esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Statistical Analysis and Differentially Regulated Metabolites  
\"Differential metabolites\" were defined as substances that were identified among different samples but showed significant variations in their respective concentrations. For differentials among the CON vs. IR-alone, CON vs. HY, CON vs. Sup-HY, CON vs. HY + IR, and CON vs. Sup-HY + IR, separate PLS-DA analyses were conducted on their respective metabolic datasets. Metabolites that contributed to the differentiation were identified based on a variable importance in the projection (VIP) value of the PLS-DA model (threshold \(>\) 1).  
Furthermore, metabolites specific to the IR-alone, HY, Sup-HY, HY + IR, and Sup-HY + IR groups were individually identified, as depicted in Figure 6A-E. By consolidating the results from the Student's \(t\)-test (with a threshold of \(p\) < 0.05) and a VIP value greater than 1.0, we identified 18, 9, 10, 17, and 15 key differential metabolites in the \(B\). _dorsalis_ larvae treated with IR-alone, HY, Sup-HY, HY + IR, and Sup-HY + IR, respectively.  
Importantly, 2-dodecan and nonadecane were unique to the IR-alone group; hexadecanoic acid, ethyl ester, and heptadecane were specific to the Sup-HY group; (E)-2-decenal and squalene were exclusive to the HY + IR group; and 11-tricosene was only detected in the Sup-HY + IR group, as illustrated in Figure 7.  
Figure 5: PLS-DA score plot of the metabolic profiles in the \(B\). _dorsalis_ (Diptera: Tephritidae) larvae exposed to different MAs under IR (116 Gy).  
Figure 6: A variable importance plot showing the contribution of each metabolite to the first component (ranked by VIP scores) in the CON vs. IR (**A**), CON vs. HY (**B**), CON vs. Super-HY (**C**), CON vs. HY + IR (**D**), and CON vs. Sup-HY + IR (**E**) groups.
# Prediction of Remaining Fatigue Life of In-Service Bridge Cranes
## 6 Conclusions  
This paper provides a method for obtaining the load spectrum of an overhead crane, which is accomplished by field tests, measurements, comparisons, and simulations. Compared with the load spectrum obtained by actual measurement, the load spectrum obtained by this method is more consistent and can replace the load spectrum obtained by experiment. In addition, the method is more convenient and faster.  
The stress spectrum of the metal crane structure was obtained by finite element simulation analysis. Subsequently, the cycling technique was performed on the crane-loaded stress spectrum using the rainflow counting method, and then, after determining the working stresses under specific operating conditions, it was found that the stress cycle showed a tendency of the pulsating cycle with a stress ratio R between 0.40 and 0.99.  
For the tested prototype, by comparing the residual fatigue life of the simulated and tested stress spectra, the error is about 9.145%, which proves the feasibility of the method. The remaining life obtained by simulation is about 25.37 years, and the method has the advantages of lower cost and shorter cycle time.  
There are still many shortcomings in this paper, such as not considering the influence of corrosion and other factors on the remaining life of the bridge crane, and the working conditions considered are relatively simple, which may cause some errors. In the future, the work will continue to be refined and improved.  
Methodology, investigation, writing--original draft, editing, visualization, data curation, conceptualization, and funding acquisition, Y.L.; review, A.J.; provision of study materials, D.Y. and B.Z.; partial model experiments, Y.D. All authors have read and agreed to the published version of the manuscript.  
This works was funded by National Natural Sciences Foundation of China, Grant/ Award Number: 51968069.  
Not applicable.  
Data Availability Statement: Data are contained within the article.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Simulation Result** & **Test Results** & **Inaccuracies** \\ \hline number of cycles & 231,501 times & 210,331 times & \\ remaining life & 25.37 years & 23.05 years & 9.145\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of simulation and test result calculations.
# The Impact of 6G-IoT Technologies on the Development of Agriculture 5.0: A Review
## 2 Agriculture 5.0: Revolutionizing Agriculture with Cutting-Edge Technologies
### The Ultra-High Requirements of Agriculture 5.0
#### 2.2.2 Large-Scale Deployment, Sustainability, and Accountability  
By definition, rural locations are remote and, in most cases, lack adequate network coverage. Due to the low magnitude of the transmitted data, this is not a serious issue for the existing precision agricultural systems [46]. In Agriculture 5.0, however, we acknowledge that the issues that arise in terms of required network capacity, latency, and reliability may well be insurmountable with the currently deployed networks due to the vast density of sensors and the amount of data exchanged. Furthermore, these issues are amplified when cultivating extremely remote locations in accordance with Agriculture 5.0's objective of maximizing the use of all arable land [22; 47].  
