esa-sceva/satcom-chunk-collection huggingface dataset:
# Efficient Dissemination of Safety Messages in Vehicle Ad Hoc Network Environments
## 1 Introduction  
Studies using a vehicular ad hoc network (VANET) to improve user driving convenience and traffic safety are currently being conducted [1; 2; 3; 4; 5]. In particular, services are being implemented that provide users with safety messages to avoid road accidents and find less congested routes, thus reducing fuel consumption and air pollution. The most basic safety message dissemination scheme is based on broadcasts [6; 7; 8; 9; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21]. However, broadcast-based message dissemination causes the broadcast storm phenomenon, which causes excessive transmission delay, packet loss, transmission failure, and dissemination interference, and degrades overall network performance. Furthermore, broadcast storms occur more frequently in urban environments with high vehicle density.  
Several schemes have been proposed for disseminating messages in a VANET environment [12; 13; 14]. However, the existing schemes are based on probabilistic schemes, which can cause unacceptable communication delays in real road environments [12]. Moreover, the existing schemes do not consider the frequent subscriptions and departures of vehicle clusters [13]. The more frequently vehicles departure from the cluster, the more messageexchanges are required to maintain the cluster result in substantial safety message packet loss. In addition, they do not consider the various message types and the roadside unit (RSU) environment [13; 14]. We need to use the V2I environment to efficiently disseminate the safety messages.  
In this paper, we propose a safety message dissemination scheme that uses collaboration between vehicles in a VANET environment. The proposed scheme does consider various message types and the RSU environment. The urban environment considered in the proposed scheme has a higher need than rural environments for safety messages due to the higher traffic volume and a greater chance of sudden congestion and emergency situations arising. Traffic in urban environments also moves in different directions, such as at intersections, in contrast to one-way roads such as highways. Therefore, the proposed scheme disseminates safety messages considering the high mobility and density of vehicles. In particular, we propose a scheme to quickly recover the cluster if the cluster collapses due to the departure of the vehicles. It also performs cluster-based safety message dissemination considering both vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) environments.  
This paper is organized as follows. Section 2 introduces related research and analyzes the differences and features of each scheme. Section 3 describes the cluster management, V2V, and V2I communication techniques for the proposed safety message dissemination scheme. Section 4 presents the results of performance evaluations conducted to demonstrate the superiority of the proposed scheme. Section 5 concludes this paper and describes future research.
# Preliminary Analysis of a Lightweight and Deployable Soft Robot for Space Applications
###### Abstract  
The rising interest in soft robotics, combined to the increasing applications in the space industry, leads to the development of novel lightweight and deployable robotic systems, that could be easily contained in a relatively small package to be deployed when required. The main challenges for soft robotic systems are the low force exertion and the control complexity. In this manuscript, a soft manipulator concept, having inflatable links, is introduced to face these issues. A prototype of the inflatable link is manufactured and statically characterized using a pseudo-rigid body model on varying inflation pressure. Moreover, the full robot model and algorithms for the load and pose estimation are presented. Finally, a control strategy, using inverse kinematics and an elastostatic approach, is developed. Experimental results provide input data for the control algorithm, and its validity domain is discussed on the basis of a simulation model. This preliminary analysis puts the basis of future advancements in building the robot prototype and developing dynamic models and robust control.  
soft robotics; pose estimation; space robotics; robot kinematics; characterization; inflatable structures; elastostatic model +
Footnote †: journal: Applied Sciences
## 3 Results and Discussion
### Analysis of VOCs by Fingerprint  
According to Figure 3, one can see that the concentration and kinds of VOCs of Zhenba bacon from four different processing stages are significantly different. It can be seen that the fresh stage contains substances that are either absent or at low concentrations in subsequent processing. The substances in region A mainly include acetone, pentanal, 1-pentanol, etc. In the flavor study, pentanal, the major straight-chain aliphatic aldehyde, contributes to the fruity and ester aromas. Due to its low flavor threshold, it could affect flavor or aroma in meat foods [24]. The variety and concentration of volatile substances were the highest in the YZ stage. The cured processing is involved in enzymatic hydrolysis of protein [25]. Therefore, it could contribute to the formation of bacon flavor. From left to right, substances in region B in the figure are mainly E-2-heptanal, 2, 3-butanedione, 2-methylpropanol, octanal, 3-methylbutanol, 1-propanol, ethyl hexanoate, heptanal, dimethyl disulphide, 3-methylthio-propanol etc. On the basis of structural considerations, it is assumed that 3-methylbutanal and 3-methylbutanol are derived from leucine while 2-methylbutanol is derived from isoleucine [26]. It is possible that these substances came from the local baiji during the curing of bacon. 3-methylbutanol has a banana-like and pear-like aroma [27]. 2, 3-butanedione is found in numerous common flavors such as \"buttery/creamy\" and fruit flavors [28], and the smell is strong. It is speculated that this is the main flavor in the bacon curing period. The concentration of substances in the C region of CP sample rose to the highest, namely 2-butanone, ethyl propanoate, butanal, 2-methylbutanal and 3-methylbuanal, etc. Meanwhile, it was observed that among the four stages, XZ and CP were the most similar, while fresh and YZ had the biggest difference. Ethyl propanoate was associated with a green-sweet-fruity note [29], and butanal was the most abundant VOC in the meat samples [30].  
