esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 The LAGOON NLG Results and Discussion
### The LAGOON NLG Overall Far-Field Acoustic Results  
As mentioned above, the SCL3 is chosen to ensure that the FW-H integration is computed along where the flow is fully correlated. Figure 10a,b represent the PSD noise level of all the five C-S at different receiver locations (2D and 3D microphones), respectively, and compared with the experimental  
Figure 9: Effect of different SCL values on the acoustic prediction due to the lower part of the LAGOON NLG at: (**a**) Sec.3; (**b**) Sec.4; and (**c**) Sec.5.  
data [12]. For the 2D microphone (2 DMic), located at 6 m above the center of each C-S, some peaks are visible at the PSD results of Sec.1, Sec.2, and Sec.4 around \(f_{\text{Exp.}}\approx 1.5\) kHz, as illustrated in Figure 9(a). However, for Sec.3 and Sec.5, no peaks are captured around \(f_{\text{Exp.}}\) Figure 9(b) shows the PSD results of all the five C-Ss collected at 3D microphones (3 DMic). The 3 DMic is located in the sideline microphones arc (M6) at 56\({}^{\text{o}}\) from the flyover microphones arc as per the experimental measurements [12], as shown in Figure 9(b). Based on these noise contributions from all the five C-Ss, the overall far-field noise is calculated using Equation (3).  
Figure 10(a) presents the total PSD noise level of all the five C-Ss and compared them with the experimental [12] and 3D numerical results [44]. For the 2 DMic, the result of the present approach has a good qualitative agreement with the experimental data at the medium-to-high frequency ranges (1.55-2.2 kHz), as illustrated in Figure 10(a). It is important to note that, the tonal peak at \(f_{\text{2D}}\approx 1.02\) kHz captured by the 3D numerical results is successfully reproduced by the present study (for both 2 DMic and 3 DMic). However, this peak is not evident in the far-field noise experimental measurements. In fact, this tone was captured in the near-field unsteady surface pressure measurements conducted by [PERSON] et al. [12] using a Kulite(r) pressure transducer located at K15, as shown in the schematic diagram of Sec.4 in Figure 10(a).  
In contrast, Figure 10(b) shows the total PSD noise level obtained using the present approach at the 3 DMic compared with experimental [12] and 3D numerical data [44]. The result of the 3 DMic reveals a better qualitative matching with the experiment. It can be observed that a very tiny peak at \(f_{\text{Exp.}}\approx 1.5\) kHz is successfully captured, as shown in Figure 10(b). This tonal peak would be more visible if the  
Figure 10: Comparisons of obtained PSD for all C-Ss of the LAGOON NLG with experimental results at: (a) 2D receiver locations relative to each domain; and (b) 3D microphone location at M6, as per the experiment.
## 5 System Implementation and Field Testing
### Testing Results
#### 5.2.3 Attraction Reservation Scheme  
We tested the function of attraction reservation using the recommended attraction result shown in Figure 11a. This result was provided by the personalized dynamic scheduling function, and we started  
Figure 10: Experimental results for case \(T_{i}^{Arr}>T_{i}^{Ideal}\).  
Figure 9: Experimental results of the Hottest First strategy.  
to reserve this recommended attraction. The app showed the information including the available session and capacity for visitors, as shown in Figure 11b. Once selecting the preferable session, we provided the number of visitors to reserve, as shown in Figure 11c.  
Figure 12a shows the result of attraction reservation if this booking is validated. In addition, our mobile app immediately generated a personalized booking ticket in the form of a QR code, as shown in Figure 12b. This experiment confirms that the attraction reservation function works correctly.  
Figure 11: Testing steps of the attraction reservation function: (**a**) example result of the recommended attraction; (**b**) list of available sessions and capacities; and (**c**) selection of the number of visitors to book.  
Figure 12: Testing result of the attraction reservation function: (**a**) result of the successful reservation of attraction; and (**b**) the booking ticket of the visitor.
## 5 Results and Discussion  
### The Multi-Dimensional Environment Evaluation System Based on AHP  
Firstly, the weights of the criterion layer were calculated. Then, each criterion's weight was allocated to the factors in index layer according to the judgement matrix. The multi-dimensional environment evaluation system reflected by residents' satisfaction was thus established. As shown in Figures 8 and 9, in the criterion layer, the living environment had the largest weight, reaching 34.48%. The management environment and economic environment were weighted as 27.16% and 23.71%, respectively. The human environment ranked last with the weight of 14.66%. In the index layer, economic income, working environment and ecological environment were the strongest indicators affecting resident's satisfaction, and the inhabited by outsiders had the weakest influence (Figures 8 and 9).
