esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
In conclusion, we have proposed a methodology that allows for the consideration of local characteristics in the design of auditory warning interfaces, applicable to the engineering and research of ADAS auditory interfaces. Using Shanghai as a case study, we first established a sound map of the city and analyzed the most significant sounds. In terms of marketing, consumer expectations for future ADASs and auditory warning systems were explored. Then, through perceptual studies, ecological sounds extracted from the Shanghai environment were used to observe the impact of familiarity, learning, and context on sound perception. Finally, a simulation test using sounds designed based on our recommendations was performed, and the results indicated that sounds designed according to local guidelines are more easily understood and show quicker response and lower workload. Based on these observations, this work draws two conclusions: (1) Perception is highly dependent on local parameters. Cultural factors, road environment factors, and many other parameters significantly influence local consumers' perceptual characteristics and cannot be overlooked. (2) However, by applying methodologies as described in this paper, it is indeed possible to improve and adapt human-computer interfaces. These research findings create an optimal and safe solution for users.  
The following supporting information can be downloaded at [[https://www.mdpi.com/article/10.3390/app142210733/s1](https://www.mdpi.com/article/10.3390/app142210733/s1)]([https://www.mdpi.com/article/10.3390/app142210733/s1](https://www.mdpi.com/article/10.3390/app142210733/s1)).  
Conceptualization, J.M. and Y.Z.; methodology, O.J.; software, Y.Z.; validation, O.J., Y.Z. and W.X.; formal analysis, Y.Z.; investigation, Z.G.; resources, J.M.; data curation, O.J.; writing--original draft preparation, Y.Z. and O.J.; writing--review and editing, Y.Z.; visualization, O.J.; supervision, J.M.; project administration, W.X.; funding acquisition, J.M. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflicts of interest.  
The NASA TLX test allows an evaluation of the user's workload. It is composed of two parts: self-evaluation of criteria and paired comparison of these criteria.
## 5 Conclusions  
We tested the influence of tillage system, rate and from of nitrogen fertilizers on canopy and soil fluxes of CO\({}_{2}\) and soil fluxes of N\({}_{2}\)O during a growing season of winter wheat cultivated in the US SGP. Canopy fluxes of CO\({}_{2}\) (net ecosystem exchange, gross primary production and ecosystem respiration) varied by 10% between no-till and conventional till, and opposite responses were observed on the two study sites. Results indicated the two forms of tillage applied to winter wheat production in the US SGP influence crop growth in a similar way. Application of nitrogen fertilizer had a stronger influence on canopy fluxes of CO\({}_{2}\) than cowpea-based green N, since the fluxes increased with increasing rates of nitrogen fertilizer. Tillage and fertilizer treatments did not influence soil respiration but large seasonal dynamics were observed from all treatments. Fluxes of N\({}_{2}\)O remained low from all treatments throughout the study and no response to tillage systems or fertilizer treatments were observed. Overall, results from this study indicate canopy fluxes of CO\({}_{2}\), an indicator of growth and yield by winter wheat, were controlled more by forms and rates of nitrogen fertilizers than system of tillage. Since cowpea was not effective at improving gross photosynthesis as a green manure compared to an unfertil control, future studies should focus on improving the fertilizer value of summer legumes grown to supply N for winter wheat in the US SGP.  
Conceptualization, T.P.K.; data curation, T.P.K.; formal analysis, T.P.K.; funding acquisition, P.H.G.; investigation, T.P.K.; methodology, T.P.K. and B.K.N.; project administration, P.H.G.; resources, B.K.N.; writing--original draft, T.P.K.; writing--review and editing, T.P.K., P.H.G. and B.K.N. All authors have read and agreed to the published version of the manuscript.  
The authors wish to recognize ARS technicians [PERSON], [PERSON], [PERSON], and [PERSON] for their assistance in managing the experiment, collecting, and analyzing samples. Mention of trademarks, proprietary products, or vendors does not constitute guarantee or warranty of products by USDA and does not imply its approval to the exclusion of other products that may be suitable. All programs and servicesof the USDA are offered on a nondiscriminatory basis, without regard to race, color, national origin, religion, sex, age, marital status or handicap.  
The authors declare no conflict of interest.