Figure 2: The gallery plots of VOCs from different stages using CC–IMS. Fresh: raw sample; YZ: cured sample; XZ: smoking sample; CP: finished sample; VOCs: volatile organic compounds.
## 3 Results
### Analysis of Cis-Acting Elements in the Promoter Region of RcTPS Genes  
Most of the functional _cis_-acting elements is concentrated within proximal promoters, usually in the region from \(-\)1,000-bp to + 200-bp relative to the transcription start site (TSS) [63]. In this study, we analyzed the 2000-bp nucleotide sequence located upstream of the ATG initiation codon for all _RcTPS_ genes using the PlantCARE database [64]. We identified three categories of _cis_-acting regulatory elements that share similarities with previously reported motifs associated with plant growth and development, phytohormone response and stress responses (Figure 6). _Cis_-acting regulatory elements involved in plant growth and development mainly include flowering regulation elements (CCAAT-box, AT-rich element, and MRE), endosperm-specific expression elements (GCN4-motif and AAGAA-motif), a shoot and root meristem expression element (CAT-box), a seed-specific regulation element (RY-element), and a shoot-specific expression element (As-1 element). Phytohormone-responsive elements included a salicylic acid-responsive element (TCA-element), gibberellin-responsive elements (P-boxes, GARE motifs, and TATC boxes), an auxin-responsive element (TGA-element), an ethylene-responsive element (ERE), an abscisic acid-responsive element (ABRE) and jasmonic acid-responsive elements (TGACG motifs and MYCs). The stress response category included defense and stress responsiveness (TC-rich repeats), wounding responses (WRE3 and WUN-motif), anaerobic induction (ARE), drought response (MBS), dehydration response (DRE), low temperature response (LTR) and stress response (STRE) (Figure 6). These data indicate that _RcTPS_ genes are likely regulated by multiple phytohormones and abiotic stresses.  
Figure 5: Conserved motifs analyses of RcTPS proteins identified by the MEME program. Motifs are indicted with different color boxes.
## 5 Simulation and Experimental Verification
### Simulation Verification  
AE simulation is carried out on steel plates with dimension of 800 mm by 700 mm by 5 mm. The sensors are arranged as Figure 5 with a spacing of 200 mm. The x-coordinates of AE source are set to 350 mm, 450 mm, 550 mm, 550 mm, and 750 mm, respectively, and the y-coordinates are set to 300 mm, 400 mm, 500 mm, and 600 mm, respectively. According to the method and operation steps introduced in Section 2.2, the localization results are shown in Table 5.  
From Table 5, it can be concluded that after the x-coordinates of AE source are initially located by the TDOA method, the beamforming localization is carried out, and the localization results are more accurate. Due to the beamforming method scanning a quarter of the entire plate, the calculation cost of the localization is greatly reduced. The TDOA localization results in the Table 5 are also relatively accurate because the signal from the simulation data has no noise interference. There is a certain error in the actual TDOA.  
Figure 7: Schematic diagram of joint location method.
