esa-sceva/satcom-chunk-collection huggingface dataset:
# Performance Analysis of On-Demand Scheduling with and without Network Coding in Wireless Broadcast
## 6 Performance Evaluation
### Simulation Results in Real-Time Models
#### 6.1.2 Impact of Deadline Range  
Recalling that the relative deadline of requests is uniformly selected from the range \(\mu^{-}\sim\mu^{+}\). A larger value of the relative deadline gives a looser request deadline. Consequently, the DMRs of all the algorithms (Figure 3a) drop with an increase mean value of \(\mu^{-}\sim\mu^{+}\). Evidently, SIN_N outperforms others.  
In the coding versions, when the deadline becomes loose, the system has more opportunities to encode and disseminate data items to serve clients. As shown in Figure 3a,b, for the coding versions, the DMRs drop and the ABPs increase slightly. However, a loose request deadline does not have much impact on the AELs (\(<\)0.1) of EDF_N and SIN_N (Figure 3c). In the non-coding versions, when deadline range increases, the ABP of EDF does not change, whereas SIN shows a downward trend. This is because EDF only considers the request urgency, but SIN considers both the request urgency and the item popularity. As data items with less popularity may have better chance to be disseminated when the request deadline is getting looser, the ABP of SIN drops.
# On the Use of Bright Scatterers for Monitoring Doppler, Dual-Polarization Weather Radars
###### Abstract  
\"Bright\" scatterers are useful for monitoring modern weather radars. In order to be \"bright\", a point target with deterministic backscattering properties should be present at a near range and be hit by the antenna beam axis. In this note, a statistical characterization of the echoes from a metallic tower located on Cimetta, at an 18 km range and at the same altitude as the Monte Lema radar, is presented. The analysis is based on five clear sky days (1440 samples with a spatial resolution of \(1^{\circ}\times 1^{\circ}\times 83.33\) m). The spectral and polarimetric signatures are striking: The spectrum width is perfectly stable; the mean radial velocity is very stable; the radar reflectivity is also quite stable, with the vertical (V) polarization being more variable than the horizontal (H) one. As far as the polarimetric information is concerned, the daily average of the differential reflectivity is approximately 1 \(\pm\) 0.9 dB. The copolar correlation coefficient between H and V is remarkably large (0.9962, on average) and stable. It is believed that these unique and stable ground clutter signals could be used to monitor operational dual-polarization weather radars.  
monitoring; quality checks; relative calibration; dual-polarization weather radar 2018 10.1007; doi:10.3390/rs10071007
## 5 Case Study
### Results and Discussion
#### 5.3.3 Variation of Transmission Efficiency with Input Power  
Various power inputs were analyzed under the same input speed, and the transmission efficiency of the herringbone gear planetary transmission system was calculated. As shown in Figure 18, power loss due to friction on the tooth surface increases rapidly, whereas the power loss of the bearing increases, and the power loss at the bearings 2 and 4 is higher than the power loss at locations 2 and 3. Frictional power loss is greatly affected by the friction factor and load on the tooth surface.  
Figure 17: Variation of efficiency loss with input speed. (**a**) Gear meshing efficiency loss; (**b**) windage efficiency loss.  
Figure 18: Variation of power loss and transmission efficiency with input power. (**a**) Gear tooth frictional power loss; (**b**) bearing power loss; (**c**) total power loss; (**d**) total transmission efficiency.  
As gears rotate lubricant is flung off the gear teeth in small oil droplets owing to the centrifugal force acting on the lubricant. These lubricant droplets create a fine mist of oil that is suspended inside the gear housing. The effect of this oil mist is an increase in 'friction resistance' on the gears, and hence an increase in the power consumption. With the input torque increases, the expulsion of the oily atmosphere from the tooth spaces as the gear teeth come into engagement creates turbulence within the gearbox and increases the power consumption. As the power increases, the torque also increases, resulting in larger loads on the tooth surface and an increase in gear tooth frictional power loss. Moreover, the transmission efficiency of the herringbone planetary transmission system slowly increases as the input power increases.
