esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Simulation Studies
### Simulation Studies and Discussion
#### 4.2.2 Following Optimization Algorithm (FOA)  
In FOA, [49], which is a swarm-based algorithm, search factors are indeed members of the community that try to improve the community by 'following' each other. (12) to (14) are the main equations of the FOA.  
\[x_{i}^{d}=(1-fr)x_{i,0}^{d}+fr\ x_{leader}^{d} \tag{12}\]  
\[f=1-exp\left(\frac{-t^{1.5}}{T}\right) \tag{13}\]  
\[x_{leader}=\left\{\begin{array}{ll}for\ minimization\ problem:\ location\ of\ min\Big{(}fit_{j}\Big{)}&j\in\{1:N\}\\ for\ maximization\ problem:\ location\ of\ max\Big{(}fit_{j}\Big{)}&j\in\{1:N\} \end{array}\right. \tag{14}\]  
In the above equations, \(x_{i,0}^{d}\) refers to the initial balance point along with the \(d\) dimension of member \(i\), and \(r\) represents a random number with a uniform distribution within the [0-1] span used to preserve the search random state. \(x_{leader}\) shows the values of the problem variables for the best member of the community, which is named the leader. Moreover, \(x_{leader}^{d}\) is the \(d\)-th dimension of the position of the mentioned leader. Symbol '\(f\)' represents the 'following' co-efficient, \(t\) is the iteration count, \(T\) is the maximum number of iterations, and \(fit\) is the community fitness vector.
# Strengthening Design of RC Columns with Direct Fastening Steel Jackets
## 5 Worked Example
### Evaluating Lateral Load Capacity of Fire-Damaged Column  
After the concrete is exposed to fire, the concrete strength is impaired. The concrete strength post fire can be estimated according to the recommendation in EN-1992-1-2 [35]. The residual cylinder strength (\(f_{c}^{\prime}\)\({}_{\eta\eta}\)) of the RC column following exposure to 570 \({}^{\circ}\)C fire is 0.5\(f_{c}^{\prime}\).  
According to the study on the mild steel post fire in [22], the residual strength and elastic modulus of rebar can be readily determined. The residual strength of longitudinal rebar is given as:  
\[\begin{array}{ll}f_{yl,pf}&=f_{yl}Rs_{y}\\ &=\ 500\times(1.0-0.2\times\frac{570-400}{600})\\ &=\ 470\ \text{MPa}\end{array}\]  
Figure 10: Reinforcement details of column: (**a**) front view; (**b**) plane view.  
The residual strength of the stirrup is given as:  
\[\begin{array}{ll}f_{yst,pf}&=f_{yst}R_{Sy}\\ &=\ 400\times(1.0-0.2\times\frac{570-400}{600})\\ &=\ 376\ \text{MPa}\end{array}\]  
The residual elastic modulus of rebar is given as:  
\[\begin{array}{ll}E_{s,pf}&=E_{s}R_{SE}\\ &=\ 200\times(1.0-0.07\times\frac{570-350}{500})\\ &=\ 194\ \text{GPa}\end{array}\]  
According to (20) and (21), the stresses of the four rows of longitudinal rebar and thus the compressive height of the plane section can be determined.  
\[N_{0}+\frac{4A_{i}\sigma_{l1}+2A_{i}\sigma_{l2}+2A_{i}\sigma_{l3}+4A_{i} \sigma_{l4}}{1.2}-\frac{\alpha f_{c,pf}^{\prime}d_{c}\beta x_{c}}{1.5}=0\]  
The compressive height of the plane section is solved as:  
\[x_{c}=304\ \text{mm}\]  
The moment capacity is derived by (22).  
\[\begin{array}{ll}M_{cap}&=N_{0}\frac{d_{c}}{2}-\frac{af_{c,pf}^{\prime}d_{c }\beta x_{c}\frac{6x_{c}}{15}}{15_{0}}\\ &+\frac{4A_{i}\sigma_{l1}(d_{c}-c-\phi_{id}-\frac{1}{2})+2A_{i}\sigma_{l2}(d_ {c}-c-\phi_{id}-\frac{\phi_{l}}{2}-s_{l})}{1.2}\\ &+\frac{2A_{i}\sigma_{l3}(d_{c}-c-\phi_{id}-\frac{\phi_{l}}{2}-2s_{l})+4A_{i} \sigma_{l4}(d_{c}-c-\phi_{id}-\frac{\phi_{l}}{2}-3s_{l})}{1.2}\\ &=\ 311\ \text{kN}\cdot\text{m}\end{array}\]  
The lateral load resistance of the fire-damaged RC column is given by (23).  
\[V_{cap}=\frac{M_{cap}}{0.5L}\ =\ \frac{311\times 10^{3}}{0.5\times 3000}\ =\ 207\ \text{kN}\ <\ V=300\ \text{kN}\]  
Thus, the fire-damaged RC column is not safe, and the required enhancement ratio of the RC column is around 1.4.
