esa-sceva/satcom-chunk-collection huggingface dataset:
# UAV Formation Shape Control via Decentralized Markov Decision Processes
## 6 Conclusions  
In this paper, we developed decentralized control method for UAVs in the context of formation control. Specifically, we extended a decision-theoretic formulation called _decentralized Markov decision process_ (Dec-MDP) to develop near real-time decentralized control methods to drive a UAV swarm from an initial formation to a desired formation in the shortest time possible. As decision-theoretic approaches suffer from the curse of dimensionality, for computational tractability, we extended an approximate dynamic programming method called _nominal belief-state optimization_ (NBO) to approximately solve the Dec-MDP. For benchmarking, we also implemented a centralized approach (Markov decision process-based) and compared the performance of our decentralized control methods against the centralized methods. In the context of the formation control problem, our results show that the average computation time for obtaining the optimal controls and the time taken for the swarm to arrive at the formation shape are significantly less with our Dec-MDP approach compared with that of the centralized methods. We also studied the impact of neighborhood threshold on multiple performance metrics in a UAV swarm.  
The formation control approach discussed in this thesis can be extended to 3D formation, and these formations can be used to sense the environments for 3D reconstruction of a scene. The vantage points of the UAVs in the swarm in 3D formation can be exploited for the efficient reconstruction of the scene in 3D, while extending tomography-type approaches. The decentralized control strategies presented in this thesis can be extended to control the motion of the UAVs in the swarm to maximize the efficiency of the above 3D scene reconstruction process. These methods have several applications, including the use of drones to map unexplored and unsafe regions (e.g., caves, underground mines, toxic environments).  
Figure 7: Average pairwise distance with respect to neighborhood threshold.  
**Author Contributions:** Conceptualization, M.A.A., H.D.M. and S.R.; Methodology, M.A.A. and S.R.; Validation, M.A.A. and S.R.; Writing and Editing, M.A.A. and S.R.; and Paper Review, H.D.M. and S.R. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported in part by Air Force Office of Scientific Research under Grant FA9550-19-1-0070.  
**Acknowledgments:** This work was supported in part by Air Force Office of Scientific Research under Grant FA9550-19-1-0070.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Real-Time Human Activity Recognition on Embedded Equipment: A Comparative Study
## 1 Introduction  
Based on sensor data, human activity recognition (HAR) focuses on detecting automatically what an occupant is doing. It is at the core of various applications such as smart homes [1, 2], assistance of elderly people [3, 4, 5, 6, 7], and industrial applications such as Human-Robot applications [8].  
In many real-time scenarios, human activities must be tracked online, in real-time. This requires that the HAR frameworks use only pre-segmented datasets. Segmentation consists of dynamically dividing the sensor stream over time into segments, forming a set of sensor events associated with one activity; which is complicated in real-time. However, human activities must be tracked online with streaming data to provide real-time services or scenarios. Related works in this research field are relatively few. For example, Ref. [9] investigated a dynamic segmentation technique of sensor events. The study used two scenarios, non-overlapping and overlapping time windows. Authors in [10] proposed another dynamic segmentation technique on streaming data based on semantic analysis and statistical learning. In order to achieve the goal of dynamic adaptation, this methodstudies the input event sequence and selects the more appropriate time-window size. The work presented in [11] proposed a method of dynamic segmentation on streaming data that integrated time correlation and event correlation and performs real-time HAR on streaming data. In [11], a real-time HAR algorithm in smart homes is developed. It is based on real-time dynamic segmentation that determines the beginning and the end of each activity and the classification using the concept of triggering sensor and the habitual duration of activities. The work reported in this paper is a continuity of the work presented in [11]. The main contributions of this work are:  
* Demonstrate that, through the dynamic sensor windowing and the identification of the triggering sensor, we can improve the classification of activities.
* Test the efficiency of the HAR algorithm in terms of processing time, memory usage, and current consumption in three hardware architectures and prove that the proposed framework can be implemented in a low-power, low-cost device while preserving good performance in terms of classification accuracy.  
This paper is organized as follows: The related works are summarized in Section 2. The proposed methodology is detailed in Section 3. The methodology of implementation on embedded equipment and the obtained results are discussed in Sections 4 and 5, respectively. Finally, concluding remarks are given in Section 6.
## 5 Simulations
### Simulation Results
#### 5.2.1 Case 1: LOS Scenario without Noise  
Simulation Case 1 is executed by placing the relay node initially at random locations all around the simulation grid area in each trial. The intention of Case 1 is to understand the distance range of the relay node from the global optimum position over which the proposed algorithm can prove effective. The settings for the parameters used in this case are as follows: propagation constant for relay-client link \(\eta_{c}=2.52\); propagation constant for server-relay link \(\eta_{s}=2.52\); reference path loss at 1-m distance \(PL_{d_{0}}=40\,\mathrm{dB}\); learning rate constant \(\kappa=0.01\); IIR filter coefficient \(\alpha=0.8\); sensors' separation distance \(\Delta x=\Delta y=0.2\) m; RSS threshold \(RSS_{\mathrm{threshold}}=-55\,\) dBm; threshold in difference between the RSS from the server and client nodes \(RSS_{\mathrm{difference}}=2\,\mathrm{dBm}\); gradient threshold \(g_{\mathrm{threshold}}=0.1\).  