## 2 Literature Review
### Multivariate Phase Space Analysis and Its Applications
[PERSON] [22] constructed a short-term traffic flow prediction model based on phase space reconstruction and regularized extreme learning machines to improve the short-term traffic flow prediction accuracy. The optimal time delay and embedding dimension of the traffic flow time series were determined using the C-C algorithm, and the phase space was reconstructed. The G-P algorithm was used to calculate the correlation dimension of the sequence, and chaos characteristics were identified for short-term traffic flow sequences. [PERSON] et al. [23] introduced a multi-time series reconstruction method based on data fusion. A social cognitive algorithm and adaptive weighted fusion estimation were used to optimize the weight of each component. The phase space was reconstructed by fusing different phase spaces through data fusion, providing a new idea for the data fusion of heterogeneous sensors. To address the large number of monitoring variables and high redundancy in chemical production systems, [PERSON] et al. [24] proposed a multisource data fusion method based on phase space reconstruction. The parameters of phase space reconstruction were obtained using the mutual information and Cao methods, providing new insights into solving multisource heterogeneous sensor data fusion problems. [PERSON] et al. [25] presented a modular macroscopic traffic model with two traffic light formulations, and the proposed model allowed arbitrary functional forms of the fundamental diagram defined by a small number of parameters; then, the moving density gradients (jam fronts) were represented accurately, and the model parameters were physically meaningful and could readily be estimated from measurement data. [PERSON] [26] aimed to improve the accuracy of short-term traffic flow predictions by studying a prediction model based on phase space reconstruction and particle swarm optimization Gaussian process regression. To overcome the nonlinearity, complexity, and randomness of traffic flow time series, the phase space was reconstructed based on chaos theory to obtain the best delay time and embedding dimension of the original time series, which were used as the input-output data set of the model to maintain the same dynamic characteristics as the original data. [PERSON] [27] proposed a method for predicting connected traffic flow data with chaotic characteristics by using an improved phase space reconstruction method to reveal chaotic dynamics in data and a hybrid deep learning model to extract features from phase space data and optimize model parameters, improving the prediction results.
## 2 Materials and Methods
### Revision of the U-Net  
The original U-Net was introduced at the 2015 IEEE 12 th International Symposium on Biomedical Imaging [14]. The U-Net is a variant of fully connected network architecture that retains the Encoder-Decoder structure. The Encoder part consists of convolution and downsampling operations that extract image feature information. A feature map is generated in each downsampling process. The Decoder part consists of deconvolution operations and performs upsampling on the feature maps. Unlike typical fully connected network structures, the U-Net uses skip-connections to fuse the feature maps from the downsampling process with the feature maps from the upsampling process. The authors claimed that this operation can compensate for the loss of edge features during downsampling. They achieved very good results without using huge data sets. Thus, we assumed a revised U-Net could provide a new tool for quantitative analysis.  
Figure 1 shows our modified network structure. We modified all the 2D convolutional layers into 1D convolutional layers with a kernel size of 3 and a stride of 1. Thus, the revised U-Net can process 1D NIR spectra. At the same time, we constructed \"ResBlocks\" in our architecture. Each ResBlock is composed of three 1D convolutional layers and a residual network [20] followed by an activation operation called Softplus. The output from upsampling is introduced into a maximum pooling residual module (MPR) and an average pooling residual module (APR). The APR module consists of two residual convolutional layers to perform sampling through average pooling. The MPR module consists of two residual convolutional layers that extract features through maximum pooling.  
These two modules extract feature information at different levels. The outputs from the APR and MPR modules are fused by channel concatenation. The fused layer is transferred into the final output. In Figure 1, the output channel is channel 4 and is aimed at CGL data. One can be changed into other values depending on their target property.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Data Set** & **Total Samples** & **Training Set** & **Validation Set** & **Test Set** & **Features 1 ** \\ \hline CGL & 231 & 139 & 46 & 46 & 116 \\ IDRC-2002 set 1 & 655 & 391 & 131 & 131 & 281 \\ IDRC-2002 set 2 & — & — & — & 655 & 281 \\ IDRC-2016 A1 & 248 & 248 & — & — & 740 \\ IDRC-2016 A2 & 248 & — & — & 248 & 740 \\ IDRC-2016 A3 & 248 & — & — & 248 & 740 \\ IDRC-2016 B1 & 646 & — & — & 646 & 740 \\ IDRC-2016 B2 & 248 & — & — & 248 & 740 \\ IDRC-2016 B3 & 248 & — & — & 248 & 740 \\ IDRC-2016 T & 248 & — & 248 & — & 740 \\ IDRC-2016 V & 248 & — & — & 248 & 740 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The classifications of three data sets.
## 2 Related Works
At present, in edge computing research under HAP drones and satellite collaborative networks, HAP drones directly collaborate with satellites to provide computing services for ground users [25; 26; 27; 28]. In this paper, HAP drones can collaborate not only with satellites but also with each other to jointly provide computing services to users on the ground. This method can expand the computing resources available to each user, and it is beneficial in reducing the delay in task processing. In addition, many current works, such as [23; 25; 29; 30], directly design reinforcement learning algorithms to output task offloading and resource allocation strategies. In this way, the output size of the neural network (including task offloading strategy and resource allocation strategy) is larger, resulting in a large network scale and a long training time. In this paper, we propose an optimization framework that combines deep reinforcement learning with convex optimization. Reinforcement learning only outputs task splitting strategies, and the optimization of resource allocation is realized by the convex optimization method, which can reduce the output size of neural networks and reduce the difficulty of network training.
