esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Impact of Seasonal Drought on the SIF Estimation  
Despite the plentiful water and heat resources of the subtropical forests, changes in their seasonal cycles lead to frequent occurrences of seasonal drought, which affect the function of forest ecosystems (e.g., photosynthesis) [54; 55]. In our study, the high VPD interfere the performance of the GPP backward approach for the SIF estimation. Photosynthesis contains light reaction and dark reaction, but these two reactions show different sensitivities to environmental factors such as illumination, temperature and water availability [56; 57]. The emission of ChIP occurs in the light reaction stage, while the CO\({}_{2}\) fixation in the dark reaction stage is directly restricted by stomatal conductance. Water stress usually leads to the closure of stomata, thereby slowing the dark reaction rate and, thus, photosynthesis rate. The light and dark reactions are interdependent and restrict each other, so the decrease in stomatal conductance will theoretically-inevitably lead to the reduction of fluorescence quantum yield [58]. Some researchers found that the ChIP would decrease due to water stress while the vegetation canopy greenness would remain unchanged during drought [58; 59]. Thereby, SIF anomaly was applied to monitor the drought events [60; 61; 62].  
However, recent research found that with the treatment of stomatal closure, no obvious change occurred in SIF or F\({}_{\text{s}}\), despite evident reductions in stomatal conductance and carbon assimilation in leaves [63]. Responses of fluorescence emission and photosynthesis could decouple during drought, representing that vegetation photosynthesis might still be suppressed in spite of the variation in ChIP signal. Most works on SIF assumed a constant ratio of photosynthetic quantum efficiency (\(\epsilon_{\text{P}}\)) and fluorescence quantum efficiency (\(\epsilon_{\text{P}}\)) to establish the linear relationship between GPP and SIF, which was consistent with observations at large scales. Nevertheless, the assumption was questioned by a recent research, which suggested that SIF captured the variations of linear electron transport rate,thus only providing insights into the light reactions of photosynthesis, without the dark reactions [17]. This explained why the SIF estimated by the backward approach under high VPD was poorly correlated with the SIF simulated by the SCOPE model. When fluorescence and photosynthesis are decoupled under environmental stress, the improved backward approach cannot be used validly. While the backward approach from GPP offers new insight into estimating canopy SIF, we suggest that the application of the approach under drought stress should be utilized properly and further experiments and research need to be done to interpret the influence of drought interfered with the stomata in response to future climate change.
## 5 Low-Duty Turbo Compressor Isentropic, Energy, and Exergy Analysis
Exergy destruction and exergy of compression are shown in a Sankey diagram in Figure 8 [68]. The same two running points were considered as in the Sankey energy flow. Electric energy brought to the process in kW was compared with the actual exergy required for the compression process, electric motor destruction, and exergy destruction. In the first running point, the electric motor exergy destroyed 10%; the rest remained for the exergy destruction of the first running point, which was 84.83%. The useful process exergy consumption was only 5.17%. At the last measured point, 10% was again the electric motor exergy destruction, 64.34% was exergy destruction, and 25.66% was usefully consumed exergy. According to the Sankey diagram, the share of exergy destruction increased with increases in compression exergy requirements, which is different compared to that of the energy losses. Figure 8: Sankey exergy flow diagram for the lowest and highest measured turbo compressor loads. Figure 7: Energy loss and exergy destruction. Figure 9 compares the isentropic and exergy efficiencies of the low-duty turbo compressor. Exergy efficiency analysis considered the whole unit, similar to the energy efficiency analysis. According to the exergy efficiency analysis, the low-duty turbo compressor efficiency in the lower running zones is 7.6% on average, with the lowest efficiency of only 4.07%, which is very poor. In the mid-running zone, the average exergy efficiency was 16.01%, and 27.87% in the upper running zone, with a peak at 29.87%, which is below 30%. In the fourth running point, the exergy efficiency increase was slightly higher compared with the energy