esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Throughflow Model
### Inviscid Blade Force Model  
To simulate the flow deflection effects made by a blade, many attempts have been made, which are generally based on two kinds of ideas, i.e., the relaxed method and the direct method. The former introduces a fictitious evolution equation for \(\mathbf{f}_{b}\), then relaxes to the steady state [21; 26; 38], while the latter usually directly expresses the circumferential component of \(\mathbf{f}_{b}\) as the gradient of the circulation [29; 34]. However, both of these kinds of methods have their shortcomings. In terms of the relaxed methods, the relaxation factor needs to be manually selected, of which an unsuitable value may significantly degrade the convergence and accuracy of the throughflow calculation. As for the direct method, projection of the circumferential component of the \(\mathbf{f}_{b}\) to the other two orientations may cause severe computing instabilities in the case of a large blade angle. To ensure the stability of throughflow simulations for the centrifugal compressor, an inviscid blade force model developed by [PERSON] et al. [36] was adopted. This model derives the modulus of the \(\mathbf{f}_{b}\) directly from the discretized normal momentum on the mean flow surface, which has proven to be of great benefit to the solver robustness and can be expressed as  
\[f_{b}=\mathbf{R}\cdot\mathcal{L}/\left|\zeta\right| \tag{5}\]where **R** is the residual vector term in the RHS of the discretized momentum equations. In the solving process, \(f_{b}\) is calculated at each iteration in conjunction with a slip condition constraint  
\[\textbf{W}^{*}=\textbf{W}-(\textbf{W}\cdot\zeta)\zeta/|\zeta|^{2} \tag{6}\]  
\(\textbf{W}^{*}\) represents an updated relative velocity.
# A Random Forest-Based Precipitation Detection Algorithm for FY-3C/3D MWTS2 over Oceanic Regions
## 4 Results
### A Time-Series Analysis
#### 4.2.2 Analysis of the Deviation Between the Observed and Simulated Brightness Temperatures (O-B)
The O-B RMSEs introduced by both precipitation detection methods are 1.266 K and 1.276 K for channel 6; 1.463 K and 1.462 K for channel 7; 1.111 K and 1.110 K for channel 8; and 1.016 K and 1.011 K for channel 9, respectively, with negligible differences of approximately 10\({}^{-3}\) K.  
Figure 16: The statistical O-B results obtained for channels 6–9: (**a–d**) after removing the AMSU-A precipitation pixels via the _SI_ and _RF_SI_ methods at 18:00 UTC on 6 June 2017 (before performing bias correction).
## Materials and Methods
### Sample Design and Data Extraction  
The researchers used a sample of Spanish university organizations. The selection of sampling elements was based on two of the most recognized rankings for assessing the activity of university institutions: the Webometrics list [33] and the Academic Ranking of World Universities (ARWU), also known as Shanghai ranking [34].  
Figure 1: Stages of the methodology.  
The authors took as their starting point the institutions located in the first fifteen positions of the Webometrics ranking in Spain in 2019 to then check whether these organizations appeared among the global top 500 of the ARWU of that same year. The authors selected only those institutions that rank in the top fifteen in Spain on the Webometrics list and, at the same time, among the top 500 in the world, according to the ARWU. This screening reduced the sample to ten organizations. The institutions were the University of Barcelona, Complutense University of Madrid, Autonomous University of Barcelona, University of Valencia, University of Granada, Autonomous University of Madrid, Polytechnic University of Catalonia, Polytechnic University of Valencia, Polytechnic University of Madrid and Pompeu Fabra University.  
Once the sample was selected, the researchers extracted from the Twitter platform, all the content published by the official accounts of the ten organizations over a one-year period. Following the procedure of previous studies [23; 35], the data were extracted through Twitter's API using the service provider Twitonomy. This process led to the gathering of 21,771 publications, in addition to the recognition obtained by each of them in terms of retweets and favorites.
# On the Use of the Multi-Agent Environment for Mobility Applications
## 2 Environment as a Spatio-Temporal Reference
### Space-Time Environment in Mobility Simulation
We considered 20%, 50%, 80%, then 100% of connected passengers, respectively, in these scenarios. In the scenarios with local information (all the simulations except the reference configuration), we placed local information agents in all the network stops. We report the average travel times for the passengers. We considered 30,000 passengers (approximately one-quarter of the actual number of passengers). We executed this system twice: one time with a broadcast of the information about disturbances to all the connected passengers and once using the spatiotemporal network to circumscribe the sending to the only connected passengers. The impact of using the space-time environment on the number of exchanged messages is reported in Table 1. It saves more than 60% of the messages in all the scenarios. The use of the space-time multi-agent environment in this simulation platform makes the information exchange between agents more efficient and saves communication bandwidth. The impact of travellers' real-time information is reported in [15].
