esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Droplets Size Spectra Measured  
The VMDs, or D50 values, distribution per liquid hose positions are shown separately by nozzle type in Figure 5. In general, the cannon (Figure 5a) generated finer droplets than the hand-type nozzle did (Figure 5b), with median values in their conventional insertion position of the liquid hose (CP) of 76 vs 95 \(\upmu\)m, respectively. This fact is consistent with the observations made in previous droplet size characterization tests [53]. Nonetheless, there was a higher capacity to proportionally increase D50 values in the cannon (265% vs 223% total increase from CP to XP, Figure 5), which makes it possible to produce relatively similar drops in the XP in both the cannon- and hand-type nozzles (270 vs 307 \(\upmu\)m, Figure 5). When looking at the distribution of the D50 droplets in the different tested positions, values in the cannon spout were more homogeneously distributed than those in the hand spout. Thus, the first nozzle generated a D50 value of 74 \(\upmu\)m at the reference position (CP), whereas values were 112, 205 and 270 \(\upmu\)m at AP, OP and XP positions, respectively. The last three values corresponded to 51%, 177% and 265% droplet diameter increase when compared to the reference position (CP). For the hand-shaped nozzle, a 95 \(\upmu\)m droplet diameter was recorded at CP. Diameters at AP (145 \(\upmu\)m), OP (160 \(\upmu\)m) and XP (307 \(\upmu\)m) were 53%, 68% and 223% larger, respectively, than those recorded at CP. As it can be seen (Figure 5), values were more heterogeneous in the hand-type nozzle (b) when compared to the cannon ones (a). The D50 variability (shown by the interquartile rate in Figure 5) in both nozzles increased with increasing distances from CP. This is also consistent with previous works characterizing pneumatic sprayer droplet size, as the droplet size indicators (D50 among them) can be predicted with linear models, which are affected by the liquid flow rate and air speed [53]. When comparing these global results with the air speed decrease rate (Figure 5), data were consistent in the cannon-type nozzle. Air speed decrease did not present extreme values along any of the measured sections, so a progressive droplet size increase was expected. On the other hand, the hand-type nozzle presented more extreme air speed decrease values, so the higher heterogeneity of data would be explained.
## 1 Introduction
Generally, extraction is being carried out using conventional technologies, such as solvent extraction (liquid-liquid and solid-liquid extraction) with the assistance of external factors (e.g., mechanical agitation, pressing, or heating systems). In addition, as per the environmental requirements and economic impact, the food industry prefers green extraction and processing to ensure a safe and high-quality extract [34]. Recently, more rapid and automated methods, including ultrasound extraction (UAE) and accelerated solvent extraction (ASE), have been used [35]. The above extraction methods are advantageous compared to conventional methods because they can be carried out in the absence of light and oxygen, cope with the demand for a reduction in organic solvent consumption, and improve the extraction time due to the possibility of working at elevated temperatures or pressures in inert atmospheres. The literature analysis did not bring any references or reports on the comparison of extraction with UAE, ASE, and SSE (shaking solvent extraction) of phenolic compounds from freeze-dried leaves of three apple cultivars (Ligol, Gala, Gloster). The objective of the present study was to investigate the potential of apple tree leaves of different Poland apple cultivars (Ligol, Gala, Gloster) as an under-utilised source for the recovery of polyphenic compounds. For the extraction, environmentally friendly water, water-methanol, and water-ethanol solution were used. First, we prepared water and water-alcoholic extracts from the freeze-dried leaves of three apple cultivars using classical shaking solvent extraction (SSE), ultrasound extraction (UAE), and accelerated solvent extraction (ASE). After, we compared extracts from different apple leaf cultivars in terms of antioxidant activity, total phenolic content, and concentration of different individual phenolic compounds. The colour parameters (in the CIEL*a*b system) of the obtained extracts were also determined.
