esa-sceva/satcom-chunk-collection huggingface dataset:
# Reduced Complexity Sequential Digital Predistortion Technique for 5G Applications
## 2 Sequential Digital Predistortion Using Under-Sampled Feedback Signals
### Proposed Sequential Digital Predistortion
Conversely, when the memory polynomial function is identified, a coarse-delay alignment algorithm with a one-sample resolution will be used. This will contribute to reducing the computational complexity associated with the identification of the memory polynomial sub-function in the proposed S-DPD.
## 6 Proposed MAC Protocol for Wireless Healthcare Environments--WhMAC
### Timing Considerations  
TDMA frame and slot timings are determined by communication data rates and packet size, which are determined by the sample rates. For example, if the communication data rate is \(f_{c}\) bit/s and the status packet takes a maximum of \(n\) bits in total, the duration of the time slot does not need to be longer than \(T_{s}=n/f_{c}+T_{guard}\) for optimal channel utilisation. \(T_{guard}\) is the guard time and it is determined by clock accuracy and resynchronisation time. On the other hand, the number of time slots needed for streaming is determined by the sample rate, data rate and TDMA frame. The guard time is necessary to avoid overlapping of the transmissions from different sensors due to clock drift. Usually there would be more than one packet per TDMA frame for streaming, depending on BER (bit error rate) and packet overhead, for optimal communication. Also, when slots are assigned, there should be some free time slots left in reserve, in case of the errors. This is especially important in case of the real-time streaming.  
On the other hand, it has to be noted that wake up receiver communication would work at a lower data rate than normal communication, in order to achieve low power. For example, the wake up receiver would work with data rate of 5 kb/s to 10 kb/s, which is significantly lower than the transceivers data rate of for example 200 kb/s. This means that even if the wake up packet has less bits than the communication packet, it will probably take more time to be transmitted. Therefore, if the same transceiver is used to send both types of packets, the wake up packet could take more time slots.
# Model Updating of Bridges Using Measured Influence Lines
## 3 Results
### Updated FE Model
#### 3.3.1 Strains  
This section compares the measured and modelled E_MG1 and E_MG2 strains under V2_F vehicle crossings. The entire strain histories are presented, and not just the strain ILs for the E_MG1 and E_MG2 sensors. Figures 24 and 25 compare the responses of E_MG1 and E_MG2 sensors, respectively. First, the strain records of three crossings at approximately 30 km/h are shown; the colours denote the exact measured speed. The mean of the three maximum values is shown with a marker. Then, three responses are added for the initial (\(\alpha_{\text{MG1}}=1.00\), \(\alpha_{\text{MG2}}=1.00\), \(\alpha_{\text{OTHER}}=1.00\)), frequency and MAC-based (one of the candidates with \(\alpha_{\text{MG1}}=1.20\), \(\alpha_{\text{MG2}}=1.10\), \(\alpha_{\text{OTHER}}=1.00\)), and strain IL-based updated FE models (one of the candidates with \(\alpha_{\text{MG1}}=1.30\), \(\alpha_{\text{MG2}}=1.50\), \(\alpha_{\text{OTHER}}=1.40\)).  
The strain IL-based FEMU did, and the frequency and MAC-based FEMU did not significantly improve the strain response at E_MG1 and E_MG2. The initial FE model overestimated the maximum measured E_MG1 strains for 51%, the frequency and MAC  
Figure 23: E_MG1 (**a**) and E_MG2 (**b**) strain ILs: comparison of measured, initial, frequency and MAC-based, and strain IL-based updated models.  
based updated FE model by 40%, and the strain IL-based FE updated model by 19%. For E_MG2 strains, the corresponding values were 49%, 38%, and 6%. These values resemble the overestimations of the strain ILs, shown in Figure 23.  
A similar conclusion can be drawn for strains and displacements. The strain-IL-based FEMU improved the displacement response even more than the strain response. The initial FE model overestimated maximum measured D_MG1 vertical displacements by 36%, frequency and MAC-based by 28% and strain IL-based FE updated model by 5%. For D_MG2 vertical displacements, the corresponding values were 35%, 25%, and \(-\)2%.
