esa-sceva/satcom-chunk-collection huggingface dataset:
# Data Element Sharing in Convergence Media Ecology Based on Evolutionary Game
## 3 Data Element Sharing Model Based on Evolutionary Game
### Node Classification
Business organization nodes involved in the convergence media ecological data element sharing mainly as data producers for common users or other institutions to provide raw data and resources, such as articles, pictures, audio, video, statistics, etc., these nodes usually adjust strategy according to the incentive mechanism to share data element, in order to obtain relatively higher income. 2. Common user node (\(U\)). Common user nodes participate in data element sharing in the convergence media ecology mainly as consumers of data elements participate in the acquisition, use and quality feedback of data elements. 3. Regulator node (\(R\)). The participation of regulator nodes in data element sharing in the convergence media ecology is mainly responsible for the regulation of the quality of data element sharing. It urges business organization nodes to actively participate in the production and sharing of high-quality data elements and common user nodes to participate in data element consumption and quality feedback. The regulator nodes distribute rewards according to the behavior of the user nodes and punish the node violations. Data element sharing node relationship is shown in Figure 4.  
In the data element sharing based on the evolutionary game, the user nodes can cooperate and compete with each other to realize the data element sharing. Evolutionary game theory can help us to analyze the strategy selection and evolution between nodes so as to design appropriate incentive mechanisms to promote the healthy development of the whole ecosystem.
# Fine-Grained Boundary Conditions in Field-Based Routing
## 6 Performance Evaluation
### Simulation Scenarios and Parameters
Specifically, in an ideal scenario without contention, we simply consider the maximum network-wide allowable load as 100%, calculated as the product of the channel bandwidth and the number of gateways. We then adjust the Poisson distribution rate to align with the specified offered load, which results from UDP packet generation by all source nodes. This calculated rate is then applied to the simulation. However, it is up to the user's discretion to choose how to set the rate. The transmission range and interference range are set to 250 m and 550 m, respectively. We also enable RTS/CTS and hello-message-jittering. Default values are applied to any other parameters or models not explicitly mentioned. To analyze the effects of our proposal, we measure the packet delivery ratio and throughput over a 600-s period in the middle of a 1000-s simulation. To ensure the reliability of the results, all simulations are repeated 10 times per scenario, and the averages are used in the graphs, with the standard deviation represented as vertical bars.
## 5 Discussion  
### Spatiotemporal Evolution Characteristics  
This study estimates the WUE of the Yunnan-Guizhou Plateau in China using the commonly applied GPP-to-ET ratio method. The results show a significant increasing trend in WUE from 2003 to 2020 in this region. Similar conclusions were drawn by [PERSON] et al. [29] in their analysis of WUE changes from 2009 to 2013. Furthermore, this study also reveals the spatial distribution characteristics of WUE, which follow a \"high-low-high\" pattern from the southwest to the northeast. Specifically, WUE is relatively lower in the northwest and central regions, whereas the southeast and eastern regions exhibit significantly higher WUE. This spatial variation is closely related to the distribution of vegetation types in the study area. Different vegetation types exhibit different WUE [30], which in turn influences the spatial variation of WUE in the region. In the southwestern part of the region, forest predominates, while other areas are primarily covered by grasslands. Forest, particularly the leaf growth processes in forested areas, is more conducive to water absorption and utilization, leading to higher WUE values in this region. This finding is supported by [PERSON] et al. [31], who found that WUE in forest-covered areas is generally higher than in grassland areas. This is likely due to the more efficient transpiration process of forests and their better utilization of water.