analysis; the exergy analysis showed a more similar trend to isentropic efficiency, but with different results. The fourth measured point was related to the full ahead command to the engine telegraph, which results in higher mass flow through the turbo compressor with fully opened inlet guide vanes and slightly increased turbo compressor speed at 12,338 rpm. The effects of surrounding temperature change on exergy efficiency and its sensitivity are evaluated in Figure 10. The temperature range for the evaluation of the low-duty turbo compressor exergy efficiency was 0 to 50 \({}^{\circ}\)C. This zone was selected because the vessel spends most of the sailing time in that temperature range. In the lower running zones of the low-duty turbo compressor, exergy efficiency decreases evenly for each 10 \({}^{\circ}\)C by about 0.6%. In the upper running zones, the low-duty turbo compressor is more sensitive to temperature changes; the exergy efficiency decreases in that zone by about 1%. The total average exergy efficiency deterioration with surrounding temperature changes was an average of 3.34%; the turbo compressor was more sensitive to surrounding temperature change in the higher running zones and with lower temperature gas. The ideal for this type is higher unit exergy efficiency if the vessel is sailing in colder areas, so the turbo compressor will have to destroy less exergy for the same task. Figure 4:0 Isentropic and exergy efficiency.
###### Abstract  
The rational design of highly efficient, noble metal-free metal oxides is one of the main research priorities in the area of catalysis. To this end, the fine tuning of ceria-based mixed oxides by means of aliovalent metal doping has currently received particular attention due to the peculiar metal-ceria synergistic interactions. Herein, we report on the synthesis, characterization and catalytic evaluation of ZnO-doped ceria nanorods (NR). In particular, a series of bare CeO\({}_{2}\) and ZnO oxides along with CeO\({}_{2}\)/ZnO mixed oxides of different Zn/Ce atomic ratios (0.2, 0.4, 0.6) were prepared by the hydrothermal method. All prepared samples were characterized by X-ray diffraction (XRD), N\({}_{2}\) physisorption, temperature-programmed reduction (TPR), scanning electron microscopy with energy dispersive X-ray spectroscopy (SEM-EDS) and transmission electron microscopy (TEM). The CO oxidation reaction was employed as a probe reaction to gain insight into structure-property relationships. The results clearly showed the superiority of mixed oxides as compared to bare ones, which could be ascribed to a synergistic ZnO-CeO\({}_{2}\) interaction towards an improved reducibility and oxygen mobility. A close correlation between the catalytic activity and oxygen storage capacity (OSC) was disclosed. Comparison with relevant literature studies verifies the role of OSC as a key activity descriptor for reactions following a redox-type mechanism.  
CeO\({}_{2}\)/ZnO mixed oxides; zinc oxide; ceria nanorods; oxygen storage capacity; CO oxidation +
Footnote †: journal: Accepted: 26 October 2020; Published: 28 October 2020
# Enhanced Peer-to-Peer Botnet Detection Using Differential Evolution for Optimized Feature Selection
###### Abstract  
With the growing prevalence of cybercrime, botnets have emerged as a significant threat, infiltrating an increasing number of legitimate computers annually. Challenges arising for organizations, educational institutions, and individuals as a result of botnet attacks include distributed denial of service (DDoS) attacks, phishing attacks, and extortion attacks, generation of spam, and identity theft. The stealthy nature of botnets, characterized by constant alterations in network structures, attack methodologies, and data transmission patterns, poses a growing difficulty in their detection. This paper introduces an innovative strategy for mitigating botnet threats. Employing differential evolution, we propose a feature selection approach that enhances the ability to discern peer-to-peer (P2P) botnet traffic amidst evolving cyber threats. Differential evolution is a population-based metaheuristic technique which can be applied to nonlinear and non-differentiable optimization problems owing to its fast convergence and use of few control parameters. Apart from that, an ensemble learning algorithm is also employed to support and enhance the detection phase, providing a robust defense against the dynamic and sophisticated nature of modern P2P botnets. The results demonstrate that our model achieves 99.99% accuracy, 99.49% precision, 98.98% recall, and 99.23% F1-score, which outperform the state-of-the-art P2P detection approaches.  
