocatalysts
### Morphology  
The interface between the catalyst and the electrolyte plays an important role in water electrocatalysis [95]. As a very important aspect of a surface structure, morphology has attracted much attention in recent years [23; 96]. To date, many methods to control the morphology have been proposed [23; 62].  
The morphology of the catalyst can be modified by adopting a suitable preparation strategy, such as the template method, the solvothermal method, or the seed crystal method [97; 98]. A nano-porous or ultra-thin structure can increase the number of exposed active sites. [PERSON] et al. prepared a new kind of Ir nanowire with a diameter of 1.7 nm by a wet chemical method (Figure 6a) [62]. Due to the high aspect ratio and large specific surface area, the OER activity increased greatly. The overpotential at 10 mA-cm\({}^{-2}\) in 0.5 M HClO\({}_{4}\) is only 270 mV, which is significantly higher than that of Ir nanoparticles.  
In fact, the electronic structure of catalytic centers can be modified by defects as well as the morphology. For example, [PERSON] et al. prepared a core-shell Ru@IrO\({}_{\text{x}}\) icosahedral nanocrystal structure by the sequential polyol method, in which a highly distorted lattice can be observed (Figure 6b) [99]. Due to the interaction between the ruthenium core and the iridium shell, ruthenium is subjected to compressive strain, which was confirmed by the decrease in the distance between ruthenium and ruthenium atoms observed by EXAFS. This strain may lead to a shift in the d-band center, which can regulate the binding energy of oxygen intermediates and the activity of the OER. In addition to the lattice strain, morphology control can also lead to changes in the chemical composition, which directly affects the electronic structure. An example is the ultra-thin ruthenium oxide nanosheets prepared by [PERSON]'s group (Figure 6c) [100]. Owing to the acid treatment in the stripping process, the actual composition of ruthenium oxide is H\({}_{\text{x}}\)RuO\({}_{2}\), in which the valence of ruthenium is +3/+4. Therefore, ruthenium oxide nanoparticles exhibit enhanced OER activity and stability.  
Figure 5: OER pathway under acidic conditions and local structural configurations of intermediates on the Ir\(-\)NiCo\({}_{2}\)O\({}_{4}-\)V\({}_{\text{O}}\)[28].
## 4 Results
Finally, the device presented in [28] has a lower bandwidth and similar radiating efficiency and directivity using AFSIW technology. As previously explained, it is important to consider that a greater height between the ground plane and the patch results in lower directivity. This explains why the directivity of the proposed antenna is lower compared to the works presented in [10; 27]. However, despite this difference in directivity, the presented antenna demonstrates an outstanding compromise among the key figures of merit, bandwidth, efficiency, and directivity, showcasing excellent bandwidth. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Work** & **BW\({}_{\mathbf{-10 dB}}\) (\%)** & **fc (GHz)** & \begin{tabular}{c} **Measured Radiation** \\ **Efficiency (\%)** \\ \end{tabular} & \begin{tabular}{c} **Measured Directivity** \\ **(dBi)** \\ \end{tabular} & \
\begin{tabular}{c} **Technology** \\ **Technology** \\ \end{tabular} \\ \hline This work & 19 & 10 & 90.48 & 8.11 &
\begin{tabular}{c} Floating full ESIW \\ integration \\ floating patch \\ \end{tabular} \\ \hline
[10] & 10.41 & 2.4 & 96 & 9.63 &
\begin{tabular}{c} Floating full \\ dielectrics microstrip \\ integration patch \\ \end{tabular} \\ \hline
[26] & 9 & 2.45 & 67 & 6.90 &
\begin{tabular}{c} Printed 3D microstrip \\ integration. \\ \end{tabular} \\ \hline
[27] & 1.8 & 4.8 & 90 & 12.80 &
\begin{tabular}{c} Microstrip integration. \\ \end{tabular} \\ \hline
[14] & 4.8 & 28 & 92 & 8.10 &
\begin{tabular}{c} Floating dielectricless \\ slot integration. \\ \end{tabular} \\ \hline
[28] & 10 & 28 & 93 & - &
\begin{tabular}{c} AFSIW substrate \\ integration. \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparative table. Figure 9: Measurement of the patch in anechoic chamber.
