esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Work  
### SLAM with Object-Level Landmarks  
Compared with a traditional visual-SLAM framework that provides maps containing points or line features, object SLAM integrates in-frame object detection into measurement, resulting in more meaningful mapping and more robust localization results [8]. In recent years, the object-detection model based on deep learning has shown potential in SLAM. [PERSON] et al. [16] proposed the WiDiCet-YOLO detection model, which can realize automatic high-performance object detection. [PERSON] et al. [17] proposed a precise single-stage detector (PSSD) by adding extra layers to single-stage object detectors (SSD). The multiple graph learning neural networks (MGLNN) proposed by [PERSON] et al. achievedmultiple graph-learning and multi-view semi-supervised classification [18]. In studies of object SLAM, various representations have been used to model the objects in the scene. The authors of [19] proposed an object-SLAM system using an RGB-D camera by matching CAD models of tables and chairs in the database. In [7], proposed by [PERSON] et al., every point in the dense map carries a semantic label given by the pixel segmentation to make the robot object-aware. [PERSON] et al. [10] proposed cube-SLAM that described objects with a cuboid bounding box, while ellipsoids were used in [11] to approximate the position and size of objects. The above studies show that the geometrical model used in object-SLAM is evolving to be more lightweight, computation-friendly and compatible for general objects.  
The low computational complexity is obtained by a reduction in fineness of the object model. To overcome the loss of model accuracy that limits the performance of dual quadric-based object SLAM, a variety of geometric properties have been implemented as prior constraints to improve the robustness and accuracy of the systems. The authors of [12] proposed that objects should stand on a plane instead of floating in the air. In [13], the authors introduced a texture plane to avoid the observability problem during common forward-translating camera movements, while [PERSON] et al. [20] proposed a different dual quadric initialization method to solve similar issues. Furthermore, attempts have also been made to extend the range of object representations, such as introducing super-ellipsoids to unify cuboids and ellipsoids [21] and using a pre-trained variational auto encoder as an efficient and optimizable object descriptor [22]. Meanwhile, the quadric-based method still combines well with these new methods and can act as an initial approximation, providing a coarse-to-fine estimation of objects [23].
## 5 Results and Discussion
### Scenario 2  
Table 2 summarizes the FS, MAE, and RMSE metrics of the two baseline models and the three proposed models supplemented with astronomical input variables. The analysis is performed for several meteorological stations to better understand the nuanced impacts on the accuracy of solar radiation forecasting.  
Figure 7: Comparative FS performance of three distinct models—ST_CNN_v1, ST_CNN_v2, and ST_Dilated_CNN—across various meteorological stations in Scenario 1.  
In contrast to Scenario 1, where the ST_Dilated_CNN model achieves the best results for all stations over the rest of the models, in Scenario 2, the greatest FS percentages have values between 27.39 and 37.21 (except for stations P03 and ZA02, where the ST_CNN_v2 model delivers a better FS with 27.47 and 37.72, respectively). The LR and RF models achieve higher RMSE values than the proposed models. Although the FS, MAE, and RMSE values obtained by the ST_CNN_v1 model demonstrate an imbalanced performance, the ST_CNN_v2 and ST_Dilated_CNN models stand out for delivering the best metrics. Nonetheless, the ST_Dilated_CNN model appears to be capable of capturing data variability.  
Each station presents unique conditions that test the limits of the models. For example, station LE05 shows the strengths of the ST_CNN_v2 model with the lowest MAE values, indicating its strong predictive accuracy. The two baseline models have a similar behavior, with a difference in some cases of one percentage point. Meanwhile, the ST_CNN_v1 model has a difference of six in terms of FS for the SO01 station, which shows that the shallow depth of the network prevents it from capturing patterns that are more complex.  
Figure 8 shows how the ST_CNN_v2 and the ST_Dilated_CNN models stand out with a better forecast skill than the rest of the models for all seasons. These models show a more stable behavior, unlike the baselines that seem to have behaviors linked to the data of each station. The RF model for stations BU04, P03, P07, SG01, and SO01 improves on the other baseline model, while the LR model improves on the other baseline model for station LE05. The ST_CNN_v1 model only improves the two baseline models for the ZA02 station. In the P03 and P07 stations, it outperforms LR, but not the RF model.  