## 4 Analysis of Test Results
### Ultimate Compressive Bearing Capacity  
Compared with the unstrengthened specimen column C1, the load-bearing performance of specimen columns C2 to C6 with the enlarged cross-section method were all  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Test Number** & \(P_{U}\)/**kN** & \(\Delta_{m}\)/**mm** & \(\Delta_{y}\)/**mm** & \(\Delta_{\text{u}}\)/**mm** & \(u_{\Delta}\) \\ \hline C1 & 778.4 & 3.83 & 3.29 & 4.41 & 1.34 \\ \hline C2 & 1612.4 & 6.87 & 6.33 & 9.06 & 1.43 \\ \hline C3 & 1641.3 & 7.10 & 5.86 & 9.11 & 1.55 \\ \hline C4 & 1712.1 & 6.21 & 4.63 & 9.54 & 2.06 \\ \hline C5 & 1641.8 & 5.48 & 4.28 & 8.32 & 1.94 \\ \hline C6 & 1811.8 & 7.56 & 6.09 & 10.50 & 1.72 \\ \hline \hline \end{tabular} Note: \(P_{U}\) is the ultimate bearing capacity; \(\Delta_{m}\) is the displacement when the specimen reaches the ultimate bearing capacity; \(\Delta_{y}\) is the nominal yield displacement; \(\Delta_{\text{u}}\) is the ultimate displacement when unloaded to 0.85; \(u_{\Delta}\) is the ductility factor, \(u_{\Delta}=\Delta_{\text{u}}/\Delta_{y}\).  
\end{table}
Table 6: Displacement and load test results.  
Figure 6: Specim load–displacement curve.  
significantly improved. On the whole, the ultimate bearing capacity was increased by 107.0%, 110.8%, 119.9%, 110.8%, and 132.7%, respectively.  
When considering a chiseled joint surface, the ultimate bearing capacity of C3 (strengthened with LC50 ceramsite concrete) was improved compared with that of C2 (strengthened with C50 ordinary concrete) by 1.7%. This indicates that compared with ordinary concrete, all-light ceramsite concrete was more suitable as reinforcement material.  
When all-solid concrete was made of ceramics concrete, the type of bonding interface had a greater impact on the reinforcement effect: (i) The least increase in the compressive ultimate bearing capacity of the specimen occurred when the bonding interface treatment was chiseled (specimen C3). (ii) The compressive ultimate bearing capacity was comparable to that of the chiseled specimen when taking the post-installed rebar (specimen C5). (iii) The ultimate compressive load-carrying capacity improvement was very obvious when the interface agent was used. (iv) When the bond interface was post-installed rebar+ interfacial agent (specimen C6), the ultimate bearing capacity was most obviously improved. It improved by 5.8% over the bonding surface with interface agent only and by 19.7% over that with post-installed rebar only (specimen C5).
## 1 Introduction
For this reason, they usually rely on the expertise of AM service suppliers or engineering bureaus, which often take advantage of Manufacturing Execution Systems (MES) for keeping track of their AM resources' operation and availability, while utilising Computer Aided Manufacturing (CAM) systems or slicing software that are associated with their AM equipment. Special machine profiles, comprising different materials and process configurations, are used by CAM software for generating the machine process instructions (G-code) and for estimating the time, materials usage, and cost of the AM production process [18]. Although interfacing different Computer Aided technologies is a fairly complex process, recent studies have shown that even SMEs have started addressing with moderate success the need for having integrated systems in different product development phases [19]. Regarding how the configuration of AM processes can be facilitated, a number of approaches have been used and reported and will be discussed in the following sections.