## 6 Results and Discussion
Figure 7: Results of all algorithms. Figure 8: Confusion matrix for all algorithms: (**a**) RF, (**b**) NB, (**c**) KNN, (**d**) SVM, (**e**) LR, (**f**) ANN, (**g**) DNN, (**h**) GBoost, (**i**) XGBoost, (**j**) NB-AGA, (**k**) RF-AGA, (**l**) XGB-AGA. Three different analysis approaches, static, dynamic, and hybrid, are used for Android malware detection. In addition to the literature, in Table 4, a comparison of the current static, dynamic, and hybrid analysis studies with the proposed approach is presented. The most widely used method for Android malware detection is static analysis. The biggest advantage of static analysis is that early measures are taken by analyzing the application before it is run. In current studies using static analysis, [PERSON] et al. [11] and [PERSON] [36] used the deep learning approach. Deep learning methods have high accuracy, but deep learning models consume large amounts of resources and have high computational costs [37; 38]. Despite this, accuracies of 0.9666 and 0.9818 were achieved, respectively, in these studies. [PERSON] et al. [11], [PERSON] et al. [39], and [PERSON] et al. [40] classified static analysis information using classical machine learning methods. In these studies, accuracy values of 0.9510, 0.9516, and 0.9110 were obtained, respectively. [PERSON] et al. [41] used a CNN for feature extraction and ANFIS for classification. As a result of the study, they reached 0.9466 accuracy. In studies using ensemble learning-based static analysis, [PERSON] [42] achieved an accuracy of 0.9933 with a fuzzy logic and machine learning-based stacking method, and [PERSON] [43] achieved a 0.9866 accuracy with a genetic algorithm and machine learning-based stacking method. The results obtained using static analysis are high. However, the results of studies using machine learning are lower than our study. Although the results of the studies using the ensemble learning approach are similar to our study, they are lower than the results we obtained. The dynamic analysis approach takes time to implement compared to static analysis, but it is difficult for applications to bypass detection systems due to their behavior at runtime. [PERSON] and [PERSON] [9] classified the features obtained using dynamic analysis with random forest. As a result of the experiment, an accuracy of 0.9290 was obtained. [PERSON] et al. [44] reached a 0.9370 accuracy using dynamic analysis and LSTM architecture. [PERSON] et al. [45] found an accuracy value of 0.95 with the weighted voting method. These studies, in which dynamic analysis was performed, also obtained lower results than our proposed approach. Figure 9: ROC curve for all algorithms. Using hybrid analysis approaches, [PERSON] et al. [12] achieved an accuracy of 0.9110 using deep neural networks. In our study, PSO-based feature selection was performed using hybrid analysis. At the same time, the performance of machine learning algorithms is optimized with the AGA. Our presented detection approach, XGB-AGA and RF-AGA, has achieved successful detection accuracy compared with existing studies.
# EMI Threat Assessment of UAV Data Link Based on Multi-Task CNN
## 4 MIMT-CNN Modeling
### Network Structure  
The input of the network is divided into multiple channels, as shown in Figure 6. The VPP, STFT spectrogram and NDC of the data link at the same interference state are input into different channels, and then go through the feature extraction layer, the feature fusion processing layer and finally the multi-task output layer.
## 4 Experiments and Results
### Experiments  
The trained DDPG, KDDPG, and D*-KDDPG models are further compared with D*-DWA to benchmark their performance, where D*-DWA and D*-KDDPG rely on a global map for navigation, and DDPG and KDDPG perform mapless navigation. The following metrics were used for the performance comparison:  
* Navigation Time (NT): The time required to complete the navigation.
* Path Length (PL): The total length of the generated path.
* Curvature Smoothness (CS): The integral of the square of the curvature, which is used to measure the smoothness of the path [18]; lower values indicate smoother paths.
* Safety Distance (SD): A distance used for collision detection, which is calculated by subtracting 0.141 m (a reference value based on the robot's dimensions) from the radar's detection distance. A collision is assumed to happen when SD reaches zero.  
In particular, when a collision occurs, the path planning fails, and NT is set to the timeout MAXTIME as specified in Section 3.3. It is noteworthy that a larger safety distance can enhance the safety of the planned path but may increase navigation time and path length.
