esa-sceva/satcom-chunk-collection huggingface dataset:
# A New Face Recognition Method for Intelligent Security
## 5 Experimental Results and Analysis  
The experimental environment in this paper is shown in Figure 10 and is composed of a projector, camera, and computer. Both the camera and the projector are connected to the computer. The projector is a product of SHARP, with a resolution of \(1280\times 720\) and a brightness of 2000 lumens; it is lightweight and small-sized and thus suitable for our measurement environment. The camera is powered by Canon, with a resolution of \(1920\times 1080\). It is easy to place in a variety of measurement environments. The Central Processing Unit (CPU) model of the computer is Core i7-8550U, and the software used in this experiment is MATLAB R2018a.  
Figure 10: Schematic diagram of the system.  
When the layered matching is used for rank recognition alone, the recognition rate is 91.5%. Additionally, when the point-distance matching is used for rank recognition alone, the recognition rate is 76.5%. The layered elastic matching contains the local and global features of the curve, but since the method only uses the two-dimensional coordinate information of the sampling points and discards their one-dimensional information, the recognition rate is not ideal. The point-distance matching further uses the spatial distance of the sampling points relative to the nose-tip point. The recognition rate here may be lower, but it retains the spatial three-dimensional information well, which helps in the shortcomings of layered elastic matching. In this paper, we use a combination of point-distance matching and the layered elastic matching and obtain a higher recognition rate. The cumulative match characteristic (CMC) curves of the three experiments are shown in Figure 15.  
To compare the performance of the face recognition method based on grating projection, we compare this method with other methods. Table 2 lists the recognition rates and speeds for iterative closest point (ICP), subspace pursuit (SP) and local binary pattern (LBP). It can be  
Figure 14: 3D face reconstructed in various poses.  
Figure 13: Reconstructed 3D human face.  
Figure 15: CMC curves between the two matching methods.  
seen that the recognition rate of this method is higher, and the speed is faster, and it has better recognition performance.  
Table 3 shows the identification of the method in this paper under different lighting conditions. Among the 120 three-dimensional face images with different facial attitudes under natural light, 116 images are accurately identified with a recognition rate of 96.67%. The rate has been dropped by 0.82% compared to the recognition in the dark environment. The recognition rate in both natural light and dark environment is above 90%, which indicates that the 3D face recognition method based on grating projection in this study has little influence from natural light.  
In the experiment, we considered images in different attitudes. It can be seen from Figure 16 that the overall recognition rate of the proposed method is higher than that of the Gabor-based face recognition method [28] and that of the LBP algorithm [29].
## 4 Results
### GPM IMERG-E Product Evaluation
Additionally, in the arid western China with high altitudes, the performance of IMERG is less satisfactory than in south and east China, indicating a significant impact of topography [62]. Moreover, the IMERG presented better performance at low latitudes than at middle latitudes over mainland China [37]. Overall, the results of this statistical evaluation indicated that the IMERG-E achieved a better performance at the basin scale at hourly and daily time scales rather than at the pixel scale.
# A Simple Approach to Dynamic Optimisation of Flexible Optical Networks with Practical Application
## 6 Testing and Evaluation  
AlgR has polynomial complexity. In the initialisation part, switching off all relays has complexity \(O(n)\). DSPA (AlgH) is bounded by \(O(n^{3})\)[18] and can generally be implemented as \(O(n^{2})\)[24]. Similarly, AlgV is \(O(n^{3})\), at worst, but \(O(n^{2})\) in practice. Anyway, this part is only executed once. The more significant non-deterministic _while_ loop, in fact, iterates at most \(n\) times (a maximum of \(n\) nodes/relays to potentially switch on). Within it is a maximisation search of \(O(n^{2})\), giving an overall complexity of \(O(n^{3})\).  
