esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Ferry Node Ranking Clustering Algorithm (FNRCA)
### Ferry Checkpoints Locations  
To decide the location of the ferry's checkpoints, we first create a virtual grid based on the specified sensing range. Each virtual grid will be of size \(r\)\(\times\)\(r\). A checkpoint will be initially placed in the center of each square in the virtual grid. Then NCRA will be applied on each square in the grid where nodes will be ranked according to their energy levels and their distance from the checkpoints. After the first round of the ferry, the checkpoints positions will be changed in each grid by the BS based on the collected related information by the ferry in the first dispatched round. Each checkpoint in each virtual grid will be placed closer to the larger number of neighbouring cluster heads. The checkpoint coordinates, Xcp(j) and Ycp(j), in each virtual grid are calculated by the following equations:  
\[\mathrm{Xcp(j)} =\frac{1}{\mathrm{N_{j}}}\sum_{k=1}^{n_{\mathrm{j}}}\mathrm{X_{ k}^{j}} \tag{3}\] \[\mathrm{Ycp(j)} =\frac{1}{\mathrm{N_{j}}}\sum_{k=1}^{n_{\mathrm{j}}}\mathrm{Y_{ k}^{j}} \tag{4}\]  
where N\({}_{j}\) is the total number of attached cluster heads associated with the same checkpoint.  
Following is the pseudo code for choosing the checkpoint location in each grid:  
* **Input**: a subset of cluster heads CPch in each virtual grid, the virtual grid dimensions and the sensing range r between the ferry and clusters heads;
* **Output**: if the subset of all cluster heads which can be covered by a circle with a radius at most r, return the circle's center (Equations (3) and (4)) or false otherwise and no change in the checkpoint position _i.e._, it will be its previous position.  
* radius \(>\) r **then*
* false; // no change in checkpoint position  
* center(x, y) = (Equations (3) and (4))
* **return** center. // checkpoint position will be the center (x, y)
* **end if**
## 5 Discussion
### Differences between Single- and Multi-Component Diffusion Approach
The sulphate concentration in the porewater limits the contribution of chlorite to uranium migration. In addition to uranium, other anions from the porewater such as chloride or sulphate are also enriched in the DDL of chlorite to compensate the positively charged surface of the mineral. Chloride ions are mainly present in the DDL of illite, whereas for sulphate, similar to for uranium, the highest concentrations have been observed in the DDL of chlorite (Figure 4b). Due to the same negative charge of two, both anions compete with each other in the DDL. This was also determined by the concentrations along the x-axis. With decreasing uranium concentration in the DDL of chlorite, the sulphate concentration simultaneously increases (Figure 4c,d). Accordingly, a higher initial sulphate concentration in the porewater (Table 1) is associated with a potentially lower uranium concentration in the DDL of chlorite and thus less impact of chlorite on the diffusive uranium transport (Figure 3). This also explains the difference between the simulations MC\({}_{4 Clay}\) and MC\({}_{2 Clay}\) between the individual facies. Due to the higher initial concentrations of sulphate in the Shaly-76 and carbonate-rich facies (Table 1), chlorite does not contribute significantly to the uranium transport and so mineral selection only affects sorption (Figure 3a,b). On the contrary, the lower sulphate concentration in the porewaters for the sample Shaly-61 and of the sandy facies (Table 1) enable an enhanced uranium transport via the chlorite DDL (Figure 3c,d). Consequently, the chlorite DDL accelerates uranium migration only for low sulphate concentrations. Besides sulphate, the concentrations of calcium and magnesium in the porewater are also decisive for the influence of the DDL on uranium migration. For instance, [PERSON] and [PERSON] [36] observed in anion diffusion experiments that the negative surface charge was more strongly shielded at the same ionic strength by bivalent cations, such as calcium, compared to monovalent ones, such as sodium. Consequently, the anion exclusion effect is reduced by a higher concentration of bivalent cations [36]. In relation to our results, this means that the anion exclusion effect in the sandy facies is intensified by the lower concentrations of bivalent cations in the porewater compared to the other facies (Table 1) in addition to the thicker DDL. In that way, the geochemistry in the porewaters governs how strong the different DDL affect uranium migration. Thus, we confirm the findings of [PERSON] et al. [11] that the geochemistry is more decisive than the clay minerals. Furthermore, we can supplement that the geochemistry of the porewater not only controls sorption processes but also the diffusive transport of uranium in the Opalinus Clay. Our results also provide indications for the migration of uranium or other anionic radionuclides in clay formations with similar mineralogy and underline the importance of a detailed determination of the porewater and the geochemical system for each potential site.