Agriculture 5.0 is anticipated to impose policies and requirements on the economic, environmental, and social levels, with each level adopting the technologies, agricultural practices, and economic dimensions that will support this new vision [48]. In this direction, sustainable Agriculture 5.0 is committed to meeting people's immediate and long-term needs for food and fibers while preserving and enhancing the living conditions of farmers and the broader society. To accomplish this, all aspects of agriculture must comply with sustainability [49; 50]. Agriculture 5.0 also incorporates green concepts and the widespread use of renewable energy and energy harvesting technologies in order to reduce the overall cost of agricultural practices while benefiting the environment. Although there is increasing interest in these technologies, widespread adoption has not yet been achieved. Last but not least, one of the key objectives of Agriculture 5.0 is to ensure that farmers, suppliers, and other directly involved parties receive accountable and transparent security services. In addition, providing irreversible records of the process from farm to store aids in building trust between consumers and sellers of agricultural products [51].  
In conclusion, this new concept of smart agriculture, which aims to accomplish more with less, will be crucial in the coming years because it satisfies the need to provide people with the agricultural products they require while simultaneously increasing production sustainably and protecting the environment. The employment of sophisticated technology in agriculture, which will provide vast information on environmental conditions, crop health, and soil conditions and permit the precise application of plant protection treatments, is a rational response to these challenges [52].
# A Mathematical Study on a Fractional-Order SEIR Mpox Model: Analysis and Vaccination Influence
## 2 Preliminary Concepts  
In this part, we recall fundamental descriptions and theorems related to fractional calculus, including the Riemann-Liouville integral and derivative, the Caputo derivative, and other relevant concepts [9].  
**Definition 1**.: _The Riemann-Liouville integral operator of the function \(h(x)\) of fractional-order \(0<\rho\leq 1\) can be expressed by_  
\[I^{\rho}h(x)=\frac{1}{\Gamma(\rho)}\int_{0}^{x}h(s)(x-s)^{\rho-1}\,ds,\ \ \ x>0. \tag{1}\]  
In the following content, we list below some properties of the Riemann-Liouville integral operator:\[I^{\rho}h(x)\to h(x)\text{ as }\rho\to 0, \tag{2}\] \[I^{\rho}(x-a)^{\
u}=\frac{\Gamma(\
u+1)}{\Gamma(\rho+\
u+1)}(x-a)^{ \rho+\
u},\ \ \
u\geq-1,a\in\mathbb{R},\] (3) \[I^{\rho}I^{\lambda}h(x)=I^{\lambda}I^{\rho}h(x),\ \ \rho,\lambda\geq 0,\] (4) \[I^{\rho}I^{\lambda}h(x)=I^{\rho+\lambda}h(x),\ \ \rho,\lambda\geq 0. \tag{5}\]  
Definition 2: The Caputo fractional derivative operator of the function \(h(x)\) of fractional-order \(0<\rho\leq 1\) can be expressed by  
\[{}^{C}D_{*}^{\rho}h(x)=\frac{1}{\Gamma(1-\rho)}\int_{0}^{x}\frac{h^{\prime}(s )}{(x-s)^{\rho}}\,ds,\ x>0. \tag{6}\]  
Herein, we also list in what follows, some further properties of the Caputo derivative operator:  
* For a constant \(c\), we have \({}^{C}D_{*}^{\rho}c=0\).
* For \(a\in\mathbb{R}\), we have \[{}^{C}D_{*}^{\rho}(x-a)^{\lambda}=\left\{\begin{array}{ll}\frac{\Gamma( \lambda+1)}{\Gamma(\lambda-\rho+1)}(x-a)^{\lambda-\rho},&\lambda>\rho-1,\\ 0,&\text{otherwise.}\end{array}\right.\]
* For constants \(a\) and \(b\), \({}^{C}D_{*}^{\rho}\) is a linear operator, i.e., \[{}^{C}D_{*}^{\rho}(ah(x)+bg(x))=a\ ^{C}D_{*}^{\rho}h(x)+b\ ^{C}D_{*}^{\rho}g(x).\] In the same regard, it is important to remember the following basic property: \[I^{\rho}\ {}^{C}D_{*}^{\rho}h(x)=h(x)-\sum_{k=1}^{n}h^{k}(0^{+})\frac{x^{k}}{k!},\ \ x>0,\] (7) where \(n-1<\rho\leq n\) such that \(n\in\mathbb{N}\).  