# Cancelable Multimodal Biometrics Based on Chaotic Maps
## 3 Proposed Approach
### Combined Face-Fingerprint Cancelable Approach  
During the enrollment stage, the transformed fingerprint minutia points and the transformed face features are converted into a bitstream and concatenated together in order to form one feature vector. The concatenated feature vectors are then directly saved into the system's database. The transformation parameters are stored in a system's database using a hash map. Figure 1 illustrates the overall process of the proposed scheme (the enrollment stage).  
In the authentication stage, we consider the score level fusion of both biometric modalities: the fingerprint and face. The fusion of several biometric modality data represents the crux of multimodal biometrics. The fusion of biometric characteristic data can be achieved at three levels: (1) the decision level, wherein various decisions are combined; (2) the score level, wherein scores from different matching processes are combined; and (3) the feature level, wherein various biometrics features are combined. Whilst the most meaningful data are found in feature sets, incompatibilities can occur in the features from these modalities. Furthermore, data may be unimportant and/or superfluous due to the significant proportions of feature space. In addition, because data are minimal at the decision level, fusion here is somewhat inflexible. Contrarily, the score level fusion is less complex and yields better results. At this level, identifying and merging modality scores--produced using individual classifiers--is a relatively simple process [23].  
Figure 2 depicts the authentication phase of the proposed approach involving the subsequent steps:  
* Firstly, the system scans the query biometric features (face and fingerprint);
* Secondly, using the user key ID, the system retrieves the user parameters and the protected biometric features from the system's database;
* Thirdly, the system generates the user-specific kernel using the Logistic map and the user's initial value \(x_{0}\) to transform the query face image;
* Fourthly, the Torus Automorphism parameters are recovered from the system's database to produce the transformed fingerprint features;
* Fifthly, user authentication is conducted using the score-level fusion of the similarity scores between the fingerprint and face features of the query and the database templates [24].  
In this work, Euclidean distance and Cosine distance were employed to compute the similarity scores of the fingerprint and facial features, respectively. In general, a specific set of factors can ensure that the ability to recover the original biometric features is sufficiently difficult to be broken by an attacker. In the proposed approach, we use a set of parameters that controls the transformation process.  
Figure 1: Proposed cancelable approach architecture (enrollment stage).
## 5 Conclusions  
In this paper, we propose a novel satellite-based WSN, which integrates the WSN with the cognitive satellite terrestrial network. Then, the sensing-based spectrum sharing scheme in the  
Figure 8: Ergodic capacity versus sensing time for different \(P_{av}\) and \(m_{sp}\) with \(I_{av}=0\) dB.  
Figure 7: Ergodic capacity versus sensing time for different \(P_{av}\) with \(m_{sp}=1\).  
cognitive satellite terrestrial network is presented for the perfect sensing condition and the imperfect sensing condition, respectively. For both scenarios, the ergodic capacity of the satellite uplink under the transmit and the interference power constraints is studied. In the context, the expression of the ergodic capacity can be formulated as a nonlinear fraction programming problem in both conditions, and the Lagrange duality method is adopted to solve the problem. Computer simulations have shown that, in a perfect sensing scenario, the proposed method can increase the channel capacity more than 20% when \(P_{av}=10\) dB compared with the traditional opportunistic spectrum access. In an imperfect sensing scenario, \(P_{av}=15\) dB and \(Q_{av}=5\) dB, the optimal sensing time achieving the highest ergodic capacity is about 2.34 ms when the frame duration is 10 ms. In future works, we will investigate the bounded CSI error model affect on the proposed method.  
[PERSON] and [PERSON] proposed the architecture of an integrated wireless sensor and the cognitive satellite terrestrial network. [PERSON] and [PERSON] derived the problem formulations for the satellite user. The simulations were implemented by [PERSON] and [PERSON]. [PERSON] wrote the manuscript. [PERSON] and [PERSON] read and approved the final manuscript.  
This work was supported by the National Science Foundation of China (No. 61971440).  
The authors would like to extend their gratitude to the anonymous reviewers for their valuable and constructive comments, which have largely improved and clarified this paper.  
The authors declare no conflict of interest.
## 4 Level Division
### Data Path  
Passive caching is used in ICNs where the node stores the requested content that passes through it, rather than pre-storing some content on the node. This allows the caching system to cache current hot spots and eliminate outdated content adaptively to network access. But it also brings some uncertainty to cache research. Also, content name routing is supported in ICN, so forwarding no longer depends on the location of the content. As a result, we do not know at which node the request will be answered or will eventually arrive at the content server to respond. To solve these problems, we set up a special scenario for analysis: assuming that neither node in the network caches anything, or that the content of this request is not cached in any node in the network.  
In this case, no node in the network can respond to the received request, and the request will be forwarded to the next node by each node it passes through.  
