esa-sceva/satcom-chunk-collection huggingface dataset:
# Joint Channel Estimation Algorithm Based on DFT and DWT
## 2 System Model  
Assume that there are \(N\) subcarriers in an OFDM system; in order to avoid inter-symbol interference (ISI), a cyclic prefix of length \(N_{\text{CP}}\) is usually inserted before each OFDM symbol, i.e., the cyclic prefix length is greater than channel pulse length. \(X[k]\) is the frequency domain symbol modulated on the \(k\)th subcarrier. The time-domain signal \(x[n]\) formed by the IDFT of \(X[k]\) is denoted as [13]:  
\[x[n]=\frac{1}{N}\sum_{k=0}^{N-1}X[k]\cdot e^{\frac{2\pi\text{dB}}{N}},-N_{\text {CP}}\leq n\leq N-1. \tag{1}\]  
The time-domain channel impulse response of a multipath fading channel can be written as [13]:  
\[h[n]=\sum_{l=0}^{L-1}h_{l}\delta[n-\tau_{l}], \tag{2}\]  
where \(L\) denotes the number of paths of the multipath channel, and \(h_{l}\) and \(\tau_{l}\) denote the complex channel gain and channel delay of the \(l\)th path, respectively. Correspondingly, the channel frequency response is [11]:  
\[H[k]=\sum_{l=0}^{L-1}h_{l}e^{-j\frac{2\pi}{N}k\tau_{l}},0\leq k\leq N-1. \tag{3}\]  
Assuming ideal synchronization of the receiver, the received signal in the time-domain can be expressed as [11]:  
\[y[n]=x[n]*h[n]+w[n], \tag{4}\]  
where \(w[n]\) denotes the additive Gaussian white noise, and \"\(*\)\" represents linear convolution. After removing the cyclic prefix from Equation (4), the DFT can be used to obtain the received frequency domain signal of the \(k\)th subcarrier, i.e., [11]:  
\[Y[k]=X[k]H[k]+W[k],0\leq k\leq N-1, \tag{5}\]  
where \(W[k]\) denotes the noise component of the \(k\)th subcarrier with a variance of \(\sigma_{wf}^{2}\).  
If \(X[k]\) is used as the pilot symbol in the frequency domain, the LS estimation of the channel frequency response of the \(k\)th subcarrier can be given by [11]:  
\[\hat{H}_{\text{LS}}[k]=H[k]+\frac{W[k]}{X[k]}. \tag{6}\]The MSE of the \(k\)th subcarrier of the LS channel estimate can be expressed as [11]:  
\[\text{MSE}_{\text{LS}}[k]=\frac{\beta}{\text{SNR}}, \tag{7}\]  
where \(\beta=E\Big{[}|X[k]|^{2}\Big{]}E\Big{[}|X[k]|^{-2}\Big{]}\). Without a loss of generality, let \(E\Big{[}|X[k]|^{2}\Big{]}=1\), so \(\beta=1\). Since \(\text{SNR}=E\Big{[}|X[k]|^{2}\Big{]}/\sigma_{wf}^{2}\), we have [11]  
\[\text{MSE}_{\text{LS}}[k]=\frac{1}{\text{SNR}}=\sigma_{wf}^{2}. \tag{8}\]  
That is, the MSE of the LS algorithm is the same as the average power of the frequency domain noise.
## 2 Materials and Methods
### Data Processing and Analysis
#### 2.4.2 Processing and Analysis of Sensor Data  
The sensor output data collected from 06:00 on June 14 to 06:00 on June 16, 2022, saved in the 15 s and 1 min data files, were normalized using the Min-Max normalization technique. This process, aimed at reducing sensitivity discrepancies among the sensors, applies a linear transformation to the original dataset. For every sensor, the 15 s and 1 min data were adjusted by subtracting the minimum signal value and dividing by the range of the maximum and minimum values [Equation (5)].  
\[x_{norm}\ =\ \left(\frac{x-x_{min}}{x_{max}-x_{min}}\right) \tag{5}\]  
where \(x_{norm}\) is the normalized sensor output; \(x_{min}\) is the minimum output value of the sensor; and \(x_{max}\) is the maximum output value of the sensor.  
Following normalization, the sensor data over the 48 h period were averaged at 5 min intervals, beginning at 06:00 on June 14. The initiation times for each 5 min observational dataset and the corresponding timestamps from the sensor data were aligned and recorded in an MS Excel spreadsheet. This facilitated a side-by-side comparison and analysis of sensor detection and visual observation data.  