Figure 8a depicts the path taken by the relay node from its initial random position (red dots) to the optimum position (green dots) (the client node and server node are shown as blue dots). It can be observed from Figure 8b that the farther the relay node is from the theoretical optimum, the faster the algorithm works, because of the learning rates selection to obtain the optimum convergence rate, as explained in the Section 4.5.1. For Case 1, the MAE value is 1.7 m (the initial relay node is randomly located within a range of 10 to 50 m from the theoretical optimum) and the success rate achieved is 83%.  
In reality, the relay node is always at a lower distance range (usually within 15 m) to the global optimum at any given point in time, because the solution using the relay node is implicitly dynamic in nature, and the relay node keeps optimizing its position to reflect the changes in the client or server node  
Figure 8: Case 1 simulation results for 100 trials with random relay node initial positions. (**a**) Relay node path from the initial position (red) to the optimum position (green). Blue dots indicate the server and client node locations. Theoretical optimum (*) is at the center (0,0); (**b**) Scatter plot of the distance of initial relay node position from the optimum against the speed of convergence.  
positions. Therefore, in further simulations, we fix the relay node initial position at (\(-\)15,15) to simulate the realistic conditions.
# Generalized Spoof Detection and Incremental Algorithm Recognition for Voice Spoofing
## 5 Conclusions  
This study designed a voice spoofing detection and spoofing algorithm recognition system. The system can detect the authenticity of a voice and accomplish the recognition of voice spoof algorithms under continuous data streams, which is not available in other voice spoofing detection systems. This helps prevent synthetic voice spoofing attacks, e.g., phone scams, and thus protects the privacy and security of individuals in society. The system is divided into voice spoof detection and spoof algorithm recognition. The voice spoof detection module learns a real voice distribution compactly using a one-class loss function. In contrast, the voice spoofing exists in the external feature space at a certain angle. This makes the system highly robust for voice spoof detection compared to binary classification methods. The voice spoof algorithm recognition module learns multiple prototypes in the feature space for each spoof algorithm type. It is then applied to classification in incremental scenarios. The experimental results show that our voice spoof detection module has a significantly improved performance compared to the baseline, traditional loss function, and adaptive domain methods. The voice spoof algorithm recognition module outperforms iCaRL. Since our system is oriented at practical applications, there may be a problem of excessive system resource usage in small devices. For this reason, we will further lighten the system according to different usage scenarios while ensuring its detection performance in the next work.  
Conceptualization, J.G.; Methodology, Y.Z.; Software, H.W.; Validation, J.G.; Formal analysis, J.G.; Investigation, J.G.; Resources, H.W.; Data curation, Y.Z.; Writing--original draft, Y.Z.; Writing--review & editing, H.W.; Visualization, Y.Z.; Supervision, [PERSON]; Project administration, [PERSON]; Funding acquisition, [PERSON] All authors have read and agreed to the published version of the manuscript.  
National Natural Science Foundation of China: 61806218.  
The used publicly archived dataset is available at [[https://datashare.ed.ac.uk/handle/10283/3336](https://datashare.ed.ac.uk/handle/10283/3336)]([https://datashare.ed.ac.uk/handle/10283/3336](https://datashare.ed.ac.uk/handle/10283/3336)).  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Attacks** & **Prototype + Weight** \\ \hline A1 & 87.30 \\ A2 & 99.73 \\ A3 & 99.89 \\ A4 & 49.56 \\ A5 & 92.65 \\ A6 & 81.36 \\ A7 & 62.63 \\ A8 & 95.97 \\ A9 & 98.09 \\ A10 & 55.20 \\ A11 & 89.60 \\ A12 & 88.18 \\ A13 & 92.71 \\ A14 & 98.44 \\ A15 & 92.14 \\ A16 & 98.09 \\ A17 & 99.93 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Recognition results after the last iteration of training.
# Experimental Solution for Estimating Pedestrian Locations from UAV Imagery
## 2 Algorithms of Pedestrian Detection from Images
### Histogram of Oriented Gradients  
A histogram of oriented gradients (HOG) is an older algorithm of object detection; however, it is still very popular for human recognition, see [5] or [6]. Unlike most modern algorithms, a HOG is based on feature detection rather than being trained as a neural network. This means that it follows certain computational steps and does not require training. The first step of the algorithm is to normalize the gamma and color of a given image frame. The step is not always required, but it is often used as a means of preparing the image for gradient calculation. The gradients are calculated using a Gauss filter, which simplifies the image at the cost of some data loss. To further simplify the process of detection, the image is divided into smaller image cells. A weighted voting system is then utilized to evaluate each pixel. The output values from voting for each pixel are assigned to each histogram cell.  