With on-path caching, requests are forwarded by the shortest path from the user to the content server, each node along the way tries to respond to them. Finally, requests are forwarded to the content server.  
With an off-path caching strategy, requests access each node in turn according to the HASH calculation results until the request reaches the content server.  
Caching makes it uncertain which node responds to requests, but the direction of request forwarding is determined, and requests are forwarded to the content server regardless of the caching strategy. For analysis purposes, this paper considers only scenarios with on-path caching strategies, further dividing the content domain into data paths as shown in Figure 6.  
Figure 6 divides content domain A into eight data paths, each of which is the shortest path from the user to the content server. If there are several shortest paths between the access node and the source node, each constitutes a data path. Different data paths may also contain the same node, but since requests on each path come from different users, they  
Figure 6: The Data Paths in Content Domain A.  
are logically isolated. These data paths clearly show the forwarding paths of requests and content in the network.
## 3 Dynamic Multi-Objective (DYMO) Auction-Based Task Allocation  
We propose quality-aware DYMO-Auction for MRTA problems in dynamic scenarios. DYMO-Auction selects an auctioneer randomly for each task to avoid a single point of failure. Initially, all robots are deployed in the area of interest, either randomly or for a purpose, as in [20]. The tasks appear in the area over time and must be assigned on time. A task may be discovered either by mobile sensors or by a static WSN [21; 22]; either way, we assume that the details of a newly emerged task can reach the robotic network (i.e., can contact any robot in the network). The auctioneer (the receiver) opens an auction for the discovered task, and other robots bid on that task. Then, the auctioneer assigns the task to the robot with the minimum cost. A robot \(R_{i}\) bids on a task \(T_{j}\) using the bidding rules that are designed to maximize the quality satisfaction, distribute the workload among the available robots, and minimize the total traveled distance. The winner robot starts visiting and executing the assigned tasks \(A_{i}\), and it can also participate in the upcoming auction processes.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{6}{c}{**Multi-Objective**} \\ \hline
**Reference** & **D** & **L** & **Q** & **DY** & **SY** \\ \hline
[2] & Yes & No & No & Yes & No \\
[3] & Yes & No & No & Yes & No \\
[4] & Yes & No & No & No & No \\
[5] & Yes & Yes & No & No & Yes \\
[6] & Yes & No & No & No & No \\
[7] & Yes & No & No & Yes & No \\
[8] & Yes & No & No & Yes & No \\ Proposed approach & Yes & Yes & Yes & Yes & Yes \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of the related works and the proposed method in terms of distance (D), load (L), quality (Q), dynamicity (D), synergy (SY) objectives [18].
## 3 The Details of the Proposed LE-KCR Algorithm  
### K-Means Clustering  
The K-means algorithm, regarded as one typical and widely used clustering algorithm, can sort elements with common characteristics (e.g., Euclidean distance). It is employed to partition the network into multiple non-overlapping clusters, with the minimized intra-cluster distance (i.e., the distance between nodes and the cluster centroid) and the maximized inter-cluster distance (i.e., the distance between different cluster centroids) [42]. The clustering steps are as follows:  
1. Generate a sensor network with n nodes.
2. Randomly select K (i.e., number of clusters) samples as the initial clustering center, a = a\({}_{\rm{i}}\), a\({}_{\rm{2}}\),..., a\({}_{\rm{k}}\).
3. For each sample, compute the distance between it and each cluster center and sort it into classifications.
4. Recalculate the cluster center of each classification (i.e., the centroid of the classification). Note that the clustering centroid in subsequent iterations is a geometric centroid rather than specific nodes. \[\rm{a_{j}}=\frac{1}{|c_{i}|}\sum_{x\in c_{i}}x\] (13) where x, a\({}_{\rm{j}}\), and \(c_{i}\) denote each sample, the cluster centroid, and the set of intra-cluster samples, respectively.
5. Repeat steps 3 and 4 until the results of each category remain basically unchanged, and then terminate the iteration (the complexity of the algorithm can be controlled by changing iteration times and minimum error).  
In order to determine the value of K, this paper confirms it by employing the gap statistic method [43]. Although the elbow method [19] is usually adopted to determine K, the elbow method needs to find the inflection point manually and lacks enough automaticity. Therefore, the gap statistical method is adopted in this paper as follows:  
\[\rm{D_{k}}=\sum_{r=1}^{k}\frac{1}{2n_{r}}\sum_{i,j\in c_{r}}d_{ij} \tag{14}\]  
\[\rm{Gap(k)}{=}\ E(logD_{k})-logD_{k} \tag{15}\]  
In Equation (14), \(\rm{D_{k}}\) is the loss function, \(\rm{d_{ij}}\) is the distance between any two nodes of the intra-cluster, and \(\rm{n_{r}}\) is number of the nodes of the intra-cluster. In Equation (15), \(\rm{E(logD_{k})}\) is the expectation of logD\({}_{k}\). This value is generally generated by Monte Carlo simulation. We randomly generate the same number of random samples as the actual samples according to the uniform distribution in the area where the sensor network is located, and we cluster the random samples with K-means to obtain \(\rm{D_{k}}\). By repeating it multiple times (e.g., 20 times), we obtain 20 logD\({}_{k}\). Then, the 20 values are averaged to obtain the approximate value of \(\rm{E(logD_{k})}\). By altering the number of clusters, the final Gap(k) takes the maximum value, which determines the best K, as shown in Figure 2.
