esa-sceva/satcom-chunk-collection huggingface dataset:
# Signal-to-Noise Ratio Analysis of Bandpass Sampling Time-Modulated Fourier Transform Spectroscopy
## 5 Conclusions  
Signal-to-noise ratio analysis is crucial for FTS. However, the randomness of noise increases the difficulty of signal-to-noise ratio analysis of BPS-FTS. Based on the basic principles of BPS-FTS, this paper starts from the radiation energy transfer process, establishes the interferogram SNR model and the average SNR correlation model between the interferogram and the reconstructed spectrum, and conducts theoretical analysis. Finally, the analysis results are verified by computer simulation. Through modeling analysis and simulation verification results, it can be seen that for BPS-FTS, its SNR is subject to the dual constraints of \(m\) and the ratio of the change in OPD to the sampling interval of OPD (\(r_{B}\)), which is specifically manifested in:  
1. When \(m\) is a fixed value, as \(r_{B}\) increases, both the interferogram and the reconstructed spectrum of the average SNR increase.
2. When \(r_{B}\) is fixed, as \(m\) increases, the sampling interval of bandpass sampling increases with the coefficient of \(m\) compared to FTS. Therefore, as the integration time increases, the average SNR of the interferogram also increases, which is higher than that of FTS. However, when the ratio \(r_{B}\) is equal, no matter what value \(m\) takes, the average SNR of the reconstructed spectrum of BPS-FTS can basically remain the same as that of FTS.
3. When \(r_{B}\cdot m\) is constant, the integration time \(\Delta t_{B}\) is a constant. At this time, the average SNR of the interferogram of BPS-FTS remains unchanged, which is consistent with FTS. However, the average SNR of the reconstructed spectrum is affected by the number of sampling points of the interferogram and decreases with the increase in \(m\).  
The above analysis has good guiding significance for the engineering implementation of BPS-FTS.  
Conceptualization, X.C., Q.L. and Z.T.; methodology, X.C. and Q.L.; software, X.C.; investigation, Y.T., J.W., N.Z., Y.Z., J.S. ([PERSON]), Y.L., J.S. ([PERSON]) and W.L.; resources, Y.L.; data curation, X.C. and Q.L.; writing--original draft preparation, X.C.; writing--review and editing, X.C.; supervision, Q.L., Y.L. and Z.T.; project administration, Q.L. and J.W. All authors have read and agreed to the published version of the manuscript.  
Figure 14: Average SNR of reconstructed spectrum when \(r_{B}\cdot m=0.5\)  
Figure 13: Average SNR of interferogram when \(r_{B}\cdot m=0.5\)  
**Funding:** This study was supported by the National Key Research and Development Program of China, 2022 YFB3903000 (2022 YFB3903001).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Dataset available on request from the authors.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
# A Dynamic Service Placement Based on Deep Reinforcement Learning in Mobile Edge Computing
## 6 Conclusions  
In this paper, we study the service placement problem under the continuous provisioning scenario in mobile edge computing. We propose a novel dynamic placement framework DSP-DRL based on deep reinforcement learning to optimize the total delay without overwhelming the constraints on physical resources and operational costs. In the learning framework, we propose a new migration conflict resolution mechanism to avoid the invalid state in the decision module. We formulate the service placement under the migration conflict into a mixed-integer linear programming (MILP) problem. Based on that, we propose a new migration conflict resolution mechanism to avoid the invalid state and approximate the policy in the decision module according to the introduced migration feasibility factor. Finally, we conduct extensive evaluations under various scenarios to demonstrate that our scheme outperforms existing state-of-the-art methods in terms of delay of users under the constraints on resources and cost in edge computing. For future work, we will investigate the dynamic service placement with multiple replications in mobile edge computing, in which the constraints on physical resources and consistency are also taken into consideration.  
Figure 8: The average delay with 10 trajectories of each user group.  
Figure 9: The average delay with 20 trajectories of each user group.  