# Estimating the Observation Area of a Stripmap SAR via an ISAR Image Sequence
## 6 Conclusions  
In this article, we propose a novel method for estimating the area observed by spaceborne SARs operating in boresight stripmap mode. We establish the corresponding relation between the observation area and the fixed attitude of the spaceborne SAR antenna, and parameterize the time-varying area observed by the spaceborne SAR into a fixed look angle of the spaceborne SAR antenna. Based on the coplanarity of the scatterers on the parabolic antenna edge of the spaceborne SAR and the principle of minimizing the dihedral angle between the plane containing the ideal estimated scatterers and the plane containing the actual parabolic antenna edge of the spaceborne SAR, an objective function is established and a linear search algorithm is used to estimate the look angle of the spaceborne SAR antenna. Then, experiments are designed to validate the feasibility and robustness of the proposed method. Under the assumption of no errors in the range and Doppler values of the scatterers on the parabolic antenna edge extracted from the ISAR image sequence, the estimation errors of the proposed method are within the quantization unit of the search scope. Therefore, the feasibility of the proposed method is validated. Under the Gaussian noise conditions of 10 dB, 15 dB, and 20 dB in the ISAR image sequence, the estimation performance of the proposed method is greatly superior, and even under a low SNR condition of 10 dB, the performance of the proposed method is still good. Therefore, the robustness of the proposed method is validated in this article.  
Conceptualization, T.F., D.C., H.C., J.W. and B.L.; methodology, B.L.; software, B.L., H.L. and S.Z.; validation, B.L.; formal analysis, B.L.; investigation, B.L., H.L., L.Z. and S.Z.; writing--original draft, B.L.; writing--review and editing, T.F., D.C., H.C., J.W., H.L., S.Z. and L.Z. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China under Grant 62071041 and the National Natural Science Foundation of China under Grant 62201041.  
The data presented in this article are available upon request from the corresponding authors.  
The authors are grateful to the anonymous reviewers for their careful review and constructive comments.  
The authors declare no conflicts of interest.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Arc Segment** & **SNR** & \(\mu\) & \(\sigma\) \\ \hline \multirow{3}{*}{LowEleArc} & 10 dB & 0.0156\({}^{\circ}\) & 0.1487\({}^{\circ}\) \\  & 15 dB & 0.0026\({}^{\circ}\) & 0.1063\({}^{\circ}\) \\  & 20 dB & 0.0057\({}^{\circ}\) & 0.0545\({}^{\circ}\) \\ \hline \multirow{3}{*}{HighEleArc} & 10 dB & 0.0031\({}^{\circ}\) & 0.0337\({}^{\circ}\) \\  & 15 dB & 0.0041\({}^{\circ}\) & 0.0202\({}^{\circ}\) \\ \cline{1-1}  & 20 dB & 0.0008\({}^{\circ}\) & 0.0131\({}^{\circ}\) \\ \hline \hline \end{tabular}
\end{table}
Table 5: Eigenvalue statistics for the look angle estimation in the low and high elevation arc segments.
# A Forwarding Latency Optimization Method for Software Data Plane Based on Spin-Polling
## 6 Evaluation  
Consistent with the test conditions in Section 3.2, we still use the L4 forwarding application built on the ip_pipeline provided by DPDK to evaluate our scheme. In general, the Tx queue length in DPDK is coupled with \(\theta_{Tx}\). We can make the length of the Tx queue decoupled from \(\theta_{Tx}\) by a simple code modification, and make \(\theta_{Tx}\) dynamically adjustable at program runtime. We can do this easily due to the open source nature of DPDK, and this also applies to similar open source high-performance packet processing frameworks. In addition, for comparison, we also implemented Smart Batching [19] on our software data plane. The testbed used in this section is the same as in Section 3.2, as shown in Figure 3a. Specifically, we used a Dell R740 server running our software data plane and an Intel E810 NIC (4 \(\times\) 25 Gbps mode) to send and receive packets. In addition, an Ixia XGS12 network test platform is our packet generator. Other detailed configuration information is shown in Table 1. Unless otherwise specified, we use 512-byte fixed-length  
Figure 10: The two-state finite state machine.  
Figure 9: The multiple Rx queues.  
IPv6 TCP packets for experiments, and some flow tables and flow entries are installed on the data plane to support packets forwarding.
