esa-sceva/satcom-chunk-collection huggingface dataset:
# On the Impact of Multiple Access Interference in LTE-V2X and NR-V2X Sidelink Communications +
## 1 Introduction
Extensive Monte Carlo (MC) simulations are used to validate the proposed analytical model's performance predictions and approximation theory, and it is shown that there is good agreement between the approximated and simulated results for both LTE-V2X Mode 4 and NR-V2X Mode 2. In summary, the main contributions of this paper are the following. * A vehicle platooning use case scenario is illustrated and analyzed by considering the evolution of the standards from LTE-V2X to 5G NR-V2X sidelink operations. * An analytical model of the multiple access interference due to the sensing-based semi-persistent scheduling operation, based on the preliminary work on LTE-V2X in [22], is provided and validated for the considered scenario. * A framework for comparison of the successful packet delivery performance of two technologies is provided, accounting for the scheduling of available resources, the number of interfering vehicles, and their relative positions. * As an interesting insight, the interplay between an improved sensing-based scheduled access and the enhanced physical layer specification in 5G NR-V2X Mode 2 is shown to be particularly beneficial for the performance as the number of vehicles and the inter-vehicle distance increase. The rest of this paper is organized as follows: The system model is presented in Section 2. The Cellular-V2X technologies and the analytical characterization of the sidelink operations are described in Sections 3 and 4, respectively. The comparison of LTE-V2X Mode 4 and NR-V2X Mode 2 is validated by the proposed analytical model and Monte Carlo simulations in Results Section 5. Finally, Section 6 concludes the paper.
## 1 Introduction
This opacity limits their application in medical diagnosis, as human experts cannot clearly understand the decision-making process of the model, making it difficult to collaborate effectively with these kinds of AI systems. In this work, we propose a hybrid model that combines a rule-based Bayesian network and a deep language model based on machine learning. The deep-language model, trained on human-written symptom descriptions, diagnoses the most probable conditions. The Bayesian network built by experts, on the other hand, uses clinically validated information from diagnostic manuals to refine this initial diagnosis of the large language model. By integrating these models, we achieve high diagnostic accuracy, allowing the system to process user-provided symptom descriptions without the need for expert intervention, while also ensuring transparency and explainability, enabling users and professionals to understand the reasoning behind the diagnoses. This fusion aims to create a highly accurate diagnostic tool for mental conditions that adheres to clinical standards and provides clear, understandable results. A diagram summarizing the model is shown in Figure 1.  
This work is organized as follows:  
In the Methods section, we define the system and establish a set of desirable requirements to ensure that it serves as a useful tool for both users and experts. We then detail the components of the system--specifically, the deep language model trained to predict condition categories based on symptom descriptions from users and the Bayesian neural network used to refine the initial response while providing an explainable and transparent diagnostic decision. We describe the construction of the dataset and network necessary for these models and outline the primary features of each one. In the Results section, we present the performance of both models on two datasets designed to evaluate the accuracy of the system in classifying clinically valid condition categories according to the DSM-V diagnostic manual. We compare these results with other models in the literature and with classifications made by psychology professionals. Finally, in the Discussion section, we discuss the results and present our conclusions.
# Measuring the Centrality of DNS Infrastructure in the Wild
## 6 Name Server Centrality Analysis
### The Response of Name Server  
Typically, the authoritative name servers should not respond to any lookup from domains that are not served by them. We measured the collected authoritative name servers. Table 9 shows the response code section of measurement results. As expected, most authoritative name servers \(REFUSE\) our lookups, but 51,216 name servers respond with \(NOERROR\). Furthermore, we filtered the empty and resolver doctored responses and obtained 17,628 name servers that respond to correct records, posing potential security risks.  
In analyzing the 17,628 authoritative name servers, we found 9,739 servers perform recursive resolution; and 7,889 servers simply forward queries to public DNS or other private iRDNSes. What worries us is that 23 servers belonging to OVH SAS, which is a leading IP infrastructure provider, answer correct responses.  
**Summary:** The results of our analysis are summarized in Figure 12. Nearly half (49%) of all domain names are supported by a few name server providers (Top 10), whose infrastructure is centralized into a small number of IP providers, posing quiet yet fatal threats. It is worrying that the multiple NS providers configuration is rare, and 98% of domain names apply to a single NS provider. The centralization of domain authoritative servers could cause disruptions in domain resolution services. The lesson of Facebook tells us that just relying on a single NS provider is dangerous. We recommend that domain owners employ a more diverse set of authoritative server configurations to reduce the probability of single-point failures, while also preventing the concentration of resolution traffic on a specific service provider.  
\begin{table}
\begin{tabular}{l r} \hline
**Rcode** & **\#** \\ \hline
5 (REFUSED) & 545,941 \\
0 (NOERROR) & 51,216 \\
2 (SERVFAIL) & 12,095 \\
3 (NXDOMAIN) & 3517 \\
9 (NOTAUTH) & 481 \\
4 (NOTIMP) & 42 \\
10 (NotZone) & 4 \\
1 (FORMERR) & 1 \\ \hline \end{tabular}
\end{table}
Table 9: Response code distribution.
