esa-sceva/satcom-chunk-collection huggingface dataset:
# OrientedDiffDet: Diffusion Model for Oriented Object Detection in Aerial Images
## 2 Related Work  
### Object Detection  
Remote-sensing image object detection often relies on prior anchors to represent oriented boxes [13; 14; 15]. [PERSON] et al. (2018) [16] proposed the unconstrained detection algorithm ICN (Image Cascade Network), which utilizes adaptive weight sharing to combine the image pyramid with the feature pyramid, thereby enriching the semantic information. However, it does not perform well in detecting small objects. [PERSON] et al. (2021) [17] introduced the Gliding Vertex algorithm, which effectively slid the four vertices to represent boxes and significantly improved the precision of small object detection. [PERSON] et al. (2021) [18] presented SCRDet (Small, Cluttered and Rotated object Detection) to address the angular boundary problem. Although these algorithms achieved good performance as two-stage detectors, the inherent complexity of natural structures led to a heavy computational burden. [PERSON] et al. (2019) [19] proposed R3 Det (Refined Rotation RetinaNet), a single-stage object detector that fine-tuned the model to solve the mismatch problem between predicted boxes and ground truth boxes.) [PERSON] et al. (2021) [20] proposed RSDet (Rotation Sensitive Detector), which improved its detection accuracy to 72.2% by improving the angular regression sensitive error. Therefore, how to improve the representation of the rotated boxes to avoid introducing the special periodic variable of angle or improve the sensitivity of angle regression is one of the main research directions of the later rotated boxes detection algorithm. Although single-stage rotated box detection algorithms exhibit slightly lower accuracy, they offer higher detection efficiency and can be widely applied in edge devices. While these methods have effectively improved the efficiency of one-stage detection algorithms, they still fall short in terms of accuracy. In this work, we propose a novel detector using oriented box representation, named OrientedDiffDet. We directly generate rotated detection boxes from a set of random rotated boxes using the diffusion model, eliminating the need for obtaining candidate anchors from prior knowledge.
## 4 Maneuvering Policy Generation Algorithm Designing in 1-vs-1 WVR Air Combat Based on MADDPG
### Reward Shaping  
Deep reinforcement learning (DRL) has always suffered from restricted practical applications owing to its high requirements in terms of training time and computational power. For example, the training of AlphaZero training required a considerable amount of computational power, and the training period was three days. Therefore, shortening of the training time is a prominent issue in this field.  
Directly training an air combat maneuvering strategy in \(1\)-vs.-\(1\) WVR air combat is challenging because its reward function is very sparse. Therefore, in this study, a reward shaping method is proposed in order to accelerate the training process and, consequently, shorten the training time.  
Shaping reward is a reward for designers' rewards for nature's gains from the environment. Shaping reward provide a visual representation of the domain knowledge, especially for designers who may have already designed the environmental rewards [23]. In addition, it does not require the modification of the agent or environment, thereby resulting in a relatively simple implementation. However, if the design of the shaping reward is inappropriate, then it may slow down the convergence time of the training algorithm or even lead to failure of the policy to converge to Nash equilibrium.  
Potential-based reward shaping (PBRS) is proposed for the above problem [24]. The shaping reward \(\mathcal{R}_{rs}\) given in this approach is the difference of a potential function \(\Phi\) defined over a source state \(s\) and a destination state \(s^{\prime}\):  
\[\mathcal{R}_{rs}=\mathcal{R}+\gamma\Phi(s)-\Phi(s^{\prime}) \tag{17}\]  
where \(\mathcal{R}\) is the original reward from the environment and \(\gamma\) is the discount factor in the MADDPG algorithm.  
PBRS has been proven to not change the Nash equilibrium of multi-agent systems; however, it affects the exploration of shaping agents [25]. Therefore, it can change the convergence of the joint policy, because a modification in the exploration of one agent can entirely redirect the search of the joint policy space to different equilibrium points. Therefore, we need to combine prior knowledge of air combat in order to design a shaped reward that allows two UCAVs to converge to a better Nash equilibrium policy.