To evaluate AlgR, we use two standard test examples [25] and compare with a 'brute force' approach [23]. Figures 3 and 4 shows the standard 'US NSF' and 'COST-266 European' optical networks with distances ignored (since we initially only consider node adjacency). For COST-266, there are \(2^{28}\) possible relay 'on/off' combinations, which is manageable for a simple exhaustive search algorithm with some patience. Table 1 compares AlgR with the known optimum generated by brute force. Each solution has been coded in C# running on a standard desktop PC. AlgR ran within a tenth of a second in each case; the exhaustive search, considerably longer.  
Table 1 shows the amount (number of powered relays) by which AlgR performs _worse_ than the 'perfect' exhaustive search algorithm for different \(m\) constraints (not all values of \(m\) are meaningful: when \(m\) reaches the length of the longest path between any nodes, for example, \(no\) relays are needed) It might be reasonable to expect the performance of the AlgR heuristic to improve with larger values of \(m\) as smaller numbers of relays suffice, but this pattern does not emerge clearly in these two test instances.  
Figure 3: The US NSF network (topologically, not geographically, accurate).  
For these small examples, individual analysis may be specious; however, Table 1 represents a mean increase of 14% in \(C(p)\) above the known optima over all sensible values of \(m\) in both cases. This is comparable with (in fact, somewhat better than) similar examples in which a crude/fast (polynomial) heuristic replaces a complex/slow metaheuristic [26] or (exponential) exhaustive search [13]. However, additional testing is still clearly needed.
## 2 Methodology
The main processing steps of the SARscape software to implement the SBAS technique are (Figure 1): (a) the generation of the connection graph, which includes defining the reliable InSAR pair connections distributed in different subsets according to the user-defined temporal and spatial baselines' maxima values; (b) the possibility of choosing an area of interest in the full SAR imagery; (c) the interferometric workflow, providing (i) the co-registration of the SAR stack list with respect to a \"super master\" SAR acquisition, (ii) the generation of interferograms, (iii) the phase noise filtering, and (iv) the phase unwrapping; (d) refinement and re-flattening to remove the residual phase ramps due to orbital uncertainties; (e) the first inversion step, providing (i) the velocity and residual topography estimation, (ii) the mean power image and precision component estimation, (iii) the second unwrapping phase; (f) the second inversion step, implementing (i) newly the refinement and re-flattening by means of a set of user-defined ground control points (GCPs), (ii) the displacement component estimation, (iii) the atmosphere pattern removal, and (iv) the coherence estimation; and (g) the geocoding of ground velocity and time series displacement. Further details of the SBAS technique are provided in [PERSON] et al. (2018). Figure 1: General flowchart of the adopted methodology.
## 5 Conclusions and Future Work  
In this paper, we proposed a new coverage flight path planning algorithm based on footprints' sweeps fitting and a sparse waypoint graph for unmanned aerial vehicles' (UAVs) navigation in three-dimensional (3D) urban environments with fixed convex obstacles. The main goals of the proposed algorithm are to reduce the computational time, number of turns, and path overlapping while finding a minimum length path that passes over all reachable points of an area or volume of interest for UAVs flying at low-altitudes in 3D urban environments. We devised a novel footprints' sweeps fitting method by considering the UAV sensor footprint as a coverage unit that guarantees complete coverage of the AOI with fewer and longer sensor footprints' sweeps. Furthermore, we generate a sparse waypoint graph by connecting footprints' sweeps endpoints by considering footprints' sweeps visiting sequence, obstacles' effect, and maneuverability constraints to obtain a complete coverage flight path. Simulation results have shown that the proposed algorithm can achieve better performance compared to the closely related global coverage flight path planning algorithms. It finds a feasible path from the urban environments inhabiting substantial number of obstacles without sacrificing the guarantees on computing time, number of turns, perfect coverage, path overlapping, and path length.  
Figure 10: Footprints sweeps’ comparisons with existing algorithms.  
Figure 9: Turns comparisons: proposed algorithm versus BCDH-CPP and CA-CPP algorithms.  
