esa-sceva/satcom-chunk-collection huggingface dataset:
# Characterization of the Key Aroma Compounds of Shandong Matcha Using HS-SPME-GC/MS and SAFE-GC/MS
###### Abstract  
Shandong matcha has the quality characteristics of bright green color, seaweed-like aroma and strong, fresh and brisk taste. In order to identify the characteristic aroma components and clarify the contribution of the grinding process to the aroma of Shandong matcha. Three grades of Shandong matcha and corresponding tencha material were firstly tested with sensory evaluation, and the volatile components were extracted with headspace solid-phase microextraction (HS-SPME) and solvent-assisted flavor evaporation (SAFE) and analyzed using GC-MS. The sensory evaluation results showed that high-grade matcha (M-GS) had prominent seaweed-like, fresh and roasted notes, whereas medium and low-grade matcha (M-G1, M-G2) were gradually coupled with grassy, fatty and high-fired aromas. GC-MS results showed that in the HS-SPME method, heterocyclic compounds (45.84-65.35%) were the highest in Shandong matcha, followed by terpenoids (7.44-16.92%) and esters (6.91-15.27%), while in the safe method, esters were the highest (12.96-24.99%), followed by terpenoids (10.76-25.09%) and heterocyclic compounds (12.12-17.07%). As a whole, the composition of volatile components between M-G1 and M-G2 is relatively close, and there are more differences in volatile components between them and M-GS. The volatile components unique to M-GS were screened using the odor activity value (OAV) evaluation method, with components such as 3-methyl-2-butten-1-thiol, 3-ethyl-Phenol, 2-thiophenemetanethiol, 2,4-undecadien, (E,E)-2,6-nonadienal, (E,Z)- being evaluated. There were other differentially volatile components, that is, volatile components that coexist in the three grades of matcha, but with different concentrations and proportions. M-G1 and M-G2 contained more volatile substances with high-fired aroma, such as 2-ethyl-3-methyl-pyrazine, coumarin and 5,6,7,8-tetrahydrodroquinoxaline. The grinding process not only changes the appearance of tencha, but also increases the content of volatile components of matcha as a whole, enhancing the aroma and flavor characteristics of matcha. In this study, the contents of 24 volatile components in matcha were mainly increased, such as benzene, (2,2-dimethoxyylethyl)-, cis-7-decen-1-al, safranal and fenchyl acetate. The dual factors of material tencha and match grinding technology are indispensable in forming the differences in aroma and flavor of Shandong matcha at different levels.  
Keywords:Shandong matcha; tencha; solvent-assisted flavor evaporation; headspace solid-phase microextraction; aroma characteristics +
Footnote †: journal: _foods_
# Effect of MXene Nanosheet Sticking on Supercapacitor Device Performance
## 3 Factors Influencing Sticking in MXene Electrodes
### Surface Chemistry of MXene Flakes  
The surface chemistry and hydrophobicity of MXene flakes are vital factors that influence the sticking behavior in MXene electrodes. In this paper, we provide a closer look at their impact.  
Figure 4: SEM images of MAX powders and MXenes obtained by etching at different conditions. (**a**) Ti\({}_{3}\)AlC\({}_{2}\) (MAX) powder showing the compact layered structure and multilayered Ti\({}_{3}\)C\({}_{2}\)T\({}_{x}\) powder synthesized with (**b**) 30 wt%, (**c**) 10 wt%, and (**d**) 5 wt% HF. Accondion-like morphology is produced after etching in (**b**) 30 wt% HF (or higher HF concentrations). (**e**) Multilayered NH\({}_{4}\)-Ti\({}_{3}\)C\({}_{2}\)T\({}_{x}\) powder synthesized with ammonium hydrogen fluoride and (**f**) the MILD method (etched with LiF in HCI), both showing negligible opening of MXene lamellas [47].  
Figure 5: Morphology and structure characterizations of MXene@MF and N–MXene: SEM image of (**a**) MXene@MF; (**b**) N–MXene; (**c**) N–MXene–F [49]. [PERSON] et al., Molecules, copyright (2022), MDPI (open access).