P2P botnet; feature selection; metaheuristic algorithm; differential evolution; DEAP library
# Analysis of Potent Odour-Active Volatile Thioids in Foods and Beverages with a Focus on Wine
## 3 Thiol Isolation--Extraction and Derivatisation  
Due to the analytical challenges previously mentioned, particularly the instability of volatile thiols, but also their low abundance, efficient isolation using the routine extraction techniques employed for aroma analysis is hard to achieve. This reflects on the fact that many of the established thiol isolation methods have combined rather sophisticated forms of sample pre-treatment, specific extraction, derivatisation, clean-up, and enrichment (concentration). From the methods that are summarised in Table 1, it is evident that the isolation of volatile thiols from wine (and similar applications in foods and other beverages) is an evolving process that has advanced from traditional, time-consuming methods to better-sequenced and much simplified procedures. Although being proposed in various formats, the currently available thiol preparation methods can generally be categorised into three main groups:  
* non-specific extraction, e.g., straightforward application of headspace solid-phase microextraction (HS-SPME), solid phase extraction (SPE), liquid-liquid extraction (LLE), purge and trap (P+T), and vacuum distillation;
* selective extraction with metal ions (e.g., Hg\({}^{+}\) or Ag\({}^{+}\));
* derivatisation (coupled with LLE, HS-SPME, SPE, or gas purge microsyringe extraction (GP-MSE)).  
As the non-specific extraction techniques for thiol isolation are principally similar to those that are commonly applied for other volatile compounds, selective extraction and derivatisation-based methods have been selected as the focus in this review.
# Semantic Segmentation of Aerial Imagery Using U-Net with Self-Attention and Separable Convolutions
###### Abstract  
This research addresses the crucial task of improving accuracy in the semantic segmentation of aerial imagery, essential for applications such as urban planning and environmental monitoring. This study emphasizes the significance of maintaining the Intersection over Union (IOU) score as a metric and employs data augmentation with the Patchify library, using a patch size of 256, to effectively augment the dataset, which is subsequently split into training and testing sets. The core of this investigation lies in a novel architecture that combines a U-Net framework with self-attention mechanisms and separable convolutions. The introduction of self-attention mechanisms enhances the model's understanding of image context, while separable convolutions expedite the training process, contributing to overall efficiency. The proposed model demonstrates a substantial accuracy improvement, surpassing the previous state-of-the-art Dense Plus U-Net, achieving an accuracy of 91% compared to the former's 86%. Visual representations, including original patch images, original masked patches, and predicted patch masks, showcase the model's proficiency in semantic segmentation, marking a significant advancement in aerial image analysis and underscoring the importance of innovative architectural elements for enhanced accuracy and efficiency in such tasks.  
s _applied_  
_sciences_
## 3 An Interactive Learning Network That Maintains Sentiment Consistency
### Task-Sharing Layer  
[PERSON] et al. [19] believe that different layers of BERT can capture different levels of information; for example, the underlying network captures surface information, the middle network captures syntactic information, and the higher network captures semantic information. The research of this paper requires not only semantic information but also syntactic information, but as the number of layers of BERT increases, the syntactic information in the output features will decrease, so we can take the front \(l\) layer of BERT as the shared layer, that is, the output of the \(l\) layer is the shared feature representation of the two subtasks \(H_{share}\), which contains context information such as aspect item information, sentiment information, syntactic information and semantic information of text, and the \(l\) layer to the \(L\) layer of BERT is used for the aspect item extraction subtask. That is, the L-layer output is the feature representation of the aspect extracted by the \(H_{ate}\). The definitions are shown in Equations (2) and (3).  
\[H_{ate}=H^{L} \tag{2}\]  
\[H_{share}=H^{l} \tag{3}\]  
Figure 1: A framework for interactive learning network models that maintains sentiment consistency.  