**Author Contributions:** Conceptualization, S.L. and J.W.; methodology, S.L.; software, J.S. and P.L.; validation, S.L.; formal analysis, J.W.; investigation, S.L.; resources, J.S. and P.L.; data curation, S.L. and H.L.; writing--original draft preparation, S.L.; writing--review and editing, S.L. and J.W.; supervision, J.W. and J.F.; project administration, S.L. and J.F.; funding acquisition, S.L. and H.L. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by China Postdoctoral Science Foundation (2021M700366), and the Fundamental Research Foundation (040000546320508).  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Material and Method
### Finite Element Model
#### 2.2.1 Workbench  
ANSYS Workbench r1 2024 is a comprehensive software platform for engineering simulation. It provides a unified environment for modeling, analyzing, and visualizing engineering problems. Workbench software provides a range of tools for geometry creation, mesh generation, analysis, and post-processing. ANSYS Workbench can be used for both simple and complex linear or nonlinear analyses [21] ([PERSON], 2024).  
Materials used for electric poles can be delineated as commands pertaining to the material properties of concrete and reinforcement, which are commonly utilized in the ANSYS Workbench program. The electric pole is a composite structure and given that both solid and link elements are employed in its modeling, establishing a connection between these components is essential [22] ([PERSON] et al., 2023). In the ANSYS Workbench FE model, the SOLID65-3D element has been designated to represent the concrete (ANSYS, 2025). The SOLID65 element is recognized for its efficacy in the 3D modeling of reinforced concrete solids. Its advanced capabilities in simulating cracking and crushing behavior in structurally reinforced concrete elements are notably effective. This element facilitates the accurate definition of nonlinear material properties, enabling the simulation of cracking in three vertical directions, crushing, and plastic deformation. The reinforcement element demonstrates both tensile and compressive capabilities. The LINK 180 element type, sourced from the ANSYS element library, is utilized to model vertical and horizontal steel bars and is applicable for both linear and nonlinear deformations within its plane. The described solid model possesses the ability to crack under tension and crush under compression. Each SOLD65 element, representing the concrete, consists of eight nodes, each possessing three degrees of freedom--accounting for translations in the x, y, and z directions [24; 25] ([PERSON] et al., 2022; [PERSON] et al., 2021). The geometry of the element, along with node locations and the coordinate system, is illustrated in the SOLID65 geometry in Figure 2.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Factor of Safety** & **Pole Length (m)** & **Minimum/Maximum Peak Force (kg)** \\ \hline  & 10–11–12–13–14 & 300/3500 \\ \cline{2-3}  & 15–16 & 300/3300 \\ \cline{2-3}  & 17–18 & 500/3000 \\ \cline{2-3}  & 19 & 500/2600 \\ \cline{2-3}  & 20–21 & 500/2600 \\ \cline{2-3}  & 22–23–24–25 & 600/2500 \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Cont._The damping ratio is used in various studies in the range of 0.7-10% for uncracked reinforced concrete members [26; 27; 28] ([PERSON] et al., 2007; [PERSON] et al., 1998; [PERSON] et al., 2020). In structural design applications, a viscous damping ratio of approximately 5% is generally accepted to represent the energy loss due to damping in reinforced concrete systems [29; 30] ([PERSON] et al., 2023; [PERSON] et al., 2025). In this study, a viscous damping ratio of 5% was also accepted.