For many reasons, such as insufficient lighting, the contrasts of different cells may not be properly adjusted. Mitigating this problem involves using a sliding window of so-called \"blocks\". A block is a square-shaped area consisting of four or more histogram cells. The HOG algorithm slides select blocks over all of the cells and normalize the gradient values. The normalized blocks are labeled as histograms of oriented gradients and can be used for object classification. Support vector machines are employed to classify objects based on gathered histogram data.  
The HOG algorithm, along with a specific machine learning algorithm, has also been tested for pedestrian detection by [PERSON] and [PERSON] [7]. They report that better results are achieved by the convolutional neural network (CNN). We also considered the HOG algorithm but it will not be used in the proposed solution.
## 4 Experimental Results and Analysis
### SOH Estimation of Four Batteries Under Different Proportions of Training Data  
Four battery samples (CS2_35, CS2_36, CS2_37, and CS2_38) from the Maryland dataset are used to evaluate the proposed method. For each battery, the dataset is split into training set and testing set subsets with varying proportions: 50%, 60%, and 70% of the data is used for training, while the remainder is reserved for testing. Figure 9 presents the SOH estimation results along with the corresponding errors for each battery across different training set proportions. In the box plot of errors, the horizontal line represents the mean error. It can be observed that the errors are concentrated around 0%, following a normal distribution pattern. As depicted, the proposed method closely follows the actual SOH values and demonstrates high predictive accuracy for various training data splits. Additionally, the method successfully captures the capacity regeneration effect during battery aging. The R\({}^{2}\) values for all cases exceed 0.985. Table 2 summarizes the error values for SOH predictions across the four batteries using different training set proportions.  
Figure 9: SOH estimation results and errors for four batteries in different training data. SOH estimation results of **(a)** CS2_35; **(c)** CS2_36; **(e)** CS2_37; **(g)** CS2_38; SOH estimation result errors of **(b)** CS2_35; **(d)** CS2_36; **(f)** CS2_37; **(h)** CS2_38.
# Numerical Study on Aerodynamic Noise Reduction of Pantograph
## 5 Conclusions  
In this paper, we studied the feasibility of applying the wavy rods to reduce the aerodynamic noise of a Faiveley CX-PG pantograph based on a hybrid method. The influence of contact strip modification on aerodynamic noise was also studied. The effect of the above noise suppression techniques on the acoustic source intensity and the noise magnitude at different frequencies was explored in detail. The main conclusions drawn in this work are as follows:  
1. The panhead area is a primary acoustic source, of which the noise is stronger than that of the middle and bottom areas at an inflow velocity of 350 km/h. The sound energy of the panhead noise is concentrated in the high-frequency range (\(f>500\) Hz).
2. Applying the wavy rods to change the cross bar surface can effectively reduce the panhead noise, especially the sound energy around the peak frequency. However, changing the arm surface has little effect on weakening the noise radiated out from the middle area.
3. Modifying the shape of the contact strip to a hexagon can suppress the vortex shedding and decrease the surface pressure level. By combining the modification of the strip shape and the application of the wavy rods, the panhead noise intensity can be diminished by 5.52 dB.  
It can be concluded that applying the wavy rods and modifying the contact strip shape are effective ways to reduce the aerodynamic noise of the pantograph.  
Conceptualization, F.S. (Fangcheng Shi); Formal analysis, F.S. (Fangcheng Shi); Funding acquisition, T.W.; Investigation, F.S. ([PERSON]); Software, X.T.; Visualization, F.S. ([PERSON]); Writing--original draft, F.S. ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Key R & D Program of China Grant No. 2020 YFA0710903-C, Fundamental Research Funds for the Central Universities Grant No. 531118010787, the National Natural Science Foundation of China Grant Nos. 51905547 and 52078199, the China National Railway Group Limited Grant No. P2021J036, and the Young Elite Scientists Sponsorship Program by CAST Grant No. 2020 QNRC001.  
Not applicable.  
Figure 17: A-weighted sound pressure level spectra of noise radiated from the panhead of the wavy rod model and hexagonal strip model.  
