esa-sceva/satcom-chunk-collection huggingface dataset:
# Enhancing Smart City Safety and Utilizing AI Expert Systems for Violence Detection
## 1 Introduction
Further, a long short-term memory (LSTM) network classifies the action attacks involving violent objects by using the recorded key points as the input. Subsequently, the LSTM network undertakes classification using these obtained body key points or features. Finally, the trained model is deployed into the edge device for testing the performance of the proposed model using unseen test data. In general, the main contributions of this proposed work are mentioned here as follows:  
1. Our proposed model can handle the small dataset problem using the stable diffusion image generative method, in which new image samples can be generated using previous images to increase the number of images for the object detection model to enhance the performance. Figure 1: The proposed model of attack alerting system—setup involving an image-to-image generation model, object detection model (YOLO v7), pose estimation model (MediaPipe), and action classification using the LSTM model. Our model architecture combines violent object detection (YOLO v7) and pose estimation models (MediaPipe) and an LSTM classifier to improve the performance of the violent attack detection system. * An edge computing device is implemented and the whole model is deployed in the computing device to test the model using violent-attack testing data in the city. * A commercial social media API is implemented here for sending the violent object and criminal clip as an alert to the registered number. Further, the rest of this paper is explained in the following sections: Section 2 describes the methodology part that has a brief explanation of the proposed model architecture, involving steps such as data collection, data labeling, the image-to-image data synthesis method, the object detection model, the pose estimation model, the LSTM classification method, the edge computing device, and the attack alerting method. Section 3 presents the results of the paper and Section 4 presents the conclusion part of the research work.
## 5 Conclusions  
In conclusion, we designed and fabricated an ultra-bend-resistant 4-core SXC in the O-band used for short-reach DSDM optical transmission. We experimentally clarified the characteristics of transmission loss, macro-bending and XT between adjacent cores due to the cabling, respectively. The ultralow macro-bending loss and small XT degradation induced by the bending indicate that the prepared 4-core SXC has an excellent bending resistance. Then, we demonstrated a low BER signal transmission by using a 100 GBASE-LR4 transceiver. By optimizing the fiber fabrication and cabling process, the transmission loss, XT and BER performance will be further improved. In the future, combined with an MCF connector, MCF transceiver and low-loss splicing of MCF [30; 31; 31; 30; 31; 32; 33], the prepared  
Figure 8: BER performance and eye diagram over the 1.2 km-long 4-core SXC of the (**a**) 1# core, (**b**) 2# core, (**c**) 3# core, and (**d**) 4# core.  
ultra-bend-resistant 4-core SXC would have potential applications to further release the space pressure, reduce power consumption and improve the transmission capacity on short-reach DSDM optical transmission. Furthermore, by using plasmonic material as fiber coating, the 4-core fiber may also be extended in the industry for applications in advanced sensors, surface-enhanced Raman spectroscopy and so on [32; 33].  
Conceptualization, Z.Z., J.Z., C.W. and L.X.; methodology, Z.Z. and L.X.; software (Comsol Multiphysics 6.1), C.W. and J.Z.; validation, Z.Z., J.Z. and L.X.; formal analysis, Y.Q. and Z.Z.; investigation, Z.Z., Y.Q. and X.J.; writing--original draft preparation, Z.Z.; writing--review and editing, L.X. and Y.S.; funding acquisition, L.X. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the Open Project of the Shanghai Institute of Optics and Fine Mechanics, Chinese Academy of Sciences (kijg-2021-2), in part by the Shanghai 2021 Science and Technology International Cooperation Project \"Program of Action for Science and Technology Innovation (21530710400), and in part by the Key Laboratory of Optoelectronic Devices and Systems of Ministry of Education and Guangdong Province.  
All the data presented in this study are available in this article.  
