esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Results and Their Analysis  
### The Effect of Transverse Reinforcement  
Experimental results of samples series A reflecting the effect of transverse reinforcement for physical-mechanical properties of annular cross section of centrifuged concrete are presented in Figure 4. The authors are analyzed the ratio of transverse (spiral) reinforcement \(\mu_{cir}\) = 0.25% to 1.25%, concrete strength \(f_{c}\) = 30 MPa to 50 MPa and relative wall thickness of annular cross section element \(\beta\) = \(t/r_{1}\) = 0.25 to 0.4.  
The effect of the spiral reinforcement on the modulus of elasticity \(E_{cir}\), on the cylindrical strength \(f_{cir}\) and strain \(\varepsilon_{cir}\) of the centrifuged concrete, should be evaluated by the following formulae:  
\[\alpha_{cir\ 1}\approx 1 \tag{13}\]  
\[\alpha_{cir2}\ =\ 0.81+0.9\mu_{cir}-0.6\mu_{cir}^{2} \tag{14}\]  
\[\alpha_{cir3}\ =\ 1+0.7\mu_{cir}(1-0.01f_{c}-0.16\mu_{cir}) \tag{15}\]  
Thus, experiments (Figure 4) demonstrate that the intensity of the spiral reinforcement has practically no effect on the modulus of elasticity of the centrifuged concrete. The reason for this is that in the initial stage of loading in which the initial modulus of elasticity is determined, i.e., under relatively low stresses, the reinforcement has no noticeable effect on the stress-strain properties of concrete without microcracks.  
Short-term compression tests have shown that longitudinal strains of reinforced concrete specimens with transverse spiral reinforcement were larger by 10%-25% than those of the specimens not reinforced by spiral reinforcement. These results match the scarce data provided by other authors [32] on testing the specimens of annular cross section reinforced by transverse (spiral) reinforcement. The experiments conducted by authors and by [PERSON]. [32] have shown that the mechanical properties of centrifuged concrete are influenced not only by longitudinal reinforcement, but also by transverse reinforcement. This influence is similar to the effect produced on concrete by the core of the column's casing with confinement reinforcement. It was stated that a decrease in the pitch of the spiral (with a ratio of transverse reinforcement \(\mu_{cir}\) = 0.25% to 1.25%) causes an increase in the structural compressive strength of concrete up to 20% [2; 18]. This relatively insignificant effect of the transverse reinforcement on the structural strength of the centrifuged concrete may be accounted for by the fact that the stress-strain state of the tubular sample is closer to the two-dimensional (plane) state than to the three-dimensionalstate. It is known [32] that plane biaxial compression has no effect on the strength of concrete. It is close to its prism strength.
# Monitoring Acute Heart Failure Patients Using Internet-of-Things-Based Smart Monitoring System
## 2 Related Work
The accuracy of the technique is low compared to other techniques, yet it is a remarkable step toward automated disease diagnosis. The complete summary of IoT-based works is shown in Table 2.  
Along the same lines, Reference [41] developed a system consisting of cloud, IoT sensors, and IoMT devices to deal with cardiac patients. The system measures the patient's eye movement, body temperature, and oxygen level for heart disease. Similarly, authors designed a model to monitor heart disease in [42]. The proposed approach utilizes sensor data and an ensemble model in a fog environment. The model performs early diagnosis of heart patients. In [43], an Adaptive Neuro-Fuzzy inference system with multiple kernel learning is used to identify cardiac disease. The system provides better results, although the computational complexity is high. An automated approach is designed in [44] to distinguish between people at high risk of heart failure and those at low risk. The authors used the classification and regression tree (CART) and achieved specificity and sensitivity values of 63% and 93%, respectively. The existing state-of-the-art studies on cardiac disease is shown in Table 3. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Ref.** & **Standards** & **Purpose** \\ \hline
[26] & Bluetooth & Monitoring and detection of heart sounds \\ \hline
[27] & Bluetooth & Fitness health monitoring when engaging in physical activity \\ \hline
[28] & Wifi & Remote monitoring of health condition \\ \hline
[29] & 3G & Provide fast data communication during emergencies \\ \hline
[30] & Satellites & Track patients and approach them in an emergency \\ \hline
[31] & NFC & Home surveillance for less-skilled individuals \\ \hline
[32] & 2G, 3G & Skincare in real-time \\ \hline
[33] & RFID & Locating and tracking medical equipment quickly \\ \hline \hline \end{tabular}
\end{table} 
Table 1: IOT connectors and their applications. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Ref.** & **Year** & **IoMT** \\ \hline
[34] & 2016 & Cloud-based remote ECG system \\ \hline
[36] & 2016 & Secure healthcare system for patient \\ \hline
[37] & 2016 & Intelligent healthcare system for patients in medicine \\ \hline
[38] & 2016 & IoT-based kidney anomaly recognition system \\ \hline
[39] & 2016 & IoT enabled healthcare \\ \hline
[40] & 2016 & IoT-based mobile medical health care system \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Summary of IoT-based works.