# Design and Analysis of a Hybrid MPPT Method for PV Systems Under Partial Shading Conditions
## 2 Materials and Methods
### Design of the Proposed MPPT Algorithms
#### 2.3.2 Design of Kalman Filter and Perturb & Observe Hybrid MPPT Algorithm
\[{v_{i}}^{t+1}=w\times{v_{i}}^{t}+c_{1}\times r_{1}\times\left({p_{best}-{x_{i}} ^{t}}\right)+c_{2}\times r_{2}\times\left({g_{best}-{x_{i}}^{t}}\right) \tag{14}\]  
\[{x_{i}}^{t+1}={x_{i}}^{t}+{v_{i}}^{t+1} \tag{15}\]  
The velocity of the particle is calculated in Equation (14). In Equation (15), the position of the particle is determined. Here, the letter \(i\) is the particle number. \(t\) indicates the current state. The expressions indicated by \(t+1\) indicate the next state. \(v_{i}\), represents the velocity vector of the particle. \(w\) is the coefficient of inertia and determines how much the particle maintains its previous speed. The cognitive and social components are expressed as coefficients \(c_{1}\) and \(c_{2}\). The coefficients \(r_{1}\) and \(r_{2}\) consist of random numbers (between 0 and 1) and make the particle motion random. The current position of the particle can be expressed as \(x_{i}\). The best position of the particle is shown by \(p_{best}\). The best position of the entire swarm is expressed as \(g_{best}\)[60]. Figure 5: Flowchart of the Kalman Filter and Perturb & Observe MPPT algorithm. In this algorithm, a MATLAB code containing the Perturb & Observe algorithm and the PSO algorithm is prepared in the MATLAB function block. First, voltage and current information are obtained from the PV array. The power value is calculated using these measurements. The Perturb & Observe method starts the algorithm at the first stage. The Perturb & Observe algorithm compares the previous states of the power and voltage information and produces the reference voltage. The generated reference voltage enters the PSO algorithm. Here, the best solution is tried again. Finally, the PSO algorithm produces a suitable reference voltage. These algorithms will work continuously one after the other in order to follow the MPP. In this algorithm, a PI controller is used. The PWM signal is generated in the control circuit and the DC-DC boost converter is switched. The peak value of the carrier signal is selected as 0.6 and its frequency is 20 kHz. Figure 6 shows the flowchart of the applied P&O and PSO Hybrid MPPT method.
# Multi-Object Tracking with Grayscale Spatial-Temporal Features
## 4 Results  
### Datasets, Evaluation Metrics, Environment, and Implementation Details  
Datasets. We conduct the experiments on the MOT16 pedestrian tracking dataset. MOT16 contains seven training sequences and seven test sequences, and these videos were captured in high-density scenes with heavy occlusion. The video frame rate is around 30 FPS.  
Evaluation metrics. We use the official evaluation tools. These evaluation tools contain many evaluation metrics. There are two most used tracking performance evaluation metrics, which are multiple object tracking accuracy (MOTA) and IDF1.  
MOTA focuses more on the detection side, while IDF1 can better reflect the tracking side. IDF1 reflects the tracker ability to maintain identities over time and emphasizes association accuracy.  
In addition, the percentage of mostly tracked targets (MT) and the percentage of mostly lost targets (ML) are also commonly used metrics.  
TFPS is tracking FPS, which does not consider the detection time.  
Environment. The proposed approach is implemented in visual studio 2019, and the program is performed on a laptop with a CPU i7-11800H and GPU RTX3060 laptop.  
Implementation Details. We use the public detector deformable parts model (DPM) provided by MOT16 to detect the target, and yolov5 is only used for target cutting.