# A Multi-Frequency Omnidirectional Antenna Based on a Ring-Shaped Structure
## 1 Introduction
This method can improve the design of multi-frequency communication equipment.
## 3 A Model of Deployment-Independent, Self-Organizing, Cyber-Physical Systems
### Cyber System
#### 3.1.1 Formalization: Device Semantics
* Communication interfaces define a labelled transition system \(\langle\mathit{Set}(\chi),\rightarrow_{\mathbf{c}},(\mathit{Set}(e)\cup \mathit{Set}(m))\times\{\uparrow,\downarrow\}\rangle\): a transition \(\chi\xrightarrow{\downarrow e}\chi^{\prime}\) models a communication interface component that moves from state \(\chi\) to \(\chi^{\prime}\) internally receiving export \(e\), \(\chi\xrightarrow{\uparrow e}\chi^{\prime}\) internally producing export \(e\), \(\chi\xrightarrow{\downarrow m}\varsigma\chi^{\prime}\) receiving message \(m\) from outside (namely from another device), and finally \(\chi\xrightarrow{\uparrow m}\varsigma\chi^{\prime}\) sending message \(m\) outward--notethat an export is a general representation of neighbor values (either imported or exported), and the interface component is in charge of turning it into a set of messages for neighbors, and vice-versa. * State component \(\kappa\) is as described in the grammar: it keeps track of the status of a component, of the information useful in that status, and evolves as detailed by the rules in the following. The semantics of a whole logical device (also called a node in this semantics) can be defined in terms of the behaviors and interactions of its logical components, which we associate to node identifiers to form _situated components_. Interactions between two such situated components can produce an observable action \(\sigma\):  
\[S ::= \left\langle i,C\right\rangle\] situated component \[l ::= \operatorname{\mathtt{act}}(a)\left|\operatorname{\mathtt{sense} }(s)\left|\operatorname{\mathtt{put}}(e)\left|\operatorname{\mathtt{get}}(e) \left|\operatorname{\mathtt{comm}}(v)\left|\operatorname{\mathtt{compute}}(e,a)\right.\right.\right.\right.\] action label \[o ::= i\rhd\rhd i\] observable action  
The semantics of such interaction is given by a labelled transition system \(\langle Set(S)\times Set(S),\rightarrow_{\mathbf{N}}\), \(Set(\sigma)\rangle\), where \(\langle S_{1}:S_{2}\rangle\xrightarrow{\rhd\rhd f^{\prime}}_{\mathbf{N}} \langle S_{1}^{\prime}:S_{2}^{\prime}\rangle\) is used to mean the two situated components \(S_{1},S_{2}\) interact by action \(l\) from device \(i\) to \(i^{\prime}\) moving to \(S_{1}^{\prime},S_{2}^{\prime}\).
## 2 Sample and Methods
### Experimental Apparatus and Procedures
#### 2.2.2 Testing Process  
The testing process is divided into four stages, namely vacuuming (stage I), inflation (stage II), adsorption equilibrium (stage III), and instantaneous pressure relief (stage IV). The specific operation of the test is carried out as follows:  
1. Gas tightness detection Before the test, the airtightness of the chamber was tested with helium gas. The chamber was charged with helium gas to the pressure of 1.2 MPa. The pressure change in the chamber was monitored via a pressure sensor to record the pressure change in the chamber within 24 h. If the pressure inside the chamber remains constant, the airtightness is optimal.  
Figure 2: (**A**) Pictorial diagram of testing equipment. (**a**) Gas control system; (**b**) pressure chamber; (**c**) pressure relief device; (**d**) data acquisition system; (**B**) Schematic diagram of the test equipment. (**a**) Gas control system; (**b**) pressure chamber; (**c**) pressure relief device; (**d**) data acquisition system.  