## 5 Experimental Validation
At the same time, in the theoretical model, the axial length of the PM bearing is calculated according to the length of the four-layer PM ring, and the length of the cytoskeleton is neglected, so there may be a certain amount of difference in the axial displacements corresponding to the maximal bearing capacity of the two models. The second area is a manufacturing or assembly error in the test prototype. The standard prototype magnet blocks are uniformly arranged in the axial and radial directions; however, due to the limitations of the manufacturing means and assembly process, there may be an unevenness in the axial and radial gaps between the magnet blocks, which directly affects the real reluctance of the bearings and other parameters. The third area is an error in the experimental process. Due to the experimental device being relatively rough, the installation and debugging of the sensors may not be precise enough, and there may be a difference in the experimental process. The theoretical solution, simulation solution, and experimental values of the stiffness of the permanent magnet thrust bearing are shown in Figure 14. As the axial displacement  
Figure 13: Comparison of load-carrying capacity of permanent magnet thrust bearing. increases, the stiffness of the bearing initially increases and then decreases. At 15 mm, the stiffness becomes negative. This is because the axial length of the permanent magnets is 30 mm, and when the rotor's axial displacement reaches 15 mm, the axial bearing capacity reaches its maximum. However, with further increases in axial displacement, the outermost permanent magnets gradually disengage from contact, causing the magnetic circuit's cross-sectional area to narrow. As a result, the bearing capacity diminishes with the increase in axial displacement, and the maximum displacement cannot exceed 15 mm during the operation of the permanent magnet bearing. In terms of the stiffness values, the theoretical, simulation, and experimental results show that the maximum error of the experimental stiffness in the positive stiffness stage is 17.2% relative to the theoretical value and 23.9% relative to the simulation value. These discrepancies are mainly due to deviations in the actual parameters of the permanent magnets compared to the theoretical parameters, as well as manufacturing and assembly errors during the construction of the prototype.
## 2 Methods
### Image Fusion  
For the generated final decision map \(I_{\text{fitted}}\), up-sampling is performed to restore the image to its original resolution. The decision map is a binary image, so the up-sampled image should also be binary, without introducing pixel values other than 1 and 0. The nearest-neighbor interpolation algorithm is computationally simple and fast and does not introduce any additional pixel values, making it ideal for binary images like the decision map. The basic principle is to find the nearest pixel in the original image to the target image coordinates and assign the value of that pixel to the corresponding position in the target image. The nearest-neighbor interpolation is represented as  
\[I_{\text{up}}(x,y)=I_{\text{fitted}}([x/s_{\text{h}}],[y/s_{\text{v}}]), \tag{10}\]  
where \(I_{\text{up}}\) is the up-sampled image, and \(s_{\text{h}}\) and \(s_{\text{v}}\) are the horizontal and vertical scaling factors, respectively. When the magnification factor is 2, \(s_{\text{h}}=s_{\text{v}}=2\).  
Finally, the original image is fused with weighted addition based on the up-sampled decision map, which is represented as  
\[I_{\text{fusion}}=I_{\text{up}}\times I_{\text{src1}}+\left[1-I_{\text{up}} \right]\times I_{\text{src2}}, \tag{11}\]  
where \(I_{\text{src1}}\) and \(I_{\text{src2}}\) are the source images of different focus regions, and \(I_{\text{up}}\) is the up-sampled decision map, resulting in the final multi-focus fused image \(I_{\text{fusion}}\).
# What's in Your Fruit Juice?--Rapid Quality Screening Based on Infrared (FT-IR) Spectroscopy
## 3 Results and Discussion  
### Quality Parameters of Fruit Juices  
The reference compositional ranges for various quality parameters in tested FJs are outlined in Table 1. Based on these findings, it was observed that employing a diverse array of FJ samples led to a broad spectrum of compositional levels (Table 1). Similar compositional quality parameter levels have been reported for commercially available FJs [40; 41; 42; 43]. Examination of the sugar content of orange juices included in our study was found to align with the findings of [PERSON] and [PERSON] [33], demonstrating consistency in sucrose, glucose, fructose, and total sugar content for most samples. Interestingly, two samples (out of 12) of 100% orange juice in our study deviated from this trend, exhibiting higher levels of sucrose, fructose, and total sugar compared to the corresponding samples in [PERSON] and [PERSON]'s investigation.  
A comparison between the total sugar content indicated on the nutrition fact labels of commercially available FJ samples and our HPLC analyses revealed some discrepancies. Among the 68 samples examined, 14 (21%) did not align with the total sugar content declarations. These non-compliant samples exhibited a difference of more than 20% in total sugar content compared to the declared label values. Twelve out of fourteen samples showed higher sugar content (>20%), while the remaining two showed lower (<20%) sugar content, than declared.  