In this scenario, it can be observed that the ST_CNN_v2 model improves the FS results of the ST_Dilated_CNN model for station ZA02, while the ST_Dilated_CNN model outperforms it for the rest of the stations. In Figure 8, it can also be appreciated that stations LE05 and ZA02 are the stations with the highest FS for all models, with a difference of up to 5 points with respect to the rest of the stations. Stations BU04, P03, P07, and SG01 have similar results, with the ST_CNN_v2 and the ST_Dilated_CNN models always exceeding the baseline. The atypical case is station SO01, with the lowest FS values for all models.
# Three-Dimensional Path-Following with Articulated 6 DoF Robot and ToF Sensors
## 3 Results
### Algorithm Implementation, Constraints, and Supporting Solutions
To calculate IK solution, the Matlab generalised IK object [40] is used, which internally uses the Broyden-Fletcher-Goldfarb-Shanno (BFGS [41]) gradient projection algorithm solver. As a starting point for the solver to find the solution for the next position, the previous robot position is used. There are also some additional solver constraints applied that limit the robot body elements to stay inside safety cell, force the solver to prefer the downward tool orientation, and limit maximum tool rotation around its axes to less than 90\({}^{\circ}\) to prevent tool reversal, which will cause a collision with the path. * Send new joint positions to the robot controller. * Execute robot move: Robot moves are executed with an arbitrary chosen constant speed. Read, in a loop, current robot position and wait until it reaches the assigned position. Once the algorithm performs the desired number of iterations, the finalising moves are executed, i.e., the detection tool is stored back in its holder, the robot moves to the safe position, and program execution logs are saved. During the development of the software for communication between the PC and the Nachi robot controller, a few minor software elements and information on Nachi MZ04 robot performance provided in [42] were employed. The full software developed for the purpose of this paper is available as the Supplementary Data provided as [S1]. This includes the main program for the robot controller, the User Task for the robot controller, the STM32 project for STM32G431 microcontroller, and the Matlab implementation of the path-following algorithm.
## 1 Introduction
### Related Work  
Many existing stress classification studies have used trivial feature extraction and selection methods. These studies use either raw data (data collected through the sensors) [25; 26] or common features of the collected data [27; 28; 29], such as rate of change, mean, standard deviation, variance, mean absolute deviation, and skewness. This set of features does not fully describe the characteristics of the dataset and is also unable to be generalised to another time-series dataset. This is because the underlying patterns and characteristics of a particular dataset are specific to itself and cannot be applied to another dataset. Furthermore, the abovementioned features are also affected by the conditions under which data are collected and the pre-processing steps performed on them.  
[PERSON] et al. [12] proposed tsfresh, a machine-learning time-series feature extraction library that has been used in several studies. The library uses a method called AutoTS (Automatic Time-Series Feature Extraction) and is based on some pre-defined feature estimation algorithms. It estimates the trends, seasonality, periodicity, and volatility of the time-series data and applies the feature selection method to select the most relevant features for further modelling or analysis. Several studies have used the tsfresh library for feature engineering. [PERSON] et al. [30] used the tsfresh feature extraction library to detect anomalous power consumption by users. They extracted 794 features that were used as input to the supervised binary classification (to detect abnormalities). However, the authors did not perform any feature selection, which makes their approach computationally expensive and not feasible to be implemented in real-time. [PERSON] et al. [31] proposed unsupervised anomaly detection using DBSCAN and feature engineering. The authors used the tsfresh library in their feature engineering process. They performed features selected based on Maximum-Relevance Minimum-redundancy and variance technique along with Maximal Information Coefficient. However, the proposed approach works best with historical data (as the calculation of relevance, redundancy, and information coefficients is performed on complete data) and cannot be applied to real-world or streaming data. In the field of healthcare, [PERSON] et al. [32] recommended a solution for the classification of flawed sensors using tsfresh feature extraction and selection. The algorithm automatically calculated and selected features using univariate hypothesis tests with a controlled false discovery rate [33]. The selected features were fed into a Long-Short-Term Memory (LSTM) model for classification. However, the extracted features were still over hundreds of features.  