###### Abstract  
The Zeeman shift plays an important role in the evaluation of optical lattice clocks since a strong bias magnetic field is applied for departing Zeeman sublevels and defining a quantization axis. We demonstrated the frequency correction and uncertainty evaluation due to Zeeman shift in the \({}^{87}\)Sr optical lattice clock at the National Time Service Center. The first-order Zeeman shift was almost completely removed by stabilizing the clock laser to the average frequency of the two Zeeman components of \(m_{\rm F}=\pm 9/2\). The residual first-order Zeeman shift arose from the magnetic field drift between measurements of the two stretched-state center frequencies; the upper bound was inferred as \(4(5)\times 10^{-18}\). The quadratic Zeeman shift coefficient was experimentally determined as -23.0(4) MHz/T\({}^{2}\) and the final Zeeman shift was evaluated as \(9.20(7)\times 10^{-17}\). The evaluation of the Zeeman shift is a foundation for overall evaluation of the uncertainty of an optical lattice clock. This measurement can provide more references for the determination of the quadratic coefficient of \({}^{87}\)Sr.  
Zeeman shift; optical lattice clocks; strontium atoms; optical lattices; laser cooling and trapping +
Footnote †: journal: Applied Sciences
## 4 Simulation and Discussion
### Simulation of the Vehicle Crossing the Water Surface during Diving  
The simulation of the breakthrough trans-medium effluent has been discussed in detail in the previous section. In this section, we briefly describe the effectiveness of the controller we designed during the water-entry phase.  
In the diving simulation, the initial position and the attitude of HAUV are set as \(x_{0}=0.5\), \(y_{0}=2\), \(z_{0}=10\) and \(\phi_{0}=0,\theta_{0}=0,\psi_{0}=0\).  
The water-entry path we designed is determined as follows:  
\[x_{d}=\left\{\begin{matrix}sin(0.1\pi t)+1,t\in[0,30)\\ 1,t\in[30,50]\end{matrix},y_{d}=\left\{\begin{matrix}cos(0.1\pi t)+2,t\in[0, 30)\\ 1,t\in[30,50]\end{matrix},z_{d}=\left\{\begin{matrix}-0.25t+8.5,t\in[0,30)\\ -0.15t+5.5,t\in[30,50]\end{matrix}\right.\right.\right. \tag{46}\]  
According to the simulation results of trajectory tracking during the trans-medium process, as shown in Figures 14-16, the vehicle first flies in a descending spiral arc in the air, gives up the horizontal maneuver at 1 m from the water surface, carries out a vertical landing and enters the water, and then dives vertically to 2 m below the water surface. The simulation process ends here. Based on the above results, by comparing the tracking effects of the two controllers, we can conclude that the ANFTSMC can more accurately return the aircraft from the air to the water.  
In addition, Figure 17 shows that at 35-40 s, the attitude angle tracking error suddenly decreases after a sudden change, which is due to the trajectory switch at 30 s. Although the feedforward PD controller can also track the desired attitude angle, it is obvious that its reaction speed and control accuracy are weaker than those of the ANFTSMC designed by us. The control input of the vehicle is shown in Figure 18. In the initial phase, the upward thrust u1 is stable until t = 35 s. Then, as it begins to enter the water, the thrust decreases gradually, and at full immersion, the thrust stabilizes around a specific value and continues for some time. It is worth noting that the observations in Figures 19 and 20 show that the estimates of the adaptive parameters still converge to a constant value, indicating that the upper bound of lumped uncertainty is effectively estimated.  
Figure 12: Response of the NFTSMC surface during trans-medium motion.  
Figure 13: Variation of the linearized switch function.  
Figure 16: Entry-water tracking responses of the coaxial HAUV in the 2D plane.  
Figure 14: Tracking results of the HAUV’s actual position and expected position.  
Figure 15: Effect of the coaxial HAUV’s 3D water-entry trajectory tracking.  
Figure 19: Estimated values of the adaptive parameters of the position loop.  
Figure 17: Attitude response when tracking the desired trajectory.  
Figure 18: Theoretical control input of the water-entry process.