# A Payload Based Malicious HTTP Traffic Detection Method Using Transfer Semi-Supervised Learning
## 4 Experiment
### Experiment Setup
#### 4.1.3 Parameter Settings  
Through a series of grid search experiment, we set the parameters of the proposed method as follows. For the TextCNN in Section 3.2, we set the length of the input payload \(l=64\) and the dimension of the word embeddings \(d=32\). We defined three window sizes (i.e., \(h\times d,h=2,3\), and 4) for the convolutional filters. For the keyword mining algorithm in Section 3.3, we set the TF-IDF score threshold \(\sigma=0.5\) based on a grid search experiment. For the transfer semi-supervised learning framework in Section 3.4, we set the number of frozen layers \(n=2\) and the trade-off weight of the loss function \(\lambda=1\) based on the previous experiences.
## Appendix A Attack Simulation Parameters  
To evaluate Edge-FLGuard under realistic threat conditions, synthetic attack scenarios were generated alongside the use of public datasets. The attack simulations aimed to replicate common IoT-targeted threats and assess both the accuracy and robustness of the system in adversarial environments, as follows:  
* DDoS Attacks: We used hping3 and Scapy to generate high-volume packet floods targeting simulated edge nodes, with packet rates ranging from 10,000 to 100,000 packets per second. Attacks were scheduled in bursts of 30-60 s during each simulation cycle.
* Spoofing Attacks: IP and MAC address spoofing were emulated using custom Python scripts that randomly altered device identifiers within session payloads. Spoofing frequency was configured to mimic periodic intrusion behavior without overwhelming traffic volume.
* Unauthorized Access Attempts: Synthetic logs were crafted to include credential stuffing and privilege escalation patterns, simulated via system call and process injection anomalies (inspired by TON_IoT attack taxonomies).
* Model Poisoning: In the federated setting, up to 30% of clients were intentionally poisoned by either label-flipping their local datasets or submitting inverted gradient updates to the server. These adversarial clients were randomly selected at each training round.
* Labeling Strategy: Attack intervals and injected traffic were timestamped and annotated to create labeled datasets. For evaluation, anomaly labels were assigned at the flow level for network data and sequence-level for telemetry data (LSTM inputs).  
All attack traffic was injected into emulated IoT environments using Mininet, with fixed random seeds to ensure reproducibility.
## 2 Motion-Planning Graph-Based Algorithm
### Curvature Approximation of the Trajectories  
As it is described above, the main goal is to determine the possible lateral positions of the nodes. Since the longitudinal velocity of the vehicle is known, the lateral acceleration can be computed as:  
\[a_{cp,max,i}=c_{max,i}v_{x}^{2}, \tag{2}\]  
where \(c_{max,i}\) gives the maximum value of the curvature for the \(i^{th}\) trajectory segment and \(v_{x}\) is the longitudinal velocity of the vehicle. The maximum lateral acceleration of the vehicle can be determined using the designed trajectory segment between two nodes (see Appendix A). The lateral accelerations are used in two aspects:  
* computing the possible lateral positions of the nodes,
* weighting the edges using the computed lateral accelerations.  
The maximum value of the lateral acceleration is chosen by taking into account the comfort and safety requirements. In this paper, the algorithm is built for everyday traffic situations, in which the maximum value the lateral acceleration is chosen for a reasonable value. The linear assumption for the bicycle model gives a sufficiently accurate result. The lateral acceleration is limited through the steering angle, which results in the limitation of the yaw rate. Therefore the limitation of the lateral acceleration is sufficient. However, the adhesion coefficient of the given road segment can be decreased. In these scenarios, the maximum value of the lateral acceleration must be chosen for smaller value. The computation of the predictive optimization can be time consuming, therefore the curvature is approximated using a function \((f)\). In order to determine this function feasible trajectories are computed to various longitudinal and lateral lengths \((x_{l},y_{l})\), and also the yaw angle is taken into account \((\psi_{initial})\). The maximum value of the curvature is approximated using a polynomial, which can be found in the form of \(\sum\limits_{i,j,k=0}^{N_{1},N_{2},N_{3}}p_{i,j,k}x_{l}^{i}y_{l}^{j}\psi_{initial}^ {k}\), where \((N_{1},N_{2}N_{3})\) represent the dimensions and \(p_{i,j,k}\) are the coefficients of the polynomial. During the fitting, it is recommended to select the dimension of the polynomial as small as possible to avoid the high complexity of the optimization problem. The determination of the coefficients in function \((f)\), the following optimization problem must be solved:  
\[J_{LS}=\sum\limits_{i=1}^{n}(c_{max,i}-f(x_{l,i},y_{l,i},\psi_{initial,i}))^{2} \rightarrow\text{min}! \tag{3}\]  
The calculated function is used for the computation of the lateral positions of the nodes and plays an important role in the weighting of the edges.