## 2 System Models and Definitions
### SSF-EDF Scheduling  
SSF-EDF assigns high priority to tasks with early deadlines. We also chose a global scheduling strategy. We establish a global ready task queue and extract tasks for scheduling according to demand. Our algorithm allows the preemption of tasks and allows tasks to be migrated between processors of different speeds. In reality, this migration introduces additional overhead. We measure this overhead by considering the number of task migrations in our experiments. The time complexity of the SSF-EDF scheduling algorithm is O(mn), where n is the number of tasks and m is the number of processors. According to the time complexity, it can be seen that SSF-EDF is used in real-time systems.  
The SSF-EDF scheduling algorithm operates on a performance asymmetric multiprocessor as follows: (1) when there is an active job waiting to be executed, no processor is idle; (2) fast processors are idled when the number of processors is greater than the number of active jobs; (3) we execute higher-priority jobs on slower processors.  
A given set of sporadic tasks is SSF-EDF schedulable if SSF-EDF scheduling is able to meet the deadlines of all jobs for the given set of sporadic tasks on a given platform. We will derive a schedulability test for SSF-EDF in Section 3. This can guarantee the schedulability of SSF-EDF.  
Figure 1: Task scheduling in SSF-EDF.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Notation** & **Meaning** \\ \hline \(\Lambda\) & Set of tasks \\ \(\pi\) & Asymmetric multiprocessor platform \\ \(\tau_{i}\) & Task \(i\in\Lambda\) \\ \(\tau_{ij}\) & The \(j_{th}\) job of task \(\tau_{i}\) \\ \(E_{i}\) & Worst-case execution time of \(\tau_{i}\) \\ \(D_{i}\) & Relative deadline of \(\tau_{i}\) \\ \(T_{i}\) & Period of \(\tau_{i}\) \\ \(p_{i}\) & \(i_{th}\) processor \\ \(s_{i}\) & Speed of the \(i_{th}\) processor \\ \(S_{k}\) & Sum of the first \(k\) processor speeds \\ \(U_{i}\) & Utilization of \(\tau_{i}\) \\ \(\delta_{i}\) & Density of \(\tau_{i}\) \\ \(W(t_{p},t_{q})\) & The total amount of work done within \([t_{p},t_{q})\) \\ \(Q(t_{p},t_{q})\) & The total amount of time required in \([t_{p},t_{q})\) \\ \(J_{p}\) & The time at which \(v\) processors are executing \\ \(\beta\) & The number of carry-in jobs \\ \hline \hline \end{tabular}
\end{table}
Table 1: The symbols.
## 3 Systematic Odometry Error Evaluation and Correction
### Calibration Trajectories Depicting a Characteristic Flower-Shaped Figure  
This paper proposes the improvement of the odometry of the omnidirectional mobile robot APR-02 using a set of specific individual calibration trajectories that globally depict a characteristic flower-shaped figure. Figure 9 shows the proposed trajectories and Table A1 (listed in Appendix A) presents the motion command required to implement each calibration trajectory and the values of the corresponding target angular rotational velocities of the wheels required to implement each trajectory that will be specific for each mobile robot type.  
The target angular velocities of the wheels shown in Table A1 (Appendix A) are in revolutions per minute (rpm) because this unit is normally used by the PIDs controlling the angular rotational velocity of the motors of the mobile robot. The calibration trajectories comprise straight displacements (Figure 9, red line labeled with an R followed with a number), clockwise displacements (blue line labeled with a B followed with a number), and counterclockwise displacements (green line labeled with a G followed with a number). These 36 combinations of angular rotational velocities of the wheels are a short representation of the infinite set of possible motion combinations. The linear displacement of all trajectories has been limited to 1 m in order to generate a characteristic and easy to remember flower-shaped figure. In this paper, each trajectory has been repeated and registered 10 times in a total of 360 trajectory experiments. Each trajectory register contains the sequence of instantaneous angular velocities of the three wheels \(E(k=1\ldots n)\), where \(n\) is the number of samples available; the vector containing the position of the mobile robot estimated by the odometry \(O(k=1\ldots n)\); and the vector containing the ground truth position \(GT(p=1\ldots m)\) estimated by the SLAM procedure.  
The adequacy of the trajectories proposed in Figure 9 to the calibration of the odometry mobile robot APR-02 will be evaluated in the following section.