## 4 Discussion
However, these benefits are accompanied by significant cybersecurity and data integrity concerns, particularly in high-security environments such as transportation hubs, critical infrastructure, and defense facilities [68; 69]. From a cybersecurity standpoint, the expanded attack surface of edge devices--often deployed in physically accessible or unsecured areas--introduces vulnerabilities to threats such as denial-of-service (DoS), malware injection, and man-in-the-middle (MITM) attacks [67; 68; 70]. These threats are exacerbated by the heterogeneity and resource limitations inherent in IoT deployments, which hinder the implementation of standardized and robust security protocols [65; 69]. Moreover, edge-based fire safety platforms operating in sensitive sectors must address the elevated risks associated with unauthorized system access and data tampering. As noted by [PERSON]. [71; 72], edge devices are vulnerable to both intentional and accidental data corruption, which may compromise real-time decision logic and response actions. Efficient verification mechanisms for edge data integrity--such as lightweight cryptographic protocols, third-party auditing, and context-aware authentication--have been proposed as viable countermeasures [72; 73; 74]. Emerging research points to a multifaceted approach to securing edge-enabled fire safety systems. Cryptographic and biometric-enhanced authentication methods ensure baseline privacy and integrity [66; 75], while federated learning and machine learning-based intrusion detection offer scalable protection against evolving cyber threats [76; 77; 78]. Furthermore, blockchain-based decentralized trust frameworks and secure firmware update mechanisms are gaining traction as essential tools for tamper-proof data management and resilient system maintenance [79; 80]. Collectively, these studies underscore that while IoT edge platforms hold great promise for next-generation fire safety applications, ensuring their secure and trustworthy deployment requires the integration of adaptive, scalable, and context-sensitive cybersecurity strategies. Future implementations should prioritize the incorporation of end-to-end se -cavity features, redundant communication protocols, and compliance with sector-specific regulatory standards to mitigate the operational risks associated with cyber compromise.
## 4 Discussion
### Circularity Assessment Frameworks--Scopes and Objectives
Still, most existing tools are developed to support design decisions and perform comparative analyses. They do not develop solutions and strategies to implement circularity in buildings nor sufficiently consider the role of design which is not merely at the initial planning process but rather persisting along the lifecycle of products and services and remains relevant at any point. Indicators and circularity evaluation methods for building are still under development [40]. The emergence of Building Information Modeling (BIM) has created new opportunities to improve process efficiency and productivity, which have already been considered in multiple tools frameworks, e.g., the Design Criteria for Circular Buildings [40]and C-Calc [74]. However, the application of BIM to address these issues is still in its early stages. Among the tools presented above, only one tool has been found using the tool of BIM to track information flows: the D-DAS. Among the several applications of BIM for the construction industry, authors have recognized its influence on building sustainability, mainly on decision support, material information storage, managing the building end of life scenarios, and waste minimization [60; 75]. Despite the great opportunity to link BIM with CE principles, it is still a growing topic with few related investigations. BIM has been widely integrated into some circularity-related fields, such as automated LCA, LCC, or sustainability assessment [76]. The role of BIM for circular thinking concerns the ability to accumulate multidisciplinary lifecycle information about a building, together with the possibility of process automation [77]. Lately, [PERSON] et al. [77] developed a BIM-based Whole-life Performance Estimator to forecast the end-of-life performance of building materials by simulating their reusability or recovery scenarios. They have also contributed to a BIM-based disassembly and deconstruction system to manage the end-of-life performance of buildings during the design and construction stages [60]. A couple of attempts have been made in another CE-related field to develop BIM-based Material Passports [78; 79]. Concerning the practical assessment of circularity, [PERSON] et al. [80] developed a BIM-based framework to evaluate circularity through an Autodesk Revit add-in, based on MP, circular business models, LCA, and LCC. Still, researchers have recognized the infancy and barriers of BIM integration on CE, as well as the lack of circularity-oriented tools, the existence of different material databases and data gaps during the early modelling stage [78; 80].