Authors [PERSON], [PERSON], [PERSON] were employed by the company Zhongtian Technology Group. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## 3 Methods
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Variable** & **Estimate** & **Standard Error** & **Z Value** & **Pr (\textgreater{}1 \text@underline{z}\) \(\downarrow\)**)** & **VIF** \\ \hline Intercept & \(-\)1.006 & 0.155 & \(-\)6.482 & \textless{}0.001 \({}^{*}\) & - \\ Gender—Male & \(-\)0.171 & 0.030 & \(-\)5.751 & \textless{}0.001 \({}^{*}\) & 1.059 \\ Driver Age & 0.015 & 0.002 & 7.560 & \textless{}0.001 \({}^{*}\) & 1.042 \\ Unbelted & 1.420 & 0.055 & 25.872 & \textless{}0.001 \({}^{*}\) & 1.010 \\ DUI & 0.202 & 0.061 & 3.300 & \textless{}0.001 \({}^{*}\) & 1.042 \\ Speeding & 0.021 & 0.036 & 0.573 & 0.566 & 1.100 \\ Vehicle type—Truck & \(-\)0.035 & 0.044 & \(-\)0.798 & 0.425 & 1.028 \\ Vehicle type—SUV & 0.162 & 0.036 & 4.538 & \textless{}0.001 \({}^{*}\) & 1.028 \\ Vehicle type—van & \(-\)0.085 & 0.062 & \(-\)1.356 & 0.175 & 1.028 \\ Vehicle age & 0.003 & 0.002 & 1.156 & 0.248 & 1.020 \\ Area—rural & 0.096 & 0.030 & 3.220 & 0.001 \({}^{*}\) & 1.063 \\ Roadway—Interstate & \(-\)0.094 & 0.055 & \(-\)1.711 & 0.087 & 1.018 \\ Roadway—Local Road & \(-\)0.094 & 0.032 & \(-\)2.956 & 0.003 \({}^{*}\) & 1.018 \\ Intersection & \(-\)0.140 & 0.037 & \(-\)3.781 & \textless{}0.001 \({}^{*}\) & 1.031 \\ Curved roads & 0.031 & 0.030 & 1.038 & 0.299 & 1.032 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The estimation results of the logistic model with all variables.
# Towards Agent Organizations Interoperability: A Model Driven Engineering Approach
## 2 Organizational Models for MAS
### Models Review
#### 2.2.6 Opera  
In OperA (Organizations per Agents) an agent organization is specified in terms of four structures: The social, the interaction, the normative and the communicative structures. In the _social structure_ are defined _roles_, _objectives_, _groups_ and _role dependencies_. Roles identify activities and services necessary to achieve social objectives. Groups provide means to collectively refer to a set of roles. Role dependencies describe how the roles are related in terms of objective realization. The _interaction structure_ defines how the main activity of an agent organization is supposed to happen. This definition is done in terms of _scenes_, _scene scripts_, _scene transitions_ and _role evolution relations_. Scenes are representations of specific interactions. A scene script is described by its players (roles or groups), scene norms (expected behavior of actors in a scene) and a desired interaction pattern. Scene transitions are used to coordinate scenes by defining the ordering and synchronization of the scenes. Role evolution relations specify the constraints that hold for the role-enacting agents as they move from scene to scene respecting the defined transitions. The normative structure gathers all the norms that are defined during the specification of roles, groups, and scene scripts. Norms are specified as formal logical expressions. Finally, the communicative structure describes the set of performatives and the domain concepts used in the interaction structure by the role enacting agents.  
OPERA is a model that addresses all the identified modeling dimensions. Nevertheless, we note that the functional and structural modeling of OPERA is less developed than the others models, the interactive modeling is comparable to what is found in ISLANDER, and the normative modeling is the most elaborated of all models analysed. In OPERA norms are expressed in a formalism called LCR (Logic for Contract Representation).
# Special Issue on \"Advances and Applications in Computational Fluid Dynamics\"
## 5 Nozzle and Jet Flow  
This section includes three papers on nozzle and jet flow, focusing on nozzle design and jet flow effects. [PERSON] et al. (Contribution 1) examine the influence of structural parameters on the jet flow field of self-propelled water jet nozzles for pipeline dredging. Using SolidWorks for modeling and Fluent with the k-\(\epsilon\) turbulence model for simulations, the study compares jet flow distribution, velocity, and displacement for nozzles of varying sizes and configurations. The results indicate that a 35\({}^{\circ}\) inclination angle minimizes jet velocity attenuation, and increasing the nozzle aperture from 2 mm to 3.5 mm reduces vortex formation and velocity decay, offering insights to optimize water jet nozzle design.  
Two papers focus on nozzle design advancements. [PERSON] et al. (Contribution 7) investigate the performance of a fluidic oscillator nozzle for cleaning sensors on autonomous vehicles, aiming to enhance cleaning efficiency. Simulations and experiments reveal that parameters like flow speed and feedback-channel inlet flow rate impact spray angle and frequency, with test results closely matching simulations, showing an error margin of under 6%. [PERSON] et al. (Contribution 17) improve the energy separation efficiency of a vortex tube by accurately modeling its geometry. Unlike previous simplified models, this approach shows that adjusting the inlet nozzle shape and increasing the inlet expansion ratio by 15% enhances energy separation while maintaining the nozzle's original length.