###### Abstract  
This paper presents a novel collision avoidance (CA) algorithm for a cooperative fixed-wing unmanned aerial vehicle (UAV). The method is based on maneuver coordination and planned trajectory prediction. Each aircraft in a conflict generates three available maneuvers and predicts the corresponding planned trajectories. The algorithm coordinates planned trajectories between participants in a conflict, determines which combination of planned trajectories provides the best separation, eventually makes an agreement on the maneuver for collision avoidance and activates the preferred maneuvers when a collision is imminent. The emphasis is placed on providing protection for UAVs, while activating maneuvers late enough to reduce interference, which is necessary for collision avoidance in the formation and clustering of UAVs. The CA has been validated with various simulations to show the advantage of collision avoidance for continuous conflicts in multiple, high-dynamic, high-density and three-dimensional (3D) environments. It eliminates the disadvantage of traditional CA, which has high uncertainty, and takes the performance parameters of different aircraft into consideration and makes full use of the maneuverability of fixed-wing aircraft.  
Keywords:fixed-wing UAV; Collision avoidance; conflict resolution; maneuver coordination +
Footnote †: journal: Applied Sciences
# Comparison of Functional Connectivity Analysis Methods in Alzheimer's Disease
## 3 Functional Connectivity Analysis Methods
### Model/Seed Based Methods
#### 3.1.3 Statistical Parametric Mapping (SPM)  
SPM refers to the application of the general linear model (GLM) and Gaussian random field (GRF) theory in combination to produce classical inferences about spatially extended data using statistical parametric maps. SPM utilizes GLM to estimate the parameters that potentially explain the data and GRF to overcome the multiple comparison difficulties. Following scaling and filtering processes of the overall brain voxels, this technique averages the voxels in a specific seed. It uses it as a variable of interest in the first-level analysis, followed by a deeper analysis utilizing the random effect. MATLAB (2017b) and SPM12  
Figure 3: Correlation matrices were generated using CCA and CA for NCs and AD patients. Both methods measure the FC between white and grey matter at resting-state. The intensity of FC is highest at yellow blocks (strong correlation) and lowest at dark blue blocks (weak correlation).  
are used to preprocess the fMRI data [30; 31], and some sample fMRI images of random subjects and the volume (age-wise) time series are shown in Figure 4.  
Figure 4: Functional images generated from SPM viewer for both NC (**left**) and AD patients(**right**). The interpretation of generated images requires extensive knowledge of fMRI and neurological disorders. Voxel time series for both subjects are displayed using signal graphs (**down**).  
Some studies have employed a variant of the model/seed-based methods. Rather than analyzing time-series correlations voxel by voxel, they parceled the brain by using an AAL atlas, averaging the time-series inside each ROI, and examined correlations across them [32]. They discovered a substantial drop in anterior-posterior positive correlations in AD. In contrast, rises in positive correlations in AD were often limited to intra-lobe connections. There was also a substantial reduction in the volume of negative connections in the parietal lobe in AD.
# In-Orbit Attitude Determination of the UVSQ-SAT CubeSat Using TRIAD and MEKF Methods
## 3 Attitude Determination Methods
### Multiplicative Extended Kalman Method
#### 3.2.4 Update  
This subsection aims to compute the post-update of the different variables at time \(t_{k}\). The covariance matrix \(P\) can then be post-updated in Equation (22):  
\[P_{k}^{+}=[I-K_{k}H_{k}(\mathbf{\hat{x}}_{k}^{-})]P_{k}^{-} \tag{22}\]  
At this phase, a reset is applied to the pre-estimate of the error angle in Equation (23):  
\[\mathbf{\delta\hat{\mathbf{\delta}}_{k}^{-}}=\mathbf{0_{3}} \tag{23}\]  
It is then possible to update the state vector in Equation (24):  
\[\mathbf{\hat{x}}_{k}^{+}=\mathbf{\hat{x}}_{k}^{-}+K_{k}[\mathbf{y}_{k}-\mathbf{h}_{k}(\mathbf{\hat {x}}_{k}^{-})] \tag{24}\]  
where \(\mathbf{\hat{x}_{k}}=[\mathbf{\delta\hat{\mathbf{\delta}}_{k}^{T}\hat{\mathbf{\beta}}_{k}^{T} \hat{\mathbf{s}}_{k}^{T}\hat{\mathbf{k}}_{U_{k}}^{T}\hat{\mathbf{k}}_{\mathbf{k}_{k}}^{T}]\). \(\mathbf{h_{k}(\mathbf{\hat{x}}_{k}^{-})}\) is the estimated observation that is given in Equation (25). The measurements are given in Equation (26).  
