esa-sceva/satcom-chunk-collection huggingface dataset:
# Long-Tail Instance Segmentation Based on Memory Bank and Confidence Calibration
## 4 Experiments
### Main Experiments
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Model** & **Backbone** & **AP\({}_{\text{bbox}}\)** & **AP\({}_{\text{r}}\)** & **AP\({}_{\text{c}}\)** & **AP\({}_{\text{f}}\)** & **AP\({}_{\text{segm}}\)** \\ \hline Mask & & 20.8 & 3.1 & 19.4 & 29.8 & 21.2 \\ R-CNN & & - & 14.5 & 24.3 & 28.4 & 24.4 \\ RFS & Res50 & 23.6 & 8.5 & 23.9 & 29.3 & 24.0 \\ EQL+RFS & & 25.4 & 16.0 & 25.4 & 29.1 & 26.1 \\ Ours & & 25.8 & 17.9 & 24.8 & 30.1 & 26.9 \\ \hline Mask & & 22.3 & 3.8 & 20.9 & 30.8 & 22.8 \\ EQL & Res101 & 25.9 & 9.2 & 26.9 & 31.1 & 25.9 \\ EQL+RFS & & 27.1 & 15.9 & 27.9 & 30.6 & 27.4 \\ Ours & & 27.8 & 19.2 & 27.3 & 31.9 & 29.3 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of the results on LVIS v0.5. Figure 7: AP with different number of image detections. Figure 8: Visualization of experimental results. Figure 9: Limitations of the model in this paper. The left column is the label of the ground truth, and the right column is the detection result of the proposed method. The area indicated by the red arrow shows the limitations of the proposed method. In addition, the proposed method was compared with end-to-end training models. When using the most primitive cross-entropy loss function, the proposed method achieves 2.2% and 0.5% improvements in segmentation accuracy on both backbone networks, respectively. Moreover, the 2.9% and 2.9% improvements in mask accuracy of our proposed method are substantially better than the results of the above two models. Finally, the proposed method was also compared with other more recent models, including one used for incremental learning [30] and hierarchical classification [31]. The experimental results show that our model performs substantially better than LST, which illustrates the absolute advantages of our model. Compared with Forest R-CNN, it is slightly inferior when ResNet50 is used as the backbone, but slightly superior when ResNet101 is used as the backbone. In addition, we compared the proposed method when X101 is used as the backbone network with Forest R-CNN. The proposed model still achieves a 0.4% higher AP\({}_{\text{bbox}}\) and 1.4% higher AP\({}_{\text{m}}\). The experimental results of the proposed model using different backbones illustrate that it has a significant advantage over general instance segmentation models. We can also solve the problems of long-tail datasets better than some recent state-of-the-art models.
# Lacase in Biorefinery of Lignocellulosic Biomass
## 2 Pretreatment of Lignocellulosic Biomasses
### Alkali Pretreatment for Lignocellulosic Biomasses  
Alkali pretreatment involves the use of alkali to break down the lignin and hemicellulose fractions of lignocellulosic biomasses. The most commonly used alkali for this purpose is sodium hydroxide (NaOH), although other alkalis, such as potassium hydroxide (KOH) and calcium hydroxide (Ca(OH)\({}_{2}\)), have also been used. For alkali pretreatment the range of temperature varies from 70 to 120 \({}^{\circ}\)C, with a pH of 10-12. Alkali pretreatment is highly efficient in the removal of lignin and thereby enhances enzymatic hydrolysis. However, the generation of inhibitory compounds, such as phenols, is a major disadvantage which can inhibit downstream enzymatic hydrolysis [19].
# Advancements and Prospects of Electronic Nose in Various Applications: A Comprehensive Review
## 3 Application Areas
### Food Analysis  
#### 3.1.1 Quality Assurance  
Quality assurance in food analysis through e-nose technology involves meticulous control measures to mitigate the impact of ambient odors or environmental factors on scene-based measurements. Researchers conduct experiments in controlled environments with regulated temperature, humidity, and airflow to minimize external factors. Before scene-based measurements, the background odor in the testing environment is analyzed and characterized to identify any potential interference. Electronic nose systems are calibrated to account for baseline readings and variations in ambient odor levels. Machine learning algorithms and advanced signal processing techniques are then employed to distinguish between the target odor of the food sample and background odors. Validation studies compare electronic nose system results to reference methods or human sensory evaluations to ensure accuracy and reliability.  
\begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Computational Treatment** & **Advantages** & **Disadvantages** & **Trends** \\ \hline PCA & - & Efficient dimensionality reduction & - & Subjective interpretation—relies on human understanding for classification & Continued usage with efforts to enhance objectivity through algorithmic autonomy and automation. Increased integration with machine learning for automated interpretation and decision-making. \\ \hline LDA & - & Optimizes class separability & - & Requires a training dataset—potential overfitting concerns & Increasing popularity due to robustness in classification tasks and ability to handle complex datasets. Adoption in real-time applications for immediate decision-making, especially in industrial settings. \\ \hline PLSR & - & Integrates features of PCA and multiple linear regression & - & Handles multicollinearity among predictors & Growing adoption for predictive modeling tasks, especially in forecasting dependent variables from extensive independent variables. \\ \hline SVM & - & Powerful for classification tasks, applicable to regression & - & Computationally intensive & Continued use, especially for effective class separation and handling nonlinear data distributions. Ongoing advancements in kernel methods for improved performance and scalability. \\ \hline ANN & - & Mimics brain-like processing, adept at handling complex, nonlinear data & - & Requires large amounts of labeled data for training & Increasing adoption due to advancements in deep learning techniques, improving accuracy and reliability. \\ \hline \hline \end{tabular}
\end{table}
Table 2: Summary of computational treatment features and trends.
## 5 Experimental Analysis
### Results and Discussion
However, from both cases, we obtained from the KNN, random forest, and decision tree classifiers 99.99% accuracy, while, from logistic regression and LSTM, we obtained only 40% accuracy, as shown in Figure 10.1  
A comparison between this work and the previous literature with respect to attack detection is illustrated in Figure 10.2 Table 3 indicates that the decision tree, random forest, and KNN schemes achieved higher precision, recall, and F1-scores compared with the literature [23; 28; 29] on the UAV frame Holybro S500 dataset. Furthermore, the works of [PERSON] et al. [23], [PERSON] et al. [28], and [PERSON] et al. [29] considered single-class classification spoofing, jamming, and benign traffic. However, this work considered a combined dataset of spoofing, jamming, and benign cases for detecting cyber threats that can reduce the computational overhead of executing individual classifiers in the literature [23; 28; 29]. Additionally, the literature [23; 28; 29] does not provide the exploratory capability of the detected threats. In this work, the proposed cyber-physical safety analyzer can detect all kinds of attacks such as jamming and spoofing from the attitude control systems and explain the root cause of radio frequency-based communication signals. To this end, the developed AI-enabled exploratory cyber-physical safety analyzer framework can successfully predict potential cyberattacks on civilian UAVs. The developed framework \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**AI Model** & **Precision** & **Recall** & **F1-Score** & **Accuracy (\%)** \\ \hline KNN & 1 & 1 & 1 & 100 \\ Logistic regression & 0.40 & 1 & 0.58 & 40 \\ Random forest & 1 & 1 & 1 & 100 \\ Decision tree & 1 & 1 & 1 & 100 \\ LSTM & 0.40 & 0.52 & 0.45 & 40 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Accuracy comparison among the AI models during jamming and spoofing attack prediction. Figure 15: Impact of switching states and received signal indicator of switching states on cyberattack status. also finds the cause of a potential cyberattack using the exploratory capability. Thus, the proposed AI-enabled exploratory cyber-physical safety analyzer framework can reduce the risk of cyberattacks on commercial UAM operations as well as protect UAVs from thieves. In the future, the proposed framework can be extended by deploying a reinforcement learning-based mechanism to capture the dynamic patterns of cyber threats. In other words, the nature of cyber threats evolves. Thus, to protect UAV operation from such new kinds of threats, we also need to deploy the evolving capability of the system. Therefore, we will extend this research in such a way that the system can adapt to the new environment and be capable of detecting and analyzing the dynamic behavior of cyber threats. Figure 16: Pair plot analysis between attack and radio status.
# A Novel Aerodynamic Modeling Method Based on Data for Tiltrotor evtol
## 3 Data-Driven Modeling Methods  
The analysis of the mechanism and influencing factors of the transition phase of the tiltrotor evtol in the Section 2 reveals that its complex aerodynamic characteristics result from the interaction of multidimensional factors. This interaction determines a nonlinear relationship between the input and output of the aerodynamic characteristic model that needs to be established. This type of relationship is reflected not only between factors but also between layers. Therefore, we selected an MLP neural network for aerodynamic modeling. This type of network is capable of solving complex nonlinear relationships.
# Immobilization of Pectinolytic Enzymes on Nylon 6/6 Carriers
## 3 Results and Discussion
### Characterization of Different Commercial Pectinase Preparations  
Five commercial preparations of pectinolytic enzymes: Pectinex Ultra SPL (from Sigma-Aldrich), Pectinex BE XXL, Ultra Mash, Ultra Tropical (from Novozymes), and Panzym-Univers (from Begerow) were evaluated regarding their specific pectin depolymerizing activity (Figure 2).  
