esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 DBSCAN Algorithm
### Decision of the Epsilon and Minimum Sample  
There is no general way to determine \(\varepsilon\) and minPts, which are important parameters for applying the DBSCAN algorithm [11]. However, \(\varepsilon\) can be calculated through the reachability distance derived by the Ordering Points To Identify the Clustering Structure (OPTICS) algorithm [35]. The parameter \(\varepsilon\) calculated at the reachability distance is almost identical to that of the DBSCAN algorithm [36]. In OPTICS, the core distance connecting the core point is defined as the following equation:  
\[\textit{core dist}_{\varepsilon,\ minPts}(o)\coloneqq\left\{\begin{array}{ ll}\textit{UNDEFINED if }|\{x|d(x,o)\leq\varepsilon_{max}|<minPts\\ minPts\ dist(o)|otherwise\end{array}\right. \tag{4}\]  
where \(minPts\ dist\) is the distance to the nearest neighbor of the \(minPts\). In the case of \(\varepsilon_{max}\), the \(\textit{UNDEFINED}\) value can be omitted and the reachability of point \(p\) from point \(o\) can be defined as the following equation:  
\[\textit{reachability}(p\gets o)\coloneqq max\{dist(o,p),\ minPts\ dist(o)\} \tag{5}\]  
In accordance with this equation, the reachability plot applied to the arrival and departure data used in this study is shown in Figure 7. Therefore, the value of \(\varepsilon\) that can adequately calculate the number of clusters is 0.2.  
In order to set minPts, prior knowledge of the dataset to be used in the research area is required. In this study, as a result of inputting various minPts values to classify sub-clusters based on \(\varepsilon=0.2\), derived earlier, the most valid result was derived when minPts = 50.  
Figure 6: Example of the density-based spatial clustering of applications with noise (DBSCAN) algorithm.
# A Literature Review of Parameter-Based Models for Walkability Evaluation
## 3 Results and Discussion
### Gross Sidewalk Walkability Index  
The Gross Sidewalk Walkability Index (GSWI) is a methodological tool for the evaluation of the physical infrastructure of pedestrian areas developed by [PERSON] et al. [73]. It refers to the parameters related to sidewalk infrastructure. The model was developed based on the level of service of the pedestrian network and structured for evaluation based on five levels scale from A to E (from best to worst). Basic measurable indicators are the width, the length of the sidewalks, the general state of the infrastructure, equipment, and part of the physical environment.
# Mechanical Strength Characterization of Plastic Fiber Reinforced Cement Concrete Composites
## 3 Results
### Ultimate Compressive Strength  
The ratio of ultimate compressive strength of plastic fiber concrete to that of plain concrete is defined as compressive strength ratio, which gives a direct measure of strength improvement. It can be noted that all plastic fiber incorporated concrete mixes showed a gradual increase in compressive strength compared to the reference concrete (as noted in Figure 7). Test results provided in Table 6 show that the plastic fiber addition showed a reasonable strength increase in the case of all three fiber concrete mixes. Among the various concrete mixes, a maximum compressive strength of 33.20 N/mm\({}^{2}\) with an increase up to 17.73% was noted in the case of the plastic fiber substituted concrete mix (PFC-15H).
# Double Deep Q-Network with a Dual-Agent for Traffic Signal Control
## 2 Model and Problem Presentation
### Problem Presentation  
ATSC based on DRL methods under a four-phase signalized intersection is a difficult task. On the one hand, the traffic signal may be changed in a random sequence. It is because agent actions that match each phase of a traffic signal are chosen according to the maximal Q-value, which does not change in a specific sequence. On the other hand, it is hard for the information of states and rewards to be extracted accurately and adequately by the traffic signal control agent.  
In this paper, we target controlling a traffic signal in a four-phase signalized intersection effectively. Problems lie in how to keep the phasing sequence fixed and extract information from intersections adequately. For the first problem, our approach adopts a dual-agent (i.e., agent \(P\) and agent \(A\)) architecture, in which each agent possesses two actions to match two traffic phases. To keep the phase sequence of traffic signals fixed, each agent denoted by busy or waiting state shifts the control of green signals (more details can be seen in Section 3). Due to each action matching each phase, non-stationary transitions that easily appear in the algorithm based on binary actions are avoided. Therefore, the DRL agent in our environment learns an effective control policy faster. For the second problem, we present a reward function and a state representation. The state is represented by a matrix of vehicle positions extracted from the observation area, which is divided into small square-shaped grids according to [17]. It can provide much critical information for agents, such as distances between vehicles and the stop line. The reward function is based on the difference between the number of vehicles that pass through the intersection during the last time interval and those that are staying. All used information is easily obtained from the traffic signal simulator (e.g., SUMO), which is based on a vehicular network [20; 21]. Further, to improve the expansibility and stability of two agents in the traffic signal control, we build a deep neural network to map states to rewards.