\[\mathbf{h_{k}(\mathbf{\hat{x}}_{k}^{-})}=\begin{bmatrix}A(\mathbf{\hat{q}}^{-})\mathbf{B_{o}} \\ A(\mathbf{\hat{q}}^{-})\mathbf{S_{o}}\\ A(\mathbf{\hat{q}}^{-})\mathbf{N_{o}}\end{bmatrix}_{t_{k}} \tag{25}\]  
\[\mathbf{y_{k}}=\begin{bmatrix}\mathbf{B}\\ \mathbf{S}\\ \mathbf{N}\end{bmatrix}_{t_{k}} \tag{26}\]  
The quaternion's update is performed through two steps that aim to compute the quaternion corresponding to the error estimate in Equation (27a) and to preserve the unit quaternion norm in Equation (27b).  
\[\mathbf{\hat{q}}^{*}=\mathbf{\hat{q}_{k}^{-}}+\frac{1}{2}\Xi(\mathbf{\hat{q}_{k}^{-}}) \mathbf{\delta\hat{\mathbf{\delta}}_{k}^{+}} \tag{27a}\] \[\mathbf{\hat{q}_{k}^{+}}=\frac{\mathbf{q}^{*}}{\|\mathbf{q}^{*}\|} \tag{27b}\]  
where \(\mathbf{\delta\hat{\mathbf{\delta}}_{k}^{+}}\) corresponds to the first three components of the state vector \(\mathbf{\hat{x}}_{k}^{+}\) and the \(\Xi(q)\) matrix is defined in Equation (28):  
\[\Xi(q)=\begin{bmatrix}q_{4}I_{3}+[\mathbf{q_{1:3}}\times]\\ -\mathbf{q_{1:3}}^{T}\end{bmatrix} \tag{28}\]
# _Apis mellifera_ Bee Verification with IoT and Graph Neural Network
## 7 Experiments
### Experiments
#### 7.2.1 Bee Recognitions at the Beehive Entrance  
This experiment allows us to measure the accuracy of bee recognition in the beehive entrance, through the software system; the result is considered accurate when the software system outputs the bee recognition result at the beehive entrance, and this result is verified by the researcher. This experiment is performed every time an image is transmitted from the apiary to the DPC, and it is necessary to verify if the object in the image is a bee because other different insects, such as wasps, ants, or beetles may be contained in the image; see the table of images of intruders that may appear in the hive's entrance. Sets of 50 images are incrementally considered to obtain the recognition percentage total, i.e., the result is obtained by dividing the number of times that the system accurately recognized the bee at the beehive entrance by the number of verified images. The number of images in each set is incremental, i.e., the previous set of verified images is considered; for example, the first 50 images are taken and the recognition percentage is obtained; then the first 50 images are considered again, 50 more images are added and result is obtained again, and so on.  
Observations. GNN is a recognition technique that provides higher recognition rates if the sample size is increased. The recognition rates, as shown in Figure 9, reach almost 90%, but the processing time is higher. The image transmission time between the apiary and the data-processing center is 1 min, which facilitates the system response time; but now, if we process 100 images per minute, the system response time for each image is not acceptable. In the case of the kNN technique, although the recognition rates are lower, the recognition speed is acceptable. If we evaluate the precision with the speed, we obtain better recognition responses with GNN, but at the cost of high processing times. This can be verified in the experiment Section 7.2.7 explained below.
# A 100-Gb/s PAM-4 DSP in 28-nm CMOS for Sredes Receiver
## 5 Conclusions  
A DSP for ADC-based SerDes receiver has been presented in this paper. It achieves an equalization ability of 33 dB at an operating speed of 100 Gb/s with a 1.2 mm\({}^{2}\) area and 7.22 pl/bit power. This design adopts the 28 nm process, it customizes a dedicated high-speed 4-to-1 MUX in the design of the DFE and adopts the speculative structure of the fixed-point operation strategy to support its high-speed operation. At the same time, an LMS algorithm based on random data selection and an average calculation based on the sigma-delta algorithm are proposed to improve the overall power efficiency of the system. Finally, by analyzing the equalization effect of different input data precisions, it is decided to determine the data precision output by ADC as 8 bits. Table 4 compares the key indexes of this design and other designs. It can be seen that for the 7 nm FinFET process, due to its advanced technology, its DSP speed will have advantages. However, if more advanced technology is adopted in this design, the speed can reach more than 100 Gb/s. As for the design of the same process [10], it has advantages in power consumption due to the fewer  
Figure 16: The layout of the whole system.  