# Convolutional Sparse Modular Fusion Algorithm for Non-Rigid Registration of Visible-Infrared Images
## 5 Experimental Methods
### Comparison of Downstream Application Experiments  
The training set for downstream application experiments was created by randomly selecting RGB/IR image pairs from the MSRS dataset. In this experiment, 100 sets of images were randomly selected from the LLVIP [31] and M3 FD [32] datasets to serve as the validation set, ensuring the reliability and generalizability of the experimental results. The LLVIP dataset contains RGB/T image pairs and pedestrian labels, while the M3 FD dataset includes RGB/IR image pairs with six labels: person, car, bus, motorcycle, truck, and light. YOLOv8 [33] was used as the object detection comparator, and the object detection training was conducted on the M3 FD dataset. The dataset was split into training and validation sets in a 9:1 ratio. The mean Average Precision [EMAIL_ADDRESS] for each label was used to evaluate the impact of fused images on object detection performance.  
Table 3 shows the Quantitative results of object detection experiments with the proposed algorithm and mainstream alternative algorithms as preprocessing methods on M3 FD and LLVIP datasets. The \"MIX\" method is the baseline approach where random sampling is performed between original images of different modalities. ICSF achieved the best results regarding the average [EMAIL_ADDRESS] metric on both the M3 FD and LLVIP test sets. Compared with the MIX method, ICSF improved up to 20.1% in average relative accuracy.
# Multiset-Trie Data Structure
## 1 Introduction
Similar to a trie, it uses common prefixes for a shared data representation. Unlike the compact prefix tree [25], the multiset-trie does not support path compression. However, the absence of path compression makes the multiset-trie a perfectly height-balanced tree. Moreover, when multiset-trie is full, it forms a complete \(n\)-ary tree. **Contributions. ** The main contributions of this paper are as follows. First, a multiset-trie is a novel data structure for storing and querying a set of multisets that provides efficient multiset containment operations. Second, a mathematical model is developed to analyze the complexity of multiset containment operations. In particular, we estimate the size of a subtree traversed by a containment query and give an insight into the time complexity of containment queries. In addition to the mathematical analysis, the size of a subtree visited by a multiset containment query is also the central focus of the empirical analysis. We carefully designed the experiments to unravel the main features of the search space. We observe how the number of visited nodes depends on various parameters, such as the density of the tree and the ordering of multiset elements. Finally, the mathematical as well as empirical analyses show the influence of ordering the elements of multisets on the efficiency of storing and processing multisets. We show that the ordering, which is based on the frequencies of the multiset elements, can speed up the multiset containment queries by orders of magnitude. **Paper organization. ** The paper is organized as follows. In the following Section 2, we present the multiset-trie data structure in detail. Section 3 presents the operations of the multiset-trie. These include the basic operations of search trees and multiset containment operations. The algorithms are presented in detail using pseudocode. The description of multiset-trie operations is followed by the mathematical analysis of their complexity in Section 4. The main assumption is that multisets are constructed uniformly at random with bounded cardinality. By using probabilistic tools, we describe the time complexity of the algorithms and the space complexity of the structure. In Section 5, we present an empirical study of the multiset-trie. Synthetic and real-world data sets are used in experiments that are designed to study the performance of multiset containment operations. Further, the multiset-trie is empirically compared to the inverted index in a separate experiment. The experiments highlight the methods for optimizing a multiset-trie. The related work is reviewed in Section 6. This section describes a set-trie data structure, the inverted indexes, the signature indexes, and the multisets in relational database systems. Finally, the concluding remarks and the future work are presented in Section 7.
# Comparative Analysis of Major Machine-Learning-Based Path Loss Models for Enclosed Indoor Channels
## 4 Machine-Learning-Based Models
### Artificial Neural Network Model  
Artificial neural networks have been developed based on biological neural network functionality. The ANNs are a network that contains a group of neurons, various layers, and activation functions, all of which get activated based on inputs. The proposed model is based on ANN architecture that includes four hidden layers of feed-forward neural networks. The first two hidden layers contain 96 neurons, and the last two hidden layers have 32 neurons, with each hidden layer followed by the ReLU activation function. The input for the network accepts four features from the preprocessed data. The output layer is a single neuron with a linear activation function as the transfer function that leads to the predicted value. Figure 4 depicts the architecture of our proposed ANN model. The output result is a real value representing the path loss. The hyperparameter values of the proposed ANN (number of layers, number of neurons, activation functions, learning rate = 0.001) are obtained using the Optuna technique, which gives the best hyperparameters that fit the measured data.  
The proposed ANN model learns to move up or down depending on the trend feature extraction from the data, giving the fit curve. The parameter weight at every epoch is adjusted using the gradient descent with momentum to reach the global minimum error. The proposed model uses a comprehensive hyperparameter set up to identify the best weights of the parameters for the path loss prediction.  
Figure 3: The KNN regression model with \(K=2\).