### Contribution and Structure of the Paper  
While the methods discussed in the previous section offer notable improvements in transient stability, frequency response, and adaptive control, they often come with trade-offs such as reliance on accurate models, computational overhead, or risk of over- or under-damping. Importantly, the methods known from the literature do not address the conflicting relation between the RoCoF and oscillation damping. In order to address this research gap and further improve controllability, this study proposes the development of a method for controllability enhancement and RoCoF minimization suitable for power systems with VSGs.  
The key characteristics of the proposed controllability-oriented method are as follows:  
* Integrated multi-criteria optimization approach that combines RoCoF minimization and maximization of controllability Gramian-based metrics to simultaneously optimize virtual inertia constants \(H\) and damping coefficients \(D\) using a genetic algorithm (GA);
* Integration of oscillatory modes' damping ratio (\(\zeta\)) constraints to ensure improved small-signal response;
* Analysis of optimization performance (objective function values and modal parameters) of five controllability metrics based on the Gramian matrix for both constrained and unconstrained \(\zeta\) cases: 1. Minimum eigenvalue (EV\({}_{\text{min}}\)), 2. Sum of two minimal eigenvalues (EV\({}_{\text{2 min}}\)), 3. Maximum eigenvalue (EV\({}_{\text{max}}\)), 4. Trace (TR), and 5. Determinant (DET).  
The IEEE 9-bus system is used as a test system with VSGs modeled as a second-order system, meaning a simplified structure is used for analysis. With multi-criteria optimization of inertia constants and damping coefficients, we can improve the controllability and reduce RoCoF while ensuring adequate oscillation damping. At the outset of this work, we carefully considered the appropriate level of model fidelity needed. While power systems are inherently nonlinear and best described using NDAEs, the primary focus of this paper is on small-signal stability analysis around nominal operating points. In this context, a linearized model is adopted, which provides sufficient accuracy for small deviations while significantly reducing model complexity and parameter requirements. This simplification is especially beneficial, as the model also serves as a foundation for further optimization-based studies.  
The rest of this paper is organized as follows. Section 2 briefly reviews stability and controllability analysis, and Section 3 explains the proposed method using a multi-objective optimization approach. Section 4 presents and Section 5 discusses the simulation results. Section 6 concludes the paper and outlines the directions for future research.
# ACE: A Routing Algorithm Based on Autonomous Channel Scheduling for Bluetooth Mesh Network
## 6 Evaluation
### Impact of Hop Numbers  
Next, we investigate the impact of the hop count of packets before reaching the intended destination. It is more realistic for a mesh network to have communication flows that span multiple hops to arrive at the intended destination in larger deployment scenarios. To this end, we increase the number of relay nodes in which the packets can be forwarded by one to four relays and evaluate the Bluetooth Mesh and ACE performance, respectively. In particular, we only enable nodes 1, 3, 7, 11, 13 on the central axis to work to simplify a multi-hop scenario. The results are plotted in Figure 6. The performance gap between Bluetooth Mesh and ACE becomes more significant with the increase of the number of hops, which can be inferred from the lower and more stable latency of ACE with the rise of the number of hops compared to Bluetooth Mesh. In particular, ACE can reduce the latency by 16% and the delay variance by 80% in contrast to Bluetooth Mesh. These performance improvements benefit from the connectionless single-channel routing scheme of ACE.  
Figure 5: Comparison for the latency using a varying number of neighbors. ACE has an inherent low and stable latency characteristic due to the single-channel scanning and broadcasting scheme.
# A Review of Failures and Malfunctions in Hydraulic Sandblasting Perforation Guns
## 5 Intelligent Diagnosis of Gun Failure Causes and Future Prospects
By continuously tracking cumulative fatigue damage and detecting peak vibration stress points, the system can issue early warnings or coordinate with automatic pressure regulation valves, flow controllers, nozzle angle adjustment mechanisms, and downhole deployment rates. Such interventions remove the root conditions for failure, mitigating or even preventing fatigue crack formation. Moreover, predictive maintenance strategies can be employed while the gun is still in normal operation--guided by intelligent diagnosis results to plan maintenance windows and spare parts inventories--thereby reducing economic losses and safety hazards resulting from unexpected failures [110; 111; 112]. Looking ahead, with the continued advancement of the Internet of Things (IoT), 5G/6G high-speed communications, cloud and edge computing, and high-performance computing chips, intelligent diagnosis of perforation gun failure causes will become more real-time and distributed [113; 114]. Multidimensional data can be synchronized to the cloud and preprocessed at the edge with ultra-fast communication networks, and big data platforms will provide abundant training and iterative opportunities for deep learning models [115]. Meanwhile, improvements in privacy-preserving computation and secure data transmission will ensure that sensitive industrial data remain protected. On this foundation, intelligent diagnostic systems can evolve continuously within multi-machine collaborative and cross-platform ecosystems--potentially within an \"industrial metaverse.\" In short, the future trajectory of intelligent diagnosis for gun failure points toward full-life-cycle intelligent supervision, interdisciplinary knowledge integration, distributed decision-making optimization, and dynamic predictive maintenance, collectively supporting the safe, efficient, and sustainable development of hydraulic sandblasting perforation technology.