Further analysis of the sensor detection data was conducted to explore variations in sensor outputs. Real-time outputs at 1 Hz were examined to present sensor responses. In addition, the 15 s interval data were analyzed to reveal variations in pig activity at different pen locations within the room, such as those near the door, wall fans, and air circulation fans.
# Formation Control Technology of Fixed-Wing UAV Swarm Based on Distributed Ad Hoc Network
## 3 Waypoint-Based Formation-Switching Method  
The UAV studied in this paper was a highly dynamic fixed-wing UAV. Due to its high speed, it was difficult for the control system to update flight parameters in real time, and the errors were relatively large. Therefore, this article mainly considered the waypoint-based formation switching, and the online design of a small number of waypoints to complete the flight process of the UAV swarm formation, without the need to participate and change the design of the aircraft control system.
# Performance Study of a Fluidic Hammer Controlled by an Output-Fed Bistable Fluidic Oscillator
## 4 Results and Discussion
### Experimental Validation  
In order to verify the accuracy and reliability of the simulation results obtained by the CFD analysis, experimental tests were conducted on the SC-86H experimental prototype (Case 1). As shown in Figure 11, the tested fluidic hammer was driven by a high pressure triplex pump which supplied clean water to it. The flow rate of the feed water was measured by an ultrasonic flow meter and was adjusted by changing the speed of the electric motor, which was the power source of the triplex pump whose speed can be varied by an inverter. A pressure-stabilizing cylinder was used to reduce the fluctuation of flow rate. Besides, a noncontact measuring system based on the Hall sensors was employed to measure the impact velocity and impact frequency of the impact body. The noncontact measuring system mainly contained a cylindrical magnet, a pair of Hall sensors and a data analysis system. The integrated Hall sensors can provide an output voltage proportional to the magnetic intensity. The cylindrical magnet was fastened to the lower end face of the mass block. Two Hall sensors with the same specifications, one above the other, were mounted in pairs on the anvil. The distance  
Figure 10: Variations of \(E,f\) and \(P\) versus stroke of the impact body \(L\).  
Figure 9: Variations of \(E,f\) and \(P\) versus mass of the impact body \(m\).  
\(\Delta Y\) between the two Hall sensors was 1.4 mm. The cylindrical magnet moves up and down with the movement of the impact body. When the distance between the upper Hall sensor and the magnet was \(Y\), assuming that the corresponding output voltage of the sensor was \(U\), and when the impact body moves continuously with a distance of \(\Delta Y\), then the distance between the lower Hall sensor and magnet was also \(Y\), thus the produced corresponding output voltage of the lower sensor was also \(U\). The moving time \(\Delta t\) corresponding to the distance \(\Delta Y\) was recorded during the movement of the impact body. As the distance of \(Y\) was small enough, the impact velocity of the impact body was approximately equal to \(\Delta Y/\Delta t\).  
Figure 12 presents the comparison of the results obtained by CFD simulation and experimental tests. It can be found that the result data obtained by CFD simulation is slightly different with that of the experimental tests, but the differences are very small and do not exceed 7%, showing considerable agreement between them. The relative error may be caused by the fact that leakage and frictional force in the working chambers was ignored in the numerical calculation, and as the value of \(\Delta Y\) cannot be infinitely small, the measurement of impact velocity was an approximation.  
Figure 11: Schematic of the experimental system.  
Figure 12: Comparison of the results (single-impact energy and impact frequency) obtained by computational fluid dynamics (CFD) simulation and experimental tests (the difference is smaller than 7%).
# A Secure and Efficient Multi-Factor Authentication Algorithm for Mobile Money Applications
## 6 Performance Analysis
### Communication Overhead  
Communication overhead is associated with estimating the number of bytes in every communication message exchanged in the enrolment, authentication, and transaction phases. It is calculated by computing the number of bytes transmitted at the time as the message exchange process. Each packet size is calculated by summing the size of each message using the information in Table 2, and the results are shown in Table 3. In addition, it excludes the size of symmetric and asymmetric encryption.  
As shown in Table 3, fifteen (15) messages are exchanged during user enrolment, authentication, and transaction phases in the proposed algorithm. Hence, the communication overhead is the sum of computing the bytes of these messages. The proposed algorithm has a high communication overhead, with a total of 744 bytes, as compared to the relevant related schemes in [15; 16; 19; 20; 21; 93]. However, it provides strong security against many security attacks, as discussed in Section 5.