The feature representation of the ATE task is input into the classification layer, and the aspect entity sequence \(Y_{a}\) is obtained. The formula is as follows: \(W_{a}\) is the weight matrix of the linear layer, and \(b_{a}\) is the bias term.  
\[Y_{a}=softmax(W_{a}H_{ate}+b_{a}) \tag{4}\]  
The results produced here are not used as the final aspect items to extract the task results but only play an auxiliary role for later use of the multi-head attention mechanism to consider the interaction between the aspect items.
# Research on Prediction of TBM Performance of Deep-Buried Tunnel Based on Machine Learning
## 3 Prediction Model of TBM Penetration Rate Based on Machine Learning
### Prediction Model of TBM Penetration Rate Based on BP Neural Network
#### 3.2.2 BPNN Model Structure and Parameter Setting  
The structure of the BPNN model will significantly affect the accuracy and convergence rate of the prediction model. The input data are composed of four parameters screened by a random forest algorithm, namely thrust (TF), rotational speed (RPM), uniaxial compressive strength (UCS) and number of rock volume joint \(J_{\text{V}}\). The output parameter is the penetration rate (PR) of the TBM. After the input and output layers are determined, the number of hidden layers and the number of hidden neurons need to constantly adjust the parameter settings to determine the best settings; otherwise, the prediction model will be overfitting or underfitting [18]. [PERSON] [19] proposed that the BPNN of a single hidden layer can approximate a continuous nonlinear function within a limited number of hidden neurons, so the hidden layer is determined as 1. The number of hidden neurons is determined according to the empirical Formula (17) proposed by [PERSON] [20].  
\[K=\sqrt{a+b}+c \tag{17}\]  
where \(a\) and \(b\) are the number of input and output nodes, \(c\) is a constant between 1 and 10 and \(K\) is the number of hidden neurons.  
The numbers of nodes in the input layer and output layer are 4 and 1, respectively. Then, \(K\) is a constant between 3 and 13. The selection of the activation function also has a crucial impact on the accuracy of the BPNN [21]. The common activation functions include tanh, sigmoid and ReLU functions. In order to determine the best prediction model, the statistical section is divided into 170 driving units, in which 110 groups of data are used as training sets to train the BPNN model, and 30 groups of data are used as test sets to measure the prediction performance of the model under different parameter settings. The number of hidden neurons and different combinations of the activation function of the BPNN are compared. BPNN models with different combinations of hidden neuron numbers and activation functions are numbered. A, B and C, respectively, mean tanh, sigmoid and ReLU functions selected as activation functions. Numbers represent the number of hidden neurons. The mean square error (MSE) and mean absolute percentage error (MAPE) are selected to evaluate the prediction accuracy of the model. The comparison results are shown in Figure 6. It can be seen from Figure 6 that when the activation function is the tanh function and the number of hidden neurons is 11, the BPNN model obtains the minimum mean square error and average absolute percentage error, and the prediction effect is the best. Accordingly, by adjusting the parameters, batch size, that is, the number of samples for a training, is set to 5; learning rate, that is, the learning rate, is set to 0.001.  
Figure 5: Back propagation neural network structure diagram.