2. In the prepared test piece, the strain gauge was attached to the strain data acquisition system with a soldering iron, followed by the resistance test. If the resistance value reached \(120\pm 0.1\)\(\Omega\), the next step could be performed.
3. The test piece was placed into the pressure chamber for fixing, sealing, and connecting with the pressure sensor;
4. Vacuum treatment The vacuum pump was turned on to evacuate the coal sample chamber for 1.5 h. The purpose of vacuuming is to remove air from the coal sample and reduce the test error. The initial vacuuming was fast and then gradually stabilized with the increase of time. During the vacuuming process, the macroscopic performance of the coal sample exhibited micro-shrinkage deformation. The pressure and strain were monitored to judge whether the air in the coal body was exhausted
5. The data acquisition system was turned on to monitor the coal strain and the pressure change in the pressure chamber. Simultaneously, the inlet valve was turned on to charge the gas. When reaching the set pressure, the inlet valve was closed to conduct the adsorption experiment at a constant temperature. At this stage, the pressure reducing valve was turned on to charge the pressure chamber with high-pressure gas, in which the gas underwent a seepage and adsorption process inside the coal body. In the meantime, the compression of the coal matrix caused compression deformation and reduced porosity. As a result, the characteristic of the coal sample continuously remained shrinkage deformation.
6. When reaching the preset adsorption time (usually 24 h), the solenoid valve was turned on to relieve the pressure in the chamber, where the rapid desorption and migration of a large amount of gas caused damage to the coal body. After the test data and the coal sample images were saved, the next set of experiments was performed.
## 5 Simulation and Analysis
### Simulation Results and Analysis  
In a cycle, two pairs of legs alternately swing and support. In the initial stage, the robot is supported by four legs. Next, RF and RH legs are in the swing stage. Then, RF and RH legs are in the supporting stage. The motion process of the quadruped robot is shown in Figure 7.  
To measure the motion of the hydraulic quadruped robot, the displacement curves of the centroid of the robot trunk are shown in Figure 8. Figure 8a-c shows displacement curves along x, y and z-axes, respectively. It can be concluded that the motion of the robot tends to be stable after two cycles of acceleration. When the RF leg and the RH leg are raised, it marks the beginning of the motion cycle. At the beginning of the cycle, the centroid of the trunk has a small displacement in the opposite direction of the forward direction. Figure 8a shows the forward displacement of the hydraulic quadruped robot is two meters in ten motion cycles. Figure 8b shows that the robot has generated laterality motion during pacing gait. The main reason is that the center of gravity of the trunk moved to the same side when the one pair legs of the robot first touched the ground. When the other side legs touched the ground, the center of gravity of the trunk moved to the other side. As shown in Figure 8c, the centroid of the robot trunk varies from \(-16\) mm to \(-10\) mm in the vertical direction. In order to reduce the impact force of the ground, a shock absorber spring is added to the shank of the robot in the design of the mechanical structure. The fluctuation of the hydraulic quadruped robot trunk is small in the vertical direction by the proposed control method, which verifies the effectiveness of the proposed height control strategy.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Values** \\ \hline cycle time & 0.8 s \\ stride length & 200 mm \\ max foot height & 50 mm \\ simulation time & 8 s \\ gravity acceleration & 9.8 m/s\({}^{2}\) \\ static friction coefficient & 0.6 \\ dynamic friction coefficient & 0.2 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation parameters of hydraulic quadruped robot.  
Figure 7: Snapshots of the robot pacing on the flat ground. (**a**) Initial stage. (**b**) The right foreleg (RF) and the right hindleg (RH) are in the swing stage. (**c**) The left foreleg (LF) and left hindleg (LH) are in the swing stage. (**d**) Four legs are in the supporting stage.  
The velocity curves of the centroid of the robot trunk are shown in Figure 9. As seen from Figure 9, the speed of the robot in the forward direction is stable at about 0.25 m/s. The lateral velocity range of the robot trunk varies from \(-0.1\) m/s to 0.1 m/s, and changes symmetrically along the y-axis. The sudden change of velocity in the vertical direction is caused by the impact of the ground on the robot when the foot touches the ground.  
