esa-sceva/satcom-chunk-collection huggingface dataset:
# An Effective and Efficient Adaptive Probability Data Dissemination Protocol in VANET
###### Abstract  
Mobile network topology changes dynamically over time because of the high velocity of vehicles. Therefore, the concept of the data dissemination scheme in a VANET environment has become an issue of debate for many research scientists. The main purpose of VANET is to ensure passenger safety application by considering the critical emergency message. The design of the message dissemination protocol should take into consideration effective data dissemination to provide a high packet data ratio and low end-to-end delay by using network resources at a minimal level. In this paper, an effective and efficient adaptive probability data dissemination protocol (EEAPD) is proposed. EEAPD comprises a delay scheme and probabilistic approach. The redundancy ratio (\(r\)) metric is used to explain the correlation between road segments and vehicles' density in broadcast probability decisions. The uniqueness of the EEAPD protocol comes from taking into account the number of road segments to decide which nodes are suitable for rebroadcasting the emergency message. The last road segment is considered in the transmission range because of the probability of it having small vehicle density. From simulation results, the proposed protocol provides a better high-packet delivery ratio and low-packet drop ratio by providing better use of the network resource within low end-to-end delay. This protocol is designed for only V2V communication by considering a beaconless strategy. the simulations in this study were conducted using Ns-3.26 and traffic simulator called \"SUMO\".  
Keywords:data dissemination; road segments; vehicles' density; safety message application; broadcast storm problem +
Footnote †: journal:
## 1 Introduction
If we can capture the aural-visual correspondence with a tractable and meaningful representation, then we will be able to trace the path towards the aimed direction. Due to the complexity involved, previous studies adopted simplified approaches using data with low content diversity and/or supervision/self-supervision. In contrast, we propose to perform cross-modal S2I translation addressing diverse audiovisual content. Although the addressed sonic universe is restricted to five sound classes, namely, Baby cry, Dog, Rail transport, Fireworks and Water flowing, our models were trained on over eight thousand distinct scenes from a dataset characterized by both inter- and intraclass diversity. We also highlight that our translator model does not require supervision or self-supervision for training. Sound class annotations were used solely for data preparation, just enough to ensure that the acoustic related element/event was present in both aural and visual modalities. The use of supervision or self-supervision for training the translator would restrict the aural and/or  
Figure 1: S2I translator training scheme. visual representations to pre-defined concepts. Additionally, in these approaches, it is hard to know if the model performed well due to a connection between modalities or if the generated image merely fitted the class features. The system we present (Figure 1) constitutes an end-to-end solution obtained after the training of an autoencoder to define the audio latent space and a generative adversarial network (GAN) [9] equipped with a deep densely connected generator to perform cross-modal translation and synthesize the images. Additionally, we introduce an approach that employs informativity classifiers as a way to quantitatively evaluate the performed S2I translation. This enables us to analyze the influence of network-bottleneck variation on the translation process. The results subtly indicate a trade-off between pixel space convergence and informativity, with better results observed, respectively, for higher and lower dimensionalities of the audio embedding space. Though the specified S2I translation task is quite challenging, we were able to obtain models that effectively translated over 14%, on average, of unknown sounds into informative images. To the best of our knowledge, this is the first study to tackle S2I translation with such diversity of audiovisual content, all achieved without resorting to any type of supervision. Furthermore, we present the techniques that we have developed to address issues like latent space continuity, model generalization, and GAN training stabilization. This text is organized as follows: In the next section, we provide a review of prior research on cross-modal processes. Section 3 explains the challenges inherent in performing S2I translation. In Section 4, the details of the S2I translator are outlined. Section 5 presents the results we have obtained with our translator. Lastly, we conclude the study by sharing our final considerations and offering suggestions for future works.
## 5 Conclusions  
MHT has been utilized in various services to efficiently verify a data segment set. However, since the performance of MHT depends on the number of data segments, some applications/services requiring fast delivery of large amounts of content, such as multimedia services and connected vehicles, need to optimize MHT operations to enhance service performance. Therefore, the proposed scheme using saved node values and the shortest path was presented to reduce the computational overhead and service latency due to the MHT's iterated hash computation process.  