## 4 Results and Discussion
### Analysis of Changes in the Area of Propagating Flames  
The effects of turbulence and obstacle shear near wall boundaries are significant when flames propagate in small-sized pipes and are disturbed by obstacles. Turbulence not only increases the heat transfer process but also increases the interfacial area between the unburned mixture and the combustion products. As shown in Figure 11, the flame area gradually increased as the flame front advanced toward the deflagration port and did not begin to decrease until the flame washed out of the pipe. Combining Figures 7-9 shows that the flame area peaks after the flame reaches the pipe relief port. This may be due to the obstacles near the pipe outlet preventing the development of the flame front surface, leading to the formation of flame backflow near the pipe pressure relief port. As part of the  
Figure 10: Pressure changes at the same position for obstacles with different angles.  
flame rushes out of the pipe, the fuel in the pipe is depleted, the flame is extinguished, and the flame area is subsequently reduced.  
Observing Figure 11, the 500 mm position at the obstacle caused by the flame area is much larger than other conditions. When the flame accelerated out of the obstacle gap, the shear effect at the edge of the obstacle caused the flame to break into folds, increasing the flame area. When the obstacle was away from the ignition source and close to the vent, the flame area was the smallest, because at this time the flame area on the left side of the obstacle was not much different from the area under other conditions, mainly because the flame passed through the obstacle, leaving less space for flame development, and soon rushed out of the pipe.  
The velocity and flame evolution at the moment of maximum flame area for each condition are shown in Figure 12. Figure 12 is obtained by calculating the flame surface as an equivalent surface with a reaction process variable of 0.5. The explosion process of a combustible premixed gas within a weakly confined structure is subjected to violent oscillations of obstacles and reflected waves. The effect of the Helmholtz instability and the Rayleigh-Taylor instability [35] wrinkles the flame front surface, which participates in the combustion reaction. The increase in the area involved in the combustion reaction leads to a faster combustion rate and faster propagation of the flame front surface. The increase in the combustion rate also produces a stronger expansion flow, which in turn accelerates the flame flow, with a consequent increase in turbulence intensity, and the flame surface thus became less smooth and changed internally. According to Figure 12, the flame area is 500 mm > 200 mm > 800 mm. It can be observed that when the obstacle is located at 500 mm, the flame has a relatively long evolutionary distance between the obstacle and the vent, mainly for the folded surface. Although the obstacle was located at 200 mm when the flame had a longer evolutionary distance, the flame front surface is mostly smooth, greatly reducing the flame area.  
Figure 11: Change in flame area over time.
# V2X Communications in Highway Environments: Scheduling Challenges and Solutions for 6G Networks
## 1 Introduction
Moreover, in the current paper's study, the very challenging highway scenario and a broader set of V2N UCs are used for the evaluation of SOVANET+ in this challenging environment. The new algorithm, SOVANET+, uses a more efficient metric that combines the modulation and coding scheme (MCS) assigned to each user, along with the Proportional Fair (PF) metric, to increase the performance of the scheduler. The combined metric arranges resources for critical services as follows. If the vehicle has an excellent Channel Quality Indicator (CQI) and, subsequently, a suitable MCS, the scheduler provides slightly higher resources than the requested ones. If the CQI decreases, additional resources are allocated to the critical session to ensure that throughput and delay requirements will be met. However, vehicles with poor signal quality are not getting any resources as their support would no longer be possible, no matter how many resources would be allocated to them. Compared to its previous version, SOVANET+ considers additional parameters, including distinct treatment for UL and downlink (DL), the data traffic characteristics, and the performance requirements of V2X UCs. Simulation results demonstrate that the proposed schedulingscheme outperforms the traditional PF scheduler and the SOVANET algorithm regarding the system's throughput, delay, jitter, and PRR making it a good candidate for autonomous driving supported by Sixth-Generation (6G) networks. Our work highlights the following key contributions:  
* The utilization of the V2X UCs in the highway environment, using the ns-3 [8] and the SUMO [9], both calibrated under 3 GPP and 5 GAA guidelines. * The development of the SOVANET+ scheduler. * The proposed scheduler's performance comparison against the PF and the SOVANET schedulers. The remainder of the paper is organized as follows: Section 2 discusses widely accepted scheduling schemes for 5G NR and research proposals for V2X communication in highway conditions. Section 3 presents the operation of SOVANET+. Section 4 presents the simulation environment used. Section 5 evaluates the performance of the SOVANET+ scheduler. Section 6 discusses the next steps of our work and concludes the paper.
