esa-sceva/satcom-chunk-collection huggingface dataset:
# An Improved Detail-Enhancement CycleGAN Using AdaLIN for Facial Style Transfer
## 1 Introduction
Among GAN-based methods, CycleGAN [20] has become a research hotspot in the field of portrait style migration due to its convenient data acquisition, ability to achieve diverse transformations, and excellent generalization ability. Research based on CycleGAN addresses the challenge of acquiring challenging datasets and implements a transformation process from portrait photographs to stylized images [21]. However, these methods encounter several issues. Many current approaches struggle with overly uniform normalization methods that fail to adapt to varying learning rates. Moreover, their regularization techniques often inadequately control model complexity, resulting in problems such as overfitting. Consequently, generated images may lack texture details, exhibit unevenly colored patches in facial highlights, introduce artifacts from noisy input data, or appear unnaturally saturated with colors [22; 23; 24], as shown in Figure 1. Compared to ordinary images, the face structure is more delicate and requires intensive transformation during the style transfer process, making existing methods unsuitable for generating stylized portrait transfers. To address this issue, considering the difficulty of obtaining pairwise datasets, this paper designs a non-pairwise dataset based on CycleGAN. It utilizes an improved generative network and discriminative network for adversarial training and balancing, while also adopting semantic constraints to ensure similarity between the generated image and the real photo. This approach retains rich detail information and preserves identity details, resulting in more lifelike and richly detailed stylized portraits. The work carried out includes the following three points:* In this paper, we propose an improved CycleGAN using AdaLIN, which can select appropriate Instance Normalization (IN) and Layer Normalization (LN) parameter weights during the training process. This approach balances the contradiction between style and content, enhances image details, and preserves the detailed features of the original picture, particularly excelling at relocating facial highlights. * We introduce a Laplacian regular module for denoising the image. This approach helps to prevent noise features from affecting the color transfer of the image and reduces the color artifacts caused by noise. * Compared to other methods, the Improved Detail-Enhancement CycleGAN using AdaLIN proposed in this paper yields superior results in image detail enhancement and artifact removal. This advancement significantly enhances the quality of the generated face images. The rest of this paper is structured as follows: The related works are introduced in Section 2. Section 3 describes the methods proposed in this paper in detail. Section 4 shows the experiment results and evaluations. Section 5 concludes this paper and describes our future work.
# Complete Systematic Error Model of SSR for Sensor Registration in ATC Surveillance Networks
## 7 Results
### Complete Model  
Next, we will show the results with variations of the complete model developed in this paper. Several configurations of the model have been used with the objective to test the final correction contribution of each parameter of the model. In Table 2 the error deviation due to the biases is expressed for different configurations of the models (in the first column the bias parameters used in each configuration are indicated). The first row is the basic model from the previous section. Second row estimates the biases due to the deviation of the antenna and the rotation axis. Third row estimates the biases produced by the encoder. Finally the fourth row estimates all the biases presented in the model.  
Figure 17: (**a**) Straight trajectory; (**b**) Take off trajectory corrected with basic model; (**c**) Several trajectories allocated at the upper-right corner of the scenario corrected with the basic model.  
The encoder eccentricity seems to be a very important parameter in the bias estimation. For example if the encoder has a diameter of 25 cm and an error in the rotation center of 0.25 mm produces a maximum bias of 200 m at a range of 200 km. As could be expected, the estimation with all the parameters is the best one and in the Figure 18 (equivalent to Figure 17) the corrected tracks are shown. In this case, as the complete model has azimuth dependent biases, every track in every azimuth is better corrected in comparison with the simple model. But even reduced parameters bias models result in relevant accuracy gains with respect to the usual basic model.  
The algorithm determines bias parameters for both radars to minimize the mean squared error between the ideal position and the corrected position. As consequence of results we should conclude that (locally in the in the shown areas) the radar 1 is better aligned to ideal position than radar 2, although measures from both radars are corrected.