# Prediction Equation for Horizontal Ground Motion Peak Values in the Loess Plateau Region
## 5 Model Regression Results  
Regression analyses are conducted using magnitude grouping methods with Models I and II to predict the equations for horizontal PGA, EPA, and PGV, utilizing the ground motion attenuation dataset for the Loess Plateau region. The corresponding results are listed in Table 5, which reports the prediction equations for Models I and II and their corresponding standard deviations, i.e., the standard deviation of the fitting residuals. Model II demonstrates better fitting performance than Model I, indicated by lower residuals. For instance, the standard deviation for the PGA prediction equation established by Model I is 0.457 while it is 0.342 for Model II.
# Comparison of the Chemical and Sensorial Evaluation of Dark Chocolate Bars
## 3 Results and Discussion  
### Headspace Compositions  
The headspace (HS) composition of all the hard discount (HD), organic (BIO), and supermarket (SM) samples are reported in Tables 2-4, respectively. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Compounds**} & \multirow{2}{*}{**1.r.i. 1**} & \multicolumn{8}{c}{**Relative Abundance (\%)**} \\  & & **HD1** & **HD2** & **HD3** & **HD4** & **HD5** & **HD6** & **HD7** & **HD8** \\ \hline ethyl acetate & 616 & - 2 & - & 10.7 & 9.9 & 5.9 & 12.6 & 3.3 & 5.3 \\
2,3-butanediol & 787 & 3.3 & - & 4.8 & 2.0 & 4.8 & 1.6 & 5.9 & 1.2 \\ isovaleric acid & 834 & 2.6 & - & 2.5 & 1.3 & - & - & 3.0 & 0.4 \\ isopentyl acetate & 876 & - & - & 1.7 & 0.2 & - & 1.0 & 1.6 & 0.9 \\
2,6-dimethylpyrazine & 914 & - & - & 4.0 & 0.3 & - & - & - & - \\
2,5-dimethylpyrazine & 915 & 6.0 & 1.5 & - & - & - & - & 3.0 & 0.3 \\ benzaldehyde & 963 & 1.9 & - & - & 1.2 & - & - & 1.3 & - \\ myrcene & 990 & - & - & 0.2 & - & - & - & 0.6 & - \\ \(n\)-decane & 1000 & - & - & 0.6 & - & - & - & - & - \\
2-ethyl-3-methylpyrazine & 1002 & - & - & - & - & - & - & 0.3 & - \\
2,3,5-trimethylpyrazine & 1004 & 13.4 & 5.6 & 12.1 & 6.2 & 7.1 & 9.4 & 12.9 & 5.5 \\ limonene & 1031 & 1.6 & 7.2 & - & - & - & - & 0.2 & - \\ (Z)-\(\beta\)-ocime & 1041 & - & - & - & - & - & - & 0.2 & - \\
2-heptyl acetate & 1043 & - & 2.4 & 1.1 & 1.2 & 1.3 & 1.3 & 0.5 & 0.5 \\
2-acetylyrrole & 1060 & 3.2 & 2.3 & 0.8 & - & - & - & - & - \\ acetophenone & 1067 & - & - & - & - & 1.2 & - & 1.0 & - \\
3-ethyl-2,5-dimethylpyrazine & 1080 & 2.3 & 1.5 & - & 0.7 & 1.0 & - & 1.6 & - \\
2-ethyl-3,5-dimethylpyrazine & 1085 & - & - & 2.0 & - & - & - & - & - \\ tetramethylpyrazine & 1086 & 43.7 & 47.3 & 37.1 & 65.7 & 59.9 & 62.0 & 50.8 & 58.1 \\ linalool & 1100 & - & - & - & - & 0.2 & 0.7 & 0.2 & - \\ nonanal & 1103 & 6.3 & 6.6 & 1.5 & 0.3 & 3.0 & - & 1.1 & - \\ phenylethyl alcohol & 1110 & 3.1 & 7.4 & 1.8 & 1.5 & 4.7 & 1.9 & 1.7 & 0.7 \\
3,5-diethyl-2-methyl pyrazine & 1160 & 1.2 & 0.5 & 1.2 & 1.3 & 1.0 & - & 1.2 & 1.8 \\ _trans_-linalool oxide (pyranoid) & 1177 & 0.1 & - & - & - & - & - & - & - \\ ethyl octanoate & 1197 & - & 2.7 & - & 0.7 & 2.6 & - & 0.5 & 1.0 \\ \(n\)-dodecane & 1200 & - & - & 2.4 & - & - & - & - & - \\ decanal & 1205 & 0.6 & 0.8 & 0.3 & 0.2 & 0.7 & 0.7 & - & 0.4 \\ butanoic acid, 2-methyl-3-oxo-, methyl & 1223 & 0.7 & 0.6 & 0.6 & 0.3 & 0.8 & 0.4 & 0.4 & 0.6 \\ ester & & 1237 & - & - & - & 0.1 & - & - & 0.2 & - \\ hexaridle & 1247 & 0.2 & - & 0.3 & 0.5 & - & - & 0.3 & 0.8 \\ (Z)-anethole & 1253 & - & - & 1.0 & - & - & - & - & - \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Complete compositions of the headspaces sampled for hard-discount chocolate bars (HD1 to HD8). _Appl. Sci._**2021**, _11_, 9964