All five commercial pectinase preparations used are recommended for use in fruit mash for increasing juice yield as well as for juice clarification and improved color stability. Using the above-described spectrophotometric method, pectinase activity of the analyzed preparations was found to be between 1065 and 1784 U/mL enzyme (Figure 2, white bars). The determined pectinase activity was considerably lower (3-10 times lower than the activity declared by the producer (see Section 2.1), which ranged between 3800 and 13,600 PECTU/mL. This can be related to the difference in the method and conditions used for activity measurement. For instance, [PERSON] et al. [34] demonstrated that depending on the particular combination of incubation time, pectin concentration, and reaction temperature, the same extract could be reported to have activities that differ by an order of magnitude. All preparations exhibited a polygalacturonase activity higher than their pectinase activities. The detected polygalacturonase activities measured towards polygalacturonic acid ranged from 1633 to 2861 U/mL enzyme (Figure 2, dashed bars). The difference between the two activities can be explained by the nature of the substrate, as pectin has a more complex structure than polygalacturonic acid, which includes rhamnogalacturon regions with side chains containing rhamnose, galactose, arabinose, and xylose [37]. In addition, polygalacturonic acid is characterized by low content of methyl esters which facilitates its hydrolysis by polygalacturonases.  
The analyzed commercial preparations also exhibited pectin lyase activity ranging between 2283 and 13,417 U/mL enzyme (Figure 2, grey bars). Pectin lysates act on pectin with high degree of methylation without the need of pectin de-esterifying enzymes [33]. Pectin lysates catalyze the cleavage of \(\alpha\) (1-4) glycosidic bonds by trans-elimination, resulting in a double bond at the non-reducing end of the galacturonic acid.  
Statistical analysis showed that pectinase activities of the analyzed preparations were not significantly different (\(p=0.1750\)), whereas, considering polygalacturonase and pectin lyase activities determined, Pectinex(r) Ultra Tropical preparation exhibited significantly  
Figure 2: Pectinase, polygalacturonase, and pectin lyase activities of commercial pectinolytic enzyme preparations. All values are expressed as means \(\pm\) standard deviation (n = 3).  
higher enzymatic activities than the remaining four preparations (\(p<0.0001\)). Thus, we selected this preparation for further immobilization experiments.
## 2 Materials and Methods
### Animals  
Thirty adult male Wistar Albino rats (_Rattus norvegicus_) (9 weeks old, weighing 230 \(\pm\) 10 g) were obtained from the Experimental Surgery and Animal Laboratory, College of Medicine, King Saud University (KSU), Riyadh, Saudi Arabia (KSA). This experiment was conducted at the Experimental Surgery and Animal Laboratory of the College of Medicine at KSU. This study was conducted in compliance with the ethical principle of the Declaration of Helsinki. This study was approved by King Saud University (Reference #: KSU-SE-19-37), and the study is in accordance with the Policy of the Research Centre. All rats were always housed under controlled conditions (temperature = 22 \(\pm\) 1 \({}^{\circ}\)C, humidity = 59%, and 12/12 h light/dark cycle) in polypropylene cages. Standard diet (4 RF21 certificate) obtained from (Mucedola, s.r.l, Settimo Milanese, Milano, Italy) and tap water were offered ad libitum during the experimental period. Animals were acclimatized to the laboratory environment for 7 days before the experiment began.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**FAs** & **Walnut Oil** & **Peanut Oil** & **Hazelnut Oil** & **Cashew Oil** \\ \hline C(14:0) & 2.7 & 0.0 & - & 0.70 \\ \hline C(16:0) & 7.2 & 8.9 & 5.6 & 0.53 \\ \hline C(16:1) & 0.2 & 0.1 & 0.2 & 0.05 \\ \hline C(17:0) & 0.1 & 0.1 & 0.1 & - \\ \hline C(17:1) & 0.0 & 0.1 & 0.1 & - \\ \hline C(18:0) & 2.7 & 2.6 & 2.5 & 4.88 \\ \hline C(18:1), n-9 & 15.2 & 51.3 & 72.7 & 84.36 \\ \hline C(18:1), n-7 & 1.3 & 1.3 & 2.4 & - \\ \hline C(18:2), trans & 0.3 & 0.2 & 0.1 & - \\ \hline C(18:2), n-6 & 57.1 & 23.5 & 12.9 & 8.68 \\ \hline C(18:3), trans & 0.5 & - & - & - \\ \hline C(18:3), n-6 & - & - & - & - \\ \hline C(18:3), n-3 & 11.9 & 0.2 & 0.4 & 0.13 \\ \hline C(20:0) & 0.1 & 1.2 & 0.2 & 0.36 \\ \hline C(20:1), n-9 & 0.2 & 1.5 & 0.2 & - \\ \hline C(20:2), n-6 & - & - & - & - \\ \hline C(22:0) & 0.1 & 2.5 & 0.0 & 0.14 \\ \hline C(22:1), n-9 & - & 0.1 & - & - \\ \hline C(24:0) & - & 1.4 & - & 0.52 \\ \hline SFA & 12.9 & 16.7 & 8.4 & 7.13 \\ \hline MUFA & 16.9 & 54.4 & 75.6 & 84.41 \\ \hline PUFA & 69.8 & 23.9 & 13.4 & 8.81 \\ \hline PUFA n-3 & 11.9 & 0.2 & 0.4 & 0.13 \\ \hline PUFA n-6 & 57.1 & 23.5 & 12.9 & 8.68 \\ \hline n6/n3 ratio & 4.8 & 117.5 & 32.25 & 66.77 \\ \hline trans-FA & 0.8 & 0.2 & 0.1 & - \\ \hline Total FA & 99.6 & 95 & 97.4 & 100.35 \\ \hline \end{tabular}