## 6 Results and Discussions
### Comparison with Traditional Reactive Power Compensation Device  
Figure 10 shows the effect of SVC, a traditional reactive power compensation device, used for the consumption of DG. The rated capacity of SVC is 5 MVA. It adopts the same calculation example and optimization algorithm as the FMSS. The \(X\)-axis represents time and the \(Y\)-axis is the output of DG or load values. By contrasting Figures 10 and 7d, the same capacity of SVC compared with FMSS can be seen. The former significantly improves the consumption capacity of DG less than the latter. It has been proved that under the condition of the same real-time optimization strategy, FMSS has obvious advantages over SVC because it can change both the active power flow and reactive power flow of the distribution network.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Time** & **0.5** & **1** & **1.5** & **2** & **2.5** & **3** & **3.5** & **4** \\ \hline P1 & \(-\)0.20057
# Efficient Resource Allocation for Beam-Hopping-Based Multi-Satellite Communication Systems
## System Model
### Channel Model  
Based on reality, we model the channel between the satellite and the ground as a Shadowed-Rician distribution model [26; 27]. The channel coefficient \(h_{jm,k}^{j}\) between the satellite \(j\) and the cell \(m\) served by the satellite \(j\) during the \(k\)th time-slot can be denoted as  
\[h_{jm,k}^{j}=\sqrt{P_{L}b(q_{jm,k}^{j})}(Ae^{q_{jm,k}^{j}}+Ze^{ q_{jm,k}^{j}}), \tag{1}\] \[P_{L}=(\frac{\lambda}{4\pi})^{2}\frac{1}{d^{2}},\] (2) \[b(\varphi_{jm,k}^{j})=b_{\max}\Big{(}\frac{J_{1}(u_{jm,k}^{j})}{ 2u_{jm,k}^{j}}+36\frac{J_{3}(u_{jm,k}^{j})}{(u_{jm,k}^{j})}\Big{)}^{2},\] (3) \[u_{jm,k}^{j}=2.07123\frac{sinq_{jm,k}^{j}}{sin(\varphi_{3\text{ dB}})_{jm,k}^{j}}, \tag{4}\]where \(P_{L}\), \(\lambda\), and \(d\) respectively represent the path loss, the carrier wavelength, and the distance between the satellite and the cell. \(\varphi_{3\text{dB}}\) denotes the 3-dB angle of antenna. We consider the path between the satellite and the cell as a combination of a direct path and several scattering paths. \(A\) represents the amplitude of the direct path, and \(\phi_{jm,k}^{j}\) represents the deterministic phase. \(Z\) represents the amplitude of the scattering path, and \(\psi_{jm,k}^{j}\) represents the random phase.
## 3 Results  
A comprehensive search across all databases yielded 3124 papers. After eliminating duplicates, the titles and abstracts of 2534 documents remained. From this, 47 studies were selected for an in-depth full-text review. Of these, 12 were excluded for the following reasons: 2 were in vitro studies [14,15], 5 did not evaluate the overall success [16, 17, 18, 19, 20], 2 did not perform any retrograde procedure [21,22], 2 did not specify the retrograde material [23,24], and 1 was in Chinese [25]. Ultimately, 35 articles were included in the qualitative assessment. Due to a high heterogeneity in the study design, materials, and follow-ups, a meta-analysis could not be performed. The selection process is depicted in Figure 1, in accordance with PRISMA guidelines. Table 2 outlines the key characteristics of the studies included in the qualitative analysis. The retrograde root filling materials used identified in this systematic review were Super EBA, MTA, IRM(r), Retroplast, Endosequence(r), and gutta-percha. The follow-up of the studies ranged from 6 months to 17.5 years, and the study design included non-randomized clinical trials, randomized clinical trials, and descriptive studies. Table 3 shows the results from the risk of bias analysis for randomized clinical trials. Most of the studies were classified as having low risk of bias in almost all the domains. For non-randomized clinical trials, the results from the risk of bias analysis are shown in Table 4. Almost all the studies were classified as having a low risk of bias in all domains. The risk of bias analysis for the cohort studies is depicted in Table 5, and the quality score was ranked between 4 and 6. Finally, the analysis of the risk of bias for the cross-sectional studies is showed in Table 6. The average quality score ranged between 4 and 6. The criteria that most failed were those related to the identification of confounding factors. Figure 1: PRISMA flow diagram of study selection process.
## 4 SMS-Based Telemetry Data Transmission Protocol
### The Structure of the Protocol  
The SMS communications protocol fits within the confines of a single SMS message by using a structured header and an extensible user data payload. The user data payload may spread across the capacity of a concatenated SMS. The protocol is comprised of three main components (Figure 2).  