The proposed CPP algorithm is practical, effective, feasible, and it has important significance in UAV practical application in urban environments. In addition, the proposed algorithm is directly inspired by the real-life applications of UAVs, and it is applicable for various coverage related applications in 3D urban environments with fixed obstacles. In future work, we are planning to enhance the proposed CPP algorithm for spatially distributed regions coverage.  
Conceptualization, S.L.; Data curation, A.M.; Investigation, A.M. and S.L.; Methodology, A.M.; Project administration, S.L.; Software, A.M.; Supervision, S.L.; Validation, A.M. and S.L.; Visualization, A.M.; Writing--original draft, A.M.; Writing--review & editing, S.L.  
This work was supported by Institute for Information & Communications Technology Promotion (IITP) grant funded by the Korean Government (MSIT) (No. 2015-0-00893, Technology Development of DMM-based Obstacle Avoidance and Vehicle Control System for a Small UAV).  
The authors declare no conflict of interest regarding the publication of this manuscript.
# Landslide Susceptibility Prediction Based on a CNN-LSTM-SAM-Attention Hybrid Model
## 2 Research Methodology (CNN-LSTM-SAM-Attention Model)
### Process Framework
#### 2.4.4 Classification Module  
Figure 5 depicts the classification module, which maps risk probability, using a softmax classifier to provide probabilistic outputs for decision-making.  
Function: Risk probability mapping.  
Selection justification: A minimal classifier prevents overfitting given limited landslide event data. Softmax provides probabilistic outputs essential for risk-tiered decision making.
## 3 Results
### Stability Test of MNDVI Estimation for \(f\)PAR
#### 3.3.3 Accuracy Assessment Based on Different Irrigation Scheme  
The irrigation scheme has caused varying degrees of water stress on winter wheat, as the North China Plain has a wide spatial range and uneven distribution of precipitation. To validate the stability of estimating \(f\)PAR using MNDVI under different water stress-induced growth variations, and to ensure that the estimation accuracy of relevant models is not significantly reduced when applied on a large scale and influenced by crop growth status. The validation results are shown in Figure 5.  
Figure 4: Validation of estimation accuracy of index model based on MNDVI in different light conditions and phenological stages.  
Comparing the estimated results with the actual measured results, except for the IS-C irrigation scheme, which has a relatively lower coefficient of determination, the accuracy of the other different water stress levels can generally meet the requirements of practical application. The IS-C scheme has good growth performance in almost all plot areas, which makes it difficult to obtain a high R\({}^{2}\), but it has the lowest RMSE. This indicates that MNDVI can consistently and accurately estimate _f_PAR under different levels of water stress.
###### Abstract
One approach is the synergetic classification of optical and SAR images, considered to be an effective way to improve the accuracy of ground object recognition and classification. For example, [PERSON] et al. [43] used GF-3 full-polarization SAR data and Sentinel-2 multispectral data to carry out synergetic classification of YRD wetlands, and the results were significantly superior to that of the single datum. [PERSON] et al. [44] used the dual polarization Sentinel-1, Sentinel-2, and PlanetScope optical data to map crop types. [PERSON] et al. [45] identified an optimal combination of Sentinel-1, Sentinel-2, and Pleiades data using ground-reference data to accurately map wetland macrophytes in the Danube Delta, which suggests that diverse combinations of sensors are valuable for improving the overall classification accuracy of all of the communities of aquatic macrophytes, except _Myriophyllum spacatum_ L. Thus, the fusion of available SAR and optical remote sensing data provides an opportunity for operational wetland mapping to support decisions such as environmental management. However, a review of the existing literature yields few studies focused on the synergetic classification of coastal wetlands over the YRD, especially with GaoFen-3 (GF-3) full-polarization SAR and Zhuhai-1 OHS hyperspectral remote sensing in China. Therefore, in this study, we first introduce a combination method for coastal wetland classification over the YRD with both GF-3 and OHS images, and we then evaluate the classification accuracy. Furthermore, we investigate the influence of an optimal feature subset, seasonal change, and tidal height on the final classification.