# Energy Performance of LR-FHSS: Analysis and Evaluation
## 5 Evaluation
### Battery Lifetime
Then, DR5 outscores all the other DRs, as it has the fastest bit rate and, consequently, the lowest ToA. Also, the use of ACKs reduces the theoretical battery lifetime, as the ED has to decode the downlink frame and one of the two receive windows is extended, which is especially impactful for DR5. Conclusively, the DR used significantly affects the performance, with the use of ACKs also contributing a non-negligible part. In this regard, differences between confirmed and unconfirmed transmission range from 6.4 to 7.5% for DR8/DR10 and DR9/DR11, respectively, up to 11.74 % (or 4.6 years) for DR5. This is due to the fact that as the \(T_{Tx}\) is lower, the contribution of the ACK reception, which stays the same, is higher in relative terms. Figure 14: Theoretical battery lifetime of the ED as a function of \(T_{Period}\) for DR8/DR10 and DR9/DR11 for unconfirmed transmission, the maximum FRM Payload size possible, and different sleep current consumption values. Figure 13: Theoretical ED battery lifetime as a function of \(T_{Period}\) for DR8/DR10, DR9/DR11, DR0, and DR5 with 1-byte FRM Payload transmissions and comparing unconfirmed and confirmed uplink transmission. As previously mentioned, the current consumption of a commercially ready ED, as opposed to that of a development kit, would be higher than in our case, as we directly measured the current consumption of the radio module. For this reason, we investigate how higher current consumption (i.e., emulating that of a whole device) during sleep stages would affect the battery lifetime performance (see Figure 14). We consider current consumption values of 0.5 \(\upmu\)A (the one measured in our model), 1 \(\upmu\)A, 10 \(\upmu\)A, and 20 \(\upmu\)A (the last as assumed by Semtech, the LR-FHSS chip manufacturer, in [25] to be a typical LR-FHSS ED consumption in sleep mode). As shown in the results depicted in Figure 14, battery lifetime stabilizes at around one and two years for \(T_{Period}\) of around 5 h assuming sleep current, \(I_{Sleep}\), values of 20 \(\upmu\)A and 10 \(\upmu\)A, respectively. On the other hand, the other \(I_{Sleep}\) values show better battery lifetimes: for example, 8.5 years for 1 \(\upmu\)A and 10 years for 0.5 \(\upmu\)A for a 6 h \(T_{Period}\). As expected, higher current consumption during the sleep state drastically shortens battery life, as it is the most impactful state for higher \(T_{Period}\) values. Particularly, this leads to up to a fifteen- to sixteen-fold reduction in battery life between the two most distant values (i.e., DR9/DR11 \(I_{Sleep}=0.5\)\(\upmu\)A and 20 \(\upmu\)A).
# Phase-Matching Quantum Key Distribution with Discrete Phase Randomization
## 6 Conclusions  
In this paper, we introduce the USD measurement and PNS attack against PM-QKD with imperfect phase randomization, and simultaneously, we deeply study the security of discrete phase randomization PM-QKD protocol with a decoy state in the asymptotic case. Our simulation results show that, as \(D\) increases, the key rate of adopting vacuum and one decoy state approaches infinite decoy states, and furthermore, the performance of key rate between discrete phase randomization and continuous phase randomization is almost the same. We also find that due to the intrinsic bit error rate and sifting factor, there  
Figure 3: (**a**) The fidelity of different mean photon numbers. The fidelity refers to Equation (23), which we take \(j=1\). (**b**) The key rate versus the transmission distance of the PM-QKD with a different number of discrete phase values. The solid line represents the coherent state with continuous phase randomization; the dash line represents the coherent state with discrete phase randomization.  
is an optimal discrete phase randomization value to guarantee security and performance. Therefore, for the actual PM-QKD system, we should better adopt the suitable discrete phase randomization value to apply.  
[PERSON] carried out numerical simulation and wrote the paper; [PERSON] and [PERSON] assisted in discussing the research topic; [PERSON] contributed to attack; [PERSON] and [PERSON] derived the formulas; [PERSON] and [PERSON] discussed the PM-QKD protocol. All authors participated in revising and all authors have read and agreed to the published version of the manuscript.  
This work is sponsored by National Key Research and Development Program of China (Grant No. 2020 YFA0309702), National Natural Science Foundation of China (Grants No. 61605248, No. 61675235 and No. 61505261) and Natural Science Foundation of Henan (Grant No. 202300410534 and No. 202300410532).  
Not applicable.  
The data presented in this study are available within the article.  
The authors declare no conflict of interest.