## 2 Theoretical Background
### Impulse Excitation Technique  
The Impulse Excitation Technique (IET) is a widely used experimental method for determining the natural frequencies of dynamic systems. This technique involves applying a short, high-energy impact to the system and measuring its subsequent response. By analyzing this response, the natural frequencies of the system can be accurately identified [19; 21].  
The core concept of the IET is to apply a short-duration force or impulse to a system, exciting its natural vibration modes. The system's response to this excitation is recorded, typically using sensors such as accelerometers or displacement transducers. The resulting time-domain signal is then analyzed to determine the system's natural frequencies. Mathematically, the response of a simple linear system to an impulse excitation can be described by the following equation:  
\[x(t)=A\ \sin(\omega_{n}t+\phi)e^{-\zeta\omega_{n}t} \tag{5}\]  
where \(x(t)\) is the system's response over time. \(A\) is the amplitude of the response, and \(\omega_{n}\) is the natural frequency of the system (rad/s). \(\zeta\) is the damping ratio (dimensionless), \(t\) is time, and \(\phi\) is the initial phase.  
For lightly damped systems, the response typically oscillates at a characteristic frequency, which is primarily governed by the system's natural frequency. By measuring the response and fitting it to the corresponding equation, the natural frequency of the system can be determined. In practice, the time-domain response is transformed into the frequency domain using the Fourier Transform method, allowing for a more precise identification of the dominant frequencies.  
\[X(\omega)=\int_{0}^{\infty}x(t)e^{-i\omega t}dt \tag{6}\]  
where \(X(\omega)\) is the frequency response function, and \(\omega\) is the angular frequency.  
From the frequency-domain representation, the natural frequency can be identified as the peak in the spectrum. The main peak corresponds to the system's fundamental natural frequency. For damped systems, the amplitude of the peak will be smaller, but the natural frequency can still be determined accurately from the spectral content.
# A Method for Building a Mixed-Reality Digital Twin of a Roadheader Monitoring System
## 2 Architecture of Digital Twin Monitoring System
### Overall Architecture of Digital Twin Monitoring System
#### 2.1.1 Multi-Valued Attribute Physical Entity  
The multi-valued attribute physical entity, namely the physical entity of concern in the system architecture, constitutes the basis of the entire system's composition. The multi-valued attributes of the physical entity primarily encompass five characteristic attributes: physical, behavioral, geometric, rule-based, spatiotemporal, and scale. The multi-valued attribute physical entity is a collection consisting of the roadheader body and the sensing elements of the working environment. Simultaneously, data collection is the fundamental step for realizing the digital twin of the roadheader. The core data of the roadheader during the cutting process are the position and orientation data of the roadheader and the working environment data. Both jointly form the data source of the digital twin monitoring system of the roadheader. The data generated during the operation process of the multi-valued  
Figure 1: Architecture diagram of Digital Twin Monitoring System for Roadheader.  
attribute physical entity are encoded and mapped to the digital twin virtual entity through the network transmission system and the data processing system to ensure the consistency, accuracy, and timeliness of the digital twin virtual entity.