# Systematic Literature Review of System Models for Technical System Development
## 4 Discussion
### Validation of Hypotheses  
Considering these discussion points, the hypotheses defined in the beginning shall be summarized and validated.  
**Hypothesis 1**.: _There is yet no converged overall definition of the term'system model': As most publications used different definitions for a'system model', this hypothesis was confirmed. The definition presented by [PERSON] [3] in Section 1 was the only full definition of a system model, even though it has not been referenced in any publication._  
**Hypothesis 2**.: _A'system model' can be created in different ways and is not limited to the application of Systems Modeling Language (SysML): This hypothesis was confirmed. System models are often created with and thus connected to graphical modeling languages like SysML, but are not limited to them. Mathematical modeling and direct linking of different models are also valid forms of system modeling._  
**Hypothesis 3**.: _The usage of a'system model' is not limited to the domain of System Engineers: This hypothesis was true considering all kinds of system models defined in the previous subsections. As one kind of system model may be domain-specific, different other domains can use them. With interconnection models and data models as system models domain-specific engineers can use them as well in their common tools, even though it is in an indirect form. Thus, system models can benefit all domains that are part of the system development._  
The three confirmed hypotheses support and enrich [PERSON]'s [3] definition.  
**Definition 1**.: _A system model is a (usually virtual) representation of the target system or one or more of its subsystems. It can be in the form of_  
1. _a domain specific part of the (sub)system (e.g., a domain-specific simulation model of a subsystem),_
2. _a domain-independent structure of the (sub)system (e.g., system architecture) or_
3. _a model linking the various (sub)system artifacts._  
One key aspect of this definition is that in contrast to the definition of [PERSON] [3], it specifically includes subsystems. In the previous definition, overall system interconnections were already addressed, but did not focus on the lower levels which have an important role. Additionally, domain-specific parts of systems and subsystems as well as models for linking artifacts, are included in this definition. The categorization within the definition allows us to classify upcoming research to one of these categories and thus allow an even better alignment of research conducted in that field.
# Unravelling the Molecular Regulation Mechanisms of Slow Ripening Trait in _Prunus persica_
## 2 Results
### Hormonal Differences between Normal and Slow Ripening Phenotypes
#### 2.3.1 Ethylene Production  
It is well known that SR fruits do not produce ethylene, unlike NR fruits. Methionine is the starting substrate to produce ethylene through the activity of 3 enzymes, an S-adenosylmethionine (SAM) synthase that produce SAM, a 1-aminocyclopropane-1-carboxylic acid (ACC) synthase (ACS) that produce ACC, and finally, an ACC oxidase (ACO) that produce ethylene (Figure 3, blue section). This information was analyzed with the transcriptomic data. Thus, in the expression profiles of three SAM synthases, one _ACS_ and one _ACO_, the most important differences were observed in the ACS and ACO expression levels. In NR siblings, _ACS_ showed a unique peak of expression during ripening (T7), and no expression of this gene was observed in SR individuals. On the other hand, unlike the expression pattern of _ACS_, two peaks were observed for normal _ACO_ expression, one in T2 without differences between NR and SR individuals, and the other at ripening (T7). _ACO_ was absent in SR siblings with a differential expression of this gene in T7 with a fold change of 3.2 (Figure 3). In summary, differences in _ACS_ and _ACO_ expression patterns were observed only in the last developmental stage (T7), probably associated indirectly with the absence of _NAC072_ and supporting the decreased ethylene production.
## 2 Background
### Defenses against Adversarial Attacks
#### 2.2.3 Adversarial Training Defense  
Adversarial training (AT) is a defense mechanism designed to improve the model's resilience to these adversarial attacks [2]. It incorporates adversarial examples (generated from any of the aforementioned attacks) into the training process. The intention is to enable the model to learn from these malicious examples and improve its robustness, equipping it to better classify similar adversarial examples in the future.  