Moreover, we identified discrepancies in the ascorbic acid levels of the FJs. Specifically, out of the 68 samples tested, 25 (37%) did not conform with the nutritional fact label. Among these, 15 exhibited lower ascorbic acid levels, while 10 had higher ascorbic acid content than indicated on the label.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & **Soluble** & **Glucose** & **Fructose** & **Sucrose** & **Total Sugar** & **Tittrable** & **Citric Acid** & **Ascorbic** \\  & **Solid** (°Brix) & **(g/100 g)** & **(g/100 g)** & **(g/100 g)** & **(g/100 g)** & **(g/100 g)** & **(mg/100 g)** & **Acid** \\ \hline range & 3.78–16.03 & 13.7–66.0 & 6.6–73.1 & 11.9–125.4 & 60.8–153.5 & 129.1–1139.1 & 18.6–1114.0 & 0.70–54.10 \\ avg \(\pm\) stdev & 11.58 \(\pm\) 2.25 & 26.9 \(\pm\) 9.6 & 36.2 \(\pm\) 17.9 & 54.7 \(\pm\) 30.8 & 105.4 \(\pm\) 22.2 & 559.5 \(\pm\) 230.5 & 519.2 \(\pm\) 274.9 & 23.52 \(\pm\) 15.53 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Reference analysis results for the main quality parameters of fruit juices.
# Sea Ice Detection Based on Differential Delay-Doppler Maps from UK TechDemoSat-1
## 2 Theory and Method
### Data Processing
#### 2.3.2 Incoherent Averaging and Normalization for Each DDM  
Each dataset contains a large number of DDMs and a certain number of consecutive DDMs are associated with the same type of reflected surface. To improve computational efficiency and reduce the impact of speckle noise, incoherent averaging is employed to the DDMs. However, as pointed out in [40], GNSS-R specular point ground track traverses different kinds of surface at some time intervals, and hence one DDM can be related to a transition area with different surface types, such as ice and water. Similar to the idea of [PERSON] [40], an adaptive incoherent averaging is implemented here and a shorter incoherent averaging interval is used for DDMs collected over transition areas. The TDS-1 data contain the specular point positions, so DDMs with specular points near coastline and ice-water transition areas will be processed with a shorter incoherent averaging duration. The sea ice-water transition area can be roughly detected through observing the changing trend of DDM peak power as abrupt power change occurs over sea ice-water transition areas.  
Figure 3: Left plot: Processed GNSS-R DDM collected over sea ice field (middle specular point location: 5\({}^{\circ}\)40\({}^{\prime}\)23.14\({}^{\prime\prime}\) W, 77\({}^{\circ}\)52\({}^{\prime}\)1.62\({}^{\prime\prime}\) N, PRN 24) and right plot: Processed GNSS-R DDM collected over sea water field (middle specular point location: 17\({}^{\circ}\)44\({}^{\prime}\)41.03\({}^{\prime\prime}\) E, 73\({}^{\circ}\)42\({}^{\prime}\)20.78\({}^{\prime\prime}\) N, PRN 24) by the SGR-ReSI receiver onboard the TDS-1 satellite on 19 February 2015.  
Figure 2: GNSS-R DDM collected over sea water surface and affected by noise. DDM\({}_{noise}\) denoted as noise floor is the average value of grids within the white box.  
After noise reduction and incoherent averaging, normalization is employed to the processed DDMs according to Equation (1). The examples of the resultant DDMs of sea ice and water with 5-s incoherent averaging are shown in Figure 3.
## 2 Literature Review
### Related Work
#### 2.3.4 Error-Based Learning Techniques  
[PERSON] et al. [29] developed a method using Deep Convolutional Neural Networks (DCNNs) to detect IoT device attacks. They also developed a neighborhood search-based particle swarm optimization algorithm (NSBPSO) for enhanced exploration and exploitation capabilities. The model was trained by using the NSBPSO-DCNN algorithm, achieving a 99.41% performance on the test dataset and 98.86% on the training dataset. However, due to computational costs, this study may not be feasible for real-time intrusion detection due to IoT systems' lack of data. [PERSON] et al. [30] proposed a new method for detecting IDSs due to low detection rates. The two-stage implementation process involves selecting features like chi-squared, ANOVA, and PCA. A Deep Neural Network model was developed on the NSL-KDD dataset for classifying traffic into normal and abnormal modes. However, the framework could have performed better on other datasets. A data imbalance can lead to incorrect classification of attack data, as average data are outnumbered by attack data, causing a difficult challenge. Moreover, [PERSON] and [PERSON] [31] proposed an IDS that tackles the complexities of big data, security data, and big data environments. The system identified nine new assault types in the UNSW-NB15 dataset with 96.29% accuracy, enhancing classification accuracy. Network traffic is analyzed by using SVM, and features are selected by using CGO. However, the study has limitations, such as the need for a large number of labeled data, which are costly and difficult to obtain. Additionally, [PERSON] and [PERSON] [32] developed a new IDS model by combining the RFE feature selection approach and CNN algorithms. As part of the assessment of the accuracy of the model, the MQTT-IoT-IDS2020 dataset, which comprises three layers of CNNs and multiclass classifications, was used. A CNN was the only deep learning model considered in this study, although other models, including RNNs, LSTM, and GRUs, may be more suitable for IoT networks.