## 5 Simulation Results
The \(R_{c}^{\text{min}}\) requirement is assumed to be 250 Kbps for human-type communication devices (HTC) devices and \(R_{d}^{\text{min}}\)= 25 Kbps for machine type communication (MTC) devices. degradation experienced by users due to unfeasible BS assignment solutions (i.e., when the BS capacity is exceeded), which can be caused by the unbalance of the users' distribution among the BS. In Figure 7, we observe that 84% of the users achieve a data rate of 800 Kbps for the pricing-based approach. In contrast, for the Max-SINR and the Min-PL algorithms, there is observed a degradation, since only 63% and 65% of the users achieve the data rate of 800 Kbps, respectively. Figure 5: Cumulative distribution function (CDF) of the throughput achieved by each algorithm. The number of HTC and MTC devices per cell is set 10 and 20, respectively. The backhaul capacity is assumed with a value of 12 Mbps. Figure 6: Average sum cost observed at the BSs as a result of the BS assignment solution found by each algorithm. The \(R_{c}^{\min}\) requirement was set to 250 kbps for HTC devices and \(R_{d}^{\min}\) of 25 Kbps for MTC devices.
## 5 Conclusions  
This paper has presented an innovative event-triggered networked predictive fault-tolerant control scheme for NMASs, designed to operate under the complex conditions of random communication constraints in both forward and feedback channels, as well as actuator faults. Comprehensive numerical simulations comparing the ENPC, EFTC, and ENPFTC methods clearly demonstrate the effectiveness of the proposed control scheme. The results show that the scheme successfully compensates for actuator faults and mitigates the impact of random communication constraints. Furthermore, the proposed event-triggered mechanism reduces data transmission volume by more than 19%. Future research could focus on developing adaptive and robust control strategies that can handle model uncertainties and further reduce the impact of event-triggered delays.  
Conceptualization, C.L. and P.L.; methodology, C.L.; software, P.L. and Z.D.; validation, C.L., P.L., and H.G.; formal analysis, C.-B.Z.; investigation, H.G.; resources, C.L.; data curation, P.L.; writing--original draft preparation, C.L.; writing--review and editing, C.-B.Z. and Z.D.; visualization, P.L.; supervision, C.L.; project administration, C.L.; funding acquisition, H.G. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China under Grant 62173002 and 62441304, the National Key R&D Program of China under Grant 2023 YFC3306401, the Beijing Natural Science Foundation under Grant 4222045, the Natural Science Foundation of Henan Province under Grant 252300421506, the Key Scientific Research Projects of Henan Province's Institutions of Higher Education under Grant 24A120001 and 25A120008, and the Scientific Problem Tackling of Henan Province under Grant 252102320053.  
Figure 16: Tracking errors of EFTC.  
Figure 15: Tracking errors of ENPC.  
**Institutional Review Board Statement:** Ethical approval is not required for this study as it do not involve human participants.  
**Informed Consent Statement:** Written informed consent is obtained from all participants involved in this study.  
**Data Availability Statement:** All data supporting the findings of this study are included within the article.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
## 2 Method
### Model Test
#### 2.3.2 Comparison with the Conventional Methods of Edge Detection  
In order to verify the validity and accuracy of edge detection, the conventional methods of edge detection are introduced, including four directional filters of the gradient (Figure 5a-d) and the second-order derivate filters of Laplacian 1 (Figure 5e) and Laplacian 2 (Figure 5f). According to the comparison between Figures 4a-d and 5a-d, it can be expounded that the fractal surface variance statistics method possesses the same directional sensitivity to the geological edges as four directional filters of gradient, but the difference is that the fractal dimensions of the edges are the positive values, and these ones of the edges from gradient directional filters are both positive and negative, with a symmetric  
Figure 4: Fractal dimension results. (**a**) S-N direction; (**b**) W-E direction; (**c**) WN-SE direction; (**d**) NE-SW direction; (**e**) the maximum fractal dimension; (**f**) the average fractal dimension.  