\end{table}
Table 1: Fatty acids composition of different oils used in this study (g/100 g oil).
## 3 Optimal Service Provisioning Framework
### Customized Mist-Edge-Cloud Framework
#### 3.1.3 Edge Computing Layer  
This layer consists of hospital-deployed edge servers and mobile cloudlets with moderate-to-high computational capabilities. It processes latency-sensitive services like anomaly detection in ECG signals or medication reminders. The edge also caches temporary data and forwards non-urgent requests to the central cloud. It supports container-based virtualization and uses heterogeneous connectivity (e.g., Wi-Fi, 5G, 6G) to ensure responsive service provisioning.
## 3 Methodology
### Computation of Monthly Land-Water Masks with Data Gaps
#### 3.2.3 Thresholding  
After computing the five water indexes, the resulting histograms show different land and water peaks in each histogram which must be separated as good as possible in this step. In general, MNDWI and NWI show dominant peaks for land on the right side of the histogram but almost none for water. For the AWEIsh, AWEInsh and TCwet, the histograms show wider land accumulations on the right side and smaller water peaks on the left side. One can see in Figure 8 that the resulting histograms do not clearly have bimodal shapes. This is required for common approaches such as the Otsu method [15] that would lead to unsatisfying results in the threshold computation. Therefore, we developed a new approach for an own optimized automated threshold computation by combining the information of all five indexes.  
In the initialization step, we first calculate the histogram for MNDWI. Also, a cumulative histogram is computed. Then, an initial threshold of 0.0 for the MNDWI is selected which can be used as a first rough threshold. Based on this threshold, the corresponding reference pixel (#723565) in the cumulative histogram is selected. Then, a search range for the final thresholds has to be defined. This is done by applying a manually selected boundary of \(\pm\)0.05% of all pixels on the reference pixel. This boundary is selected as a compromise between a large search range that can lead to ambiguous thresholds and a small search range in which the optimal threshold is not captured (per water index). This leads to the upper (#731653) and lower (#715476) boundary of the search range in the cumulative histogram. This restriction on the search range leads to an improved and more homogeneous threshold computation of the five indexes.  
In the next step, the pixel boundaries are applied on all cumulative histograms of all indexes as the number of land and water pixels are equally distributed. Then, the pixel boundaries are transferred from the cumulative histograms to the normal histograms of each index. The threshold is now searched in the search range which is highlighted in yellow of Figure 8. Instead of just using the minimum within the search range, we compute a standard deviation within a sliding window because of the histogram noise. The index value at the minimum standard deviation within each search range of each index is then selected as threshold and also transferred to a pixel value in the cumulative histogram which is shown as a red line in Figure 8. Then, all five pixel-based thresholds are averaged to get only one pixel-based threshold (727497 \(\pm\) 124) for all five indexes. The small standard deviation of 124 pixels already shows that the non-averaged thresholds are very accurate. Finally, the averaged  
Figure 7: Correlation matrix between all indexes for Lake Ray Roberts in January 2017 (MNDWI, NWI, AWEInsh, AWEIsh, TCwet).  
pixel threshold is transferred again to all histograms in order to get the final index threshold which will be used in the next step to derive the land-water masks.