## 4 Results
### Results on the Learn2 Reg and CHAOS Datasets
The registration results are shown in Figure 7, which shows that our DTR-GAN has a fine registration performance. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**MRI-CT**} & \multicolumn{2}{c}{**CT-MRI**} \\ \hline  & \(DSC\) **(\%)**\(\uparrow\) & \(ASD\) **(mm)**\(\downarrow\) & \(DSC\) **(\%)**\(\uparrow\) & \(ASD\) **(mm)**\(\downarrow\) \\ \hline Affine [37] & \(65.51\pm 3.10\) & \(5.63\pm 1.77\) & \(64.91\pm 3.10\) & \(6.10\pm 1.77\) \\ VoxelMorph [8] & \(68.21\pm 2.42\) & \(6.10\pm 1.37\) & \(66.60\pm 3.94\) & \(6.32\pm 1.67\) \\ CR-GAN & \(77.81\pm 1.80\) & \(5.15\pm 1.57\) & \(76.19\pm 2.70\) & \(5.50\pm 1.51\) \\ SbR [25] & \(76.52\pm 2.62\) & \(4.55\pm 1.36\) & \(75.51\pm 1.60\) & \(4.55\pm 1.36\) \\ RoT [23] & \(74.81\pm 0.78\) & \(5.05\pm 1.79\) & \(73.30\pm 2.20\) & \(5.65\pm 1.79\) \\ MSF [28] & \(74.29\pm 2.37\) & \(5.05\pm 2.12\) & \(73.79\pm 2.21\) & \(5.06\pm 1.62\) \\ DFR [26] & \(78.50\pm 2.50\) & \(4.54\pm 1.22\) & \(77.65\pm 2.50\) & \(4.74\pm 1.11\) \\
**DTR-GAN** & \(\mathbf{80.85\pm 2.09}\) & \(\mathbf{4.21\pm 1.65}\) & \(\mathbf{79.73\pm 2.03}\) & \(\mathbf{4.62\pm 1.76}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Quantitative results of different methods on the Learn2 Reg dataset. The best results are marked in bold for clarity. \(\uparrow\) means a better registration performance should obtain higher metric values. \(\downarrow\) means a better registration performance should obtain lower metric values. Figure 5: Experimental results on the Learn2 Reg dataset: (**A**,**E**) are warped images, (**B**,**F**) are deformation fields, (**C**,**G**) are checkboard grids, and (**D**,**H**) are overlapping images. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**T1-CT**} & \multicolumn{2}{c}{**CT-T1**} \\ \hline  & \(DSC\) (\%) \(\uparrow\) & \(ASD\) (mm) \(\downarrow\) & \(DSC\) (\%) \(\uparrow\) & \(ASD\) (mm) \(\downarrow\) \\ \hline Affine [37] & \(65.15\pm 6.20\) & \(7.54\pm 2.56\) & \(64.23\pm 6.40\) & \(7.66\pm 2.40\) \\ VoxelMorph [8] & \(69.59\pm 6.81\) & \(7.23\pm 2.41\) & \(68.83\pm 6.10\) & \(7.43\pm 2.40\) \\ CR-GAN & \(76.67\pm 4.20\) & \(7.08\pm 3.93\) & \(75.18\pm 4.81\) & \(7.26\pm 2.78\) \\ SbR [25] & \(75.49\pm 6.18\) & \(6.77\pm 2.65\) & \(74.63\pm 6.18\) & \(6.82\pm 2.66\) \\ RoT [23] & \(73.52\pm 5.62\) & \(7.55\pm 2.36\) & \(72.20\pm 5.29\) & \(7.62\pm 2.05\) \\ IMSE [28] & \(74.26\pm 2.07\) & \(7.17\pm 2.86\) & \(73.42\pm 2.30\) & \(7.40\pm 2.18\) \\ DFR [26] & \(78.19\pm 6.06\) & \(6.35\pm 2.41\) & \(77.53\pm 6.35\) & \(6.54\pm 2.08\) \\
**DTR-GAN** & \(\mathbf{80.17\pm 1.77}\) & \(\mathbf{6.07\pm 2.19}\) & \(\mathbf{79.04\pm 2.40}\) & \(\mathbf{6.28\pm 2.50}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Quantitative results of different methods on the CHAOS dataset. The best results are marked in bold for clarity. Figure 6: Experimental results on the CHAOS dataset. (**A**,**E**) are warped images, (**B**,**F**) are deformation fields, (**C**,**G**) are checkboard grids, and (**D**,**H**) are overlapping images.
# A Data-Driven Approach for Generating Synthetic Load Profiles with GANs
## 4 Discussion  
### Dataset Size Sensitivity  
An important strength of the proposed method is its ability to perform well on small datasets. Since the size of the training dataset is a common limitation due to various reasons, e.g., privacy concerns, data collection costs, regulations, time-related problems, such a method where the generated data resemble the real data, however small and limited it is, is important and valuable base for further studies and usages. While traditional GANs tend to overfit or collapse under limited data, the Conv1D-WGAN-GP model maintained low divergence scores and classifier accuracy even with fewer than 400 samples (often, representing data for just one year). Its compact architecture and reduced parameter complexity make it particularly well-suited for cases where real load data are scarce or noisy.