# BDS Orbit Maneuver Detection Based on Epoch-Updated Orbits Estimated by SRIF
## 1 Introduction
Our and other scholars' previous studies also used GNSS observations to detect orbit maneuvers and determine the time of the maneuver. However, the inconsistency between the observation and the geometry distance between the satellite and station has certain limitations in orbit maneuver detection. Orbit maneuvers will cause OMC anomalies in multiple measurement stations, but there are also other situations that can cause this phenomenon, such as satellite clock anomalies. In addition, the first 6-7 h of a satellite orbit after the start of the orbit maneuver cannot be obtained, the satellite status is marked as unhealthy in the broadcast ephemeris, and the orbit elements are in an invalid state, which also adds difficulty to the detection of orbit maneuvers because of the error geometry distance between satellites and stations. As is well known, the acceleration caused by the orbit maneuver thrust directly changes the satellite orbit state. If the orbit of the maneuvering satellite can be calculated and compared with the orbit before and after the maneuver, the start and end time of the orbit maneuver can be analyzed. The most direct and effective method for detecting orbit maneuvers is to analyze the orbit estimates of the maneuvering satellite, which avoids relying on the invalid broadcast ephemeris and eliminates the potential interference caused by observation errors in detecting orbit maneuvers. However, the main challenge is the presence of system errors induced by the maneuvering thrust, which makes it difficult to estimate the precise orbit of maneuvering satellites by using conventional mechanical models. In this study, we propose a novel method to detect the maneuvering start and end-time for GNSS orbit maneuvering satellites, which is applicable for both post-processing and real time cases. We adopt the SRIF for real-time POD estimation and maneuver detection, in which the effect caused by the orbit maneuver, that is, the abrupt change in orbit accelerations and the corresponding changes in orbit velocities and positions, can be properly adjusted via the covariance of the state constraint equations. The orbit difference with the predicted orbit before and after orbit maneuvering can accurately detect the start and end time of the orbit maneuvers. The proposed method can only detect the start time of the maneuver as it relies on the precise orbit of the maneuvering satellite one day after the end of the maneuvering. However, the real-time detection of the end of the orbit maneuver can also be achieved by utilizing the instability of the satellite orbit during the maneuver period. In this paper, we analyze the data from BeiDou satellites in 2022 and prove that the method can accurately detect the start and end times of orbit maneuvers. In the next section, the methodology of SRIF is introduced briefly and the description is focused on orbit maneuver detection. Then, the experiment results from one year (2022) are analyzed, followed by conclusions and discussions in the last section.
## 3 Results and Discussion
### Estimating the Drug-likeness of the Designed Molecules  
There are different well-known drug-likeness standards. On the one hand, we have [PERSON]'s rule of five [79], which establishes that a molecule with adequate drug-likeness should exhibit less than five hydrogen bond donors (NHBD), no more than ten bond acceptors (NHBA), a molecular weight (MW) lower than 500 daltons, and a logarithm of the octanol-water partition coefficient (logP) lower than five. On the other hand, [PERSON]'s filter suggests that for relatively good drug-likeness, the following conditions should exist: \(-0.4<\text{logP}<5.6\), molar refractivity (AMR) in the interval 40-130 cm\({}^{3}\)/mol, 180 daltons \(<\) MW \(<\) 480 daltons, and a total number of atoms (TNA) in the range 20-70 [80]. Last, [PERSON]'s rule takes into consideration only the number of rotatable bonds (NRB) and topological polar surface area (TPSA); only molecules with NRB \(<\) 10 and TPSA \(<\) 140 A\({}^{2}\) are considered to exhibit acceptable drug-likeness [81]. Here, we wanted to gain insights into the drug-likeness of our four designed molecules by verifying their compliance with the three aforementioned drug-likeness standards. To do so, we employed the AlvaDesc software (version 1.0.22) [82], which allowed us to calculate all the global physicochemical properties mentioned above (Table 6).  
We compared the values of the different physicochemical properties of our four designed molecules with the corresponding cutoff intervals/values for the same physicochemical properties established by these three drug-likeness-based guidelines. No violation was found. Therefore, the designed molecules have adequate drug-likeness.  
We would like to highlight, that, besides estimating the drug-likeness of the four designed molecules, we checked several public databases such as ChEMBL [55; 56; 83], ZINC [84], eMolecules [85], and ChemSpider [86]. We intended to search for molecules in the aforementioned databases whose chemical structures resemble those designed by us. No molecules similar to the ones designed in this study were found by using a similarity cutoff of 80%.