distribution of the calculated direction. The results show that the directional fractal surface variance statistics method is to some extent superior to the gradient directional method. The maximum and average (Figure 4e,f) fractal dimensions are the synthesis method with the optimization of the directional influence; the analysis with the second-order derivate filters of Laplacian (Figure 5e,f) reveals that the edges are able to be detected, but the difference is that fractal dimensions of edges superbly correspond to the geological boundaries; the later one is located in the gradient zone, and is hard to confirm in complicated and changeable conditions. Based on the comparison with the conventional methods of the gradient and the second-order derivate filters, we consider that the fractal surface variance statistics method has a certain superiority, and the results show that it can be used in the geological boundary division and detection, and can be used more clearly to detect the edge.
## 4 Comparative Analysis of RPLC with CNN through Experimental Validation  
### Comparative Pipelines for Image Classification  
The experimental validation and benchmarking of the proposed algorithm is achieved by comparing it with the state-of-the-art image classification approaches of convolution neural network (CNN) and support vector machine (SVM). The validation is based on the extensive study of comparison of our proposed algorithm. The experimental study included huge number of iterations for identifying the thresholds as represented by Figure 3. After threshold identification for validation, we kept in mind criteria such as size of dataset, image size (in terms of image resolution). The pipeline for image classification used by CNN, SVM, and luminance based algorithm is shown in Figures 4-6 respectively.  
Figure 6 represents the graphical visualization of the CNN model that has been used for the classification of image in Dark, Semi-dark and Bright categories. The convolutional layer and maxpooling layers used in the pipeline serves the purpose of reducing the spatial size of the incoming image and captures the small details of the scene and tries to decrease the computational power required to process data. The layers used in the CNN helps in extraction of dominant features. Another group of these layers including convolutional, dropout and maxpooling with stride 2 \(\times\) 2 stride is used to capture low level details even further and finally Dense layer is used to make final prediction about the incoming image in the form of three classes, i.e., Dark, Semi-dark, and Bright.  
Figure 5: RPLC Threshold rejection & identification.  
Figure 7 represents the graphical visualization of SVM pipeline. The incoming image having the label, i.e., Dark, Semi-dark, Bright, is converted into arrays and preprocessed to retain a uniform image size to avoid mismatched image size classification problems. The image dataset is divided into training and testing sets, the SVM classifier is trained and subsequently used for the prediction of image labels.  
Figure 8 represents the graphical visualization of the RPLC algorithm. The image is manipulated based on the pixel intensities counter are set and updated according to the presence of pixel intensities in terms of Dark, Semi-dark, and Bright. The counter having the highest value is finally taken as the image label.  
Figure 8: RPLC Pipeline for Image Classification.  
Figure 6: Basic CNN Model Structure for Image Classification.  
Figure 7: Support Vector Machine (SVM) Classification Pipeline.
# Validation Analysis of Drought Monitoring Based on FY-4 Satellite
## 4 Discussion  
### Impact of Cloud Cover on Drought Monitoring  
In the spatiotemporal analysis of TVDI, MCI, and SHRI, numerous stations and pixels exhibited negative correlation, but failed the significance test. In the trend analysis, the drought area shows a cliff-like decline. Comparing the results of cloud identification and local TVDI, it is found that the null pixels and the breakpoints were caused by the cloud cover.  
Because the values of the cloud in the NIR band and IR band are close [56], the ratio of the NIR band and IR band in the cloud area is close to 1. Comparison of the NIR band, IR band, true color image, NDVI, and cloud mask products from FY-4A in the Shandong Peninsula (Figure 15) revealed that there were obvious high-value areas in the red boxes of the NIR band (Figure 15a) and IR band (Figure 15b). The red box of the true color image might be a cloud cover (Figure 15c). The red area is the ratio value close to 1 (Figure 15d). The red area of NDVI was suspected to be a cloud pixel (Figure 15e). According to the above inference and the cloud mask image overlap (Figure 15f), it is concluded that CLM cloud detection is missing.  
Therefore, high-precision cloud detection results can significantly improve TVDI's ability to monitor drought events. [PERSON] et al. [57] used machine learning algorithms to construct a cloud detection model to improve the accuracy of FY-4A AGRI cloud mask products. Furthermore, many researchers focused on the reconstruction of missing values by clouds [58; 59; 60], which could help to expand the scope of remote sensing drought monitoring, and enhance the temporal continuity of values [61].