# User-Perceived Capacity: Theory, Computation, and Achievable Policies
## 4 The Cross-Layer Approach  
In this section, we prioritize addressing the dual problem of enhancing the user-perceived capacity, that is, minimizing the average power while meeting the user-perceived capacity requirements. Subsequently, we propose a search algorithm capable of determining  
Figure 2: Reading mode. The blue step-change line represents the number of packets remaining as backlog at the base station in each time-slot in a period. The red line represents the number of packets remaining that are unread at the user side.  
the achievable user-perceived capacity under power constraints. Firstly, we delve into a detailed exposition of the scheduling policy, aiming to minimize the average power consumption while ensuring a continuous supply of packets for the user. Specifically, we analytically describe the scheduling policy under a probabilistic method, which is comprised of a conditional probability distribution of \(s[t]\) under different values of \(q[t]\), \(h[t]\), and \(m[t]\). Based on this, we represent the system as a Markov chain with the system state being the pair of the queue length and time index, i.e., \((q[t],m[t])\). By exploiting the steady-state distribution of the Markov chain and the probabilistic scheduling policy, we re-write problem (12) as a linear programming problem. By solving the derived linear programming problem, we obtain the minimum average power under the continuous reading constraint, as well as the corresponding scheduling policy.
## 6 Results and Discussion
### Results Depending on the Message TTL  
Figures 5-7 present the results when increasing values of message TTL. By using social relationships among the nodes as a forwarding criterion, we intuitively expect to improve data exchange by natural encounters. The results shown in Figure 5 confirm this intuition, since the FSF algorithm achieved the best delivery ratio in all scenarios, delivering up to 7% more messages than the other algorithms. It is worth mentioning that, in all tested scenarios, the algorithms behave quite similarly, that is, the larger the message TTL is, the higher the delivered message ratio is. In addition, the Friendship Routing algorithm gets the second-best delivery ratio in all scenarios tested, confirming that the use of social ties (friendship, acquaintances, bridges, etc.) is an interesting criterion to increase the message delivery probability in OppNets.  
With respect to the metrics delivery delay and average cost, the algorithms exhibit the same behaviour: the higher the message TLL, the longer the delivery delay, the higher the average cost. In addition, for all algorithms, the higher the delivery ratio, the higher the delivery delay is. Since the FSF algorithm delivered more messages, it also gets the the longest delivery delay. Regarding the metric average cost, FSF achieved the best result in Cambridge and NCCU. The Bubble-Rap gets the best result in Sassy, while, in Reality, the PRoPHET algorithm performed better.  
Figure 5: Delivery ratio when increasing message TTL.  
Almost certainly, the number of relationships among the nodes, as well as the intercontact time between nodes with the social relationship, has impacted the FSF performance. The higher the average number of social relationships per node, the higher is the probability of forwarding a message during a contact opportunity. It clearly can also increase the FSF average cost. On the other hand, when the intercontact time between nodes with the social relationship is high, it clearly can impact the average delivery delay. From the simulation results, it is reasonable to assume that the average number of social relationships detected by FSF for each node is low and the intercontact time is high, which contributes to increasing the FSF average delay and reducing the number of messages forwarding.
# Fine-Grained Detection of Pavement Distress Based on Integrated Data Using Digital Twin
## 5 Results and Discussion
### Comparison of Results for Different Detection Scenarios  
The light and environment renderer in the UE4.26 physics engine is used to generate three types of poor detection scenarios, namely shadow, occlusion and blur, and 25 images (5 images per distress category) of pavement distress from the rendered images are selected to test the effectiveness and adaptability of the YOLOv5 network under poor detection scenarios after fully training on two types of datasets. The two types of datasets are the integrated dataset with the best virtual-to-real ratio of 3:1 and the dataset with a virtual-to-real ratio of 0:1 (the only real dataset).  
Precision, the proportion of correctly detected objects to the total number of detections, usually represents the wrong detections, while recall, the proportion of correctly detected objects to the total number of actual objects, represents the missed detections. Since missed detections of diseases in complex detection scenarios are more likely to occur than wrong detections and could lead to serious consequences, this section uses recall to evaluate the effects of the YOLOv5 network trained on different datasets, as shown in Figure 13. The YOLOv5 network fully trained with the best virtual-to-real ratio (3:1) integrated dataset achieves more than 40% recall for all five pavement-distress types in the three poor detection scenarios (presented by the bar chart), with optimal adaptation to the shadow scenario, achieving 100% recall for longitudinal cracks, cross cracks, alligator cracks and potholes. In contrast, the YOLOv5 network trained on the only real dataset achieves less than 20% recall in shadow and occlusion scenarios (presented in the line chart), and performs best in blur scenarios, but the recall is about 40% lower than the YOLOv5 network fully trained on the virtual-to-real ratio (3:1) integrated dataset.  