Thus, further research is required to obtain a well-generalizable feature engineering algorithm that can calculate and provide well-distinguishable features from large time-series data for an accurate and efficient classification (monitoring) system.
###### Abstract  
The need for wireless communication appears everywhere, and with as few interruptions as possible. An extensive and in-depth study has been conducted on the limitations of wireless communication in pedestrian tunnels. Using an analytical mathematical ray tracing model, and performing tunnel measurements, with an autonomous measuring setup, the model was validated. An extraordinary wide band was used for the experiments, which also featured optical systems to ensure accurate results. Since the tunnel is narrow, the differences between the different paths are small, and in order to distinguish them a resolution of 0.2 ns was used. The RMS delay spread, \(\sigma_{\tau}\), decreases throughout the tunnel, and at no point exceeds 1 ns.  
experiment; PDP; delay spread; indoor propagation; ray tracing; tunnels; 5G +
Footnote †: journal: Physics Letters B
## 2 Materials and Methods
### Recordings and Measurements  
The football game was recorded on a football pitch with artificial turf using two Sony HDR CX240E cameras (Sony, Tokyo, Japan) on a tripod. The ball flight speed during kicking, measured with a Stalker STS 2 radar gun (Stalker Sport, Nashua, NH, USA), as well as the time of dribbling with a change in directions with one foot over 20 m (adjusted measurement according to [36]) measured with photocells (Beam Trainer BLE, Bicyclehero, Anyangsi, Republic of Korea), were measured on a football pitch with artificial turf. The height of the counter-movement jump (CMJ) and take-off power were measured with a force plate in a sports hall with an S2P portable dual-force plate (with ARS software--Analysis and Reporting Software; S2P Ltd., Ljubljana, Slovenia, [[http://www.s2p.si/software.html](http://www.s2p.si/software.html)]([http://www.s2p.si/software.html](http://www.s2p.si/software.html)), accessed date: 9 February 2020).  
Before the actual measurements, all test subjects completed a trial with all the motor ability tests included in the study without recording the results. All measurements and recordings of the subjects in the football game were carried out within four days. The sequence of measurements was as follows: dribbling the ball with change in direction, ball flight speed when kicking, CMJ on the force plate, and the recording of the football game. Before the measurements and the recording of the football match, all test subjects completed a standardised 15 min warm-up (running and stretching exercises). Before the measurements on the force plate in the sports hall, the test subjects also completed a 15 min warm-up programme (cycling on a stationary bike and stretching exercises).  
The reliability and validity of the measurement of the dribbling with changes in direction time was validated [36]. Stalker STS 2 is a reliable and valid device for measuring the flight speed of a football by placing a radar gun behind the goal [37]. Force plate measurements are generally considered valid and reliable. For the CMJ, good to excellent reliability of the measured variables (including jump height and jump force) is observed [38]. The kinetic variables of the CMJ measured with a force plate are considered reliable measures of propulsive force in young athletes [39].
## 3 Results  
To validate our urban-rural fringe identification results, we employed a comprehensive framework incorporating five complementary approaches. The validation included an analysis of nightlight intensity patterns, building characteristics (height, area, and perimeter), POI distributions across different functional categories, human activity dynamics derived from mobile phone data, and landscape indicators. These multiple perspectives collectively validated our identification method while providing complementary evidence of the transitional nature of urban-rural fringe areas. The following sections present detailed results from each validation approach.
## 2 Materials and Methods
### Procedures
#### 2.3.2 Jump Performance Assessment  
Drop Jumps (DJs)  
Each participant was asked to perform 3 DJs [26] (arms akimbo), from a 40 cm box onto a force plate (BP600900-2000, AMTI, Watertown, MA, USA), with maximum effort, i.e., keeping the ground contact time as short as possible [11]. The rest periods between jumps was 30 s, to avoid any fatigue [27]. The 40 cm box was placed on another force plate (BP600900-2000, AMTI, Watertown, MA, USA), which allowed us to monitor for possible vertical push-off from the box, which would increase the drop height. Jumps that did not appear to be maximal (i.e., compared with the familiarization session) and jumps with an identified push-off from the box had to be repeated. The DJ with the lowest ground contact time was used for the further analysis. Before moving to the CMJs, a 60 s break was given to the participants.  