## 2 The Conceptual Framework of Smart Homes  
The term \"smart home\" is generally used to refer to a modern home which provides electronically controlled security and conveniences. Smart home technology is defined as the integration of home-based technology and services for a better quality of living [5]. Basically, smart homes are equipped with advanced automatic systems for various pre-programmed functions and tasks such as lighting, temperature control, multi-media, window and door operations, etc. This modern environment is also referred to as ambient intelligence, which is sensitive and adaptive to modern human and social needs [6]. The implementation of smart home technologies is generally meant to enhance home comfort for everyone through the automation of domestic tasks, easier communication, and higher security [7]. Smart home users are able to enhance their capacity to interact with their domestic environment, perform tasks and engage in activities that would have been previously difficult or impossible [8].  
In a digitally enriched living environment, the concept \"smart home\" is subject to various definitions and interpretations because being \"smart\" can imply various characteristics of a highly advanced modern home, such as being automatic, compact, innovative, convenient, self-adjusting, responsive, or functional. With a view to generating a sound background for further discussion, a conceptual framework of smart homes has been constructed based on a review of relevant literature (Figure 1).  
This proposed framework proposes that smart homes can be characterized or identified as having five basic features:  
* Automation: the ability to accommodate automatic devices or perform automatic functions;
* Multi-functionality: the ability to perform various duties or generate various outcomes;
* Adaptability: the ability to adjust (or be adjusted) to meet the needs of users;
* Interactivity: the ability to interact with or allow for interaction among users; and
* Efficiency: the ability to perform functions in a time-saving, cost-saving and convenient manner.  
These five features are drawn upon in the following discussion.
## 4 Experiments
### Baselines  
To evaluate the performance of the LSH-GEFR method, we compared it to the following models:  
* LDA + Temporal Ordering (LDA + TO) [46]: This method builds a single LDA topic model over the datasets and temporally orders the events under the same topic into a timeline chain. This method exemplifies the naive approach to solving the timeline structure summarization problem.
* Story Forest [8]: Story Forest adopts a bilayer keyword graph to capture event relationships. The goal is to extract and cluster events from breaking news and generate event lines based on clustering results.
* EventKG [16]: EventKG is a multilingual event-centric temporal knowledge graph. The effectiveness of the biographical timeline generation is demonstrated based on the EventKG.
* EventNET [47]: EventNET is an automatic event tree generation method based on a single-layer event network.
* LSH-GEFR (No GE): We remove the graph enhancement part of the method and directly input the original event feature based on Bert into the integrated D2E-EC clustering framework.
* LSH-GEFR (No D2E-EC): After obtaining the optimized event feature vector based on the graph enhancement model, we group the events based on the event similarity. If the similarity of two events is greater than a threshold, they are grouped into a topic. The first cluster centroid is randomly selected and then iteratively selects the most dissimilar events from the current cluster centroids as the next cluster centroid.
# A Novel Machine-Learning Approach to Predict Stress-Responsive Genes in Arabidopsis
## 1 Introduction
To further advance the understanding of stress tolerance in crops, the present work undertakes a comprehensive analysis of the Arabidopsis crop model. By employing novel hybrid gene selection methods such as IG, ReliefF, and LASSO, this study plans to identify key genes responsible for various stress responses in Arabidopsis. This comparative evaluation of different gene selection methods and classifiers explores the discovery of key genetic signatures that can serve as robust biomarkers for stress tolerance. The synergy between different feature selection methods aims to facilitate the identification of common genes, offering valuable genetic insight into the physiological responses of plants under stress. The outcomes of this research are expected to provide valuable guidance for breeding and pre-breeding programs, enhancing the resilience of crop plants to diverse environmental stresses. Moreover, the innovative methodology employed in this study introduces a promising avenue for exploring the complex genetic landscape of stress tolerance in other agricultural species. The paper is structured as follows. Section 2 delves into the detailed methodology, including the novel hybrid gene selection techniques employed. Section 3 presents the results of the comparative evaluation of the different gene selection methods and classifiers, along with the identified key genes. The section also discusses the implications of these findings, the synergy between feature selection methods, and the potential applications in breeding programs, as well as limitations and future works. Finally, Section 4 summarizes the major findings of the research.