###### Abstract  
To provide a comprehensive understanding of carbonaceous aerosol and its role in the haze formation in the Central Plains Urban Agglomeration of China, size-segregated particulate matter samples (PM\({}_{1}\), PM\({}_{2.5}\) and PM\({}_{10}\)) were continually collected from 20 December 2017, to 17 January 2018, in Xinxiang, the third largest city of Henan province. The results showed that the mean mass concentrations of PM\({}_{1}\), PM\({}_{2.5}\) and PM\({}_{10}\) were 63.20, 119.63 and 211.95 ug\(\cdot\)m\({}^{-3}\), respectively, and the organic carbon (OC) and elemental carbon (EC) were 11.37 (5.87), 19.24 (7.36), and 27.04 (10.27) ug\(\cdot\)m\({}^{-3}\), respectively. Four pollution episodes that were categorized by short evolution patterns (PE1 and PE3) and long evolution patterns (PE2 and PE4) were observed. Meteorological condition was attributed to haze episodes evolution pattern. Carbonaceous components contributed to PE1 and PE2 under drier condition through transportation and local combustion emission, while they were not main species in PE3 and PE4 for haze explosive growth under suitable RH, whatever for the short or long evolution pattern. The atmospheric self-cleaning processes were analyzed by a case study, which showed the wet scavenging effectively reduced the coarse particles with a removal rate of 73%, while it was not for the carbonaceous components in fine particles that is hydrophobic in nature. These results highlight that local primary emissions such as biomass combustion were the important sources for haze formation in Central China, especially in dry conditions.  
PM; particle size; carbonaceous compositions; haze episode; wet scavenging +
Footnote †: journal: Applied Sciences
# Journey Planning Algorithms for Massive Delay-Prone Transit Networks +
## 1 Introduction  
Computing the \"best\" journeys in schedule-based transit systems (consisting, e.g., of trains, buses, etc.) is a problem that has been faced at least once by everybody who has ever travelled [1]. In particular,the journey planning problem takes as input a timetable (or schedule), that is, the description, in terms of departure and arrival times, of transits of vehicles between stops within the system, and seeks to answer to natural queries such as \"What is the best journey from some stop \(A\) to some other stop \(B\) if I want to depart at time \(t\)?\".  
Solving (efficiently) such problems constitutes a fundamental primitive in the world of information technologies and intelligent transport systems. Nowadays, in fact, millions of people rely on computer-based journey planning to obtain, accurately and quickly, public transit directions. To this aim, most of the currently deployed journey planners employ algorithms that have been developed and refined in the last couple of decades by researchers in applied algorithmics and algorithm engineering.  
In particular, it is known that, despite its simple formulation, the problem is much more challenging than, for instance, the classic route planning problem in road networks [1; 2; 3], since schedule-based transit systems exhibit an inherent time-dependent component that requires complex modeling assumptions to obtain meaningful results. For this reason, transit companies in the last decade have invested a lot of resources to develop effective systems called journey planners (like, e.g., Google Transit or bahn.de), that store the schedule via a suitable model/data structure and incorporate algorithms to answer efficiently to various types of queries on such model, seeking best journeys with respect to different metrics of interest. Depending on the considered metric and modelling assumptions, the problem can be in turn specialized into a plethora of optimization problems [1].  
The most common type of query in this context is the earliest arrival query, which asks for computing a journey that minimizes the total travelling time from a given departure stop to a given arrival stop, if one departs at a certain departure time. Another prominent type of query is the profile query, which instead asks to retrieve a set of journeys from a given departure stop to a given arrival stop if the departure time can lie within a given range. Further types of queries can be obtained by considering multiple optimization criteria simultaneously (multi-criteria queries) or according to the abstraction at which the problem has to be solved. If, for instance, one wants to optimize the transfer time, that is, the time required by a passenger for moving from one vehicle to another one within a stop, then the journey planning problem is called realistic, while it is referred to as ideal otherwise [2]. In this paper, we focus on the realistic scenario, which is much more meaningful from an application viewpoint.
## Contribution  
The MANETs reactive routing protocols are more practical due to their ability to maintain a reasonable network convergence time and minimize flooding of packets in the route establishment process, especially in real-time communication scenarios. In practical terms, it is evident that MANETs do not rely on a fixed infrastructure or any centralized ancillary for coordination and control between the mobile nodes. Thus, mobile nodes are simultaneously considered as hosts as well as routers when establishing a connection between end users in the network. Therefore, whenever a route needs to be established between a pair of mobile nodes, the status of intermediate nodes must be considered before determining the routing in MANETs. Routing in MANETs involves establishing a stable and reliable route between a source and destination nodes. However, factors such as channel link quality, mobility, and energy resource constraints of mobile nodes, remain major challenges. These challenges are briefly discussed as follows:  
_Link Quality:_ The dynamic time-varying characteristics of the MANETs pose link failure. To overcome this, it is of paramount importance to consider the reliability of link quality as it interacts with the Media Access Control (MAC) layer for the selection of the optimal route.  