Definition 3: The Caputo operator \({}^{C}D_{*}^{\rho}\) might be outlined in terms of the Riemann-Liouville integral operator along the following lines:  
\[{}^{C}D_{*}^{\rho}f=I^{n-\rho}D^{n}f, \tag{8}\]  
where \(\rho\in\mathbb{R}^{+}\) and \(n=\lceil\rho\rceil\).  
Definition 4 ([9]): The two parameters of the Mittag-Leffler function can be expressed as  
\[E_{\gamma,\delta}(x)=\sum_{j=0}^{\infty}\frac{x^{k}}{\Gamma(\gamma k+\delta)},\]  
where \(\gamma,\delta>0\) and \(x\in\mathbb{C}\).  
Theorem 1 ([9]): Assume \({}^{C}D_{*}^{j\rho}h(x)\in C(0,T]\), for \(j=0,1,2,\cdots,n+1\) and \(0<\rho\leq 1\). Then, the function \(h(x)\) might be expanded about \(x_{0}\) in the following manner:  
\[h(x)=\sum_{j=0}^{n}\frac{(x-x_{
## 3 Satellite-to-Ground QKD Simulator  
In this section, we provide an in-depth description of the mathematical framework of the developed satellite-to-ground QKD simulator software (MTLAB 2020). To provide a clear understanding of our software's features, we categorize its functionalities into three main areas. The first area focuses on the space segment where the satellite resides. The initial step involves the calculation of elevation angle over time. For this estimation, we used the latitude, longitude, and orbital height data of each satellite, which are obtained every 10 s when employing the AGI/STK system tool (ANSYS, Pittsburgh, PA, USA) [52]. For simplicity, in this study, only a satellite pass was modeled. Considerations such as the specifications of the transmitter and receiver enable calculations of the geometrical loss, the pointing losses, and the receiving system loss (such as in telescope-to-SMF couplings). Next, we delved into atmospheric channel modeling, which accounts for all the atmospheric phenomena such as turbulence and atmospheric absorption that degrade the link's performance. Finally, the Decoy-State BB84 DV-QKD protocol was utilized, including finite key size analysis. Overall, by modeling the abovementioned protocols and methods, the software was able to provide metrics such as the SKR, Quantum Bit Error Rate (QBER), and link budget estimations. Above, a flow chart illustrates the simulation process (Figure 2). Inputs are marked in green squares, outputs in orange, and the simulation steps are highlighted in blue boxes, thus providing a visual guide through our simulation methodology.
# UAV-Based Privacy-Preserved Trustworthy Seamless Service Agility for NextG Cellular Networks
## 7 Conclusions  
In this work, we proposed a novel UAV-assisted user-agility support framework that enables trustworthy seamless service migration in a zero-trust environment. The proposed framework facilitates temporal authentication-authority delegation and proxying to enable preservice, all-party mutual authentication. In our proposed model, the UAV acts as a mediating authenticator between the UE in need of service and the guest provider's base station. We used bare-metal virtualization to provide the resources needed to host the entire experiment, and 4 UHD devices acted as the main radio interface. The experiments showed that the presented framework facilitated effective and efficient trustworthy service migration between heterogeneous service provider networks. Our future work includes adopting the presented mechanism to secure v2x communications in a highly mobile environment.  
**Author Contributions:** Formal analysis, M.A.A.-M.; Funding acquisition, M.A.; Resources, M.A.; Software, M.M.S.; Supervision, M.A.; Validation, M.M.S.; Writing--review & editing, M.A.A.-M. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by the Commonwealth Cyber Initiative, an investment in the advancement of cyber research and development, innovation, and workforce development, under Grant HS-4Q21-005, in part by the CCI SWVA grant ProCyED, and in part by the National Security Agency under Grant Number H98230-20-I-0388/VMI/CyDef - C004-22,C005-22, and other grants.  
**Acknowledgments:** The authors would like to express their appreciation to the \"IoT and Cyber Security Lab,\" USA, for supporting and hosting the activities related to this manuscript.  
**Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Home Network**} & \multicolumn{3}{c}{**Guest Network**} \\ \cline{2-7}  & \multicolumn{2}{c}{**UAV**} & \multicolumn{2}{c}{**UE**} & \multicolumn{2}{c}{**Guest-eNB**} \\ \hline
**\# of UEs** & **TX** & **RX** & **TX** & **RX** & **TX** & **RX** \\ \hline
1 & 5 & 3 & 3 & 4 & 2 & 3 \\ \hline
5 & 21 & 11 & 3 & 4 & 10 & 15 \\ \hline
10 & 41 & 21 & 3 & 4 & 20 & 30 \\ \hline
15 & 61 & 31 & 3 & 4 & 30 & 45 \\ \hline
20 & 81 & 41 & 3 & 4 & 40 & 60 \\ \hline
40 & 161 & 81 & 3 & 4 & 80 & 120 \\ \hline
100 & 401 & 201 & 3 & 4 & 200 & 300 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Total number of authentication messages at fixed number of UAVs.