# Probabilistic Modeling of Congested Traffic Scenarios on Long-Span Bridges
To address this research gap, this study investigated and simulated extreme response scenarios of congested traffic on long-span bridges using an extreme scenario simulation probabilistic model previously developed by the authors for free-flowing traffic [33]. In fact, effectively simulating extreme scenarios of congested traffic first requires a substantial number of extreme scenario samples, which need to be selected from a larger pool of normal congested traffic scenarios. As WIM (Weigh-in-Motion) traffic data are not representative of congested traffic scenarios, currently, congestion traffic data are typically obtained through bridge-mounted camera systems. Due to the slow flow speed of congested traffic, this method of data acquisition often results in low efficiency and limited samples. According to the research of [PERSON] et al. [28], congested traffic can be obtained from jammed traffic images. In this study, the inherent correlation between congested and free-flow traffic at the same location was explored, and the characteristics of congested flow from those of free flow were deduced, allowing the relevant information contained in the WIM data to be fully explored. Then, the congested flow was modeled using the cellular automata (CA) approach, providing the simulated samples of the previous proposed extreme response scenario [33]. The framework of this paper is as follows: Section 2 provides a detailed review of the methodologies employed to develop the probabilistic model for simulating extreme response scenarios, outlining its theoretical foundations; Section 3 deals with an exploration of how the CA method is utilized to simulate congested traffic, detailing the process and the adaptations made for accurate representations of traffic congestion; Section 4 analyzes the application of the extreme response scenario method to model the impact of congested traffic loading on infrastructure, particularly focusing on the process and results of simulating these conditions; Section 5 provides a focused discussion on the key findings of the study, emphasizing the practical implications of the probabilistic model for evaluating extreme response scenarios on long-span bridges under varying traffic conditions; and Section 6 summarizes the key findings of the research, discussing the effectiveness and adaptability of the extreme response scenario simulation method and highlighting potential areas for future research to enhance the accuracy and efficiency of traffic simulations to obtain maximum load effects in long-span bridges.
# Assessment of ZTD Derived from COSMIC Occultation Data with ECWMF, Radiosondes, and GNSS
## 3 Results and Discussions
Chen  
Figure 4: Horizontal distance distribution of gnsZTD matched with atmPrf in 2008. Figure 5: Statistics and distribution of residual ZTDs for matched atmPrf and gnsZTD in 2008.  
showed that the results of comparing GPS-ZTD and ECMWF-ZTD calculated by the integration method over China are \(-\)10.5 \(\pm\) 24.3 mm [4]. By estimation with \(\delta\)ZTD\({}_{1}\) and \(\delta\)ZTD\({}_{3}\) in 2008, there is about 2.3 \(\pm\) 22.3 mm between gnsZTD and echZTD at the bottom of the occultation profile. From above, we can see that \(\delta\)ZTD differs with each other, the absolute value of the bias is within 12 mm, and the standard deviation is approximately 25 mm or less. Based on the above validation, we discuss the spatial characteristics of \(\delta\)ZTD\({}_{1}\) and \(\delta\)ZTD\({}_{3}\) due to the station distribution and the matching process.
## 5 Conclusions  
For radar jamming recognition, this paper proposed Diff-SwinT, a novel integrated framework that identified various typical radar jamming more accurately under low JNR conditions. This method fully utilized denoising techniques based on the diffusion model to bolster robustness and capitalized on the strength of SwinT in extracting multi-scale features from radar time-frequency representations. Simulation results demonstrated that the Diff-SwinT algorithm achieved remarkable overall accuracy up to 83.5% at JNR = \(-\)10 dB and over 95% at \(-\)4 dB. When compared to using Swin Transformer alone, Diff-SwinT significantly improved overall accuracy by 15% to 11% from JNR \(-\)16 dB to \(-\)10 dB. In comparison to VGG-based and feature fusion-based recognition methods, Diff-SwinT showed substantially higher overall accuracy of 33% to 27% and 57% to 35%, respectively, from JNR \(-\)16 dB to \(-\)10 dB. This verified the efficacy of the proposed integrated framework and its considerable advantages over existing algorithms. In summary, the proposed Diff-SwinT framework provided a new paradigm for robust radar jamming recognition under challenging low JNR conditions.  
Conceptualization, M.S. and D.W.; methodology, F.M.; software, W.W.; writing--original draft preparation, W.W.; writing--review and editing, Y.H. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
We are sorry that the dataset of this article cannot be provided due to privacy and moral restrictions.  
The authors declare no conflict of interest.