###### Abstract  
It is important to further enhance the performance of green and efficient non-homogeneous catalysts for advanced oxidation process of Peroxymonosulfate (PMS-AOP) for green treatment of industrial wastewater. In this paper, nitrogen-sulfur co-doped MOFs-derived carbon material (CoSN@C) was prepared by one-pot synthesis followed by carbonization, and its morphological structure was characterized by XRD and SEM. After pyrolysis, the CoSN@C still maintained the dodecahedral morphology and structure of ZIF-67. The synergistic effects of S and N significantly elevated the activation of PMS. The results show that the CoSN@C + PMS system can effectively activate PMS to degrade Rhodamine B (RhB), with a rate constant (1.85 min\({}^{-1}\)) four times higher than that of the CoN@C + PMS system (0.44 min\({}^{-1}\)). The optimal catalytic process parameters of material dosage, PMS concentration, temperature, pH, and other parameters were also investigated for the activation of PMS to remove Rhodamine B. The cyclic experiment shows that the CoSN@C has excellent recyclability and the degradation rate of RhB still reached 88.9% after four cycles. Radical capture experiments and EPR tests showed that the CoSN@C + PMS system generated a large amount of Co\({}_{4}\)- and -OH radicals adsorbed on the catalyst surface and a certain amount of singlet oxygen, and the free radical pathway and non-radical pathway worked together to degrade RhB efficiently and rapidly. While non-radical pathway with singlet oxygen as main reactive oxygen species played a key role in the CoN@C + PMS system. This work provides a new idea for the rational design of non-homogeneous catalysts for PMS-AOP system.  
advanced oxidation process; peroxymonosulfate (PMS); non-homogeneous catalyst; MOFs +
Footnote †: journal: Applied sciences
# Empirical Evaluation on Utilizing CNN-Features for Seismic Patch Classification
## 3 Methods and Data
### Seismic Data  
A total of 500 synthetic seismic images were prepared, and each of them contained one fault line with different slopes and positions. The corresponding fault position in each image was indicated in white by generating binary masks, referring to the seismic amplitude information. Figure 1 presents an example of a seismic wave image (a), and a fault line (b) to be extracted from it. The dataset of the synthetic seismic wave images (501 \(\times\) 501 pixels), reproduced through open-source code, IPF [37], is an artificial implementation of sequential rock deformation over time.  
From the dataset composed of image pairs, shown in Figure 1 (where the two images on the left and right sides include seismic waves and fault lines, respectively), the fault and nonfault patches were extracted. One patch is a (45 \(\times\) 45)-dimensional matrix with one candidate pixel in the center, and 2024 pixels adjacent to it (this is the smallest patch size generating satisfactory results [4]). This patch can be classified as a fault patch or a nonfault patch, according to the following rule: If the candidate pixel is a pixel forming a fault line, then it becomes a fault patch; otherwise, it becomes a nonfault patch. For example, referring to the fault line matrix shown in Figure 1b, all possible fault patches were first extracted, and then the same number of nonfault patches were randomly extracted from the seismic wave image, shown in Figure 1a.  
Figure 1: Plots presenting synthetic seismic wave data: (**a**) a synthetic seismic image; (**b**) a fault line. Here, two blocks, marked in the red color and in blue, indicate the fault and nonfault patches, respectively.
# Cluster Analysis of Pedestrian Mobile Channels in Measurements and Simulations
## 4 Gradient Boosted Decision Tree-Based Model
### Numerical Results  
Simulation and measurement data were used to train and validate the GBDT-based model. 80% of the data are training samples, and 20% are validating samples. Training samples are used to train the model, and validating samples are used to check its accuracy. To train the simulation data, we had to reconstruct CIRs in the time domain with MPC delay and power as input. Training output is the cluster delay, AoD, and EoD. To train the data collected by measurement, CIRs are ready-made as input, and the output is from the results given by the KPMKF process, as shown in Section 2. The depth of trees was set as 100, and the maximum iteration is set as 200.  
To validate the GBDT-based model, RMSE was used to measure the differences between predicted and observed values, which is calculated by  
\[R=\sqrt{\frac{\sum_{n=1}^{N}(y^{(n)}-y^{(n)}_{sim/mea})^{2}}{N}} \tag{33}\]\(y^{(n)}\) is the \(n\)th results predicted by the GBDT-based model, while \(y^{(n)}_{sim/mea}\) is the \(n\)th results from simulations/measurements. \(N\) is the number of drops. The smaller the RMSE is, the better the fit between the predicted and observed values is.  