## 3 Theoretical Framework
### Artificial Intelligence and Technology Acceptance Model
#### 3.2.3 Adaptive Learning Technologies  
Adaptive learning technologies are at the heart of personalized education, especially in complex areas, such as language learning, where learner needs are diverse. The technologies use sophisticated algorithms to change the learning content and styles adapted to the user's learning preferences and progress, enabling optimal educational outcomes. Adaptive technologies for language learners can change the pace of the curriculum, the difficulty level, and the content types according to real-time assessments of learner performance and engagement.  
Adaptive learning technologies are crucial in the CILS. They help the system offer personalized linguistic and cultural instruction by analyzing the students' responses and tailoring the teaching strategies accordingly. If a learner learns basic vocabulary quickly but has difficulty with complex grammatical structures, the system can be adjusted so that more intensive grammar exercises are provided than vocabulary exercises, which proceed more quickly.  
Multiple platforms employ multimedia elements, interactive dialogues, and real cultural scenarios that make learning more interesting. They also promote spaced repetition, a learning strategy that includes longer intervals of time between subsequent reviews of learned material to utilize the psychological spacing effect. This method is most successful in language learning in strengthening memory.  
In addition to individual learning, adaptive technologies in CILS may support collaborative learning. Learners can be grouped according to their capabilities or interests, which is facilitated with peer-to-peer learning and interaction practice in a controlled, culturally enriched setting. This helps develop language skills and promotes cultural understanding and empathy among learners from different races.  
The application of these adaptive learning technologies will help the CILS provide a custom-made, interactive, and adaptive learning experience. This method assures that learners are passive receivers of information and active participants in a learning process that respects their original cultural and linguistic background, thereby creating more productive and meaningful learning interactions.
## 5 Conclusions  
This paper proposes a deep-learning-based method for identifying small targets in car tire marking points, which improves the accuracy of small target marking point recognition in images; the research results of this paper are summarized as follows:  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Models** & **Precision** & **Recall** & \
\begin{tabular}{c} **Training Duration** \\ **(Hour)** \\ \end{tabular} &
\begin{tabular}{c} **Detection** \\ **Speed (ms/pic)** \\ \end{tabular} \\ \hline Improved YOLOv5s & 0.96 & 0.89 & 3.4 & 22 \\ YOLOv5s & 0.87 & 0.84 & 3.2 & 19 \\ YOLOv4 & 0.78 & 0.77 & 3.8 & 31 \\ Faster-RCNN & 0.82 & 0.81 & 3.5 & 25 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Improved YOLOv5s model compared to YOLOv5s, YOLOv4, and faster-RCNN.  
Figure 12: Comparison of results: where (**a,c,e,g**) are improved YOLOv5s results, and (**b,d,f,h**) are YOLOv5s results.  
In image pre-processing, the MSRCR algorithm is introduced to improve the contrast of identification points in the image, which can adapt to different environments and reduce the difficulty of model training.  
Improve YOLOv5s model by adding a small target detection head to improve the accuracy of the model for small target recognition.  
The addition of the parameter-free simAM attention mechanism, which enhances the ability of the convolutional layer to protrude features, can better improve feature information.  
The loss function CIoU of the original network is replaced by Alpha-IoU, which is more flexible than the original CIoU in updating the parameters using back propagation and reducing the loss of predicted and true values, thus optimizing the model and enabling it to have better recognition results.  