_Mobility:_ The network topology in MANETs is highly dynamic and changes in an unpredictable manner due to high displacement and random addition/subtraction of intermediate nodes in the network, which is capable of degrading the overall performance and can disrupt the and Quality of Service (QoS) of the network.  
_Energy constraints:_ Limited energy resources and processing power of mobile nodes are major source of constraints in the design of an optimal route, which incur severe impacts on route stability and lifetime of the network.  
Although, number of multiobjective routing metrics have been suggested by researchers in the literature as discussed in Section 3; however, they did not combine multiple parameter metrics with decision-making techniques such as Q-learning algorithm to simplify information exchange throughout the network and reduce routing overhead. To the best of our knowledge, none of these studies addressed mobility, link quality, and energy efficiency of nodes simultaneously with the multipath concept in the selection of the optimal route. Therefore, in this paper, we have considered link quality, mobility, and energy resource constraints of mobile nodes factor in the proposed scheme.  
The rest of this paper is organized as follows. Section 3 reviews the existing literature pertaining to OLSR reactive routing scheme. Section 4 describes the proposed MRLAM routing scheme in detail and presents simulation parameters and the scenario used to validate the proposed scheme. Section 5 presents the results and discussion. Finally, the conclusion and future work are presented in Sections 6 and 7, respectively.
## 4 Methodology
### Characterization of Producer and Consumer Tasks  
To validate the characterization of communication between producer and consumer tasks, the specific moments when the tasks must read and write data were calculated using Equations (1) and (2), obtained from [32].  
\[P_{W,R}^{n}=\phi_{W}+\left\lfloor\frac{n\cdot T_{\max}+\phi_{\max}-\phi_{W}}{T_ {W}}\right\rfloor\cdot T_{W} \tag{1}\]  
\[Q_{W,R}^{n}=\phi_{R}+\left\lfloor\frac{n\cdot T_{\max}+\phi_{\max}-\phi_{R}}{T_ {R}}\right\rfloor\cdot T_{R} \tag{2}\]  
where \(P\) corresponds to the execution times of the producer and \(Q\) to those of the consumer. \(\phi_{W}\) and \(\phi_{R}\) are the offsets for the writing and reading tasks, respectively. \(T_{\max}\) represents the maximum values of \(T_{W}\) and \(T_{R}\), and \(\phi_{\max}\) is the offset of the task with the largest period of the pair. Finally, \(T_{W}\) and \(T_{R}\) are the communication tasks for writing and reading, respectively.  
According to Equations (1) and (2), predictability in publication and reading times reduces variability in task response times. The calculation of specific offsets allows for improved system determinism, as knowing the exact times of reading and writing enables precise forecasting of system behavior under various load and execution conditions. This not only enhances predictability but also provides flexibility in system design, allowing adaptation to different temporal and synchronization requirements without the need to change task periods. The diagram of this characterization is shown in Figure 9.
# Resource Allocation and Offloading Strategy for UAV-Assisted LEO Satellite Edge Computing
###### Abstract  
In emergency situations, such as earthquakes, landslides and other natural disasters, the terrestrial communications infrastructure is severely disrupted and unable to provide services to terrestrial IoT devices. However, tasks in emergency scenarios often require high levels of computing power and energy supply that cannot be processed quickly enough by devices locally and require computational offloading. In addition, offloading tasks to server-equipped edge base stations may not always be feasible due to the lack of infrastructure or distance. Since Low Orbit Satellites (LEO) have abundant computing resources, and Unmanned Aerial Vehicles (UAVs) have flexible deployment, offloading tasks to LEO satellite edge servers via UAVs becomes straightforward, which provides computing services to ground-based devices. Therefore, this paper investigates the computational tasks and resource allocation in a UAV-assisted multi-layer LEO satellite network, taking into account satellite computing resources and device task volumes. In order to minimise the weighted sum of energy consumption and delay in the system, the problem is formulated as a constrained optimisation problem, which is then transformed into a Markov Decision Problem (MDP). We propose a UAV-assisted airspace integration network architecture, and a Deep Deterministic Policy Gradient and Long short-term memory (DDPC-LSTM)-based task offloading and resource allocation algorithm to solve the problem. Simulation results demonstrate that the solution outperforms the baseline approach and that our framework and algorithm have the potential to provide reliable communication services in emergency situations.  
deep reinforcement learning; edge computing; task offloading 2023