# Experimental Studies of Scale Effect on the Shear Strength of Coarse-Grained Soil
## 3 Results
### Verification of Empirical Equation of the Shear Strength of Coarse-Grained Soil  
In order to verify the applicability of the above shear strength prediction equation for different types of CGS, the above conclusions need to be supported by other test data. [PERSON] et al. [29] carried out a series of large triaxial experiments on rockfill materials with different maximum particle sizes and grading structures by using the same fractal dimension grading design method and the same relative density sample preparation standard. It is mentioned that gneiss rockfill, as a medium-hard rock, is used in the test. The fractal dimensions \(D\) is 2.3, 2.6, and 2.7, representing coarse batching, intermediate batching, and fine batching, respectively. The cohesion, \(c\), internal friction angle, \(\varphi\), and the grading structure parameter obtained by the optimal fitting of the in situ grading curve are listed in Table 4. The maximum particle sizes of the graded particles are 60, 40, and 20 mm, respectively. The maximum particle size grading forms of different particles remain similar and meet the grading characteristics after scaling by the similar grading method, as given in Figure 14.  
It can be seen from Table 4 that when a sample preparation standard with the same relative density is adopted, the cohesion and internal friction angle increase with the increase of the maximum particle size, no matter what kind of gradation. This finding is consistent with the conclusion of these test results. However, compared with the sandy-grained soil, rockfill materials are less affected by the scale effect. The maximum and minimum values of the cohesion and internal friction angle of different dimensions are 5.17-17.31% and 3.16-10.33%, and the average values are 9.47% and 6.05%, respectively.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**In Situ Dimension** & **Maximum Particle Size \(d_{max}\)** & \(n\) & \(e\) & \(S\) & **Cohesion/kPa**[29] & **Internal Friction Angle(\({}^{\circ}\)) [29]** \\ \hline \multirow{3}{*}{\(D=2.3\)} & 60 & 0.7 & 0.003 & 0.512 & 185.808 & 39.68 \\  & 40 & 0.7 & 0.003 & 0.512 & 177.727 & 39.4 \\  & 20 & 0.7 & 0.003 & 0.512 & 153.650 & 35.58 \\ \hline \multirow{3}{*}{\(D=2.6\)} & 60 & 0.4 & 0.02 & 0.686 & 204.094 & 41.41 \\  & 40 & 0.4 & 0.02 & 0.686 & 200.382 & 41.14 \\  & 20 & 0.4 & 0.02 & 0.686 & 193.541 & 40.1 \\ \hline \multirow{3}{*}{\(D=2.7\)} & 60 & 0.3 & 0.09 & 0.779 & 209.852 & 41.72 \\  & 40 & 0.3 & 0.09 & 0.779 & 197.401 & 39.78 \\ \cline{1-1}  & 20 & 0.3 & 0.09 & 0.779 & 202.085 & 40.07 \\ \hline \end{tabular}
\end{table}
Table 4: Fitting results of test data of rockfill material soil.  
Figure 13: Measured and predicted values of the internal friction angle of CGS.
# Recent Applications of Artificial Intelligence in Radiotherapy: Where We Are and Beyond
## 1 Introduction  
Artificial intelligence (AI) is a field of computer science that focuses on developing computer-based approaches that mimicking humans' ability to make decisions and solve problems. From a conceptual standpoint, AI can be divided into two main categories: iterative optimization (IO) [1], also known as computationalism [2], and machine learning (ML), which in turn includes deep learning (DL), resembling a matryoshka scheme. IO algorithms are based on the progressive combination of several scripts that are independently createdas parts of the decision-making process of an experienced operator. ML uses statistical functions to develop models that self-learn patterns from collected data and make decisions on new data. ML includes DL, which involves the combination of simple algorithms in a complex hierarchical and \"deep\" architecture, inspired by the connections of neurons in the network-based structure inside the human brain.  
Currently, AI has been applied in many medical fields, thanks to its ability to find novel solutions for managing very complex, high-dimensional, and multifactorial problems, as commonly encountered in the field of therapy and imaging [3].  
Thanks to the extensive development that has occurred in radiotherapy (RT) oncology information systems (OIS), a large quantity of information (e.g., patient assessments, multi-modality images, absorbed dose distributions, machine performance, and patient-specific quality assurance) is now available. These databases are reaching dimensions beyond those manageable by individuals or groups, meaning quality assurance (QA) of the RT workflow processes and machine status checks are not possible.  