# Review and Simulation of Counter-UAS Sensors for Unmanned Traffic Management
## 2 Review of the State-of-the-Art Technology
### Detection by Data Fusion
The sensors used to detect and identify are radars, electro-optics, rangefinders, and direction finders. Its radars were described in Section 2.1), and it also identifies the potential threats via visual confirmation with a multispectral camera. Meanwhile, Dedrone provides a complete airspace security system [29]. Different types of sensors may be connected to the DedroneTracker software. The sensors provided by Dedrone are RF sensors, radars, and cameras. Depending on the application, Dedrone has different radars [48] with different performances in the Dedrone platform, such as the Counter-Drone Radar from Echodyne [15] and the Ranger R8 SS-3D from Flir [16], whose specifications were analyzed in Section 2.1. The last sensors integrated by Dedrone are PTZ cameras [49]. DedroneTracker system software has a video analysis capability, able to detect and locate UAVs in real time. Depending on the application, Dedrone can integrate one or more PTZ camera models with different performance levels. On the one hand, there is Axis Q6215-LE PTZ Network Camera from Axis Communications [42], On the other hand, there is Triton PT-Series HD Camera from FLIR [43]. They were described in Section 2.5. Another company to have its drone detection solutions analyzed in this paper is DroneShield [50]. It has a range of stand-alone portable products and rapidly deployable fixed site solutions. One of the most remarkable ones is the DroneSentry product [51], which is an autonomous fixed C-UAS system that integrates DroneShield's suite of sensors and countermeasures into a unified responsive platform. This product has as its primary detection method the RadarZero product [52], which is a radar, and/or the RfOne RF detector [53]. It has secondary detection methods such as the WideAlert acoustic sensors and DroneOpt camera sensor [54]. The main specifications of DroneSentry can be seen in Table 13.
## 6 Conclusions  
In this study, utilizing Abaqus/CAE and TOSCA, the topology optimization design of the U-frame structure for spaceborne optoelectronic tracking and sighting turntables is executed. This approach offers a valuable methodology and perspective for the lightweight design of the U-frame structure. The optimization process incorporates both static and dynamic properties of the structure, demonstrating the application of multi-constraint topology optimization in U-frame design.  
The performance analysis of the U-frame structure reveals that the optimized structure significantly outperforms the empirical design in terms of mechanical attributes. Specifically, the maximum stress value in the U-frame structure, as determined through topology optimization design, is reduced by 28.74 MPa, while the first natural frequency sees an enhancement of 11.48%. Moreover, when contrasted with the empirical design structure, the optimized version achieves a 4.3% reduction in weight, aligning with lightweight design objectives. Mechanical simulation analysis of the turntable system corroborates that the optimized turntable structure fulfills the reliability standards required for spacecraft structural design, thereby underscoring the efficacy of the topology optimization approach in U-frame structure design enhancements.  
The integration of Abaqus/CAE and TOSCA emerges as a potent strategy for optimizing the U-frame structure, attaining a balance between lightweight design and enhanced static and dynamic performances. Additionally, it offers insights into the optimal distribution of holes within the U-frame, guiding future modifications. This study enriches the domain of U-frame structure design for spaceborne optoelectronic tracking and sighting turntables, and suggests new avenues for applying topology optimization techniques to akin engineering challenges.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Nodes** & **Mode Diagram** & **Vibration Form** \\ \hline \hline mode5 & & \\ mode6 & & \\ \hline \hline \end{tabular} Note: The vibration shapes of the simulation mode and the test mode are basically the same.  
\end{table}
Table 6: _Cont._  
**Author Contributions:** Conceptualization, P.R.; methodology, J.W.; software, J.W.; validation, J.W., J.H. and B.P.; formal analysis, Y.Q.; investigation, J.W.; resources, Y.Q.; data curation, J.W.; writing--original draft preparation, J.W.; writing--review and editing, J.W., P.R., Y.X. and B.P.; visualization, J.H. and B.P.; supervision, P.R.; project administration, P.R.; funding acquisition, Y.X. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Member Funding Project of The Youth Innovation Promotion Association CAS, grant number 2022405.  
Not applicable.  
**Data Availability Statement**: The model and source data used in this article are unavailable due to privacy which protected by industry technology and can be shared as appropriate after stating the ownership institution and purpose via the author's email address.  
The authors declare no conflicts of interest.
## 2 Importance of Thermal Condition Monitoring
### Distribution Cable Lines  
Thermal properties and mechanical stress of cables influence the capacity threshold of the DERs network [35]. These are basic fundamental parameters for monitoring the cables in the DERs grid. The direct instant measurements of such temperatures are often not reflective. The use of thermal capacity models combined with the measurement of causes of internal heat sources and external conditions have not helped in the past. The temperature limitations often constrain the capacity of cables in DERs grid because of the uncertainty in power variations. The network suffers this predicament due to the variable generation forms and variability in the demand side load. The temperature and environmental conditions that vary according to the weather pattern also influence fluctuations, contributing to the conductors' thermal problems. Per the factors mentioned above, conductors' thermal performance for load capacity cannot be static. Thus, this calls for prompt monitoring of the conductors' state of thermal behavior [34; 36]. To improve on the conductor thermal impact, the cables for distributed networks should depend on real-time data measurements obtained from online monitoring. The real-time data will indicate the weather relations with ambient temperature and load conditions, particularly in the extreme wind velocity conditions [35]. The data then serves as a model for assumed homogeneous temperature distribution for the conductors. The model can be developed, as a state estimation algorithm for computing heat transfer of the line conductors.  