Figure 8: Displacement curves for centroid of robot trunk. (**a**) Position variations along x-axis. (**b**) Position variations along y-axis. (**c**) Position variations along z-axis.  
Figure 9: Velocity curves for the centroid of the robot trunk.
## 2 Analysis Methods
### Conventional Exergy Analysis  
There is absence of chemical reactions in the developed CCHP system and the total exergy can be thus expressed as [32]:  
\[\dot{E}_{j}=\dot{m}e_{j}=\dot{m}\Big{[}\big{(}h_{j}-h_{0}\big{)}-\tau_{0}\big{(} s_{j}-s_{0}\big{)}\Big{]} \tag{12}\]The specific exergy \(e_{j}\) is separated deeply into its thermal and mechanical exergy [40]:  
\[\begin{array}{ll}e_{j}&=e_{j}^{\rm PH}=e_{j}^{\rm T}+e_{j}^{\rm M}\\ &=\left[\left(h_{j}-h_{j,X}\right)-\tau_{0}\big{(}s_{j}-s_{j,X}\big{)}\right]_{ p=const}+\left[\left(h_{j,X}-h_{j,0}\right)-\tau_{0}\big{(}s_{j,X}-s_{j,0} \big{)}\right]_{\tau_{0}=const}\end{array} \tag{13}\]  
where the node \(X\) is at the pressure \(p\) and ambient temperature \(\tau_{0}\). Here, thermal exergy is mainly due to the temperature, and mechanical exergy mainly due to the pressure.  
The \"fuel-product\" definition is applied for exergy in the present analysis. At component level, the exergy balance can be expressed as [33]:  
\[\dot{E}_{\rm F,\dot{k}}=\dot{E}_{\rm P,\dot{k}}+\dot{E}_{\rm D,\dot{k}} \tag{14}\]  
and the exergy balance for overall system is:  
\[\dot{E}_{\rm F,\dot{tot}}=\dot{E}_{\rm P,\dot{tot}}+\sum\dot{E}_{\rm D,\dot{k} }+\dot{E}_{\rm L,\dot{tot}} \tag{15}\]  
where \(\dot{E}_{\rm P,\dot{k}}\) and \(\dot{E}_{\rm F,\dot{k}}\) stands for product and fuel exergy, respectively. \(\dot{E}_{\rm L,\dot{tot}}\) is the exergy that will not be used further in the system. It is noteworthy that \(\dot{E}_{\rm L,\dot{tot}}\) is considered merely for the overall system rather than for a specific component.  
The following definitions are introduced to assess the exergy conversion rate in the conventional exergy analysis [31; 32; 33]:  
Exergy efficiency of a component:  
\[\varepsilon_{k}=\frac{\dot{E}_{\rm P,\dot{k}}}{\dot{E}_{\rm F,\dot{k}}}\times 1 00\% \tag{16}\]  
The system exergy efficiency:  
\[\varepsilon_{tot}=\frac{\dot{E}_{\rm P,\dot{tot}}}{\dot{E}_{\rm F,\dot{tot}}} \times 100\% \tag{17}\]  
The ratio of exergy destruction:  
\[y_{k}=\frac{\dot{E}_{\rm D,\dot{k}}}{\dot{E}_{\rm F,\dot{tot}}}\times 100\% \tag{18}\]  
The relative exergy destruction:  
\[y_{k}^{*}=\frac{\dot{E}_{\rm D,\dot{k}}}{\dot{E}_{\rm D,\dot{tot}}}\times 100\% \tag{19}\]  
In Table 1, the fuel and product exergy is listed by referring to [35; 40].