Figure 8a displays an example of AoD results by GBDT to show the degree of fitting. The training and validating samples, and the AoDs of the three clusters, are from the above Section 3 simulation in NLoS conditions, which are shown as solid curves. The dotted lines are the fitted results calculated by the GBDT-based model. The fitted curves are very close to the samples.  
The RMSEs of the measurements and simulations are shown in Figure 8b. This shows that the GBDT validating results generally fit well with simulation/measurement results. Compared to the measurement RMSEs, those of the simulations are smaller. Moreover, the GBDT-based model performs better in an LoS condition than in an NLoS condition.
# Joint Extraction of Entities and Relations Based on Enhanced Span and Gate Mechanism
## 1 Introduction
The goal of this paper is to enhance the efficiency of the relation extraction model and solve the problems of poor information transmission between entity relations, low efficiency of span-based entity extraction, difficulty in identifying nested entities. We put forward a new kind of extraction model to enhance the entity relationship extraction task. Specifically, our research aims and contributions are as follows:  
1. A new one-to-many matching method is proposed which combines the predicted start positions and multiple end positions of spans sequentially. Specifically, a start position can be matched with multiple end positions located to its right. In addition, we design a subject binary classifier to determine whether a span represents the subject entity in the sentence. This approach effectively solves the problem of nested entities. 2. To enhance feature transfer between the subject and object prediction tasks, we present a new subject feature fusion method based on a gate mechanism. Compared with simple connections, this method can integrate information more effectively and enhance the expressiveness of the model. 3. We present a novel joint model called ESGM that combines the pointer network span with a gate mechanism. This new model addresses the challenges of nested entities and relation overlap, enabling the model to capture richer semantic information and resulting in greatly improved performance.
# Two Medoid-Based Algorithms for Clustering Sets
## 3 Medoid-Based Approaches
### Accuracy Clustering Indexes  
The proposed new distance functions in Section 3.1 make it possible to qualify the accuracy of a clustering solution achieved by repeated K-medoids or random swaps, by using some well-known internal or external indexes. For benchmark datasets provided of ground truth partition labels (see also later in this paper), the external adjusted rand index (\(ARI\)) (used in [8]) and the centroid index (\(CI\)) [12; 25; 26] can be used to qualify the similarity degree between an obtained solution and the ground truth solution. The \(ARI\) index ranges from 0 to 1, with 1 mirroring maximal similarity and 0 expressing the maximal dissimilarity. The \(CI\) index ranges from 0 to \(K\). A \(CI=0\) is a precondition for a clustering solution to be structurally correct, with the found medoids which are very close to the optimal positions. A \(CI>0\) indicates the number of medoids which were incorrectly determined.  
In addition to the \(SDM\) function cost, in this work, the internal Silhouette index (\(SI\)) [16; 27] can be used for checking the degree of separation of the clusters. The \(SI\) index ranges from \(-1\) to \(1\) (see also [12]), with \(1\) mirroring well-separated clusters. An \(SI=0\) indicates high overlapping among clusters. An \(SI\) toward \(-1\) characterizes an incorrect clustering.
## 3 Results and Discussion  
In this section, the simulation results are presented with the model presented in Section 2, for the three types of low temperature solar collectors: flat plate, evacuated tubes, and flat plate with heat pipes. The simulation results are presented for the five working fluids evaluated: water, water-ethylene glycol at 30% (EG 30%), water-ethylene glycol at 50% (EG 50%), water-propylene glycol at 30% (PG 30%), water-propylene glycol at 50% (PG 50%). The performance parameters evaluated include useful heat, \(Q_{u}\), efficiency, \(\eta\), and heat transfer coefficient, \(h\). The varied parameters include mass flow rate, fluid temperature, ambient temperature, and radiation.
## 3 Approach for Reliable Data Collection in IoT-Based Smart Farming
### Reliable Fog Computing-Based Architecture  
A Fog hierarchical architecture includes three tiers called Cloud, Fog, and Things [32]. The Cloud Tier comprises one or more Data Centers for providing services that consume many computational resources (e.g., big data analysis). The Fog Tier includes Fog Nodes (FNs). An FN is any physical or virtual entity (e.g., routers, switches, wireless access points, video surveillance cameras, controllers, and servers) that improves the interaction between the Things Tier and Cloud Tier to enhance IoT-based services, such as data collection based on wireless sensors, tracking systems, and, in general, delay-sensitive applications. The Things Tier consists of IoT-enabled devices including sensor nodes. This Tier is responsible for interacting with the environment and sending data to the Fog Tier [33].  