# Educational 5G Edge Computing: Framework and Experimental Study
## 3 5G Smart Campus System Architecture
### Smart Education Dedicated Network Architecture  
Unlike a traditional education network, which is primarily focused on the network connection needs of the school, the 5G education private network can meet the basic network connection needs of the country, region, and school, as well as the requirements for data storage, analysis, and usage. It constructs a three-level business integration service system of education center cloud (central)--education regional cloud (provincial side)--5G education edge cloud (campus side). Based on the education center cloud, the government can use the basic education data of all schools to achieve evidence-based education decision-making, as well as the access services of open education services. Furthermore, the sharing of educational business capabilities, business collaboration, and the integrated development of education ecology can be realized. Based on the education regional cloud, each education management unit can learn scientific decision-making according to regional education data and regionally share basic business platforms and educational resources. Based on the education edge cloud, the 5G education private network provides the school with independently available campus resources, education data, and the control authority of the education network by realizing the basic network connection requirements of the  
Figure 2: Applications of smart education in campus scenarios.  
school. This ensures that the school can use the education network and resource scheduling on demand.  
In addition, according to the different communication needs in different scenarios, including on-campus, school-linked, and off-campus, and the variability of educational terminals connected in each scenario, the local area network architecture of the 5G education network is further divided into a single-campus local education network, a multi-campus wide-area education network, a fixed-mobile integrated education metropolitan area network, and a 5G and WiFi integrated education network. For each scenario:  
1. Macro and micro base stations are used to serve as basic network access services;
2. Cloud-network integration gateways truncate data to serve data local shunting and campus network security management and control other services;
3. Edge computing platforms complete full business of educational data production in the school and only output business results out of school to ensure data security.  
This construction method of network integration using the cloud-network integration situational awareness platform actualizes a comprehensive perception of the campus network and simplifies network operation and maintenance, thus enabling the upgrade of the campus network and the construction of a smart campus.
# Advances in Fermentation Technology: A Focus on Health and Safety
## 3 The Effectiveness of Innovative Methods in Fermented Food Safety Assurance
### Advanced bioreactor Technologies with Real-Time Monitoring  
Recent advancements in bioreactor technology have significantly improved fermentation processes through integrated sensors for real-time monitoring and control. These sensors continuously measure critical parameters such as pH, temperature, dissolved oxygen, and biomass concentration without sample requiring removal, reducing contamination risks and enhancing batch consistency [111]. Novel sensing technologies, including fluorescence and turbidimetry, have been developed to directly measure cell mass, substrate, and product concentrations [112]. Portable, multiplexed thread-based sensing microprobes have been demonstrated for continuous pH and ammonium ion monitoring during biorementation [113].  
Furthermore, integrating advanced sensors with software models has shown promise for process fingerprinting, smart control, and outcome prediction in biopharmaceutical manufacturing. These innovations drive the industry towards automation and smart manufacturing, improving understanding and outcomes in cell culture processes [10]. For instance, a study by [PERSON] et al. [114] demonstrated that bioreactors equipped with sensor-integrated monitoring systems significantly increased product consistency and reduced contamination risks. Integrating advanced online monitoring technologies within bioreactors allows for real-time adjustments to critical parameters, thereby enhancing the stability and reliability of cell cultures. This capability is crucial for maintaining optimal growth conditions and minimising the risk of contamination, which is often a significant concern in bioprocessing environments [114]. These innovations improved the overall efficiency of bioprocesses and contributed to producing high-quality biological products, aligning with industry demands for consistency and safety.  
Figure 2: Examples of the food safety advantages derived from recent progress in fermentation technology. The combination of these modern technologies indicated a transition to more effective and efficient safety measures in the fermented food sector, enhancing customer trust and public health.  
Figure 3: Schematic illustration of technologically advanced fermentation processes as a sustainable solution to food production.