Countermovement Jumps (CMJs)  
Each participant was asked to conduct three CMJs [26] on the same force plate as the DJ. The rest between the jumps was 30 s [27]. During the whole CMJ, the participant was asked to keep their hands placed on their hips [28]. The CMJ was performed by the participant lowering their center of mass by bending their knees to a self-selected grade.  
Immediately after reaching the lowest position, the participant was asked to jump vertically, as high as possible. The CMJ with the highest jump height was used for the further analysis.  
Jump Data Analysis  
For the analysis of the CMJs and DJs, only the vertical ground reaction force was used. Data were captured (1000 Hz) using Nexus software (Oxford Metrics, Yarnton, Oxfordshire, UK). The final analysis was performed using a self-written MATLAB script (MATLAB R2021a, MathWorks Inc., Natick, MA, USA). Raw force data were smoothed using a 10 ms moving average.  
For the DJs, the ground contact time represents the time the subject was in contact with the force plate, using a threshold of 10 N. Jump height was calculated using the flight time method (threshold landing 10 N), and finally the ratio between the DJ height and contact time represents the RSI.  
For the CMJs, the jump height was calculated using the impulse-momentum method. In addition, the modified RSI was calculated by forming the ratio between the CMJ height and the time the subject needed for the CMJ until take-off. The beginning of the CMJ and the start of the flight phase (take-off) were defined using a threshold of 10 N deviation from baseline. The center of mass (CoM) displacement until the deepest squat position was defined as the difference between upright standing (0 cm displacement) and the local minimum between the beginning of the CMJ and the take-off. The CoM displacement-time curve was calculated by double integration (MATLAB trapz function) of the acceleration-time curve.
_J. Sens. Actuator Netw._**2015**, \(4\), 378-409; doi:10.3390/jsan4040378  
_Journal of Sensor and Actuator Networks_  
**ISSN 2224-2708**  
www.mdpi.com/journal/jsan  
_Article_  
**The Efficacy of Epidemic Algorithms on Detecting Node Replicas in Wireless Sensor Networks \({}^{\dagger}\)**  
**[PERSON] \({}^{1,\ast}\), [PERSON] \({}^{2,\ddagger}\) and [PERSON] \({}^{3,\ddagger}\)**  
\({}^{1}\) Department of Computer Science, Sam Houston State University, Huntsville, TX 77341, USA;  
E-Mail: [EMAIL_ADDRESS]  
\({}^{2}\) Department of Computer Science, University of The Pacific, Stockton, CA 95211, USA;  
E-Mail: [EMAIL_ADDRESS]  
\({}^{3}\) Department of Computer Science, University of Houston, Houston, TX 77004, USA;  
E-Mail: [EMAIL_ADDRESS]  
\({}^{\dagger}\) This paper is an extended version of our paper published in \"[PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON] Epidemic Node Replica Detection in Sensor Networks. In Proceedings of the Third IEEE ASE International Conference on Cyber Security (CyberSecurity 2014), Stanford, CA, USA, 27-31 May 2014\".  
\({}^{\ddagger}\) These authors contributed equally to this work.  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +936-294-1591; Fax: +936-294-4312.  
Academic Editor: [PERSON]  
_Received: 15 October 2015 / Accepted: 9 December 2015 / Published: 14 December 2015_  
**Abstract:** A node replication attack against a wireless sensor network involves surreptitious efforts by an adversary to insert duplicate sensor nodes into the network while avoiding detection. Due to the lack of tamper-resistant hardware and the low cost of sensor nodes, launching replication attacks takes little effort to carry out. Naturally, detecting these replica nodes is a very important task and has been studied extensively. In this paper, we propose a novel distributed, randomized sensor duplicate detection algorithm called **Discard** to detect node replicas in group-deployed wireless sensor networks. Our protocol is an epidemic, self-organizing duplicate detection scheme, which exhibits emergent properties. Epidemic schemes have found diverse applications in distributed computing: load balancing, topology management, audio and video streaming, computing aggregate functions, failure detection,network and resource monitoring, to name a few. To the best of our knowledge, our algorithm is the first attempt at exploring the potential of this paradigm to detect replicas in a wireless sensor network. Through analysis and simulation, we show that our scheme achieves robust replica detection with substantially lower communication, computational and storage requirements than prior schemes in the literature.  