## 3 Proportional Lumpability
For any perturbation \(X^{\prime}(t)\) of the original chain \(X(t)\) with respect to \(\kappa\), as per Definition 3, where \(X^{\prime}(t)\) has an infinitesimal generator \(\mathbf{Q}^{\prime}\) and equilibrium distribution \(\boldsymbol{\pi^{\prime}}\), the equilibrium distribution \(\boldsymbol{\pi}\) of \(X(t)\) adheres to the following property: let \(K=\sum_{s\in\mathcal{S}}\pi^{\prime}(s)/\kappa(s)\), then for all \(s\in\mathcal{S}\),_  
\[\pi(s)=\frac{\pi^{\prime}(s)}{K\,\kappa(s)}\,.\]  
**Example 1**. : _Consider the classic reliability problem concerning a system comprising \(N\) components. Each component \(i\in 1,\ldots,N\) follows an exponential distribution with rate \(\mu_{i}\) for time to failure, independent of the other components. This system type has been extensively researched, as seen in various works such as [35; 36; 37; 38; 39]. Following the approach in [38], we posit that upon system failure, it is restored to a new \"good\" state, and the restoration time follows an exponential distribution with rate \(\lambda\). At any given time, the system state can be represented as a boolean vector of size \(N\), \(\bar{x}=(x_{1},\ldots,x_{N})\), where \(x_{i}=1\) signifies the \(i\)-th component functioning and \(x_{i}=0\) denotes failure. Consequently, the set of all feasible states is \(\mathcal{S}=\{0,1\}^{N}\). Under these conditions, the system's state evolution over time can be modeled by a continuous-time Markov chain. The Markov process for a system with \(3\) components (i.e., \(N=3\)) is illustrated in Figure 1. Notably, this system is proportionally lumpable with respect to the given partition: \(S_{n}=\{\bar{x}\in\mathcal{S}:\sum x_{i}=n\}\) with \(n\in\{0,1,2,3\}\), i.e.,_  
\[ \begin{array}{lcl}S_{0}&=&\{(0,0,0)\}\\ S_{1}&=&\{(1,0,0),(0,1,0),(0,0,1)\}\\ S_{2}&=&\{(1,1,0),(1,0,1),(0,1,1)\}\\ S_{3}&=&\{(1,1,1)\}\end{array} \]  
_and the function \(\kappa\) such that for each state \(s\in S_{1}\cup S_{2}\), \(\kappa(s)=q(s)\), while for \(s\in S_{0}\cup S_{3}\), \(\kappa(s)=1\). Consequently, we can examine the aggregated Markov chain depicted in Figure 2. Utilizing Theorems 1 and 2, we are able to calculate the precise solution for the original model._  
Figure 1: CTMC representing the reliability of a system with \(3\) components.
## 3 Results  
### Baseline Clinicopathologic Features  
Baseline clinicopathologic features of 194 patients who received robotic TT with CCND during the study period (December 2008 to September 2017) are summarized in Table 1. Mean patient age was 41.8 \(\pm\) 8.8 years, and the cohort was composed of 187 females and 7 males. The mean tumor size was 1.0 \(\pm\) 0.6 cm. Multiple and bilateral thyroid gland lesions were observed in 90 (46.4%) and 70 (36.1%) patients, respectively. In all, 163 (84%) patients underwent ipsilateral CCND and 31 patients had bilateral CCND. Mean postoperative hospital stay was 3.8 \(\pm\) 1.7 days.  
There were no significant differences between the before (\(n\) = 119) and after (\(n\) = 75) the learning curve end-point with regard to primary tumor size (\(p\) = 0.084), extrathyroid extension (\(p\) = 0.518), lymph node metastasis (\(p\) = 0.219), and postoperative hospital stay (\(p\) = 0.305).  
When we compared the multiplicity, bilaterality, and extent of LN dissection between the before (\(n\) = 119) and after (\(n\) = 75) learning curve end-point, the multiplicity (35.3% versus 64%, \(p\) < 0.001), bilaterality (26.1% versus 52%, \(p\) < 0.001), and bilateral CCND (10.1% versus 25.4%, \(p\) = 0.005) were more frequent after (\(n\) = 75) the learning curve end-point, respectively.  