## 4 Measurements and Characterization
### Comparison with the Existing Literature  
Table 1 provides a performance comparison between the proposed X-band GaN MMIC power amplifier (PA) and other state-of-the-art designs. This comparison highlights several key parameters, such as output power, power flatness, power-added efficiency (PAE), gain, and the number of stages used in each design.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Ref.**} & **Process** & **Freq.** & **Pout** & **Pout Flatness** & **PAE** & **Gain** & **Stages** & **Area** & **Class** \\ \cline{2-9}  & **(\(\bm\))** & **(GHz)** & **(dBm)** & **(dBm)** & **(\%)** & **(dB)** & **(\(\backslash\))** & **(mm2)** & **(\(\backslash\))** \\ \hline
[1] & 0.25 & 9.8–10.4 & 42 & \(\pm 1.1\) & 38 & 18 & 2 & 18 & AB \\ \hline
[2] & 0.25 & 8.5–10.2 & 43 & \(\pm 1.2\) & 43 & 25 & 3 & 7.7 & F\({}^{-1}\) \\ \hline
[4] & 0.14 & 9.6–11.0 & 40 & \(\pm 1.5\) & 61 & 10 & 1 & \(\backslash\) & F \\ \hline
[14] & 0.25 & 8–12 & 43 & \(\pm 0.5\) & 38 & 23 & 3 & 7.5 & AB \\ \hline \hline \end{tabular}
\end{table}
Table 1: Performance comparison with X-band GaN MMIC PAs.  
Figure 10: (**a**) Simulated and measured S-parameters; (**b**) simulated and measured output power, PAE, and gain versus frequency.  
The realized PA achieves a saturated output power of 47.6 dBm across the 8-12 GHz frequency band, with an impressive in-band power flatness of \(\pm\)0.3 dB. This is a notable improvement over several other designs that exhibit larger output power fluctuations, typically in the range of \(\pm\)1.0 to \(\pm\)1.5 dB. In terms of power-added efficiency (PAE), the proposed amplifier demonstrates an efficiency of between 40% and 45.5%, which is competitive compared to other X-band GaN MMIC amplifiers. While some designs, such as that in Ref. [17], report higher efficiencies (up to 52%), they often come at the cost of either output power or bandwidth limitations. By combining load-line theory with CCBJ, this design has precisely defined the optimal operating impedance space, which allows for a better balance between output power and efficiency. Furthermore, the well-designed RC structure enables this PA to exhibit superior gain compared to other designs.  
These results validate our theoretical analysis and demonstrate the implementation of an effective design methodology for high-power, high-efficiency MMIC power amplifiers with excellent in-band \(P_{\text{out}}\) flatness.
## 4 Materials and Methods
### Measurement Design
#### 4.3.2 Collaboration and Participation Achieved  
The term effective interaction or effective participation is used in collaboration processes to refer to the interactions among participants where they cooperate and have a well social atmosphere, group involvement, opinions and efforts of the other members are considered [50]. We measured the level of the effective participation in CoMeS-SPL according to the participation and cohesion degrees proposed by [50].  
* Participation degree (PD): The intensity with which the individual members participate in the group's activities and their proactive commitment [50]. For this study, there are no previous values that allow comparing whether or not the degree of obtained participation is greater when applying the CoMeS-SPL method compared to another.
* Factor artifact history (\(HF_{ar}\)): This factor indicates the balance in the number of contributions to an artifact made by the different participants. It is calculated with the standard deviation and the average value of the contributions of participants in the completion of an artifact. This value is in the range [0, 1]: values close to 0 indicate that the contributions were made only by some of the actors, while values close to 1 express a higher degree of balance in the contributions of the participants [51]. The following formula is used to calculate the \((HF_{ar})\). \[HF_{ar}=1-\frac{stdev(C_{ar})}{med(C_{ar})}\] The proposal [50] of this indicator is known as equal participation degree (EPD), and in an 'effective' collaborative group all members should participate to a similar degree without any participant having monopolizing behavior. However, we considered the same weight for each role involved in the construction of an artifact. Similarly, we used the same weight for each artifact in the scope.
* Collaboration factor (CF): This describes the level of relative participation of the members of a collaborative activity [51]. It is based on the type and size of contributions or events made by the participants in the completion of the artifacts that constitute the scope activity. The result allows inferring elements of cooperation, such as the evolution of group performance of time, as well as the effectiveness of the collaboration [51]. The factor of collaboration (CF) is calculated as the average value of the collaboration factors of the artifacts. \[CF=\frac{sum_{i=1}^{n}HF_{ar_{i}}}{n}\]
* Perceived collaboration (PC): This is the degree to which participants in SPL scoping activity believe that using CoMeS-SPL encourages collaboration and effective communication among them.