This paper contains the following two contributions. First, MHT's inefficiency and the impact of such an inefficiency are demonstrated. A hash function is speedy and efficient, but even such an efficient function can lead to inefficiencies if it operates multiple times. The performance of computing hash values of MHT depends on the size of MHT, specifically on the number of MHT leaf nodes. In this paper, it is shown that in MHT, computing node values can cause more severe latency than verifying digital signatures and receiving segments.  
Second, to improve the inefficiency of MHT, the concept of the shortest verification path of MHT, reusing node values that a verifier has proven to be valid previously, is proposed. For that task, an easy way to select the shortest verification path for each segment using the sequence number of the segment is demonstrated. Except for the first segment, not every segment needs to compute node values of nodes on the full path from a corresponding leaf node to the root node. Instead, it is sufficient to utilize a shorter verification path in which the length is between 2 and \(k-1\) if the number of segments is \(2^{k}\). The shorter the length of a verification path is, the fewer the number of times to calculate the node value.  
Future developments of this type of research include applying the proposed method to NDN-based applications/services such as connected vehicles and IoT-based home security services, and edge computing services. Such services require high capability for fast processing and fast transmission for multimedia data delivery. Generally, connected vehicles, smart home applications, IoT-based disaster management, and edge computing services are considering large-capacity IoT devices with ARM v7 (or v8) and 1-4 GB of memory or higher. Hence, the time-memory tradeoff of the proposed scheme is suitable for such applications/services.  
Conceptualization, D.K. and J.L.; methodology, D.K.; validation, D.K. and J.L.; formal analysis, D.K.; writing--original draft preparation, D.K.; writing--review and editing, J.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by NRF of Korea, grant numbers NRF-2021R1F1A1062954 and NRF-2020R1F1A1070215.  
Not applicable.  
The authors declare no conflict of interest.
# Multi-Device Security Application for Unmanned Surface and Aerial Systems
## 3 Methods and Materials
### Experiment Two Application--MAUI ROS  
The second MLS application, named MAUI ROS, is built upon all components of the MAUI chat application from phase one. MAUI ROS replaces the manual generation of plaintext messages found in MAUI chat with ROS plaintext topic messages that our application subscribes to. The plaintext ROS topic messages are published by the default publisher node named **talker**[23]. By using this publisher setup, it allowed us to control the speed at which messages are published, therefore, creating a controlled message delivery environment. During this phase, we install ROS Noetic [24] on two virtual environments. As in the MAUI chat server and client setup, MAUI ROS is installed on both virtual environments. One virtual environment simulates the user creating the MLS group, and the other, the user joining the MLS group. The default ROS installation creates a development folder named **catkin_ws/src**.  
The two MAUI ROS applications send the data to the other node via TCP using MLS as the security protocol. Unlike MAUI chat, MAUI ROS allows for simultaneous bidirectional message exchange via threading implementation. The application has two threads. The first thread subscribes to a predefined ROS topic, encrypts the received topic message, and transmits it over the TCP connection. The second thread remains in a constant loop listening for incoming TCP messages over the established connection. When a message is received, it decrypts the received message. The received messages are ROS topic messages. Thus all ROS data are sent via MLS in our test environment. This data exchange simulates two different UxS from different countries or services sharing information over an ad-hoc wireless network.
## 4 Deployment of Telemedicine Services Stage
### Deployment of Telemedicine Services Stage  
This initiative was the first of its kind at a time when there were no precedents for networks of this type. From the technological point of view, it was necessary to investigate a low-cost solution that did not require connection to an electrical network (nonexistent in the Napo River), which could withstand the high temperatures and humidity of the forest along with storms and electric shocks while offering broadband connection for telephony and telemedicine services. The universities of the consortium were responsible for the research activities. URJC worked on adapting WiFi technology so that it could be used in long distance networks [2] and selected and configured the communication equipment of the project [14]. For its part, PUCP contributed its knowledge of the jungle environments, and was responsible for the adaptation of the systems to support the climatic conditions of the forest and to carry out the installation in the field. It should be noted that WiFi technology was already available in local markets at an affordable price, and technological sustainability in the short term was ensured when using it. Moreover, URJC transferred the research findings to the PUCP in order to guarantee the technological sustainability in the medium term. Additionally, the PUCP could use its role as a university to train technical staff in order to ensure technological sustainability in the long term.  