# Predicting Player Churn of a Free-to-Play Mobile Video Game Using Supervised Machine Learning
## 4 Research Methodology
### Dataset collection  
The research was conducted on a dataset provided by an internationally established game developer and consists of over 4 years' worth of player event logs from January 2018 to January 2022. The game tracks in-game player activity and creates logs which are then processed and imported into a database, as shown in Figure 7. Each log in the database represents one player-caused event that has taken place in the game and was subsequently recorded by the games' system. Based on the event invoked by the player, logs have a different label. For example, when the player starts the game, a _GameStart_ event is logged, while quitting the game triggers a _GameEnd_ event. Another example is when a player finishes reading through a story, the _StoryCompleted_ event is triggered and logged. Each event consists of various common attributes such as the device ID (which is the primary key in the database), players' usernames (which were anonymized), the time at which the event was logged, and others. Events also contain event-specific attributes. For example, the _StoryCompleted_ event will have the story ID, or the _ChoiceMade_ event will contain the ID of the choice that was selected. As for the database context, each event is represented as a  
Figure 6: Research methodology flowchart.  
separate relation table with event attributes matching the table attributes in the database. The data from the database is then subsequently processed and finally structured into a dataset fit for the selected machine learning models.  
Because of the sheer number of concurrent users, and the speed at which the logs are created, we briefly outline certain errors observed in the collected data, as such input may be of interest to other researchers collecting similar large datasets with large amounts of parallel data being collected. While most observed errors are non-essential to the current research topic, there are some that severely hinder the research validity. For example, one user's session is defined as a set of events that occurred between a start and an end point (start and end events). However, in certain cases, we observed that either the start or the end event from a session was not present in the collected data. That leads to unknown start or end times for a session and an inaccuracy in the calculations relevant to user sessions (such as session duration, average session duration, etc.). Even if all the essential events for reconstructing a specific user session are present, certain events may be missing their session number and, as such, cannot be attributed to a certain session. Finally, sessions with an end-event report happening before the start event are also present in the data. Because of this, additional data cleaning is needed before the final datasets can be reliably constructed. The percentage of users removed by these methods varies on the current task, but generally, no fewer than 73.08% were removed from the initial dataset.
# A Deep-Learning-Based Secure Routing Protocol to Avoid Blackhole Attacks in VANETs +
## 6 Conclusions  
In this paper, we proposed a DLSR and DLC protocol in VANETs to establish a secure route against blackhole attacks. The proposed DLSR protocol utilized DL at each node to select secure routing or normal routing depending on network security conditions by training a DNN model to detect abnormal node behavior. This enabled nodes to select the most suitable next-hop based on their fitness function value, which is also achieved through DNN by optimizing the weights of each parameter such as remaining energy, distance, and hop count. Furthermore, to enhance route connectivity and reduce control overhead we proposed a DLC protocol that employed DNN to optimize the weights of each parameter such as cosine similarity, cosine distance, and remaining energy, which led to electing a cluster head candidate. Additionally, we compared the impact of the mobility model, i.e., the RPGM and RWP model on network metrics. The numerical results showed that the proposed DLSR+DLC protocol with the RPGM model can establish a secure route that improved node connectivity against blackhole attacks. Overall, the proposed DLSR+DLC protocol with the RPGM model efficiently established the cluster and route, which led to improvements in network metrics such as PDR, routing delay, control overhead, packet loss ratio, and number of packet losses.  