# Seismic Resilience in Critical Infrastructures: A Power Station Preparedness Case Study
## 2 Background
### Secondary Phenomena (Aftershocks and Beyond)  
Following an earthquake, secondary events are foreseen, with the nature of these phenomena heavily influenced by the local site conditions. The terrain is susceptible to landslides along slopes in mountainous regions, whereas areas with water-saturated sandy soils face the risk of liquefaction. Conducting a geological survey to examine the soil composition is crucial for identifying the potential risks associated with these secondary post-earthquake events.  
The southern coastal area predominantly comprises kurkar rock, a relatively young geological formation that has not experienced significant geological upheavals. This lack of historical geological stress means the kurkar has yet to undergo extensive compaction, crystallization, or fossilization processes that typically strengthen older rocks. As a result, the kurkar in this region exhibits a different level of cohesion and resistance to weathering than observed in the more mature rock formations along the cliffs of Rosh HaNikra and other northern coastal parts of the country. This area is also affected by the dynamic processes of wave erosion and sand drift, further influencing the geological stability and response to seismic events.
###### Abstract  
Among many parameters characterizing atmospheric aerosols, aerosol mass extinction efficiency (MEE) is important for understanding the optical properties of aerosols. MEE is expressed as a function of the refractive indices (i.e., composition) and size distributions of aerosol particles. Aerosol MEE is often considered as a size-independent constant that depends only on the chemical composition of aerosol particles. The famous [PERSON]'s reconstruction equation and subsequent revised methods express the extinction coefficient as a function of aerosol mass concentration and MEE. However, the used constant MEE does not take into account the effect of the size distribution of polydispersed chemical composition. Thus, a simplified expression of size-dependent MEE is required for accurate and conventional calculations of the aerosol extinction coefficient and also other optical properties. In this study, a simple parameterization of MEE of polydispersed aerosol particles was developed. The geometric volume-mean diameters of up to 10 um with lognormal size distributions and varying geometric standard deviations were used to represent the sizes of various aerosol particles (i.e., ammonium sulfate and nitrate, elemental carbon, and sea salt). Integrating representations of separate small mode and large mode particles using a harmonic mean-type approximation generated the flexible and convenient parameterizations of MEE that can be readily used to process in situ observations and adopted in large-scale numerical models. The calculated MEE and the simple forcing efficiency using the method developed in this study showed high correlations with those calculated using the Mie theory without losing accuracy.  
amss extinction efficiency; extinction coefficient; polydispersed aerosol; reconstruction method; Mie scattering; harmonic mean type approximation 2020
## 2 Design and Modeling
### Modeling
The transverse deflection of the buckled beam can be approximated using the model expression in the form of  
\[w(x,t)=\sum_{n=1}^{\infty}\phi_{n}(x)p_{n}(t) \tag{14}\]  
The axial deflection is related to the transverse deflection, and it can be expressed as  
\[u(x,t)=\frac{1}{2}\int_{0}^{x}w^{\prime}(x,t)^{2}dx \tag{15}\]  
Thus, \(\uptau_{TF}\) and \(F_{m}\) are the distance vector and equivalent magnetic force between the tip magnet and the fixed magnet, which can be further written as  
\[\uptau_{TF}=(d_{0}+u(l,t))\hat{\mathbf{e}}_{x} \tag{16}\]  
where \(d_{0}\) is the initial distance between the tip magnet and the fixed magnet. \(F_{m}\) can be given as  
\[F_{m}=-\frac{3u_{0}}{2\pi}\frac{M_{A}V_{A}M_{B}V_{B}}{\left(d_{0}+\frac{(\pi p )^{2}}{16l}\right)^{4}} \tag{17}\]  
For the Lagrange function, \(p\) and \(V\) are selected as generalized coordinates, thus the governing equations of the system can be given from  
\[ \begin{array}{l}\frac{d}{dt}\left(\frac{\partial L}{\partial p}\right)- \frac{\partial L}{\partial p}=\frac{\delta W}{\partial p}\\ \frac{d}{dt}\left(\frac{\partial L}{\partial V}\right)-\frac{\partial L}{ \partial V}=\frac{\delta W}{\partial V}\end{array}  \tag{18}\]  