However, despite its effectiveness, AT has limitations. One primary challenge is that the process is computationally expensive. Moreover, it has been observed that using all adversarial examples for training may not necessarily improve model robustness, as some adversarial examples may only introduce noise into the learning process [17]. This is where our research comes in: we propose a method that judiciously selects adversarial examples for training, with the aim of improving model robustness.
# A Diffusion Modeling-Based System for Teaching Dance to Digital Human
## 4 Implementation of Digital Human Dance Movement Teaching System  
### System Functional Modules  
As illustrated in Figure 10, the realization of this system depends on three primary functional modules: the music-driven dance animation technology and video display module, the music-driven dance animation generation module, and the digital human dance rendering module. Each module performs its specific function, and together, the three modules support each other to create a digital human dance movement teaching system based on the diffusion model.  
The music-driven dance animation technology and video display module are implemented using Java Web technologies. This module provides a front-end interface for the playback of dance animation videos and a back-end portal for system management and control. Users can browse and view the generated dance animations and learn through the detailed decomposition of digital human movements. The module functions as a classic web service, utilizing Thymeleaf (version 3.0.15.RELEASE) as the template engine on the front end and employing Spring Boot (version 2.7.5), MyBatis (version 3.5.11), and MySQL (version 8.0.32) for back-end content management. Additionally, it leverages AliCloud OSS (version 3.10.2) for object storage to handle data storage needs, thereby fulfilling the requirements for displaying digital human dance movements.  
Figure 9: SMPL model diagram.  
The music-driven dance animation generation module is developed using the Vue.js framework for the front-end interface, facilitating user interaction. In this module, users can upload music files and preview the generated dance animation effects after selecting a dance type. To enable the rendering of FBX models, the system incorporates the Three.js framework. The design of this module emphasizes user experience, featuring a user-friendly and intuitive interface, and supports the import of various music formats.  
The digital human dance movement rendering module incorporates an artificial intelligence algorithm based on a diffusion model [20]. In this system, the diffusion model is trained to learn the mapping between music and dance movements. When a user uploads a music file, the module analyzes the music data and automatically generates the corresponding dance movement sequence using the trained model. A well-designed loss function and various regularization strategies are employed during model training to enhance its generalization capabilities. After generating the dance movements, the module maps the movement data to a 3D human model. PyTorch3D is used to load the classic SMPL human model, which is driven in real time by the generated action data. Ultimately, as illustrated in Figure 11, the digital human model is output in motion capture data format for visual rendering and playback in the front-end module.  
Figure 11: Digital human model.  
Figure 10: Functional Module Diagram.
# Multifunction RF Systems for Naval Platforms
## 4 MFRF System Prototype Development Programs
### M-Aesa  
#### 4.3.1 Overview  
The M-AESA program was a joint Sweden-Italy initiative aimed at developing new technology and system concepts for a next generation AESA-based MFRF system that integrated radar, EA, ES and communication functions [52]. The ultimate goal was to be able to potentially insert this technology into future Swedish and Italian ground, air and naval platforms, utilizing common RF hardware modules. The industrial consortium of Saab Microwave Systems AB (Stockholm, Sweden), Selex Sistemi Integrati (Rome, Italy) and Elettronica Group (Rome, Italy) was awarded the contract in 2005 to conduct this program.  
There were three program phases:  
* _Phase 1_ (2005-2006): Technology concept/application formulation-analysis of existing system and related technology base to outline potential future system applications. _Deliverables_: (1) Statement of Work (SOW) and Work Breakdown Structure (WBS) for Phase 2, and (2) schedule and cost estimates for Phases 2 and 3.
* _Phase 2_ (2006-2010): Concept refinement-development of RF building blocks, selection of architecture for the M-AESA system. _Deliverables_: M-AESA system prototype at TRL 4.
* _Phase 3_ (2011-2014): Technology development. _Deliverables_: M-AESA system prototype at TRL 6.  
Work completed in Phases 1 and 2 was reported in the open literature and is summarized in the next section.