All operations were successfully completed using a robotic technique without conversion to conventional open surgery. The mean follow-up period was 76.1 months (range 6-103 months).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Characteristics** & **Overall (\(n\) = 194)** & **Before (\(n\) = 119)** & **After (\(n\) = 75)** & \(p\)**-Value** \\ \hline Age, year, mean \(\pm\) SD & 41.8 \(\pm\) 8.8 & 41.9 \(\pm\) 8.4 & 41.6 \(\pm\) 9.4 & 0.788 \\ Sex, \(n\) (\%) & & & & 0.045 \\ Male & 7 (3.6) & 7 (5.9) & 0 (0) & \\ Female & 187 (96.4) & 112 (94.1) & 75 (100.0) & \\ Primary tumor size, cm, mean \(\pm\) SD & 1.0 \(\pm\) 0.6 & 0.9 \(\pm\) 0.5 & 1.1 \(\pm\) 0.6 & 0.084 \\ Extraathyroid extension, \(n\) (\%) & & & & 0.518 \\ None & 73 (37.6) & 42 (35.2) & 31 (41.3) & \\ Minimal & 77 (39.7) & 51 (42.9) & 26 (34.7) & \\ Extensive & 44 (22.7) & 25 (21.9) & 18 (24.0) & \\ Multiplicity, \(n\) (\%) & 90 (46.4) & 42 (35.3) & 48 (64.0) & \textless{}0.001 \\ Bilaterality, \(n\) (\%) & 70 (36.1) & 31 (26.1) & 39 (52.0) & \textless{}0.001 \\ Extent of LN dissection, \(n\) (\%) & & & & 0.005 \\ Ipsilateral CCND & 163 (84.0) & 107 (89.9) & 56 (74.7) & \\ Bilateral CCND & 31 (16.0) & 12 (10.1) & 19 (25.4) & \\ Lymph node metastasis, \(n\) (\%) & 85 (43.8) & 48 (40.3) & 37 (49.3) & 0.219 \\ Postoperative hospital stay, day, mean \(\pm\) SD & 3.8 \(\pm\) 1.7 & 3.9 \(\pm\) 1.8 & 3.7 \(\pm\) 1.6 & 0.305 \\ \hline \hline \end{tabular} SD = standard deviation; LN = lymph node; CCND = central compartment node dissection.  
\end{table}
Table 1: Baseline clinicopathologic features of patients who underwent robotic total thyroidectomy with concomitant central compartment node dissection, overall, before, and after the learning curve end-point.
## 4 Results
### Site S042
The distribution of volumes for these blocks compared against the 82 block volumes surveyed at the site is shown in Figure 12. This figure presents the volume distributions in 0.5 m\({}^{3}\) intervals and in 0.025 m\({}^{3}\) intervals. The average and maximum differences (in probability 0-1) between the DFN prediction and observations were 0.02 and 0.09 when volumes are discretized in 0.5 m\({}^{3}\) intervals, and 0.05 and 0.25 when discretized in 0.025 m\({}^{3}\) intervals. Figure 9: Probability density histograms of joint set orientation and fitted probability density function (PDF). J1 dip (**a**) and dip direction (**b**), J2 dip (**c**) and dip direction (**d**), J3 dip (**e**), and dip direction (**f**). Figure 11: View of one realization of the 10 m \(\times\) 10 m \(\times\) 10 m persistent DFN built for Site S042 (**a**) and discrete blocks defined by the DFN, colorized randomly (**b**). Figure 10: Probability density histograms of joint set orientation. J4 dip (**a**) and dip direction (**b**), J5 dip (**c**), and dip direction (**d**). Figure 13a shows the fragmented rockfall volume cumulative relative distribution as calculated by the persistent DFN method and field surveys. Similar to the Tornado Mountain case study, the correlation between calculated volumes and surveyed volumes appears adequate as a first approximation for engineering purposes, based on the correlations between histograms (Figure 12), but the cumulative distributions quickly deviate in Figure 13a. The DFN method overestimates the volumes for a given relative frequency when compared to observations. As mentioned previously, relative cumulative frequencies such as those in Figure 13 can be biased by the number of observations and ranges of volumes observed. Figure 13b shows the fragmented rockfall volume cumulative relative distribution as calculated by the persistent DFN method and field surveys, but truncating the DFN blocks to the range of blocks observed by the surveys. Overestimation of the DFN volumes is still evident; however, the DFN prediction is significantly closer to the observations. Figure 12: Fragmented rockfall volume distribution as calculated by the persistent DFN method and field surveys for Site S042. Figure 13: Fragmented rockfall volume cumulative relative distribution as calculated by the persistent DFN method and field surveys, for all DFN calculated volumes (**a**) and for DFN volumes truncated in accordance with the range of surveyed volumes (**b**). Site S042.