Figure 14 shows a portion of the detection results for the asphalt pavement distress. The YOLOv5 network, fully trained with the optimal virtual-to-real ratio (3:1) integrated dataset, enables the correct detection of all five pavement-distress types in the three poor detection scenarios, with a confidence level of over 90% for all predicted labels. The YOLOv5 network trained on the real dataset sometimes makes wrong detections in shadow and blur scenarios; for example, it mistakes shadow objects and normal pavements for distress, and the confidence level of the predicted labels is mostly below 50%, which makes it difficult to effectively detect the five types of pavement distress and poorly adapted to complex detection scenarios.  
Figure 13: Recall of YOLOv5 network trained on the integrated dataset with the best virtual-to-real ratio of 3:1 for three adverse-detection scenarios.  
Figure 14: Test results of YOLOv5 network trained on two datasets.
# Detection and Treatment Methods for Perfluorinated Compounds in Wastewater Treatment Plants
## 1 Introduction  
Perfluorinated compounds (PFCs) do not easily degrade biologically in the natural environment due to their extremely stable covalent bonds. Artificially synthesized and produced, PFCs are used in manufacturing various household goods and are detected consistently in water [1]. There has been much research interest in PFCs as they have been reported to affect the natural water system, causing disturbances to the ecosystem [1, 2]. Since the mid-2000s, the European Union (EU), Canada, and the USA, among others, have started regulating PFCs. At the fourth Stockholm Treaty Conference of the Parties in May 2009, it was agreed that some of these PFCs would be included in the target compounds of the treaty [3, 4, 5]. The most well-known PFCs are used in industry and broader daily life for waterproof materials, lubricants, paint, ink, paper, fiber, carpet, ovens, cooking equipment, electronic products, packaging materials, metal coating, cleaning products, semi-conductors, and firefighting products [2]. However, since many household products use PFCs as additives, it is difficult to calculate their amount of usage and the amounts released into the environment. Whereas the residual concentration of PFCs in the environment is small, at the ng/L (ppt) level or below, the effects on the ecosystem cannot be disregarded if the ecosystem then is exposed long-term to PFCs [6, 7, 8].  
The pathways of PFCs to the environment are numerous; most of them enter via wastewater treatment plants. However, little has been known about the mechanisms through which these persistent and toxic PFCs are removed. Therefore, it is difficult to improve the removal of the small amount of PFCs with existing wastewater treatment methods [9, 10].  
In this study, we aimed to investigate PFCs concentration in the influent and effluent of an actual wastewater treatment plant, which is the source of perfluorocarbons in large industrial complexes and highly populated areas. The distribution properties of PFCs were investigated from the influent wastewater treatment plant and an optimal treatment plan was designed through a pilot test, which may eventually serve as data for future surface water management plans.
# An Experimental Study of the Leakage Mechanism in Static Seals
## 3 Experimental Design for Sealing Surface Leak-Rate Measurement
### Experimental Implementation
#### 3.3.1 Effect of Sealing Surface Characteristics on Leak Rate  
The properties of the sealing surfaces include material properties and surface topography, measurement parameters of surface topography and the material parameters are shown in Table 1. In this experiment, the leak rates of the four test pieces in contact with the silicon wafers were measured. The surfaces of test pieces No. 1 and 2 were smooth, so that the amount of leakage was relatively small. For these two pieces, as the contact load of the device would gradually decrease during long-term measurement (greater than 200 min), the gas pressure was increased to 0.1 MPa to reduce the measurement time. The surfaces of test pieces No. 3 and 4 were rough, and the leakage amount was consequently larger. As a result, for these two pieces, the gas pressure was set to 0 MPa, so that the measurement conditions of the experimental device were satisfied when measuring the leakage amount of the seepage process. All experimental fluids used in this study were water, whose viscosity coefficient was \(1.01\times 10^{-3}\) Pa\(\cdot\)s. In addition, the experimental temperature is at 25 \({}^{\circ}\)C.