# Accurate Indoor Localization with IoT Devices and Advanced Fingerprinting Methods
## 3 Results
### HYBRID Location Results  
In this method, the outlier detection filter is first applied to all data so that the data containing noise is detected and removed from the database as an unreliable point. After that, finding the maximum frequency in the common points of final location is performed using the algorithm. The results of this method show that compared to the PWCL method, there is an improvement in location accuracy. Figure 9 presents the bar graph of average error (in meters) across 47 points using the HYBRID method, highlighting the improvement in location accuracy compared to the PWCL method.  
The average error in this method is 1.82 m. The error variance in this method is 0.53 and the standard deviation is 0.73.  
Figure 8: Bar graph of average error (in meters) at 47 points in the PWCL method.  
Figure 9: Bar graph of average error (in meters) at 47 points in the HYBRID method.
# A Survey: Network Feature Measurement Based on Machine Learning
## 2 Machine Learning Method in Network Measurement
### Supervised Learning in Network Measurement
#### 2.1.7 DT in Network Measurement  
One of the most well-known techniques for representing data classification is the use of decision trees [50]. The decision tree's non-leaf nodes have two branches that are classified as true or false. The leaf node can make decisions. Any input coming from the root node will always reach a leaf node or be the only input to do so. Based on the idea of minimizing the loss function using training data, the decision tree model was developed as a classification and regression technique. You can think of every route leading from the root node to the leaf node as a rule. The leaf node refers to the conclusion of the rule, whereas each internal node represents a condition. These rules, which are mutually exclusive and comprehensive, are applicable in situations when there are more than two realistic options that the decision-maker can select and more than two uncontrollable unknown circumstances.  
[PERSON] et al. [25] created a decision tree that just requires a modest set of features to be collected to forecast the RTT between IP pairs that are separated geographically by great distances. Based on the combination of the mutual information gain of various qualities and the RTT, a decision tree of the RTT is built. The learning process made use of several attributes. Tests of the chosen attributes are represented by the internal nodes of the decision tree. From the matching node used for attribute classification, each branch descended. The RTT level is represented by the leaf node. When the decision tree has a defined structure, a strict protocol, and a clear aim that the decision-maker expects to attain, the method can be used in large-scale systems. It enables the decision-maker to compute the gains or losses of various schemes under various conditions and estimate the likelihood of the occurrence of uncertain factors. A decision tree struggles to forecast continuous fields, though. When processing data with strong feature correlation, speed suffers and errors may multiply more quickly when there are many categories.
## 4 Multi-Hazard Events
### Long-Duration Wind Loads  
Similar to the earthquake, after the wind events in the project lifetime are sampled, the wind load time-history data are generated following the AIJ (2015) [30] recommendations. The natural wind speed is consisting of the mean wind speed component (\(\overline{U}(z)\)) and the fluctuating component (\(u(t)\)) in the longitudinal direction, which is changing with time. The AIJ (2015) [30] adopted the von Karman power spectrum to present the power spectral density of the fluctuating component of wind events, as given,  
\[S_{u}(f)=\frac{4\sigma_{u}^{2}\{L_{z}/\overline{U}(z)\}}{[1+70.8\{fL_{z}/ \overline{U}(z)\}^{2}]^{5/6}} \tag{10}\]  
where in the equation, \(f\) is the frequency spectrum of wind loads, \(\sigma_{u}\) is the standard deviation of the fluctuating component, \(\overline{U}(z)\) is the mean wind speed at 10 m of ground level, and \(L_{Z}\) is the turbulence scale.  