# A Statistical Approach to Detect Jamming Attacks in Wireless Sensor Networks
## 2 Related Work
[PERSON] and [PERSON] [21] proposed MULti-channel Exfiltration PROtocol (MULEPRO), a fully distributed protocol that is designed to evacuate data from jammed areas in response to a radio jamming DoS attack in a WSN. It functions by dynamically assigning nodes to different channels in the jammed area to defeat the attacker. The resilience of WSN routing protocols, such as AODV, DSR and the recent MPH to reactive jamming attack has been studied in [22]. The authors, thereafter proposed a modified version of the protocols, AODV-M, DSR-M and MPH-M that ensure that whenever the nodes running the detection algorithm detect an attack, the node is isolated and the routing protocols adapt their paths to avoid the isolated nodes. A query-based jamming detection algorithm (QUJDA) has been proposed in [23]. QUJDA is an anomaly-based technique and its deployment is distributed. It functions by first differentiating between cases of attack condition and normal network condition using metrics such as bad packet ratio, packet delivery ratio and amount of energy consumed. QUJDA also ensures that sensor nodes relate with their neighbours to achieve a higher detection rate. A game theory approach has been proposed in [24] to ensure a high network lifetime and packet delivery ratio during multiple or single jammer attack in WSN using single-leader multiple-followers Stackelberg game theory. This approach, TC-JAM, uses a topological control technique, where the sink node acts as a leader to track nodes that have been affected by jamming attack. The sensor nodes, which acts as the followers, ensure an optimum transmission power level is achieved while making sure a large number of neighbour nodes are covered. Many of these proposed techniques have used metrics from different layers to detect the presence of a jamming attack, however, there exist some network impairments such as collisions and packet failures due to interference and weak links that exhibit seemingly similar behaviour to a jamming attack. Therefore, some of these metrics that have been used in the literature to detect jamming attacks will produce high false alarm rates. In this work, we propose the EWMA algorithm and use the packet IAT as the sole metric to distinguish between normal traffic patterns and jamming attacks. EWMA is a statistical monitoring process technique that averages data and continually increases the weight of more recent values of the average variable. The packet IAT allows us to detect different forms of DoS jamming attacks in WSNs. To the best of our knowledge, this work is the first to use EWMA based on packet IAT to detect different forms of jamming attacks in WSNs.
# User-Cell Association for Security and Energy Efficiency in Ultra-Dense Heterogeneous Networks
## 3 Proposed Association Utility
### Security Metrics Definition  
PLS defines a standard metric to measure how much information can be transferred in a wireless link without eavesdropping: the secrecy capacity \(\mathbb{C}_{i,s}^{[sec]}\)[40]. The secrecy capacity is defined as the difference between the capacity of the legitimate link \(C_{u,c}\) (between the \(u\)-th UE and the \(c\)-th SBS) and the capacity of the eavesdropper \(\mathbb{C}_{E,c}\left(\hat{x},\hat{y}\right)\) in position \(\left(\hat{x},\hat{y}\right)\). Thus, \(C_{s}=\max\{0,C_{u,c}-C_{E,c}\left(\hat{x},\hat{y}\right)\}\). \(C_{E,c}\left(\hat{x},\hat{y}\right)\) is the capacity of the link from transmitting cell \(c\) and the eavesdropper in position \(\left(\hat{x},\hat{y}\right)\). The expression of the capacity is as in (3) where the SINR value is referred to [PERSON]. This metric requires to know [PERSON]'s location, which is not an information usually known in the real world. In order to drop this requirement, in [41] a secrecy capacity averaged over an area is proposed as a new metric.  