Figure 1 presents our Fog-based and reliability-oriented architecture. Unlike the traditional Fog-based architectures [34; 35], our architecture separates the Fog Tier into two layers: categorizing resources in layers depending on domain requirements leads to improve IoT reliability by properly locating management services [36]. As making decisions with reliable data are pivotal for IoT-based applications, we locate FDM and FRM mechanisms in Layer 2 FN because it is closest to the IoT devices. In this way, we avoid applications running on upper Tiers operate with inaccurate data. Note that outliers in collected data can negatively impact statistical analysis and the accuracy in estimation and forecasting models of harvest, leading to making wrong decisions and, consequently, lost money. Then, Layer 2 FN comprises several edge computing nodes that collect data from IoT devices and host our reliability-targeted mechanisms. FDM is to detect outliers (see Section 3.3), while FRM is to infer correct values for replacing the detected outliers (see Section 3.4). Layer 1 includes Fog Controllers (FCs) to coordinate the network tiers and perform preliminary data analytic because they have more processing, storage, and networking resources than Layer 2 FN.
# Cooperative Standoff Tracking of Moving Targets Using Modified Lyapunov Vector Field Guidance
## 6 Conclusions  
This paper focused on the cooperative standoff tracking of moving targets. Three subproblems were discussed: Generating the optimal flyable path for a single UAV, standoff tracking of moving targets, and intervehicle phase keeping for cooperative tracking. For the first subproblem, we discussed the influence of guidance parameter \(c\) on the trajectory. The gradient descent method was utilized to search the optimal guidance parameter \(c\). The ILVF was introduced to prevent the UAV from traversing the circle orbit. In the second subproblem, the IMM-UKF algorithm was introduced to fuse localization results from three UAVs. In this way, the state of the target was estimated and used for correcting the guidance law in standoff tracking of moving targets. At last, aiming to make a team of UAVs converge to the desired intervehicle phase, a speed-based controller was proposed. A series of numerical simulations on three subproblems were well-designed and performed to demonstrate the feasibility of the proposed algorithm. To demonstrate the usability in reality, we further carried out high-fidelity hardware-in-the-loop simulations.  
There are some potential modifications on this study. As an uncertain factor, wind can be regarded as a disturbance to flight control. Actual flight using fixed-wing UAVs will be carried out to verify the validity of our algorithm in more complex conditions.  
Conceptualization, J.L.; data analysis, F.C., J.L.; writing-original draft preparation, F.C.; writing-review and editing, F.C., J.L.; supervision, Y.N.; project administration, Y.N.; funding acquisition, L.W. All authors have read and agreed to the published version of manuscript.  
This research was funded by The National Natural Science Foundation of China (No. 61876187).  
The authors declare no conflict of interest.  
Figure 22: The phase difference. (**a**) Phase difference in the “gathering” stage; (**b**) Phase difference in the “phase adjusting” stage; (**c**) Phase difference in the “high-speed” stage; (**d**) Phase difference in the “low-speed” stage; (**e**) Phase difference in the “accelerating” stage.
## 4 Experiments
### Experimental Results
#### 4.5.4 Effect of Each Component  
To investigate the effect of each component of our model on the prediction result, we evaluate the four variants separately by removing the external module, the global correlated mechanism, the independent daily-periodic spatial-temporal component, and the independent weekly-periodic spatial-temporal component from GSTGCN. These four variants are: GSTGCN-noExt, GSTGCN-noGlo, GSTGCN-noDay, and GSTGCN-noWeek. Figure 10b illustrates the MAE comparison of the GSTGCN and its four variants predicting the next 12 steps on PeMSD7. It can be seen from the figure that the GSTGCN consistently outperforms GSTGCN-noExt and GSTGCN-noGlo, indicating the effectiveness of the external component and the global correlated mechanism. The other two models, GSTGCN-noDay and GSTGCN-noWeek, have similar short-term prediction performance as GSTGCN, but they perform worse in the long-term predictions. Therefore, it is proved that the daily-periodic component and the weekly-periodic component help to capture the long-term temporal dependencies of the traffic data more effectively.