Keywords:replica detection; duplicate detection; wireless sensor networks; emergent algorithms; epidemic gossip
# A Sun-Tracking Algorithm for Satellite-Borne Spectrometers Based on the Orbital Motion Model
## 3 On-Axis Tracking Control Method based on the Orbital Motion Model
### Predictive Filtering Algorithm
The iterative process mainly includes two steps: prediction and correction. The covariance \(P(k+1\,|\,k)\) of system state \(X(k+1\,|\,k)\) can be predicted using the covariance \(P(k\,|\,k)\) of system state \(X(k\,|\,k)\), according to the state-space expression in Equation (11), as shown in Equation (13). \[\left\{ \begin{array}{c}X(k+1|k)=F(T,b_{1},b_{2},b_{3})X(k|k)\\ P(k+1|k)=F(T,b_{1},b_{2},b_{3})P(k|k)F^{T}(T,b_{1},b_{2},b_{3})+Q_{k}\\ \end{array} \right. \tag{13}\]  
According to the predicted covariance matrix and the actual measured noise, the Kalman gain \(K(k+1)\) can be calculated, and the predicted state and covariance are corrected with the Kalman gain. The state \(X(k+1)\) of the system at time \(k+1\) is the output and the covariance matrix \(P(k+1)\) at time \(k+1\) is calculated, as shown in Equation (14). \[\left\{ \begin{array}{c}K(k+1)=P(k+1|k)H^{T}\big{[}HP(k+1|k)H^{T}+R_{k}\big{]} ^{-1}\\ X(k+1|k+1)=X(k+1|k)+K(k+1)(Y(k+1)-HX(k+1|k))\\ P(k+1|k+1)=[I-K(k+1)H]P(k+1|k)\\ \end{array} \right. \tag{14}\]  
According to Equations (13) and (14), in addition to the target motion model, the reasonable estimation of the system noise and measurement noise, along with the design of the parameter matrices \(Q_{k}\) and \(R_{k}\), are the main factors that affect the performance of the Kalman filter [22]. For the spectrometer system analyzed in this paper, the measurement noise and sampling frequency of the encoder and tracking sensor are constant, which can be taken as the direct references for \(R_{k}\) selection. When the measurement noise increases, the Kalman gain becomes smaller and the reliability of the prediction filtering algorithm for the system measurement results decreases. The process noise mainly comes from the target model error caused by the platform attitude control error. The \(Q_{k}\) matrix can be designed according to the platform attitude stability and other parameters since the dependence of the predictive status on the target model can be regulated by adjusting the elements in the \(Q_{k}\) matrix. The elements in \(Q_{k}\) are supposed to be eclectically selected. If the elements are too large, the prediction cannot work well. If the elements are too small, the predictive status would depend excessively on the target model, with the result that the algorithm may be divergent when there is a relatively large model error. When the process noise is relatively large, the impact of the target model noise on the predicted value can be reduced by increasing the Kalman gain.
## 4 Simulation Results  
The proposed method is evaluated by Network Simulator 2 (NS2) [27]. Table 1 shows the simulation parameters. Simulation is conducted by using a frequency band of 2.4 GHz. In this simulation, the energy consumption parameters of CC2420 [28] are used; Tx energy is 31.25 mW, Rx energy is 35.28 mW, Idle energy is 0.712 mW, and Sleep energy is 0.144 mW. In addition, this paper uses IEEE 802.15.4 default MAC parameters [19].  
This study assumes that the packet transmissions are failed due to collisions caused by concurrent transmissions.  
This study does not assume transmission failures due to physical layer conditions.  
Figure 4: Buffer model in the proposed method.