The algorithm can then be used, for the effect of different temperature conditions transfer associated with cable temperature behaviors [35; 37]. Again, mechanical stress or changes in physical conductor parameters affect the ohmic resistivity and thermal conductivity [20]. This effect is proved, by the heat balance of Equation (1):  
\[P_{J}+P_{S}=P_{C}+P_{R} \tag{1}\]  
where, \(P_{J}=\) heat increase by Joules, \(P_{S}=\) Solar heat (direct), \(P_{C}=\) heat loss from convection, and \(P_{R}=\) heat loss from radiation [20]. Therefore, the scenarios mentioned demands composite monitoring system to manage such impairments. For this reason, our proposed design concept seeks to resolve that. In the case of underground cables, the design concept resolves to the equivalent thermal circuit model (temperature = heat \(\times\) thermal resistivity) considering the effect of soil or Earth's concealment as a compensating factor [38]. The model of the underground cable scenario takes consideration of the heat transferred from the conductor through the thermal conduction and the thermal remanence in the insulation jacket and duct (if present). This includes the earth conditions as a result of soil temperature [38]. The model impacts on the sensor monitoring configuration for performance accuracy and threshold indication. The corresponding data and communication interface for the transmission and processing of signal applies to standard system protocols.
## 1 Introduction
* We evaluate the performance of the proposed hybrid combination of time series models, by determining five different accuracy mean errors: two relative mean errors, two absolute mean errors, and one correlation measure, such as root mean square error, root mean square percentage error, mean absolute error, and mean absolute percentage error; a statistical test, the Diebold-Mariano test; and a visual evaluation. * In this study, the results of the final best combination model are compared with the best model proposed in the literature as well as the considered baseline models and the comparative results are recorded. Based on these results, the proposed final best combination model from this work is highly accurate and efficient compared to the best models reported in the literature. * We present a methodological proposal applicable to the environmental management system in order to mitigate ozone pollution aimed at the stakeholders of the national air quality program. * Finally, the current work uses only the four district datasets in Lima, Peru. This can be extended to other districts of Lima, other regions of Peru, and even the world level to evaluate the performance of the proposed hybrid time series modeling and forecasting technique. This article describes the proposed hybrid time series forecasting methodology and explains its construction step by step in Section 2. The results of the case study for each district studied are in Section 3. Discussion about the best combination model of this study versus the standard time series models is detailed in Section 4, and the conclusions, along with limitations and future challenges, are presented in Section 5.
## Nomenclature  
AR: outlet-to-inlet area ratio  
AITD: aggressive Interturbine transition duct  
\(C_{d}\): resistance coefficient of the sweeping rod  
\(C_{\text{P0}}\ =\ \left(P_{0}-P_{\text{s,ref}}\right)/\left(P_{0,\text{ref}}-P_{ \text{s,ref}}\right)\): total pressure coefficient  
\(C_{\text{ps}}\ =\ \left(P_{\text{s}}-P_{\text{s,ref}}\right)/\left(P_{0,\text{ref}}-P_{ \text{s,ref}}\right)\): static pressure coefficient  
\(C_{\text{us}}\ =\ \left(v_{\text{x}}\omega_{\text{x}}+v_{\text{y}}\omega_{ \text{y}}+v_{\text{z}}\omega_{\text{z}}\right)\cdot\text{H}/\left(u_{\text{e} }\cdot\sqrt{v_{\text{x}}^{2}+v_{\text{y}}^{2}+v_{\text{z}}^{2}}\right)\): streamwise vorticity coefficient  
\(C_{x}\): axial chord  
\(d\): diameter of the sweeping rod  
\(d/s\): nondimensional diameter of the sweeping rod  
\(f\): reduced frequency  
\(f_{HPT}\): passing frequency of the HPT rotor  
FSTL: free stream turbulence intensity  
h: height relative to hub  
H: annulus height of the ITD inlet  
H\({}_{12}\): shape factor  
HP: high pressure  
HPT: high pressure turbine  
TID: Interturbine transition duct  
L: axial length of ITD  
L/H: nondimensional axial length of ITD  
LP: low pressure  
LPT: low pressure turbine  
LPT-GV: low pressure turbine-guide vane  
R\({}_{\text{hub}}\): radius of hub at the ITD's inlet  
R\({}_{\text{tip}}\): radius of casing at the ITD's inlet  
\(ref\): reference plane (Plane B0)  
\(s\): pitch of LPT-GV at midspan  
\(s_{\text{b}}\): the sweeping rod spacing at midspan  
T: the time of full cycle sweeping wake  
\(T_{u}\): turbulence intensity  
\(u_{\text{e}}\): mass-averaged exit velocity  
\(w\): \(w/s\): nondimensional width of wake  
\(Y\): total pressure loss  
\(y^{+}\): nondimensional thickness of the first layer grid on the wall  
\(Z\): cosecant of the relative flow angle at HPT outlet  
\(\theta\): mean rise angle/momentum thickness  
\(\beta_{\text{e}}\): the exit flow angle  
\(\xi\ =\ 2\theta/(s\text{-}cos(\beta_{\text{e}}))\): loss coefficient  
\(\Delta\)t: the minimum physical time step