## 4 Results and Discussion
### Simulation Results
#### 4.2.2 Performance of Simplified Beamforming
Furthermore, we have confirmed the effectiveness of the proposed beamforming scheme based on simplified and high efficient CSI estimation. From the complexity estimate in Table 1 and the parameters used in Table 2, the conventional SVD and the proposed scheme require 278,528,000 and 20,480,000 multiplications, respectively; thus a 92.6% complexity reduction can be achieved. To emphasize this advantage, we examine the compressed sensing based CSI estimation scheme in [18], referred as multi-grid orthogonal matching pursuit (MG-OMP). When \(L\), \(N_{\text{T}}\)Beam, \(N_{\text{R}}\)Beam, and (\(G_{0}\),\(G_{1}\)) be the number of scatters, transmission/reception training beams, and discrete angle sets, respectively, its complexity is estimated to be \(O(LN_{\text{T}}\)Beam\(N_{\text{R}}\)Beam(\(G_{0}\)\({}^{2}+G_{1}\)\({}^{2}\)), i.e., 37,365,760 if we set \(L=10\), \(N_{\text{T}}\)Beam \(=N_{\text{R}}\)Beam \(=32\), (\(G_{0}\),\(G_{1}\)) \(=\) (60,7) as the derived parameters. The proposed scheme offers a 45.2% reduction in complexity. However, it should be understood that our proposal  
Figure 8: CDFs of System throughput. can ease hardware implementation since it does only require multiplications with matrices and a vector with simplified algorithms. This is one of the remarkable advantages of our proposal.
# Virtual Marker Technique to Enhance User Interactions in a Marker-Based AR System
###### Abstract  
In marker-based augmented reality (AR) systems, markers are usually relatively independent and predefined by the system creator in advance. Users can only use these predefined markers to complete the construction of certain specified content. Such systems usually lack flexibility and cannot allow users to create content freely. In this paper, we propose a virtual marker technique to build a marker-based AR system framework, where multiple AR markers including virtual and physical markers work together. Information from multiple markers can be merged, and virtual markers are used to provide user-defined information. We conducted a pilot study to understand the multi-marker cooperation framework based on virtual markers. The pilot study shows that the virtual marker technique will not significantly increase the user's time and operational burdens, while actively improving the user's cognitive experience.  
augmented reality; marker-based system; hand gesture; multiple markers; virtual marker
## 1 Introduction  
The need to improve the efficiency of energy power plants emphasizes the importance of optimizing their equipment design and the inlet and operation conditions. For this reason, it is important to analyze how the use of fluid mixtures affects the operating conditions--mainly, plant efficiency [1]. There are different external conditions to the plant (environmental conditions) that mark the need to have a working fluid that adapts to these variable environments, both for low and high temperatures, with the objective that the plant works optimally.  
The ever-increasing necessity to reduce the environmental impact of industrial and urban energy conversion processes has led engineers to consider the use of sCO\({}_{2}\)-mixtures as working fluid for thermodynamic power and refrigeration cycles [2, 3], emphasizing the mitigation of air pollution and the emission of greenhouse gases, and reducing energy production costs [4].  
Several configurations of the Brayton cycle are currently under study [5]. One of the very interesting features of this cycle is its ability to achieve high efficiency in a variety of applications operating with intermediate temperature levels: concentrated solar power (CSP) [6; 7], waste heat recovery [8; 9], coal-fired power plants [10; 11; 12], and Gen IV nuclear reactors [13; 14; 15] among others. In this work, the influence of fluid mixtures on the recompression Brayton cycle (cf. Figure 1) has been studied. This power cycle is the evolution of the previous configuration proposed by [PERSON] [16; 17] and has clear connections with [PERSON] and [PERSON]'s work [18; 19]. The cycle is named after the re-compressor located parallel to the main compressor, which means that the flow is split into two for the compression process. The first amount of stream flows into the cooler, where its temperature is reduced to a value close to the critical temperature. The second stream is not cooled but compressed directly in the re-compressor [5]. In the work of [PERSON] [20], it was determined that the recompression cycle showed the best performance in comparison with other configurations (simple, pre-compression, and divided expansion cycles).  
The novelty of this work over the state of the art is new mixtures with mixing ratio and new fluids, in the recompression Brayton cycle. In Section 2, the methodology with which this work was developed is explained. The obtained results of the recompression Brayton cycle using sCO\({}_{2}\)-mixtures are discussed in Section 3. Conclusions and future work are discussed in Section 4. An increase in the thermal efficiency of the recompression Brayton power cycle using sCO\({}_{2}\)-mixtures is the main conclusion of this manuscript.