The fundamental mode shape functions of the clamped-clamped beam can be written as  
\[\phi_{1}=(1-\cos(2\pi x/l))/2 \tag{19}\]  
The governing equations of the piezoelectric rotational energy harvester with dynamic axial loaded can be given as \[ \begin{split}& m\ddot{p}+c\dot{p}+c_{f}p^{2}\dot{p}+\varsigma p+\rho \Big{(}p^{2}\ddot{p}+p\dot{p}^{2}\Big{)}+\mu F_{m}p+\chi V=F_{in}\\ &\dot{V}+\kappa V=\tau\dot{p}\end{split}  \tag{20}\]  
where  
\[m=\int_{0}^{l}m_{pb}\phi^{2}(x)dx+m_{c}\]  
\[c=c_{1}\int_{0}^{l}\phi(x)dx\]  
\[c_{f}=c_{2}\bigg{[}\int_{0}^{l}\phi^{\prime}(x)^{2}\bigg{]}^{2}\]  
\[\varsigma=\int_{0}^{l}E_{pb}I_{pb}\phi^{\prime\prime}(x)^{2}dx\]  
\[\rho=\int_{0}^{l}m_{pb}\Big{[}\int_{0}^{x}\phi^{\prime}(x)^{2}dx\Big{]}^{2}dx+ m_{c}\bigg{[}\int_{0}^{\frac{l}{2}}\phi^{\prime}(x)^{2}dx\bigg{]}^{2}+m_{a} \bigg{[}\int_{0}^{l}\phi^{\prime}(x)^{2}dx\bigg{]}^{2}\]  
\[\mu=\int_{0}^{l}\phi^{\prime}(x)^{2}dx\]  
\[\chi=\int_{0}^{l}I_{p}\phi^{\prime\prime}(x)dx\]  
\[\kappa=1/\big{(}R_{L}C_{p}\big{)}\]  
\[\tau=\int_{0}^{l}I_{p}\phi^{\prime\prime}(x)dx/C_{p}\]
# Blind Deconvolution with Scale Ambiguity
## 4 Discussion and Experimental Results  
In this section, we first show some discussions of the proposed method in Section 4.1: we empirically determine an optimal value of \(p\) in terms of three evaluation criterions in Section 4.1.1. According to this \(p\), we discuss the convergence property of the proposed algorithm in Section 4.1.2 and provide the parameter analysis in Section 4.1.3. The limitations are discussed in Section 4.1.4. Then we evaluate the proposed method on both synthetic and real-world blurred images and compare it to state-of-the-art image deblurring methods in Section 4.2.  
The proposed algorithm is implemented in MATLAB on a computer with an Intel Xeon E5630 CPU @2.53 G and 12 GB RAM. To process a \(255\times 255\) blurred image with kernel size \(27\times 27\), the proposed algorithm estimates the blur kernel in around 69 seconds without code optimization.  
**Parameter settings.** In all experiments, the regularization weight parameter \(\lambda\) is set to be 0.09, \(\zeta\) is set to be 0.003, and \(p\) is set to be 2.
## 3 Experimental Results and Discussion
### Pushover Tests on Isolated Structural Wall
#### 3.1.2 Diagrams of Drift versus Strain in Concrete  
Figure 14 shows the relationship between the drift of the wall and the average vertical strain of the concrete at the base of the wall, at both the dorsal face (where the actuator is placed) and the frontal face (the opposite side). In this case, a positive value of strain denotes tension, and a negative value denotes compression.  
Figure 14 shows a linear elastic behavior of concrete through the test, since the maximum measured strain is around 200 \(\upmu\)m/m under compression and 90 \(\upmu\)m/m under tension, which is smaller than maximum elastic strain of concrete (which can be estimated around 1000 \(\upmu\)m/m under compression and 100 \(\upmu\)m/m under tension). This is particularly true for the critical drift \(\varphi_{y}\), where the maximum measured strain is significantly smaller (around 50 \(\upmu\)m/m under compression and 10 \(\upmu\)m/m under tension). The results satisfy the ACI374.2R-13 [34].  
The measured vertical strain values are in accordance with the visual inspections carried out at the end of the tests, where no visible cracks in concrete wall were observed, and, of course, no concrete crushing occured.  
The diagrams in Figure 14 show an asymmetric behavior, i.e., compression strains are greater than tension strain. This could be because under tension, small microcracks in concrete occur, relaxing tension stress in concrete (and as a counterpart increasing the tension stress of the reinforcement), resulting in smaller values of tension strain.  
This result confirms that the plastic behavior shown by the isolated structural wall is completely caused by the low-cost energy dissipation device. Moreover, once the plastic behavior of the structural wall is observed, a progressive decrease of the maximum vertical strain of concrete occurs. This is because the elastic energy stored in the wall progressively flows to the energy dissipation device, preventing the wall from structural damage.