On the other hand, the simple fact of having broadband services in healthcare centers was not going to contribute to improving patient care. For this reason, the EHAS Foundation, a Spanish NGO specialized in Information and Communication Technologies (ICT) and healthcare in isolated environments, was in charge of identifying the needs of the healthcare system, and based on that analysis, designed telemedicine solutions and appropriate care protocols for the Napo healthcare system [15; 16]. These solutions were also transferred to the PUCP to facilitate the system's maintenance in the medium term.
# Formation Mechanism and Control Technology of an Excavation Damage Zone in Tunnel-Surrounding Rock
## 7 Conclusions  
In this study, the damage of protected surrounding rock by using an electronic detonator and a nonelectric detonator in tunnel blasting was compared. The advantages of using an electronic detonator to protect surrounding rock were quantified through field monitoring and numerical simulation. The research results can provide a reliable reference for the design of controlled blasting and the determination of support parameters in tunnel excavation engineering. Some valuable conclusions are drawn from this study:  
(1) In the surrounding grade IV rock, when the blasting footage was 3 m, the nonelectric detonators were used for normal construction, and the range of the loose circle was 1.5-2.3 m. The resulting loose circle ranged from 0 m to 1.4 m after the nonelectric detonators were changed to electronic detonators under the condition that the distance between the holes and the amount of charge remained unchanged. The range of the loose circle produced by the blasting of the tunnel electronic detonator was approximately 60-76% of that of the nonelectric detonator blasting, and the surrounding rock in the loose circle was less broken.  
(2) When nonelectric detonators were used for blasting, the impact of the blasting load on the loose ring was approximately 33% due to the large blasting load. The small blasting load of the single-hole electronic detonator leads to an approximately 13% effect of the blasting load on the loose circle.  
(3) The above studies show that the use of electronic detonators for the protection of surrounding rock has a beneficial effect. The preliminary support parameters of the tunnel can be optimized after excluding the interference of initial surrounding rock fissures. When using electronic detonators, the specific optimization parameters can be expressed as reducing the length of the anchor, increasing the steel arch spacing, and reducing the amount of shotcrete.  
Conceptualization, H.F., X.G. and C.C.; methodology, X.G., J.W. and Q.N.; software, N.Y.; validation, J.W., C.C. and H.F.; formal analysis, J.L.; investigation, Q.N.; resources, H.F.; data curation, Y.L.; writing--original draft preparation, H.F. and N.Y.; writing--review and editing, N.Y., Y.L. and J.L.; visualization, Y.L.; supervision, X.G. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Service Safety and Intelligent Maintenance of Rail Transit Operation Tunnels (2022 JBXT007), the General Program of Shandong Natural Science Foundation of China (ZR2022 ME043), and the General Program of National Natural Science Foundation of China (51978356).  
Not applicable.  
The data used to support the findings of this study are included within the article.  
The authors declare no conflict of interest.
## 4 Results and Discussion
### Processing Time  
To quantify the performance of our solution, we analyze first the processing speed of the different segmentation methods. We define processing time as the time that the algorithm takes since it receives a new point-cloud from the sensor until it is processed resulting in the detector output. The results  
Figure 10: Semantic map of an indoor office environment. (**a**) Estimated position of the objects with our method; (**b**) real position of the objects in the scenario. The semantic information included are doors (red points), chairs (green points), and people (blue squares). The black dot was the actual starting position of the moving person.  
presented in this section are the average time required by the different algorithms to process 1000 samples (point-clouds) provided by the sensor in use. The results for the processing time are presented in Table 3.  
Considering that the algorithms based on CNN can distinguish between multiple classes of objects simultaneously, we also ran our solution according to the scheme presented in Figure 2, i.e., we executed the different segmentation methods in parallel to detect multiple object types simultaneously. In this case, our algorithm takes an average of 21.22 ms to process a point-cloud for object detection. This approach is simpler than our previous PFF_PeD work, which required two further steps to detect moving persons. As a result, the processing time decreases by approximately 6 ms. Apart from this increase in detection speed, we are now able to identify both moving and stationary persons. In contrast, the pure 2D solutions detecting legs (ELD) is 4 times slower than our approach.  