AI can allow an integrated and comprehensive assessment of the patient's condition using all available information in the OIS to support radiation oncology staff. Importantly, a radiation oncologist's judgment can be more accurate than those made by machines, as they are often dedicated to a single task (e.g., patient identification, prioritization, monitoring, or patient workflow checks). radiation oncologists have daily interactions with patients and have the capability to understand their unspoken needs and values. In this sense, AI tools should be considered as computer aids to the staff, who would remain responsible and in charge of patient management.  
Furthermore, the rapid development of new AI-based solutions applied to RT will impact the daily clinical practice, such as the (semi-)automatic delineation of normal tissues and target volumes or treatment plan optimization. Nevertheless, AI strategies use advanced statistical techniques and complex algorithms that are often not fully understood by RT staff, with the risk of being employed as \"black box\" tools [4].  
Our overview focuses on the AI-based approaches implemented or proposed in recent years in RT to favor the more conscious use of available solutions. At the same time, we highlight possible barriers to implementation in clinical practice and identify possible countermeasures.
###### Abstract  
With a constant increase in the number of deployed satellites, it is expected that the current fixed spectrum allocation in satellite communications (SATCOM) will migrate towards more dynamic and flexible spectrum sharing rules. This migration is accelerated due to the introduction of new terrestrial services in bands used by satellite services. Therefore, it is important to design dynamic spectrum sharing (DSS) solutions that can maximize spectrum utilization and support coexistence between a high number of satellite and terrestrial networks operating in the same spectrum bands. Several DSS solutions for SATCOM exist, however, they are mainly centralized solutions and might lead to scalability issues with increasing satellite density. This paper describes two distributed DSS techniques for efficient spectrum sharing across multiple satellite systems (geostationary and non-geostationary satellites with earth stations in motion) and terrestrial networks, with a focus on increasing spectrum utilization and minimizing the impact of interference between satellite and terrestrial segments. Two relevant SATCOM use cases have been selected for dynamic spectrum sharing: the opportunistic sharing of satellite and terrestrial systems in (i) downlink Ka-band and (ii) uplink Ka-band. For the two selected use cases, the performance of proposed DSS techniques has been analyzed and compared to static spectrum allocation. Notable performance gains have been obtained.  
satellite communications; dynamic spectrum sharing; spectrum coexistence; distributed algorithms; ns-3 +
Footnote †: journal: Sensors
# Multi-Objective Optimisation in Multi-QoS Routing Strategy for Software-Defined Satellite Network
## 2 Related Works
[PERSON] et al. proposed ARPQ [22] (Adaptive Routing Protocol for Quality of Service) routing protocol, which is mainly suitable for delay-sensitive applications, such as VOIP (voiceover internet protocol). They firstly set a threshold and then classify incoming packets for transmission. When the transmission delay of packets is less than this threshold, the packets are directly transmitted to the destination node in the LEO satellite layer. If transmission delay is greater than this threshold, the transmission is carried out in the MEO layer. The disadvantage is its poor ability to predict congestion. [PERSON] et al. proposed HDRP [23] (Hierarchical and Distributed QoS Routing Protocol) based on the satellite group and group management. Comparing with MLSR and SGRP, it does not adopt virtual node strategy to shield the dynamics of LEO satellite network, but uses the actual satellite location to define the group members. In order to reduce the number of snapshots, they further propose a snapshot merge strategy. With the application of heuristic algorithm, the routing performance of QoS routing protocols have been greatly improved. [PERSON] [24] et al. proposed optimizing the routing strategy by using ant colony algorithm and genetic algorithm to improve the performance of satellite networks. The traffic balancing routing protocol is also a hot issue. It can improve total throughput and reduce packet drop rate. Because satellite network topology is predictable and cyclical, traffic congestion can be predicted and the routing strategy can be adjusted in advance. [PERSON] et al. [25] have proposed a QoS routing protocol to balance traffic and traffic is divided into three types: delay sensitive traffic, video application traffic, and general transmission traffic. The time delay sensitive traffic has the highest priority. The video application traffic is transmitted in LEO satellites and general transmission traffic is transmitted in GEO satellites. The disadvantage is that the MEO layer can be congested. In order to solve this problem, [PERSON] et al. [26] proposed that traffic is sent through several MEO satellites. What's more, [PERSON] et al. [27] further theoretically calculated the traffic deflection threshold. When the traffic exceeds this threshold, it is transmitted to MEO satellites. Otherwise, it is transmitted directly at the LEO layer.
# I/Q Imbalance and Imperfect SIC on Two-Way Relay NOMA Systems
## 5 Conclusions  
In this paper, the performance of a TWR C-NOMA system with IQI and ipSIC over the Rician fading channels was investigated. The analytical expressions of these performance metrics were obtained. In addition, the asymptotic outage behaviour and the diversity order were discussed in the high SNR region. Based on the analytical results, it was shown that the outage performance of the NOMA based system was better than that of OMA. The results revealed that the system outage performance could significantly deteriorate by the IQI and ipSIC. It was also presented that the IS resulted in a zero diversity order in the system. Moreover, the system throughput was found to improve with the increase of SNR, in which it converged to a fixed constant in the high SNR region.  