# Recent Advancement in Assessment and Control of Structures under Multi-Hazard
## 4 Multi-Hazard Assessment and Control of Bridges
### Multi-Hazard Assessment of Bridges  
To understand the consequences of multi-hazard effects on the safety/reliability of bridges, a wide range of experimental and analytical studies have been conducted and reported in the literature. One of the most studied scenarios is the interaction of earthquakes with other actions: for example, traffic-load. The interaction between these hazards, when they occur concurrently, can be amplifying or diminishing (see, for example, [63; 64]). Cascading effects might also be observed when bridges, partly damaged by earthquakes, are exposed to traffic (see, for example, [65; 66]). Ground shaking and liquefaction induced by earthquakes can have complex interactions in bridge response, both amplifying and diminishing (see, for example, [67; 68]). Another multi-hazard scenario for bridges is the simultaneous occurrence of high waves and hurricane surge (see, for example, [69; 70; 71; 72; 73]). Another scenario is foundation scour due to floods, which may increase the seismic vulnerability of bridges [74; 75; 76; 77; 78; 79; 80; 81]. Aging and corrosion of bridge elements causes structural deterioration that can amplify the effect of other hazards, such as earthquakes or wind forces. The effect of deterioration caused by seismic and traffic loads on a reinforced concrete bridge is addressed by [PERSON] and [PERSON] [82], [PERSON] et al. [83], [PERSON] et al. [84], [PERSON] et al. [85], [PERSON] and [PERSON] [86], [PERSON] et al. [87], [PERSON] et al. [88], and [PERSON] et al. [89]. Long-span bridges are especially sensitive to wind forces, but they can also be affected by seismic excitation. A framework for the assessment of vulnerability of long-span bridges subjected to multi-hazards (seismic and wind excitation) is presented by [PERSON] et al. [90]. A summary of recent advances in wind effects on long-span bridges, with a multi-hazard perspective, is presented in [PERSON] et al. [91]. Studies on the multi-hazard effects and performance of bridges is summarized in Table 1.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
# Comparison of the Biological Potential and Chemical Composition of Brazilian and Mexican Propolis
## 3 Discussion
### Antiviral Activity  
Around the world, many types of research with antiviral activity (human immunodeficiency virus (HIV), adenovirus, HSV-1, HSV-2, Newcastle virus disease, bovine rotavirus, pseudorabies virus, canine adenovirus type 2, feline calicivirus, bovine viral diarrhea virus, influenza virus types A and B, parainfluenza virus, infectious bursal disease virus, and avian reovirus) have already been conducted [109; 110; 111; 112; 113; 114].  
Few scientific articles with the antiviral activity of propolisids extracts have been verified. For example, [PERSON] (102) studied the antiviral effect of Mexican propolisis, when it has been evaluated different treatments with propolisis, flavonoids individually, and a mixture of the three flavonoids that are normally present in propolisis (quercetin, naringenin, and pinochemrin). They demonstrated that the best antiviral activity was the administration of propolisis or the mixture of flavonoids than the individual compounds. On the other hand, Brazilian propolisis has been verified in MS2 and Av-08 bacteriophages showing antiviral reduction \(\sim\)3 and \(\sim\)4.5 Log 10 PFU/mL respectively [115; 116]. In addition, in the current COVID-19 pandemic, propolisis from Brazil, specifically a standardized green propolisis extract (EPP-AF\({}^{\otimes}\)), has been evaluated in clinical treatment, using doses of 400-800 mg/day of the preparation, and a reduction in hospitalization time has been observed [117].
## 5 Discussion  
Based on descriptive analysis and regression analysis results, some countermeasures are suggested to prevent SVHFOCs to improve elderly driving safety in the future.  
Firstly, many elderly drivers are found to be still engaged in risky driving behaviors, such as not wearing a seatbelt and DUI, which could significantly aggravate crash severity. Therefore, regular driving safety education and training seem still very necessary for elderly drivers, although they might have rich driving experience. Considering the declining physiological and psychological functions of elderly drivers, agencies may consider stricter law enforcement for elderly drivers in terms of risky driving.  