## 5 Analysis of Experimental and Simulation Results
### Thermally-Stratified Water Body Conditions
#### 5.2.3 Case 7 Working Conditions
By 10 s of discharge, the jet temperature diffusion gradually approaches the stratified interface. The influence of the stratified interface inhibits the upward floating process of the jet temperature diffusion at 15 s and indicates a trend of horizontal diffusion to the right. However, due to higher temperatures from jet attenuation compared to those in the upper layer of water, there is continuous upward diffusion of the jet temperature resulting in noticeable perturbation at the stratified interface. For jets with initially high temperatures, environmental water stratification still has an inhibitory effect on their rise and rate of thermal diffusion. When reaching a stratification interface during this process, if temperatures from attenuated jets remain higher than those in upper water layers, then upward thermal diffusion occurs, but at a reduced rate. This translates gradually into horizontal thermal dispersion while exhibiting more apparent dissipation phenomena for the jet temperatures.
# MedicalSeg: A Medical GUI Application for Image Segmentation Management
## 3 Methods
### Segmentation Algorithms
#### 3.2.1 Threshold Segmentation  
One of the most common ways to extract objects from the background is to select a threshold value (\(Th\)) applicable over an entire image. Any point \((x,y)\) of the image at which \(f(x,y)>Th\) is called an object point; otherwise, the point is called background point. In other words, the segmented image \(g(x,y)\), is given by:  
\[g(x,y)=\left\{\begin{array}{l}1,iff(x,y)>Th\\ 0,iff(x,y)\leq Th\end{array}\right. \tag{1}\]  
The process given in this Equation (1) is referred to as global thresholding. The term local or regional thresholding is sometimes used to denote variable thresholding in which the value of \(Th\) at point \((x,y)\) in an image (\(I\)) depends on properties of neighbourhood of \((x,y)\). Moreover, that point \((x,y)\) corresponds to a pixel value of the matrix obtained from the image \(I\). The most challenging part of using this segmentation technique is to find the accurate threshold value [27].  
Moreover, a normalization step was performed in our algorithm. During normalization, the \(Th\) value is used as a minimum value (\(Tmin\)) and a max value (\(Tmax\)) for each image is calculated using a maximization function. In order to normalize the image according to \(T\). The following equations are applied in order to obtain the normalization matrix of the image \(TNorm\):  
\[Tmin=Th \tag{2}\] \[Tmax=max(I)\] (3) \[TNorm=(I-Tmin)/(Tmax-Tmin) \tag{4}\]
Cathode:  
\[\text{O}_{2}+2\text{H}_{2}\text{O}+4\text{e}^{-}\to 4\text{OH}^{-}\]  
Overall:  
\[2\text{Zn}+\text{O}_{2}\to 2\text{ZnO}\]  
As above, oxygen from the air is reduced to provide OH\({}^{-}\) and Zn is oxidized in response to the OH\({}^{-}\) provided. However, the corrosion of the zinc anode progresses more quickly when using an aqueous electrolyte, resulting in a hydrogen evolution reaction (HER). The following equation describes the HER that occurs during a discharge reaction.  
HER:  
\[\text{Zn}+2\text{H}_{2}\text{O}\rightarrow\text{Zn(OH)}_{2}+\text{H}_{2}\uparrow\]  
Therefore, the amount of zinc that participates in the reaction is reduced. In addition, dendrite formation occurs because of corrosion, and external environmental factors due to the open-cell structure can greatly affect the performance of the batteries. Many methods using electrolyte additives are available to solve this problem. Several additives have been studied, including organic and inorganic materials, as well as conductive polymers [22; 23; 24]. Among them, inorganic materials including metal species have been used due to their a higher reduction potential than Zn. Hg is one of the effective additives which can effectively suppress the HER. However, its use is limited due to its impact on the environment. Several researchers have reported that some transition metals, such as Ni, Mg, Sn, Fe, Bi, and Al, are effective in reducing dendrite formation and the HER [25; 26; 27]. In and Cu also have similar effects [28; 29]. Inorganic additives such as In\({}_{2}\)O\({}_{3}\), Al\({}_{2}\)O\({}_{3}\), SnO, and Bi\({}_{2}\)O\({}_{3}\) have been reported to have similar effects [30; 31; 32].  
CuO has good electrical properties; it has a band gap of about 1.2 eV and high carrier concentration. Using this advantage, CuO is used in p-type semiconductors and supercapacitors [33]. It is a low-cost material and has the advantage of being producible via a variety of synthesis methods. Additionally, depending on the synthesis method used, a variety of nanostructures (e.g., sphere-like, cross-like, hexagonal, and flower-like) can be synthesized [33; 34; 35]. In addition, the large surface area of nano CuO gives this material much better chemical properties than bulk materials. [PERSON] et al. reported that copper compounds have been shown to affect the corrosion behavior in Zn-air batteries [36].  
In this work, various shapes of nano CuO were synthesized by the hydrothermal synthesis method and used as an additive. The discharge performances of the nano CuO additives were compared. In addition, to identify the self-discharge behavior, the discharge capacities after 1 h and 12 h storage were compared. Additionally, the corrosion factors were measured using the Tafel extrapolation method.