# Detection of Wind Turbine Failures through Cross-Information between Neighbouring Turbines
###### Abstract
From experiments, it has been observed that the signal difference is stable over a range of values when the turbine is working properly, but, when it grows and remains stable over time, it is indicative of a malfunctioning system. To our knowledge, no work has examined the impact of estimating the values of turbine variables using information from neighbouring turbines geographically close to the one under analysis. The interest of this strategy is primarily to simplify the models and create a baseline to be able to detect deviations from it that indicate a malfunction of a turbine. This work investigates this strategy in detail and demonstrates experimentally that it provides simple but very useful information for monitoring wind turbine farms. The malfunction of a particular system or subsystem in a wind turbine is extremely rare, so very few cases are available. Another problem in this field is the inconsistency of labelling. Ultimately, the use of a normality model together with a regression technique instead of classification-based models seems to be a good strategy. The critical point is to train a normality model using data records belonging to different operating regimes, when the turbines are operating correctly. If accurate models are to be developed, it is necessary to have large data records to correctly represent the different conditions that may occur, for example in changing wind or weather conditions. The article is organised as follows. In Section 2, how the signals of the different turbines show very clear time-varying patterns is illustrated from examples with real data. This observation is evident from a temporally limited fragment of signals from the generator system. Such observations motivate the study. Taking advantage of the representation, problems associated with SCADA data processing such as the presence of outliers or missing data, are presented and analysed. In Section 3, Materials and Methods, the database used in this work, the data synchronisation method, the method used to filter outliers, and the way the reference signal is generated, are presented. Section 4 is devoted to the results of analysing each turbine with the proposed method. Finally, the article ends with a brief discussion in Section 5 and with the conclusions in Section 6. A list of acronyms used in this work is included before the References to facilitate the reading of the manuscript.
## 2 Materials and Methods
### Kinematic Data Analysis
#### 2.3.3 Temporal Kinematic Analysis  
The temporal evolution of the kinematic synergies (_Thumb_F(t)_, _MCPs_F(t)_, _PIPs_F(t)_, _Thumb_A(t)_, _Fingers_A(t)_) was studied to analyse the existence of temporal patterns and the factors on which they depend. A linear mixed-effects model (LME) was applied, followed by an ANOVA on the LME. This is a flexible approach that can account for complex correlation structures and random effects, and, as demonstrated by [PERSON] et al. [25], it is applicable for studying temporal evolution. Intrasubject repeatability was confirmed through visual inspection for all participants except for one patient in VBBT. This patient's data were excluded from the temporal analysis. Then, the five movement cycles were averaged by taking the mean at each normalised time point.  
Then, two analyses were performed. First, to examine differences between RBBT and VBBT within each health condition group, 10 LME models were applied: kinematic synergy (five parameters \(\times\) two health conditions) as the dependent variable, time (\(t\)) and test condition (BBBT/VBBT) as fixed factors, and including participant as random effect so that results reflect only the fixed effects after accounting for inter-individual variability. Second, to examine differences due to health conditions within each test condition group, 10 LME models were applied: kinematic synergy (five parameters \(\times\) two test conditions) as the dependent variable, time (\(t\)) and health condition (healthy/patient) as fixed factors, introducing participants as random effect. A post-hoc study was then conducted in each of the 20 analyses to examine differences between groups at each time step to determine whether differences associated with the factor emerge during specific movement phases (cube reach, cube transport, cube release, or hand return).
## 2 Background  
Government entities are responsible for protecting the information they handle, as it often includes confidential data about citizens and government employees [27]. Information management must ensure that data is collected, stored, processed, and shared securely and confidentially [3; 28], which is why organizations and government entities have adopted information security maturity models to ensure the adequate protection of data [24].  
A maturity model is a roadmap that guides the organization in implementing good practices, providing a starting point [6; 8], as well as providing systematic reference frameworks for measuring the performance of organizations in selected areas. They allow evaluating the state of development of a given organization or business process, identifying the areas in which the organization should focus to improve [29; 30].