Figure 17: The micrograph of the chip.  
taps applied, but the equalization ability is slightly insufficient. However, the equalization ability of this design can reach the maximum attenuation of 33 dB. In general, under the same process conditions, compared with other DSPS, the DSP designed in this paper has advantages in comprehensive area, power consumption and BERconsideration.  
Writing--original draft, W.L.; Writing--review and editing, M.L., X.Z., G.X., G.Y., Q.H. and Z.J. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Optoelectronic and Microelectronic Devices and Integration in the National Key R&D Program of China (Grant No. 2021 YFB2206602), and the National Natural Science Foundation of China (Grant No. 62074162).  
The authors declare no new data were created.  
The authors declare no conflict of interest.
# Non-Terrestrial Networks with UAVs: A Projection on Flying Ad-Hoc Networks
## Appendix B Position-Based Routing Protocols  
Below is the list of conventional position-based routing protocols for FANETs [19; 36; 39] with respect to their proposed time.  
2006 Ad Hoc Routing Protocol for Aeronautical Mobile Ad Hoc Networks (ARPAM) [203] 2008 Greedy-Random-Greedy (GRG) [204] 2009 Geographic Greedy Perimeter Stateless Routing (GPSR) [205] 2009 UAV Search Mission Protocol (USMP) [205] 2009 Greedy-Hull-Greedy (GHG) [206] 2010 Multipath Doppler Routing (MUDOR) [207] 2010 Greedy Distributed Spanning Tree Routing 3D (GDSTR-3D) [208] 2011 Reactive-Greedy-Reactive (RGR) [209] 2011 Greedy Geographic Forwarding (GGF) [210] 2011 Geographic Load-Share Routing (GLSR) [211] 2012 Geographic Position Mobility-Oriented Routing (GPMOR) [212] 2012 Mobility Prediction-Based Geographic Routing (MPGR)[213] 2014 Recovery Strategy for the Greedy Forwarding Failure (RSGFF) [214] 2014 Cross-Layer Link Quality and Geographical-Aware Beaconless [215] 2015 Connectivity-Based Traffic-Density Aware Routing Using UAVs for VANETs (CRUV) [118] 2016 UAV-Assisted VANET Routing Protocol (UVAR) [216] 2016 Position-Aware Secure and Efficient Routing Approach (PASER) [217] 2016 Secure UAV Ad Hoc Routing Protocol (SUAP) [218].
# A Filter Feature Selection Algorithm Based on Mutual Information for Intrusion Detection
## 4 Experiment and Results
### Experiment and Analysis
#### 4.3.1 Denial-of-Service Test Experiment  
This section presents the detection performance of the proposed feature selection model RPFMI and other models for DOS and normal samples in the KDD Cup 99 test set. In the training phase, the numbers of normal and DOS samples are 10,000 and 10,000, respectively, while in the test phase, the numbers of normal and DOS samples are 2000 and 2000, respectively.  
With the different feature selection methods and the SVM classifier, the selected features are summarized in Table 5. It is shown that the number of selected features using the MMIFS [32] method is smallest. The numbers of selected features in the MIFS-U (\(\beta=0.3\)) [21] and RPFMI methods are the same.  
Table 6 shows the confusion matrices regarding the normal and DOS data in different models. It is obvious that the sum of the FP and FN in the RPFMI + SVM model is the smallest. Comparisons between different models in terms of the ACC, DR, FPR and PR are performed. Compared with the other feature selection algorithm, the highest ACC (i.e., 99.772%) can be obtained with the proposed algorithm (Figure 1). Figure 2 shows that RPFMI + SVM model has the same DR as other feature selection methods. The FPR of RPFMI + SVM is 0.003%, which is only larger than that of the CIMI model [22], as shown in Figure 3. Although the PR of the RPFMI model ranks second (Figure 4), it is higher than the other feature selection methods. Therefore, the RPFMI algorithm is advantageous over the other feature selection methods in improving both the ACC and PR and reducing the FPR.  
Figure 3: False Positive Rate on the DOS test data.  
Figure 2: Detection Rate on the DOS test data.