# Optimized Design for NB-LDPC-Coded High-Order CPM: Power and Iterative Efficiencies
## 5 Positive Feedback Issue  
For the turbo-like receivers, an unwanted phenomenon called positive feedback, where BER performance worsens with increasing iterations, commonly occurs. A similar phenomenon also occurs in the investigated system, and this is a much more serious phenomenon, especially for low \(\text{E}_{\text{b}}/\text{N}_{0}\) due to the insufficient interleaving length and the high possibility of burst errors.  
A method of extrinsic information operation is introduced to improve iterative convergence, thereby effectively avoiding the undesired phenomenon. As shown in Figure 1, the extrinsic information from one SISO decoder must be operated and then transmitted into the other SISO decoder, that is:  
\[\mathbf{P}_{I}^{*}(\mathbf{u}^{i},O)\textbf{=}\exp\Bigl{(}\psi(\mathbf{P}_{I}( \mathbf{u}^{i},O)).\times\ln\mathbf{P}_{I}(\mathbf{u}^{i},O)\Bigr{)} \tag{22}\]  
\[\mathbf{P}_{O}^{*}(\mathbf{c}^{i},O)\textbf{=}\exp\Bigl{(}\psi(\mathbf{P}_{O}( \mathbf{c}^{i},O)).\times\ln\mathbf{P}_{O}(\mathbf{c}^{i},O)\Bigr{)} \tag{23}\]  
where \(\mathbf{P}_{O}(\mathbf{c},O)\) is the outer codeword probability of the output, and \(\psi(\cdot)\) is written as:  
\[\psi(\mathbf{\Omega})=a\times\exp(-b|\ln\Omega|) \tag{24}\]  
where \(a\) and \(b\) are satisfied with \(a\in[0.6,0.9]\) and \(b\in[0.001,0.01]\), respectively.  
Figures 8 and 9 show the comparisons between the iterative convergence of the NB-LDPC code for 8M2 RC using the proposed method and the original approach when \(\text{E}_{\text{b}}/\text{N}_{0}\) is 0.4 and 1.2 dB, where \(a\) and \(b\) are set to 0.9 and 0.01, respectively. As can be seen from Figure 8, the proposed method can be effectively curbing the positive feedback phenomenon at low \(\text{E}_{\text{b}}/\text{N}_{0}\), the value of BER can be reduced approximately by \(5\times 10^{-3}\) compared with the original method. Figure 9 reveals that the proposed method is capable of accelerating the iterative convergence, improving power efficiency and enhancing the transmission reliability (BER reduce approximately by \(2.5\times 10^{-3}\) compared with the original method) at medium-high \(\text{E}_{\text{b}}/\text{N}_{0}\).  
Figure 8: Iterative convergence of the NB-LDPC code for 8M2 RC with \(h=1/2\) using the proposed and original methods at \(\text{E}_{\text{b}}/\text{N}_{0}=0.4\) dB.
# Recovery and Characterization of Orbital Angular Momentum Modes with Ghost Diffraction Holography
## 3 Results and Discussion  
### Simulation Results  
The proof of principle of the technique is validated by simulation for various vortex modes with specific helicity and topological charge. We simulate the technique by generating a spatially incoherent light source by illuminating a random diffuser with a collimated coherent light source of wavelength 632.8 nm and a beam size of 6.5 mm. Vortex modes of zero radial index with topological charge having \(l=1\)_to m_ and \(l=0\) are utilized for generating vortex fields for the correlation function measurement. These fields are holographically added with a random reference field generated from an independent diffuser illuminated with an off-axis point source. The propagated field intensities at the detector plane (with a pixel size of 5.5 \(\upmu\)m) at 400 mm from the diffuser plane are considered for performing the cross-correlation of spatial intensity fluctuations. The simulation results corresponding to \(\varepsilon l=+1\) & \(-1\) and \(\varepsilon l=+4\) & \(-4\) are shown in Figure 4. Figure 4a-d  
Figure 3: Illustration of the recovery scheme of the technique: (**a**) step by step analysis procedure; (**b**) vortex phase mask (\(\varepsilon l=+3\)) encoded in the SLM; (**c**) zero order phase mask encoded in the SLM; (**d,e**) raw intensity images recorded by the camera corresponding to (**b,c**), respectively; (**f**) correlation hologram retrieved from cross-correlation of intensity images; (**g**) results of Fourier transform operation on correlation hologram; (**h**) amplitude distribution of OAM mode; and (**i**) phase distribution of OAM mode.  