The Find Object (FO) solution takes an average of 29.65 ms to detect any of the objects we are interested in identifying. In this case, the processing time depends on the number of images that the algorithm uses for feature extraction (FO uses a database with multiple images for comparison to process each sensor sample). In our experiments, we used 170 images for processing each sensor sample; if the number of images increases, this will have a negative impact on the processing time. For SS, instead, the dataset employed provides a large number of labels to distinguish a larger set of objects. However, this type of solution based on CNN has a high computation cost and are usually executed on machines with high-performance GPUs. For our scenario and our reference hardware platform, this resulted in 325.75 ms.  
In conclusion, the results show that our proposed segmentation methods can significantly decrease the processing time, without imposing any strict hardware requirement. Moreover, the comparable approaches, which can distinguish among different objects, are based on the use of cameras, which offer a limited FoV (60\({}^{\circ}\)for the Astra camera in comparison to 360\({}^{\circ}\)for the Velodyne 3D LiDAR). They also require high-density point-clouds, which our reference 3D LiDAR cannot provide. Nonetheless, the lower density of the point-clouds exploited in our approach does not affect significantly the detection accuracy, as we discuss next.
## 3 AMD Diagnosis  
In the last decades, new multimodal imaging techniques have been incorporated to study AMD. They have led to a remarkable improvement in both understanding of the pathophysiology of macular diseases and its progression, but most importantly, to monitor treatment response [16].  
FA is an irreplaceable dye-based invasive diagnostic tool to detect subtle neovascularization, monitor leaks, and compare them with staining and window defects. In the era of photodynamic therapy, FA has been considered the gold standard method for detecting and classifying subtle choroidal neovascularization as classic, occult, or combination subtypes [16,17]. Nowadays, it is still a useful method for the detection of \"active\" neovascularization and geographic atrophy [16]. However, fluorescein dye leaks from blood vessels, making it less ideal for visualization of details in the choroidal circulation [18]. Although FA is useful for the visualization of MNV, retinal-choroidal anastomoses and other choroidal vascular abnormalities are better visualized using ICGA [18]. In contrast to FA, it uses a dye that is 98% protein bound, providing more detailed images of the choroid and thus identifying the entire extension of the MNV [16]. However, both modalities have many drawbacks: they are time consuming and invasive, requiring intravenous dye injection, which can cause some side effects such as nausea and anaphylaxis [19].  
In last two decades, OCT has emerged as a new non-invasive diagnostic tool for the diagnosis and follow-up treatment of macular diseases. Particularly in AMD, OCT facilitates in vivo high-resolution evaluation of the retina [20], so detecting the presence of drusen, RPE atrophy, fibrovascular complex, sub and intraretinal fluid, among other features [18].  
More recently, OCTA has become available to retinal specialists. Unlike traditional angiography and ICGA, OCTA is a quick and non-invasive 3D imaging modality that does not require the use of a contrast agent [21]. OCTA detects the erythrocyte movement by analyzing the signal decorrelation within multiple B-scans performed repeatedly at the same location of the retina [12,16,21]. Changes in temporal contrast at a specific location indicate movement (erythrocyte motion) and hence vessel location [21].  
OCTA allows the direct visualization and measurement of the foveal avascular zone (FAZ) area and provides morphologic and quantitative vascular information on macular microcirculation, including the deep and superficial capillary plexus, with good reproducibility and repeatability, in vivo and without dye leakage and staining that may obscure the limits and anatomy of pathologies [22].  
However, as with any other imaging methods, several image artifacts also occur in OCTA, such as the shadow effect or those due to algorithms for data acquisition and image processing and motion-related artifacts [21].
# Study of the Ionospheric Scintillation Radio Propagation Characteristics with Cosmic Observations
## 2 Data and Methods
### Fitting Interval Analysis  
The interval of samples is also considered in the evaluation of fitting performance. Due to the errors in the data or induced during the data processing, the \"mean \(\pm\) standard deviation\" is commonly used to evaluate the fitting performance. In this study, the value of \"mean \(\pm\) standard deviation\" was adopted to count the interval length of the standard curve falling into this interval.  
The proposed framework of this analysis is described in Figure 1. The COSMIC occultation data are first downloaded, and then the right scintillation index is extracted. The data are classified to different levels. The Parson window method is then adopted to calculate the probability density function. The confidence interval is also determined. Finally, the characteristics and fitting performance of the measured curve and the standard probability density distribution curve are compared and discussed by using the statistical metrics introduced above.