With the above improvements, the resulting improved YOLOv5s model has a 9.5% improvement in mAP compared to the original YOLOv5s. The accuracy is improved by 9%, the recall rate is improved by 4%, and the convergence speed is accelerated. Compared with other small target detection models, the models in this paper all have significant advantages. Through random test experiments, the superiority of the improved YOLOv5s model for small target recognition is verified and can be applied to practical detection tasks.  
Z.G.: conception of the study, proposition of the theory and method, supervision; J.Y.: literature search, figures, manuscript preparation and writing; J.S.: programming, testing of exiting code components. W.Z.: data collection. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Liaoning Provincial Education Department Project (Grant No. IJKZ0114). Yingkou Enterprise Doctoral Entrepreneurship and Entrepreneurship Program Project.  
Not applicable.  
The authors declare no conflict of interest.
## 5 Conclusions
* Clear needs include (i) integrating various sensor data and existing data sources, (ii) employing very high-resolution data, (iii) discovering new data sources, and (iv) basing the methods on high-quality reference data  
Therefore, RS can largely contribute to more sustainable small-holder agriculture in West Africa by supporting practices such as sustainable intensification and integrated soil fertility management. However, its role is embedded in an ever-changing landscape of evolving socio-economic and climate conditions and variability in farming systems and information asymmetries. This poses challenges and risks requiring RS as a critical component to be used alongside other strategies for effective agricultural development [10; 12; 16]. The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/land14010171/s1](https://www.mdpi.com/article/10.3390/land14010171/s1)]([https://www.mdpi.com/article/10.3390/land14010171/s1](https://www.mdpi.com/article/10.3390/land14010171/s1)), Table S1: Overview of the 163 reviewed publications, including authors, title, sensor names, spatial resolution in meters, study country and crop of interest. Conceptualization, N.H. and C.K. ; writing--original draft preparation, N.H.; writing--review and editing, N.H., J.M., U.G. and C.K. ; visualization, N.H.; supervision, C.K. All authors have read and agreed to the published version of the manuscript. This research was funded by the Federal Ministry of Education and Research (Bundesministerium fur Bildung und Forschung, BMBF) under the grant number (FKZ) 01 LL2204A within the framework of the COINS project. Data Availability Statement: Data is contained within the article. The authors would like to thank native English speaker [PERSON] for the linguistic review. The authors declare no conflicts of interest.
# Improving Lossless Image Compression with Contextual Memory
## 4 The Proposed Method-Contextual Memory
### Context Modeling  
When it comes to predictive compression, we need to decide what best describes the part of the image we are currently trying to predict. This means that we need to look around the target pixel in the hope that the information will be enough to help a decision mechanism recognize which part of the image we are in and choose to use the appropriate representation of the internally created segmentation of the image.  
Before continuing, we need to define the terms used in Figure 3. The word _context_ represents the region of the image that participates in the prediction mechanism. \"Context value\" is the numeric value of the context, either a direct value or a function of that value, which will be used as an index in the _memory_ structure. The algorithm makes no assumption about the memory structure, but we provide some implementation details. The output of indexing the memory is the \"memory value\".  
We choose a simple model for contexts for predicting the bits of the pixels. We use rays in four directions and with various lengths, and the quantized derivatives along the rays. Since the pixels of the image are predicted from left to right, top to bottom, the only information we can rely on are known pixels, which means the directions are to the west, 45 degrees north-west, north, and 45 degrees north-east. The rays are depicted as gray background in Figure 4. We choose rays of varying lengths from length 1 to 7, but use this as a parameter. The derivative with respect to the intensity value is computed as the difference between the consecutive pixels of the ray and quantizing is done by masking the lower order bits from the derivative. We use three levels of quantizing, each cutting out one more bit than the other. The current pixel participates in the contexts only with the currently known bits.  
In order to compute the context value from the contexts, we use a hashing function. We chose Fowler-Noll-Vo hash function (FNV) that is a non-cryptographic one byte at a time, designed to compute fast and with a low collisions rate. It is found to be particularly suited for hashing nearly identical strings [27].  
Figure 4: Contexts as rays.  
Figure 3: Block scheme of the proposed method.  
As an optimization, since we know that we will need to compute hashes for rays, we exploit the fact that FNV computes one byte at a time hashes and pass as input only the longest ray and output all the intermediate results. We apply the same optimization for the quantized derivatives of the rays.