Considering the above-mentioned results, we derive two security metrics:  
* Secure Area \(A_{u,c}^{\text{[sec]}}\). The secure area is defined as the set of locations of an area where the capacity of the legitimate channel \(C_{u,c}\) is strictly greater than the capacity of the eavesdropper ([PERSON]) channel \(C_{E,c}\). In other words, assuming that [PERSON] is in a generic location \(\left(x,y\right)\in A\) then \(C_{E,c}\left(x,y\right)<C_{u,c}\) \[A_{u,c}^{\text{[sec]}}=\left\{\left(x,y\right)\in A\ \mid\ C_{u,c}^{\text{[sec]}}(x,y)=C_{u,c}-C_{E,c}\left(x,y \right)>0\right\}\] (6)
* Averaged Secure Throughput \(\overline{T}_{u,c}^{\text{[sec]}}\). The average secure throughput is defined as the difference between the capacity of the legitimate link \(C_{u,c}\) (between UE and BS) and the capacity of the eavesdropper \(C_{E}\) averaged over the entire area \[\overline{T}_{u,c}^{\text{[sec]}}=\frac{1}{A}\sum_{\left(x,y\right)\in A}\max \{0,C_{u,c}-C_{E,c}\left(x,y\right)\}\] (7)
# Novel Real-Time Compensation Method for Machine Tool's Ball Screw Thermal Error
## 2 Thermal Equilibrium Analysis
### Thermal Analysis of Ball Screw Feed System  
The structure, heat source, heat conduction, and heat convection of the target ball screw feed system are shown in Figure 2.  
The main heat source of the ball screw feed system is the friction in the nut and front bearing. The elastic motor coupling can be regarded as a heat insulator, owing to its large thermal resistance. In addition, the following assumptions simplify the thermal analysis of the ball screw feed system:  
1. The thermal boundary at the connection between the fixed end of the screw and the elastic coupling can be regarded as adiabatic. Thus, heat conduction from the motor to the screw can be ignored.
2. Heat generation due to friction in the free-end bearing is negligible and can be ignored.
3. Only the axial heat conduction of the screw is considered.  
Then, as shown in Figure 3, the screw can be divided into five sections:  
1. Fixed end - This is the section that is in contact with the elastic coupling. The interface between the screw and elastic coupling is considered adiabatic.
2. Fixed bearing--This is the section that is in contact with the fixed-end bearing. Heat generated by the bearing enters the screw here.
3. Nut--The section that is in contact with the ball screw nut. Heat generated by the nut enters the screw here.
# Edible Packaging: A Technological Update for the Sustainable Future of the Food Industry
## 3 Results
### Regulations  
Edible packaging is an integrated portion of the food product; ergo, edible packaging has to conform to food safety regulations. It is possible to create edible films and coatings using the food ingredients and additives that have been permitted by Codex Alimentarius, the US Food and Drug Administration, the EFSA (European Food Safety Authority), or other rational food safety authorities [30]. Edible packaging materials may be categorized as food packaging materials, food products, food contact materials, or food ingredients depending on their function. Currently, the majority of non-plastic food contact materials are not regulated by European legislation, and the present regulations only cover a limited amount of edible packaging materials, with the exception of Directive 2007/42/EC, which pertains to the regenerated cellulose films and articles intended for use in food contact materials [107].  
Several edible coatings and films contain components that might cause allergic reactions in some consumers. So, any coating that contains a recognized allergen should be properly labeled [30]. The use of organic solvents in the development of edible packaging materials is discouraged due to the risk of contamination of food products and increased toxicological concerns. Any edible packaging material must not harm human health and must be authorized by official food safety institutions to ensure compliance with food safety regulations.  
Overall, edible packaging materials have to be developed and used in a way that complies with local and national food safety rules to ensure their safety and success. To attract customers, marketing strategies such as price discounts, awareness programs, and advertisements should be used. When using edible packaging materials and additives, it is necessary to follow good manufacturing practices (GMP) [20].
# Grapevine Downy Mildew Warning System Based on NB-IoT and Energy Harvesting Technology
## 1 Introduction
### Related Work
Systems with fundamental communication based on the SMS were subsequently introduced [23]. With the growth in wireless sensor networks, Web technologies, and especially the Internet, the early warning systems and forecasting systems for grape diseases have begun to use widely available communication technologies (such as the Web [24], GSM/GPRS [25] and WiFi [26]). Some commercial solutions that use modern low-power technologies such as LoRaWAN or NB-IoT, have recently emerged (e.g., Metos [13]). Two examples of a platform-based approach consist of a weather station net, disease models, and the Internet website with specific tools and features [27]. A new line of research suggests using a machine learning approach to downy mildew forecasting [28].