Figure 2: (**a**) Schematic of the experimental system for the generation and recovery of OAM modes: He-Ne, helium-neon laser source; M, mirror; SF, spatial filter assembly; L, lens; HWP, half wave plate; A, aperture; GG, ground glass diffuser; BS, beam splitter; SLM, spatial light modulator; MO, microscope objective; and CCD, charge coupled device camera; (**b–e**) CCD recorded resultant speckle pattern for OAM modes with \(l=1,\ 4,-1\ \&\ -4\), respectively.  
represents the correlation hologram retrieved from the cross-correlation of intensity fluctuations as expressed in Equation (4). The correlation hologram preserves the signature of the vortex mode in the intensity cross-correlation function, which can be clearly seen as the generation of the fork pattern corresponding to different topological values in a way analogous to the fork hologram in conventional off-axis holography. The amplitude and phase distributions of the OAM modes are retrieved from the correlation hologram by utilizing the Fourier domain filtering approach [38], and the corresponding results are shown in Figure 4e-h and Figure 4i-l, respectively.
# Design of a Modified Compact Coupler with Unwanted Harmonics Suppression for L-Band Applications
## 3 Design of The Proposed BLC
#### Results of The Proposed BLC  
The measured and simulated scattering parameters of the proposed BLC are depicted in Figures 14 and 15. The harmonics are attenuated in this design example with the respectively measured suppression levels of 15 dB, 21 dB, 28 dB, 35 dB, 32 dB, 25 dB, and 17 dB for the \(S_{21}\) parameter, while the corresponding attenuations are 26 dB, 43 dB, 44 dB, 35 dB, 50 dB, 55 dB, and 39 dB for the \(S_{31}\) parameter. Additionally, the obtained measured isolation and return loss are 28 dB and 29 dB, respectively.  
Figure 12: Structure of the proposed BLC.  
Figure 13: Structure of the proposed and the conventional BLCs.  
The EM simulation in Advance Design System (ADS) software with adaptive step used is performed for simulation and the Hewlett Packard 8720B network analyzer with a linear measurement process with 22 MHz steps is used for measurement. The measurement and simulations results have good agreement, and a slight difference between these curves is normal.  
Additionally, to show the performance of the designed BLC near the operating frequency, the in-band frequency response is illustrated in Figure 16. As seen, the fractional bandwidth (FBW) of the designed coupler is from 0.905 GHz up to 1.105 GHz, which show a 200 MHz operating bandwidth or FBW of 20%. The magnitudes of \(S_{21}\) and \(S_{31}\) in the FBW are located in the upper \(-4\) dB line. The results show that the measured insertion loss of better than 0.2 dB is achieved at operating frequency.  
Figure 14: Measured and simulated \(S_{21}\) and \(S_{31}\) parameters of the proposed BLC.  
Figure 15: Measured and simulated \(S_{11}\) and \(S_{41}\) parameters of the proposed BLC.  
The result of the in-band phase difference is depicted in Figure 17. The measured phase difference of the presented BLC is less than 90 \(\pm\) 0.5\({}^{\circ}\) at the main frequency. Moreover, the phase difference is flat, and its value is near 90\({}^{\circ}\) at the operating bandwidth.  
The photos of the fabricated BLC under test are depicted in Figure 18. The Hewlett Packard 8720 B network analyzer is used to measure the fabricated device, the applied network analyzer has a linear measurement process with 22 MHz step.  
Figure 16: In-band simulated and measured S-parameters of the proposed BLC.  
Figure 17: Measured and simulated phase difference in the proposed BLC.  
Table 2 shows the performance comparison between the proposed microstrip coupler and the recent ones. As can be seen from the table, the proposed BLC has the best performance in terms of insertion loss, harmonic suppression and size reduction between the cited research.