###### Abstract  
Underwater visible light communication (UVLC) based on LEDs has become a competitive candidate, which is able to provide high data rates, low latency and low cost for next-generation wireless communication technologies. However, it is still challenging to achieve high-speed communication because of bottleneck problems such as bandwidth limitation and linear and nonlinear distortions. Traditional Deep-learning Neural Network (DNN)-based waveform-to-symbol converter is verified to be an effective method to alleviate them, but impractical due to high complexity. To achieve a better tradeoff between communication performance and computation complexity, a cascaded receiver consisting of a DNN-based waveform-to-symbol converter and modified Neural Network (NN)-based decision-directed least mean square (DD-LMS) is then innovatively proposed. With fewer taps and nodes than the traditional converter, the front-stage converter could mitigate the majority of Inter-Symbol Interference (ISI) and signal nonlinear distortions. Then modified NN-based DD-LMS is cascaded to improve communication performance by reducing phase offset, making received constellation points more concentrated and closer to standard constellation points. Compared with the traditional converter, the cascaded receiver could achieve 89.6% of signal Vpp dynamic range with 12.4% of complexity in the 64 APSK UVLC system. Moreover, the ratio of signal Vpp dynamic range and total trainable parameters is 1.24 \(\times\) 10\({}^{-1}\) mV, while that of the traditional converter is 1.95 \(\times\) 10\({}^{-2}\) mV. The cascaded receiver used in 64 APSK UVLC systems is experimentally verified to achieve enhanced performance, thus as a promising scheme for future high-speed underwater VLC.  
underwater visible light communication (UVLC); neural network (NN); waveform-to-symbol converter; DD-LMS +
Footnote †: journal: _Photonics_
# Privacy-Aware Access Protocols for MEC Applications in 5G +
## 3 Statement of the Problem
### Privacy Requirements
#### 3.3.1 Justification of Requirements
Therefore, if APPIFY can be used to relate messages to each other based on the device identity or IMSI, it would conclude that [PERSON] and [PERSON] use the same device or have the same IMSI. Thus, APPIFY can further assume that, with high probability, either these identifiers belong to the same person or these two users have a close relationship with each other. The MEC application does not need to have this kind of knowledge. Requirements about Outsiders (R15-R20)  
MNO, MEC, and the MEC application all take part in providing services to [PERSON]. They need to know some information regarding [PERSON]. However, outsiders are not included in this process. Therefore, all three types of outsiders should learn nothing about [PERSON], for example, the content or the volume of the usage and the identifiers she shares with MNO and MEC application. They should not know which MEC application [PERSON] uses or who uses APPIFY. In addition, outsiders should not be able to distinguish whether two messages in the network are related to the same MEC application or whether they are sent from the same user or different users.
# A Comprehensive Review of Acoustic Methods for Locating Underground Pipelines
## 3 Acoustic Methods for Locating Buried Pipelines
### Seismic Wave Methods
#### 3.4.4 Summary of Seismic Wave Methods  
An advantage of seismic wave methods is that access to the pipe of interest is not required as the excitation source is located at ground surface. Moreover, burial depth of the target can be determined from cross-sectional stacking images, which is greatly superior to other previously introduced acoustic methods. However, imaging of underground objects usually suffers from insufficient resolution and heavily depends on the wavespeed of the soil used in signal processing. Consequently, determination of wave velocities through in-situ ground is necessary, which unfortunately, is not always accurate due to the dispersion behaviours and the coexistence of multi-type waves.  
All three main types of seismic waves (compressional wave, shear wave and Rayleigh wave) have been evaluated individually [115] with the choice of wave depending mainly on the depth of target. In general, seismic wave methods are effective for locating buried objects larger than about half a wavelength. More detailed background theory behind seismic methods and signal processing algorithms can be found in [99; 100].