## 4 The Smart Shipping Use Case
### Overview of Assessments and Training Levels
Moreover, the trainee is touch the general concepts of social engineering and phishing attacks (Remembering), reviews specific examples of attacks and plays a PROTECT game with a social engineering card-deck (Understanding), and tries to classify email examples as legitimate or malicious (Applying). For the advance training (Analyzing/Evaluating) as the emulated scenario starts, a faulty (but legitimate) email, commanding the captain to go to the Thessaloniki port, is sent. The email contains the details of another journey and was sent to the trainee by mistake: (i) the trainee identifies that this is a legitimate email, (ii) since the destination port was Piraeus, the trainee understands that this email was sent to him/her by mistake, and (iii) the trainee ignores the email and reports it back to the backend office. Then, the trainee receives a malicious (phishing) email, alerting him/her that a bad weather condition will take place, thus, he/she needs to go to another port to make a stop: (i) the trainee identifies that this is a phishing email and ii) ignores the email and reports it to the backend office. Lastly, the captain receives a legitimate email with the weather forecast, denoting that the weather is good, and the destination is the Piraeus port: (i) the trainee understands that this is a legitimate email and (ii) starts the journey in the Simulation Tool (where CTTP simulation sub-models can be activated with on-ship attacks for more complex scenarios, i.e., GPS spoofing). If the trainee succeeded in all steps and has learnt the underlying concepts, he/she can act as the trainer and create the emails (legitimate, faulty, or malicious) that will be sent to other trainees during the emulation scenario (Create). Table 3 summarizes the modelling steps for the social engineering scenario of Table 2 and Figure 9. The overall accomplishments of the trainee disclose his/her level of  
Figure 10: The Smart Shipping pilot architecture and Virtual Lab deployment. understanding concerning the tampering and spoofing perspectives of social engineering attacks and the usage of the relevant countermeasures that would assure integrity and authentication, respectively.
# Enhanced Location Prediction for Wargaming with Graph Neural Networks and Transformers
## 3 Methodology
### Transformer Module  
In our model, the transformer module processes the attribute vector \(X\). The architecture consists of a sequence of transformer layers, each composed of a multi-head self-attention (MSA) mechanism and a feed-forward network (FFN). In the following, we use a single-head self-attention module for illustration.  
Firstly, a linear transformation is applied to the input attribute vector \(X\), producing \(X^{\prime}\in\mathbb{R}^{b\times T\times d}\) with a hidden dimension \(d\). Then, the self-attention module projects \(X^{\prime}\) into three subspaces, namely \(Q\) (queries), \(K\) (keys), and \(V\) (values):  
\[Q=X^{\prime}W_{Q},K=X^{\prime}W_{K},V=X^{\prime}W_{V}, \tag{1}\]  
where \(W_{Q}\in\mathbb{R}^{d\times d_{w}}\), \(W_{K}\in\mathbb{R}^{d\times d_{w}}\), and \(W_{V}\in\mathbb{R}^{d\times d_{w}}\) are learnable projection matrices. These matrices map \(X^{\prime}\) into the query matrix (\(Q\)), key matrix (\(K\)), and value matrix (\(V\)). The dimension \(d_{w}\) corresponds to the feature size of \(Q\) and \(K\), while \(d_{v}\) represents the feature size of \(V\). These dimensions are used to scale the attention mechanism.  
Next, the attention matrix is computed to capture the pairwise similarity between the input features in the sequence:  
\[Attn(X^{\prime})=\text{softmax}(\frac{QK^{T}}{\sqrt{d_{w}}})V, \tag{2}\]  
where \(Attn(X^{\prime})\) measures the similarity between each pair of projected feature vectors (queries and keys) by calculating their dot product. The resulting scores are scaled by \(\sqrt{d_{w}}\) to ensure numerical stability, particularly for large dimensions. The softmax function is applied row-wise to normalize these attention scores, enabling the model to assign appropriate weights to different parts of the sequence.  