# Backscatter Communications: Inception of the Battery-Free Era--A Comprehensive Survey
## 4 BackCom: Signal Processing and Detection
### BackCom Signal Processing Aspect  
The signal processing component in BackCom considers carrier signals and backscattered signals. Carrier signals with a higher frequency are emitted from the transmitter and propagate towards passive tags. The passive tags reflect back the incoming signals with a lower frequency as compared to the carrier waves. [PERSON] et al. discussed signal processing and BER performance analysis for Amb-BackCom [67]. Usually, in multi-tag communications, a large number of the passive nodes are unable to observe each other's transmissions. Thus, enabling concurrent transmissions is one of the primary challenges in BackCom. Backscatter spike train (BST) addresses the concurrent transmissions problem [68]. BST allows the BackCom physical layer for concurrent transmissions from multiple tags with the help of intra-bit multiplexing of OOK signals. Similarly, the MOXcatter system is suggested for multiplexing using a spatial stream BackCom with off-the-shelf WiFi devices [69]. The coding techniques for accommodating a large amount of data on backscattered signals are elaborated below.
# Compositional Analysis and Aroma Evaluation of Feijoa Essential Oils from New Zealand
###### Abstract  
Feijoa is an aromatic fruit and the essential oil from feijoa peel could be a valuable by-product in the juicing industry. An initial comparison of the essential oil extraction methods, steam-distillation and hydro-distillation, was conducted. The volatile compounds in the essential oils from four feijoa cultivars were identified and semi-quantified by GC-MS and the aroma active compounds in each essential oil were characterized using SPME-GC-O-MS. Hydro-distillation, with a material to water ratio of 1:4 and an extraction time of 90 min, was the optimized extraction method for feijoa essential oil. The Wiki Tu cultivar produced the highest essential oil yield among the four selected cultivars. A total of 160 compounds were detected, among which 90 compounds were reported for the first time in feijoa essential oils. Terpenes and esters were dominant compounds in feijoa essential oil composition and were also major contributors to feijoa essential oil aroma. Key aroma active compounds in feijoa essential oils were \(\alpha\)-terpineol, ethyl benzoate, (\(Z\))-3-hexenyl hexanoate, linalool, (\(E\))-gerariol, 2-undecanone, 3-octanone, \(\alpha\)-cubebene, and germacrene D. This is the first report on the optimization of the extraction method and the establishment of the aroma profile of feijoa essential oils, with a comparison of four New Zealand grown cultivars.  
feijoa essential oil; hydro-distillation; GC-MS; HS-SPME-GC-O-MS; aroma active compounds; aroma profile +
Footnote †: journal: molecules
# XSShield: Defending Against Stored XSS Attacks Using LLM-Based Semantic Understanding
## 3 XSShield Design
### Prompt Optimizer
#### 3.1.1 Prompt Expander  
The Prompt Expander implements gradient descent mechanics in semantic space through three phase-locked steps: (1) performance evaluation (forward pass) identifying label-prediction mismatches, (2) semantic gradient generation (backward pass) translating errors into natural-language optimization directions, and (3) prompt updates adjusting instructions against computed gradients--all without differentiable parameters. The module optimizes prompts through directional gradient updates in semantic space. This optimization process encompasses three interrelated steps: prompt evaluation, semantic gradient generation, and gradient-based prompt updates, all implemented by designing prompts to guide the LLM, systematically expanding the search space while maintaining semantic coherence. The specific prompt usage is detailed in Appendix B.  
**Evaluation Phase**: We utilize the base model (the LLM used for evaluation) to assess each prompt's performance on predefined train sets. Given a set of prompt candidates \(P\), for each prompt \(p_{i}\in P\), we extract a batch sample from training data \(D_{train\_ample}\) to efficiently obtain prediction results. Subsequently, we identify samples where predicted labels differ from true labels, defining them as mismatched samples \(D_{mismatch}=\{d_{j}\mid pred_{j}\
eq label_{j},d_{j}\in D_{train\_sample}\}\), which serve as the foundation for subsequent optimization.  
**Gradient Generation Phase:** We transform mismatched samples into actionable optimization directions. We analyze error patterns and generate semantic gradients in natural language description form. Compared to traditional numerical gradients, these semantic gradients capture subtle characteristics of prompt deficiencies, providing explicit directional guidance for improvements in semantic space. We design specialized prompts to analyze different types of errors and generate corresponding semantic guidance, effectively identifying specific aspects of prompts requiring enhancement.  
**Prompt-Update Phase:** We implement targeted corrections based on identified deficiencies. This process addresses specific issues through directional edits, adjusting prompts in the opposite direction of semantic gradients. Each optimized prompt is added to the prompt candidates set \(P\), continuously enriching the optimization space.