# Optimal Particle Filter Weight for Bayesian Direct Position Estimation in a GNSS Receiver
## 1 Introduction
This work starts with an explanation of the existing BDPE implementation [23] and illustrations in Section 2, which is followed by a detailed description of the implemented particle filter in Section 3. The existing implementation uses BDPE concepts presented in [8; 14; 16; 24], the Line-of-Sight (LOS) projection elements of a Vector Tracking Loop (VTL) for steering the GNSS internal replica signal generators as shown in [25], the concept of highly efficient synthetically generated multi-correlator values (MC) as shown in [26] and ideas of [17; 18] for mapping the correlation values to particles of a Particle Filter (PF). Section 4 shows the derivation of the optimal particle weight, which is based on a probabilistic description of the GNSS signal from [27], and Section 5 shows how the update can be performed under consideration of a finite precision of a computer. Section 6 shows that the optimal weight causes a very dense concentration of particles, which can lead to suboptimal behaviour of Bayesian filters, especially in the case of different residual user range errors coming, e.g., from orbit, satellite clock, multipath or ionosphere. Therefore, a solution is proposed in Section 7, which limits the theoretical high ranging accuracies by considering an additional nuisance parameter. The work is concluded with simulations and real-world data in Section 8.
# Applicability of Semi-Quantitative Evaluation of the Intercondylar Notch
## 1 Introduction  
The intercondylar notch (IN) has been widely investigated in the literature, as it is an important morphological structure in the knee joint that houses some of its major stabilizing structures (anterior and posterior cruciate ligaments (ACLs and PCLs, respectively), meniscofemoral ligaments (MFLs)). Therefore, changes to this osseous structure influence these structures within [1; 2; 3; 4; 5; 6; 7; 8; 9; 10; 11]. The term intercondylar space was defined to reflect this intimate relationship [12].  
Changes in the IN have been associated with ruptures and degeneration in the ACL, which highlights the importance of the definition of cut-off values for the width of the IN (notch width, NW) for risk evaluation. For the same reason, the notch width index (NWI) was defined as a ratio between the NW and bicondylar width [13]. Here, however, discussion arose on the accuracy of these measurements and the index for risk assessment in clinical practice [14].  
For easier evaluation of the morphology of the IN, different shapes were defined (A-shape, U-shape, W-shape and wave-like shape), and a correlation was found between narrower notches and ACL injury, post-injury outcomes and surgical or conservative treatment [14; 15; 16; 17; 18]. Additionally, the IN is not a static formation, with the shape andwidth changing throughout life, from a wider A-shape to a narrower \(\Omega\)-shape with the development of ridges at the osteochondral border [12, 19, 20].  
Until now, these previously described shapes have not been tested for their reliability, reproducibility and applicability in clinical praxis. Only one of these classification systems is based on and defined by specific measurements, and, therefore, a control for the distribution of shapes may be available. Thus, the aim of this study was to test the classification system of [PERSON] et al. [12] for its applicability and reproducibility in clinical routine. Our hypothesis was that semi-quantitative evaluations (the combination of shape evaluation and measurements of the NW) are reliable and comparable with the calculated classification.
# DataMatrix Code Recognition Method Based on Coarse Positioning of Images
## 5 Experiments and Result Analysis
Figure 21: Samples of DM code images of Dataset B with varying levels of Gaussian noise added: (**a**) \(6\%\); (**b**) \(14\%\); (**c**) \(37\%\). The experimental results show that this algorithm can accurately convert the DM code into a data matrix when facing problems such as DM code image blur, wear, corrosion, geometric distortion, and strong background interference in practical application scenarios, improving decoding ability in complex environments. Compared with popular DM code decoding open-source libraries libdmtx and zxing, it also has good performance in terms of resolution, anti-noise, and anti-distortion. However, it should be noted that the proposed algorithm, due to its complex processing steps including adaptive grid division, outlier detection and correction, and locally weighted linear regression, requires more computational resources than traditional decoding libraries. Experimental comparison shows that the average execution time of this algorithm is 14.971 s and average memory consumption is 329.454 KB, whereas libdmtx and zxing exhibit significantly faster decoding speeds and lower memory usage (0.004 s and 60.364 KB for libdmtx, and 0.356 s and 6.909 KB for zxing). This indicates that while the proposed method offers superior robustness under challenging conditions, it is more suitable for scenarios where decoding accuracy is prioritized over speed and resource consumption.