## 2 Basis of the QuEChERS Method
### Modifications in the Amount of Sample  
As it has been indicated, the original QuEChERS method requires large amount of sample (10 g) [17]. However, in order to spread its applicability to different matrices other than fruits and vegetables, some modifications have been suggested depending on the nature of the samples. In this sense, samples with a water content between 25-80%, such as bananas, require the addition of water before the initial extraction step to achieve a total content of 10 g of water (if 10 g of sample are employed). Conversely, in samples with a water content lower than 25%, such as cereals, dried fruits, honey, spices, aromatic herbs, flours, etc., the initial amount of sample has to be reduced to 1-5 g depending on the load of matrix interferences co-extracted and expected in the final extracts, and they require the addition of water before their extraction [39]. Table 2 shows the proposal recommended for the amount of sample and the addition of water depending on the sample nature in some food products.  
Reviewing the works published in the literature that performed QuEChERS for the multiresidue analysis of PAs in food and feed (Table 1), in general, lower amounts of samples are used than the ones proposed in Table 2 for products with contents of water below 80% and 25%. Nonetheless, the addition of water before the extraction with the organic solvent is carried out and consider by the authors (Table 1). For instance, [PERSON] et al. (2022) only used 1 g of honey and evaluated the addition of different amounts of water (0.5, 1, 2 and 3 mL) to the samples before the QuEChERS extraction [23]. It was revealed that 0.5 mL were not enough, as it seemed that honey was not completely dispersed. Conversely, the response of some PAs, such as senciephylline, decreased when the volume of water added was higher than 1 mL, due to the water solubility of these compounds. [23] Therefore, despite the recommendation to add water to honey samples in a 2:1 ratio, respectively (Table 2), in this work the best results were achieved with a 1:1 ratio [23].
# Bridge Model Updating Based on Wavelet Neural Network and Wind-Driven Optimization
## 5 Finite Element Model Updating of Ningbo Bund Bridge
### Finite Element Model of Ningbo Bund Bridge  
According to design drawings and engineering construction practice, the finite element model of the bridge was established by the finite element analysis software Midas Civil 2020. The main girder and triangular inclined tower structure were simulated by the spatial beam element, the stay cable was simulated by the truss element, and the cable force was input by the measured cable force. The corresponding element mass adopts the equivalent density method, which is equivalent to the mass density of the element according to the mass of the segment, so that the structural mass of the finite element model is close to the actual mass of the structure. The tower and the bottom of the pier were consolidated to constrain all the degrees of freedom; the beam end restraint was simulated with spring elements to restrain the vertical and longitudinal displacements of the bridge. The established finite element model has a total of 309 nodes, 64 truss elements, and 193 beam elements, as shown in Figure 9. In the model, the triangular inclined tower structure is shown in Figure 10. The design load of the bridge is vehicle load highway-level I and city-level A, and pedestrian load is 2.4 kN/m\({}^{2}\).  
Figure 8: The elevation of Ningbo Bund Bridge.  
Figure 9: Finite element model of Ningbo Bund Bridge.  
An ambient vibration test was carried out on the Ningbo Bund Bridge to obtain the structure's measured frequencies and mode shapes. The ultra-low frequency (minimum sampling frequency up to 0.05 Hz) 991-type accelerometer was used in the test, the operation details in the experimental modal analysis are detailed in the literature [47], and a low-speed gear was selected for signal acquisition for low-frequency analysis. The measuring points were arranged in the main beam's vertical, horizontal, and longitudinal direction, and the INV306D data signal acquisition and analysis system was used for data acquisition and modal analysis.  
Meanwhile, the bridge's initial finite element analysis was conducted to obtain the initial calculation results of the frequency and mode shape. The first three-order mode shapes are shown in Figure 11, and the first eight-order natural frequencies are shown in Table 7.  
Figure 11: First three-order mode shapes of the bridge.  
Figure 10: The schematic diagram of the triangular inclined tower structure.  
It can be seen from Table 6 that differences exist between the initially calculated value of the structural natural frequency and the measured value: The difference in first-order natural frequency is more than 10%, and the differences in the second-, fifth-, seventh-, and eighth-order natural frequencies are all above 5%; thus, the initial finite element model needs to be updated.