# MHBase: A Distributed Real-Time Query Scheme for Meteorological Data Based on HBase
## 3 Proposed Design
### Index Model
#### 3.2.1. Coprocessor  
A _Coprocessor_ is divided into two categories: the _Observer_ whose function is similar to a database trigger and the _Endpoint_, which is similar to stored procedures in RDBMS. It can execute custom code in parallel at the region level just like a lightweight mapreduce and it also makes it possible to extend server-side functionality. Furthermore, _Observer_ contains rich hooked functions and, for this reason, some developers have attempted to realize the index in JIRA ([PERSON], 2014; [PERSON], 2015) reports. In this paper, we add _IndexMasterObserver_ and _IndexRegionObserver_, which are two expanding instances of an _Observer_ to manage the indexes.  
_IndexMasterObserver_ is used to manage a table-level operation by a master, such as create and delete an index table automatically, while _IndexRegionObserver_ is used to perform specific data manipulations in region-level. Table 1 shows the main functions in _IndexRegionObserver_.  
Figure 5: Effect of vertical partitioning on region splits.  
When a _user region_ is split and placed into another _RegionServer_, the original startkey of the upper region remains but another one should be changed and additional work has to be done by the coprocessor. These rewrite operations are triggered when regions are being compacted after region split.  
The main steps are as follows:  
1. get the original rowkey: row\({}_{orig}\), split rowkey: row\({}_{split}\) and their length of region startkey len\({}_{orig\_start}\), len\({}_{split\_start}\);
2. calculate the length: len\({}_{remain}\)= the length of row\({}_{orig}-\)len\({}_{orig\_start}\);
3. new byte [len\({}_{split\_start}+\)len\({}_{remain}\)];
4. copy the combination of region startkey and the rest of original rowkey to new array.
## 4 Experiments and Results
### Experiments with a Random Forest Classifier  
Experiments were undertaken to assess how well the resulting Sensitivity Indices correlated with the results of other state-of-the-art classification methods, first the Random Forest Classifier (Section 4.6), and then Neural Networks (Section 4.7). Random Forest Classifiers (RFC) [39; 40] are well-known classification methods used in the machine learning community. They have shown better, or at least comparable, performance in comparison to other state-of-the-art classification algorithms, such as SVMs [41] or boosting [42], and show a series of advantages such as efficiency when used on large databases and robustness to missing data.  
For this experiment, we used an adapted version of the online algorithm proposed by [PERSON] et al. [43]. All our experiments were performed on MATLAB (R2011-64 bit) running on an Intel Xeon 3.2 GHz--Windows 7 machine (Austria, Graz). Due to the randomized nature of the algorithm, we performed 10-fold cross-validation for the datasets. For each validation run, we used a random selection of 90% of the data for learning, while the remaining part of the data was used for testing. The main performance criterion is the correct classification rate (CCR), which is the ratio between the correctly classified datasets and the number of valid test datasets. Due to the unbalanced nature of classes, individual correct classification rates were calculated to emphasise any effects on smaller classes.  
At first, we used the 32 most important features suggested by the EventTracker SA algorithm. The results obtained can be seen in the second column of Table 8.  
Figure 11: EventTracker SI value correlation behavior.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Well ID** & **CCR** & **Best Possible CCR** \\ \hline S055 & 56\% & 86\% \\ S075 & 69\% & 92\% \\ S085 & 71\% & 91\% \\ S140 & 62\% & 92\% \\ \hline \hline \end{tabular}
\end{table}
Table 8: Main results for features selected only by the SA algorithm and the best results obtained by taking into account the domain expert’s knowledge.  
As one can see, the classification rates vary between about 55% and 70%, which indicates the feasibility of the proposed EventTracker SA method. The worst performance for S055 may be explained both by the complexity of the task (This is indicated by the fact that the best possible correct classification rate is also the worst one for this dataset) and the fact that the SA algorithm did not see the specific well data during the selection procedure.  
In order to check the performance of RFC using added knowledge from an expert, an offline experiment incorporating an expert's knowledge for the selection of channels was performed. In particular, we added the first and second derivatives (H01 and H02), as well as the H12 of the base channels C0108 (mdBit), C0110 (mdHole), and C0112 (posBlock) as additional features and obtained the results shown in the third column of Table 8. The results show a correlation between the performance of the EventTracker SA method and the ones from incorporating an expert's knowledge.