**Acknowledgments:** The authors acknowledge the National Key R & D Program of China (Grant No. 2020 YFA0710903-C), Fundamental Research Funds for the Central Universities (Grant No. 531118010787), Key Laboratory of Hypersonic Aerodynamic Force and Heat Technology/AVIC Aerodynamics Research Institute Foundation, the National Natural Science Foundation of China (Grant Nos. 51905547 and 52078199), the China National Railway Group Limited (Grant No. P2021J036), and the Young Elite Scientists Sponsorship Program by CAST (Grant No. 2020 QNRC001). This work was carried out in part using computing resources at the High Performance Computing Center of Central South University.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Recent Advances and Insights into Bromelain Processing, Pharmacokinetics and Therapeutic Uses
## 6 Bromelain Studies
### Sport Injuries  
In recent years, bromelain's properties have been tested in the treatment of sport-related muscle injuries and in relieving occasional post-exercising muscle pain [93]. Muscle pain and apparent stiffness that is experienced after training (so-called \"delayed onset muscle soreness\" (DOMS)) is one of the most important limitations for athletes who perform repeated strenuous exercises [94]. In this regard, bromelain has been shown to attenuate the occurrence of contraction-induced skeletal muscle injury, which is considered to be one of the causes of DOMS [95]. However, in a recent study, forty subjects that were treated with either bromelain (300 mg t.i.d.), ibuprofen (400 mg t.i.d.), placebo (t.i.d.) and acontrol group demonstrated no significant improvement in their active range of motion and perceived pain after an eccentric exercise protocol (bromelain and ibuprofen seem to be ineffective and comparable to the placebo) [96]. In addition, the supplementation of bromelain with rutoside before and after a marathon race failed to attenuate post-race inflammation, neither did it decrease the incidence of upper-respiratory illness in runners [97].  
In a multicentre, double blind RCT involving 27 European hospitals, 721 patients with acute unilateral sprains of the lateral ankle joint were treated with a triple combination (bromelain, rutoside and trypsin), double combinations, single substances or a placebo. The results showed that bromelain had no remarkable impact on decreasing symptoms compared to either the double combinations and/or the other single substances. However, significant improvements in pain, swelling and range of motion were observed compared with the placebo [98]. In contrast to these results, patients with sport-related ankle injuries that were treated with bromelain in a double-blind placebo-controlled trial had faster recovery times than the control group [99]. Even the double-blind studies of [PERSON] [100], [PERSON] [101] and [PERSON] [102] demonstrated significant improvements in healing from sports injuries and faster haematoma resorption.  
Finally, bromelain treatment has been shown to improve pain, bruising, swelling, redness and tolerability at the site of injury when administrated to patients suffering from blunt trauma injuries to the musculoskeletal system [103]. Moreover, in subjects with haematoma, bromelain induced more rapid resorption with a significant decrease in haematoma volume compared to the placebo group [104].
# MapReduce Algorithm for Location Recommendation by Using Area Skyline Query
###### Abstract  
Location recommendation is essential for various map-based mobile applications. However, it is not easy to generate location-based recommendations with the changing contexts and locations of mobile users. Skyline operation is one of the most well-established techniques for location-based services. Our previous work proposed a new query method, called \"area skyline query\", to select areas in a map. However, it is not efficient for large-scale data. In this paper, we propose a parallel algorithm for processing the area skyline using MapReduce. Intensive experiments on both synthetic and real data confirm that our proposed algorithm is sufficiently efficient for large-scale data.  
area skyline; grid structure; MapReduce +
Footnote †: journal:  
which are the room price and the distance to the nearest bus/train station. \(h_{1}\) has the lowest room price but the longest distance from the bus/train station. In contrast, the most convenient \(h_{4}\) has the highest room price. Figure 1 shows that the hotel \(h_{2}\) and the hotel \(h_{5}\) are dominated by \(h_{3}\), while \(h_{1},h_{3}\) and \(h_{4}\) are not dominated each other. Therefore, the skyline objects are \(\{h_{1},h_{3},h_{4}\}\).  
Skyline queries have attracted much attention recently since the queries can be formulated as an intuitive process. There exist many skyline query algorithms, which are recorded in [2; 3; 4]. However, most of the existing skyline queries are zero-dimensional data. Since locations are typical two-dimensional data, the general skyline queries cannot solve the spatial relationships between objects. Based on such consideration, we consider using the spatial skyline queries to solve such problem.
# Harnessing FPGA Technology for Energy-Efficient Wearable Medical Devices
## 5 Power Optimization Strategies
### Energy-Efficient Task Scheduling  
Strategic scheduling of tasks and components to leverage specific hardware features can significantly reduce power consumption. In physiological monitoring, some tasks rely on the successful completion of other tasks. For example, detecting an anomaly in an ECG signal can trigger a subsequent arrhythmia classification step. Conversely, some tasks require only periodic activation, such as turning on the LED of a PPG sensor. Significant efforts have been made by different authors to optimize power consumption by scheduling tasks in a way which selectively activates only the necessary components at any given time. They employed various approaches, including event-driven activation, duty cycle optimization, and time sharing of hardware components using time division multiplexing (TDM). All of these approaches rely on the idea of minimizing unnecessary active components or their active time to reduce energy consumption.