**Author Contributions:** A.A.: conceptualization, methodology, software, validation, formal analysis, investigation, visualization, writing--original draft. Y.P.: conceptualization, validation, writing--review and editing. R.H.Y.P.: conceptualization, validation, writing--review and editing. K.S.: conceptualization, methodology, validation, writing--review and editing. B.A.: conceptualization, methodology, validation, writing--review and editing. B.A.: conceptualization, methodology, validation, investigation, resources, visualization, supervision, project administration, funding acquisition, writing--review and editing. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (NRF-2022R1A2B5B01001190). [PERSON] is the corresponding author.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
###### Abstract  
In this study, we present a sensorless, robust, and physiological tracking control method to drive the operational speed of implantable rotary blood pumps (IRBPs) for patients with heart failure (HF). The method used sensorless measurements of the pump flow to track the desired reference flow (\(Q_{t}\)). A dynamical estimator model was used to estimate the average pump flow (\(Q_{est}\)) based on pulse-width modulation (PWM) signals. A proportional-integral (PI) controller integrated with a fuzzy logic control (FLC) system was developed to automatically adapt the pump flow. The \(Q_{t}\) was modeled as a constant and trigonometric function using an elastance function (\(E(t)\)) to achieve a variation in the metabolic demand. The proposed method was evaluated in silico using a lumped parameter model of the cardiovascular system (CVS) under rest and exercise scenarios. The findings demonstrated that the proposed control system efficiently updated the pump speed of the IRBP to avoid suction or overperfusion. In all scenarios, the numerical results for the left atrium pressure (\(P_{ls}\)), aortic pressure (\(P_{ab}\)), and left ventricle pressure (\(P_{lp}\)) were clinically accepted. The \(Q_{est}\) accurately tracked the \(Q_{t}\) within an error of 0.25 L/min.  
heart failure; proportional-integral; fuzzy logic control; estimator model; rotary blood pump
# Recent Advances in Photodetectors Based on Two-Dimensional Material/Si Heterojunctions
## 3 Photodetectors Based on 2D Material-Si Heterojunctions
### TMD/Si Heterojunctions  
Except graphene, TMDs have been extensively studied as 2D materials due to their superior electrical and optoelectronics properties [96; 97; 98]. These properties include layer-dependent band gaps and band structure, large exciton binding energy, spin-valley effect, strong light-matter interaction and high carrier mobility [99; 100; 101]. When combined with Si to form van der Waals heterojunctions, the heterojunction devices can fully utilize the advantages of both materials, making them favorable for photodetection. [PERSON] et al. reported the first few-layer MoS\({}_{2}\)/Si pn photodiode, where the MoS\({}_{2}\) film was synthesized by sulfurizing a Mo film and transferred onto the surface of a p-Si substrate to form a heterojunction [102]. The photodiode showed strong photoconductivity, which could be adjusted by varying the thickness of the MoS\({}_{2}\) layer. Subsequently, significant efforts have been made to explore TMD/Si heterojunction-based photodetectors. [PERSON] et al. prepared monolayer MoS\({}_{2}\) flakes by mechanical exfoliation and transferred them onto the surface of Si substrates with different types of conductivity to form MoS\({}_{2}\)/Si heterojunctions [62]. They used a conductive atomic force microscope to study the photoresponse of the heterojunction devices. Both the monolayer MoS\({}_{2}\)/n-Si heterojunction and the monolayer MoS\({}_{2}\)/p-Si heterojunction exhibited photodiode-like behavior, and the monolayer MoS\({}_{2}\)/n-Si heterojunction showed better performance with the highest photoresponsivity of 7.2 A/W. The higher photoresponsivity of the monolayer MoS\({}_{2}\)/n-Si heterojunction was attributed to the larger built-in electrical field revealed by a Kelvin force microscope. [PERSON] et al. studied the photoresponse mechanism of monolayer MoS\({}_{2}\)/p-Si using a photocurrent mapping technique and found that the photosensitive region under both reverse and forward bias is the junction region [103]. They also measured the spectral response and found that it was identical to the absorption spectrum of the monolayer MoS\({}_{2}\), indicating that the photogenerated carriers were mainly present in the monolayer MoS\({}_{2}\). Their results were different to the findings reported in the literature and may be attributed to the high doping level of the Si substrates used in their study. In our study, we synthesized few-layer WS\({}_{2}\) films by sulfurizing W film and transferred them onto the surface of p-Si substrates to form