The first element in the protocol is the SMS packet itself. Each SMS message is encapsulated in a packet called a PDU. The protocol is implemented in field 12 of the PDU, the user data segment. While we must be aware of the PDU's structure and data, the PDU is pre-defined and is outside the scope of the protocol design, other than as a data source. Our protocol is not responsible for implementing the PDU packet, as this is done by the mobile device.  
The second and third elements of the protocol can be considered to be the true protocol, and these sections are encoded in the PDU's user data segment as the \"SMS message\" that we are sending. The second element is the protocol header. The protocol header contains consistent, structured information for any protocol message. This information is used for administration and control purposes (for example, identifying the message type, specifying the sender device ID, destination device ID, and so on). The header is a fixed length component of the protocol message, and is always located in the first SMS message of any protocol transmission (whether single SMS or concatenated SMS).  
The third component of the protocol is the user data payload. This is a variable length field, containing application specific information based on the current use of the message. The data segment is used in an application specific way. For example, in medical telemetry, the user data segment may contain 100 heart rate samples for a patient, or an aggregated set of values from multiple sensors, or sensor readings for different patients all monitored at the same site.  
Figure 2: Fundamental structure of the SMS Protocol.
# Comparison and Identification of the Aroma-Active Compounds in the Root of _Angelica dahurica_
## 3 Materials and Methods
### Extraction of the Volatile Compounds
#### 3.3.5 GC-O-MS Analysis  
In this work, GC-MS/MS (7890A-7000B; Agilent Technologies Inc., Santa Clara, CA, USA) was employed to analyze the aroma compounds, and a Sniffer 9000 Olfactometer ([PERSON], Schlieren, Switzerland) was used to detect their aromas. A DB-Wax (strong polarity, 30 m \(\times\) 0.25 mm \(\times\) 0.25 \(\upmu\)m; J&W Scientific, Folsom, CA, USA) and DB-5 (weak polarity, 30 m \(\times\) 0.25 mm \(\times\) 0.25 \(\upmu\)m; J&W Scientific, Folsom, CA, USA) column were used to separate the volatile compounds. The method was conducted according to [PERSON] and [PERSON] (2018) with minor modifications. Ultrahigh-purity helium was used as the carrier gas at a flow rate of 1.2 mL/min. The heating program was as follows: The initial temperature was set at 40 \({}^{\circ}\)C and held for 3 min, then it was increased to 200 \({}^{\circ}\)C at a rate of 5 \({}^{\circ}\)C/min and finally raised to 230 \({}^{\circ}\)C at a rate of 10 \({}^{\circ}\)C/min and held for 3 min. The split ratio was 5:1, and the injector and GC-MS/MS interface temperatures were set at 250 and 280 \({}^{\circ}\)C, respectively. Electron impact mass spectra were generated at 70 eV, and the m/z scan was carried out from 50 to 350 m/z. The ion source temperature was set at 230 \({}^{\circ}\)C.  
GC-O was carried out by five experienced assessors. The assessors smelled the aromas with sniffing masks and then recorded the aroma they perceived; the retention time, intensity value, and description of the aroma were all noted. Each assessor sniffed each sample twice. The compounds were considered to be aroma active when odors with the same retention time were detected by at least half of the assessors [22].
## 3 Results
### The Effects of ET Variations on Estimation of Runoff and Water Storage Changes
#### 3.2.3 The Effects of ET Variations on Water Storage Estimation  
Figure 6 shows the annual water storage estimations using the four ET products combining the AWAP precipitation and runoff data. Compared with the AWAP water storage, the water storage estimated using the MODIS ET is much larger and positive except for the negative value in 2006 which was an extremely dry year, while the water storage estimated using the TerraClimate ET is larger in most years and less in a few years. The water storage estimated using the GLDAS ET is much less than the AWAP water storage and was negative in most years except in 2003, 2007, 2010, and 2016. The water storage estimated using the CSIRO ET is similar to the AWAP's estimations, but the former was negative while the latter was positive in 2009. The mean annual water storage estimated using the ET products from AWAP, CSIRO, GLDAS, MODIS, and TerraClimate are 4.4 mm, -14.1 mm, -61.5 mm, 116.1 mm, and 18.1 mm, respectively. The CSIRO and GLDAS ET products result in the underestimation of water storage, which indicates that the water storage is in deficit. By contrast, the MODIS and TerraClimate ET products result in the overestimation of water storage. The water storage estimated using the MODIS ET is even up to 110 mm and more than the AWAP water storage.