**Author Contributions:** Conceptualization, X.L., Q.L., C.Z. and X.T.; data curation, X.L. and Q.L.; methodology, X.T., X.L. and Q.L.; software, Q.L.; supervision, X.T., H.P. and H.C.; validation, X.T., X.L., H.P., H.C., K.M.R. and R.K.; formal analysis, X.L., Q.L.; investigation, H.P., H.C., K.M.R. and R.K.; writing, original draft preparation, X.T. and Q.L.; writing, review and editing, X.L., Q.L., K.M.R. and R.K. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported in part by the Henan Scientific and Technological Research Project under Grant 181202210307, in part by the Fundamental Research Funds for the Universities of Henan Province under Grant NSFFR180309, in part by the Outstanding Youth Science Foundation of Henan Polytechnic University under Grant 2019-4, in part by the Key Scientific Research Projects of Higher Education Institutions in Henan Province under Grant 20A510007.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Automated Trucks' Impact on pavement Fatigue Damage
## 5 Conclusions  
In this study, we numerically simulated the fatigue performance of 864 unique scenarios with varying wander modes, market penetration, lane width, and flexible pavement layers' thickness and stiffness. The fatigue damage index (DI) for each scenario was calculated and compared to understand the impacts of these variables on pavement fatigue. In summary, our study's findings yield practical recommendations for vehicle designers and road practitioners. During the initial stages of AT implementation, characterised by low market penetration, we advise using the uniform-wander mode over the zero-wander mode to mitigate ATs' negative impact on pavement fatigue damage. Additionally, in the full automation era, utilising wider lanes can further safeguard against increased fatigue damage associated with ATs. However, if truck manufacturers are compelled to adopt the default zero-wander mode, the efficacy of wider lanes diminishes in terms of fatigue damage reduction. Instead, enhancing pavement layer stiffness or thickness from the outset becomes imperative. Alternatively, dedicating narrower lanes may offer cost savings without significantly exacerbating fatigue damage. Nonetheless, a comprehensive economic analysis and further investigation are requisite to thoroughly assess the associated costs and benefits.  
**Author Contributions:** The authors confirm their contribution to the paper as follows: Conceptualisation, A.Y., B.V. and A.P.; data curation, A.P.; formal analysis, A.Y.; funding acquisition, B.V. and A.P.; investigation, A.Y. and A.P.; methodology, A.Y., B.V. and A.P.; project administration, A.P.; resources, A.Y., B.V. and A.P.; software, A.Y.; supervision, B.V. and A.P.; validation, A.Y.; visualisation, A.Y.; writing--original draft, A.Y.; writing--review and editing, B.V. and A.P. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the Special Research Fund (BOF) of Hasselt University with the BOF number \"BOF19 OWB26\".  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data available on request due to privacy.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
# Tokens Shuffling Approach for Privacy, Security, and Reliability in IoHT under a Pandemic
## 3 Proposed Approach (TSA)
### Strengths and Limitations of TSA  
TSA enables the health domain to achieve many advantages, which are:  
* TSA did not use fake data protection techniques such as the Dummy approach or data obfuscation, thus maintaining the quality of service and not affecting the accuracy of its results.
* The user in TSA does not need to completely trust any of the cooperating parties, such as the service providers, the fog nodes, or even the cooperating users.  
Figure 2: Second Scenario of TSA.  
* By saving the data locally for people who have not been proven to be infected, the load on service providers has been reduced, and the privacy and security of users' data have been enhanced.
* The real data sent by each user to SP are not related to him (they are data for another user). This will enhance his privacy and encourage him to cooperate with others.
* The fog node reduces the load on the user in communicating with service providers, on the one hand, and the other hand, it enhances the privacy of users because they do not have to communicate directly with service providers.
* The use of the token greatly reduces the chances of fake users who want to tamper with the results and statistics of medical centers and service providers.
* The use of blockchain enhances data security, integrity, and reliability.  
However, there are some limitations to TSA, which are:  
* Damage to the user's device causes the loss of data that are saved locally, but this is rare.
* In the case of cooperation between a malicious fog and a malicious SP2, user data can be exposed in the first scenario. However, the problem has been resolved in the second scenario.
* TSA is based on more than one user existing in the same area, which makes sense in pandemic situations, but if there is only one user, TSA in the worst case achieves what the Blind Third Party (BTP) approach achieves, where the user encrypts their data with an SP2 key and sends them via the fog node.
* TSA cannot cover the privacy issue in all types of LBS applications.