The co-coherence of wind turbulence along with the structure height, which defines the frequency-dependent spatial correlation of wind speed fluctuation, is also considered as given below,  
\[\gamma\big{(}f,d_{j}\big{)}=\frac{\text{Re}\ \big{[}S_{i_{1},i_{2}}\big{(}f,d_{ j}\big{)}\big{]}}{\sqrt{S_{i_{1}}(f)\cdot S_{i_{2}}(f)}}=exp\left(-C_{ij}\frac{fd_{ j}}{U(z)}\right),\ i=u,v,w;\ j=v,w \tag{11}\]  
\[\gamma\big{(}f,d_{j}\big{)}=exp\left(-\frac{\sqrt{\big{(}C_{uw}\cdot fd_{y} \big{)}^{2}+\big{(}C_{uz}\cdot fd_{z}\big{)}^{2}}}{\overline{U}(z)}\right) \tag{12}\]  
where \(d_{j}\) is the distance of two referenced nodes of Euclidian distance, \(S_{u}\) is the power spectral density of turbulence in the longitudinal direction of a single point, and \(C_{\overline{ij}}\) is the decay coefficients. The current work employed the decay coefficients from the Norwegian Public Roads Administration (NPRA) Handbook N400 [38], and the power law is employed as well to estimate the wind speed force at story levels. Furthermore, the conventional spectral method introduced by [PERSON] (1991) [39] and [PERSON] (1996) [40] is adopted to estimate the fluctuating component of wind load by simulating the multivariate random process in the time domain. The entire process discussed in this section is further illustrated in the flowchart, see Figure 13, and the corresponding parameters are summarized in Table 6.  
Figure 12: The sequence of the phase spectrum earthquakes used to generate compatible earthquakes in multi-hazard scenarios (the numbers are assigned according to Table 5).
## 2 Materials and Methods
### Acquisition of Ground Control Points  
Ground control points were acquired using a portable Global Positioning System (eTrex(r) 20 GPS Receiver; Garmin, Olathe, KS, USA), Landsat OLI imagery, topographical maps, and aerial photographs covering the two study sites. Ground truth points were gathered in June 2021. Greater details and color contrasts were obtained by the false color (rgb--754) Landsat OLI composite. Within the study sites, appropriate transect lines were placed to sample the different class types, i.e., mangroves, other forests, bare ground, agriculture, and water--where the classes were more homogenous. Thirty-two coordinates with an accuracy of 3 to 5 m were gathered for each of the five classes in each of the two study areas. The coordinates were then overlaid on the resampled Landsat images. The points were converted into polygons using the TerrSet software 2020 v. 19.0 by following the edges of the respective pixels. The polygons were then used as reference data to generate regions of interest (ROIs) which represented the land cover classes.
# Adaptive Binaural Cue-Based Amplitude Panning in Irregular Loudspeaker Configurations
## 1 Introduction
This algorithm leverages measured room impulse responses (RIRs) and binaural room impulse responses (BRIRs) to account for the characteristics of the listening environment when deriving loudspeaker gains. It consists of two stages: the compensation stage and the gain and delay optimization stage. In the compensation stage, the inverse filtering algorithm is employed to eliminate discrepancies in the magnitude frequency responses of RIRs, ensuring consistent sound output from each loudspeaker at the center of the listening area. During the gain and delay search optimization stage, the interaural time difference (ITD) and interaural cross-correlation (IACC) for different gains and time delays are predicted using BRIRs measured with a dummy head. By adjusting the gains and time delay between loudspeakers, the predicted ITD of the virtual source is matched to that of the real source in the free field, creating an accurate virtual sound source at the desired azimuth angle. To resolve multiple solutions during the search process, the optimal gain pair and time delay are determined by using IACC. The effectiveness of the proposed algorithm is validated through both subjective and objective evaluations on a stereo system. The remainder of this paper is organized as follows: Section 2 introduces the proposed amplitude panning method, including the use of inverse filters to eliminate magnitude differences between loudspeakers and the determination of gains and time delay based on ITD and IACC. Section 3 presents the results of the simulation. Section 4 presents the resultsof objective and subjective experiments conducted in a listening room. Finally, Section 5 concludes the paper.