Secondly, SUVs are found to be increasingly popular among elderly drivers, but also much riskier than passenger cars regarding crash consequences. Aiming at this issue, on one hand, automakers should optimize the structures of SUVs to lower their center of gravity to prevent roll-overs once collisions occur. Additionally, they may install SUVs with advanced electronic control systems, such as traction control systems and torque vector control systems, to allocate engine power to wheels in a more reasonable and balanced way. On the other hand, agencies should consider developing some free training programs to help elderly drivers identify features of SUVs quickly and learn how to drive SUVs safely.  
Thirdly, aiming at SVHFOCs, some advanced driver assistance systems (ADASs), including forward-collision warning systems and lane-departure warning systems, have been proved to be able to effectively prevent vehicles running off roads. Automakers should consider improving these ADASs to be compatible with physiological functions of elderly drivers to make them more acceptable. Since these ADASs are generally optional and expensive, agencies might consider providing some subsidies to encourage elderly drivers to adopt them. At the same time, agencies should also improve roadway safety facilities, such as installing guardrails and flashing lights to help elderly drivers recognize fixed objects in advanced to avoid collisions [36], and installing crash cushions and barriers to mitigate crash outcomes.  
Finally, crashes are found to be more severe with age for elderly drivers, which implies the presence of heterogeneities among them. However, most existing studies consider elderly drivers homogenous, which might impair the effectiveness of the proposed safety measures. In the future, age-customized measures should be developed for elderly drivers to protect them precisely and effectively.
## 5 Three State Markov Model for a Base Station  
The base stations (BSs) follow a Markov decision process (MDP) to decide when to switch among different states. The transition among all of the three states can be represented as a three-state Markov model [18] and can be presented as Figure 4. In this figure, \(S_{0}\), \(S_{1}\) and \(S_{2}\) represents the states of the \(n\)th base station at sleep mode, stand-by and active mode respectively; and \(a_{0}\), \(a_{1}\) and \(a_{2}\) represent the action of \((n-1)\)th BS going into or staying at sleep, stand-by and active mode respectively. According to this figure, while the \(n\)th BS is in state \(S_{0}\) (sleep mode), and an action \(a_{0}\) or \(a_{1}\) is taken so that \((n-1)\)th BS goes into or remains in sleep mode or stand-by mode respectively, then the \(n\)th BS will remain in the same state \(S_{0}\) with transition probability 1. However if an action \(a_{2}\) is taken so that \((n-1)\)th BS goes into active mode, then the \(n\)th BS will go to state \(S_{1}\) (stand-by mode) with transition probability 1. While the \(n\)th BS is in state \(S_{1}\) (stand-by) then the only possible actions are \(a_{1}\) and \(a_{2}\) as per our proposed model. This is because we are following the sequence of the state transition of the BSs, which means when load increases then the \((n-1)\)th BS will go to active mode before \(n\)th BS. In other words, we can say that if \(n\)th BS is in stand-by mode then \((n-1)\)th BS cannot be in sleep mode rather it would be in stand-by or active mode. Therefore when the \(n\)th BS is in state \(S_{1}\) then action \(a_{1}\) will take it to state \(S_{0}\) with probability 1; however if action \(a_{2}\) is taken then it will take the \(n\)th BS to state \(S_{2}\) with probability \(P_{12}^{n}\) or will remain in state \(S_{1}\) with probability \(P_{11}^{n}\). Finally when The \(n\)th BS is in state \(S_{2}\) (active mode) then the only possible action is \(a_{2}\) which will take the \(n\)th BS to state \(S_{1}\) with probability \(P_{21}^{n}\) or will remain in state \(S_{2}\) with probability \(P_{22}^{n}\).
## 3 Robot Functionalities: People Perception, Human Following and Pick and Placing
### Pick and Placing  
For the Pick and Placing functionality, an approach based on the visual-servoing technique is implemented for reaching and grasping objects. The method relies on the RGB-D camera in the MBot's head to continuously estimate both the end-effector and target poses and then uses their difference as a positioning error to be minimized through proportional control of the arm. Since both poses are in the camera frame, this approach is less sensitive to errors in the calibration of both the camera and the arm joints.