## 4 Attack Surface Analysis
### Compromised Insider Nodes
#### 4.2.2 IoT Devices  
IoT devices face susceptibility to compromise through various channels, presenting considerable security challenges [60]. A prevalent vulnerability arises from default credentials and weak passwords, often preconfigured by manufacturers, which facilitate unauthorized access for potential attackers [61]. Devices are left exposed to known vulnerabilities due to insufficient software updates and patch management practices over time [62]. Additionally, attackers can exploit insecure network communication protocols to intercept or manipulate data exchanged between IoT devices, a critical concern given the limited processing power of these devices and their data-oriented nature [63]. Physical tampering, particularly in unsecured environments, stands as another avenue for compromise [64]. The escalating number of IoT devices has led to a surge in attacks targeting them [65; 66; 67], establishing them as prime entry points to WLANs. Major IoT operating systems, such as Contiki OS and FreeRTOS, are Linux-based choices that include network functionalities, with the drawback of storing network credentials in plain text [68], rendering them susceptible targets for initiating WLAN infiltration.
## 4 Experiments
### Implementation Details  
In our experiments, we refine pretrained video transformers using selection networks, which have randomly initialized parameters. We sample clips of 16 frames with a temporal stride of 4 following UniFormer-S. The spatial size of each frame is cropped to 224 \(\times\) 224 pixels. The network is trained using the AdamW optimizer [36], under a cosine learning rate schedule [37], for a total of 20 epochs. The first three epochs are allocated for linear warmup [38]. The initial learning rate is set to \(1\times 10^{-6}\) for the backbone network, and \(1\times 10^{-4}\) for the selection network. The learning rate for the backbone layers is determined to be 0.01 times that of the selection network's learning rate, with a mini-batch consisting of eight video clips. With regard to the implementation of the Video Swin Transformer, a clip of 32 frames is sampled with a temporal stride of 2 and a spatial size of \(224\times 224\). Like the UniFormer-S, AdamW is also used as an optimizer for a total of 20 epochs, with the initial 3 epochs being used for linear warmup. The learning rate for the selection networks is set to \(1\times 10^{-4}\), while the backbone model employs a learning rate of 0.01 times that. The mini-batch size is reduced to four video clips for this network. The networks are implemented in PyTorch and the experiments are carried out on four Tesla V100 GPUs that each have a memory of 32 GB. During inference, we adopt the same testing strategies employed in the original backbones to ensure a fair comparison. To gauge the performance of classification conducted by the model, metrics such as top-1 and top-5 accuracy are reported on the validation set. Measuring the computational costs is performed through the use of FLOPs, or floating-point operations, a standard metric used in data science to assess the computational demands of an algorithm regardless of the specific hardware being used. To achieve a suitable balance between computation and accuracy, a multiclip testing approach is adopted when testing for Kinetics-400.
# Skeleton-Based Dynamic Hand Gesture Recognition Using an Enhanced Network with One-Shot Learning
###### Abstract  
Dynamic hand gesture recognition based on one-shot learning requires full assimilation of the motion features from a few annotated data. However, how to effectively extract the spatio-temporal features of the hand gestures remains a challenging issue. This paper proposes a skeleton-based dynamic hand gesture recognition using an enhanced network (GREN) based on one-shot learning by improving the memory-augmented neural network, which can rapidly assimilate the motion features of dynamic hand gestures. Besides, the network effectively combines and stores the shared features between dissimilar classes, which lowers the prediction error caused by the unnecessary hyper-parameters updating, and improves the recognition accuracy with the increase of categories. In this paper, the public dynamic hand gesture database (DHGD) is used for the experimental comparison of the state-of-the-art performance of the GREN network, and although only 30% of the dataset was used for training, the accuracy of skeleton-based dynamic hand gesture recognition reached 82.29% based on one-shot learning. Experiments with the Microsoft Research Asia (MSRA) hand gesture dataset verified the robustness of the GREN network. The experimental results demonstrate that the GREN network is feasible for skeleton-based dynamic hand gesture recognition based on one-shot learning.  
one-shot learning; gesture recognition; GREN; skeleton-based +
Footnote †: journal: Applied Sciences