The MSA mechanism enhances the model ability to capture and express information by dividing the input into multiple self-attention heads. Each head calculates self-attention in parallel, and the outputs of all heads are then concatenated to increase the representational capacity of the model. The computation of MSA is as follows:  
\[MSA(X^{\prime})=||_{h-1}^{H}(Attn^{(h)}(X^{\prime})), \tag{3}\]  
where \(||\) denotes a concatenation operation and \(H\) is the number of attention heads. Similarly, each head \(h\) computes its own attention as  
\[Attn^{(h)}(X^{\prime})=\text{softmax}(Q^{(h)}(K^{(h)})^{T}\Big{/}\sqrt{d_{w}}) V^{(h)}. \tag{4}\]  
The FFN consists of two linear layers with a GELU ([PERSON] et al., 2017) non-linearity, which is implemented similarly to the approach by [PERSON] et al. (2017). Finally, the layer-wise computation in the transformer module is defined as follows:  
\[Y^{\prime(l)}=MSA(LN(Y^{(l-1)}))+Y^{(l-1)}, \tag{5}\]  
\[Y^{(l)}=FFN(LN(Y^{\prime(l)}))+Y^{\prime(l)}, \tag{6}\]  
where \(Y^{(0)}=X^{\prime},l\in\{1,\dots,L\}\) denotes the \(l\)-th layer of the transformer module and \(LN(\cdot)\) represents the normalization operation.
## 7 Conclusions  
The FBMC signal uses the prototype pulse to shape the subcarriers, relaxing the requirements for orthogonality. Strict synchronization between the subcarriers is no longer required, and the signal has a higher tolerance for the Doppler frequency offset. FBMC does not use CP to combat multipath effects; instead, OQAM is used to perform independent equalization on subcarriers, achieving a better bit error rate than OFDM used in the communication field. In addition, the good out-of-band suppression of the spectrum and the extremely high spectrum utilization efficiency can make the jamming energy more concentrated in the main lobe and increase the jamming distance. These properties make FBMC a good alternative for a joint communication jamming system.  
In this paper, we developed a joint communication jamming waveform based on the FBMC chirp. Modulating the chirp signal to different subcarrier groups can increase the number of false targets in a single pulse period. Since the subcarriers of the FBMC-OQAM signal are orthogonal, the signals are naturally orthogonal. The transmitter and receiver can then be separated to achieve multi-false-target jamming, which can raise the CFAR threshold by about 20 dB and help protect the target from detection.  
The ratio of the frequency shift of the designed jamming signal to the modulation frequency depends on the delay time. This makes the designed joint signal more robust in response to jamming and can resist frequency modulation agility. Furthermore, using an intercepted radar signal made it possible to perform channel estimation. Thus, high-speed digital transmission could be achieved while ensuring dense false target jamming. The proposed FBMC chirp joint waveform requires about 20 dB less jamming signal ratio than the existing method, and thus the energy saved can ensure the robust performance of the communication subsystem in the joint communication jamming system. The simulation results show that the joint signal can jam a pulse Doppler radar and excels in communication rate and bit error rate performance, ensuring that instructions are accurately and completely transmitted while implementing effective jamming.  
Conceptualization, G.L. and W.Y.; validation, G.L., W.Y. and Y.B.; formal analysis, W.Y. and Y.W.; investigation, Y.B. and Y.W.; writing--original draft preparation, G.L. and W.Y.; writing--review and editing, G.L., W.Y., Y.B., Y.W. and P.L.; supervision, G.L. and P.L.; project administration, G.L. and P.L.; funding acquisition, G.L. and P.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China under Grant Nos. 62171346 and 61805189 and the Natural Science Basic Research Program of Shaanxi (Program No. 2021 JM-140).  
Figure 19: SAR imaging results: (**a**) without jamming; (**b**) SAR jamming result.  
Data Availability Statement: The data presented in this study are available on request from the corresponding author.  
The authors are thankful for the academic editor's and reviewers' comments on this work.  
The authors declare no conflict of interest.