esa-sceva/satcom-chunk-collection huggingface dataset:
# Dosimetric Evaluation in Micro-CT Studies Used in Preclinical Molecular Imaging
## 2 Materials and Methods
### Dosimetric Measurements  
#### 2.3.1 Thermoluminescent Dosimetry Procedures  
Thermoluminescent chips (TLD-100) (Harshaw/Bicron, Newbury, OH, USA) (\(3.2\times 3.2\times 0.89\) mm\({}^{3}\)) were used. Dosimeters were annealed in air at 400 \({}^{\circ}\)C for 1 h followed by 2 h annealing at 100 \({}^{\circ}\)C. After each annealing, they were rapidly cooled (\(\approx\)75 \({}^{\circ}\)C min\({}^{-1}\)) to room temperature. A TL reading was performed no more than 72 h after irradiation, in a nitrogen atmosphere using a Harshaw model 3500 reader, at a heating rate of 10 \({}^{\circ}\)C s\({}^{-1}\), ranging from 10 to 300 \({}^{\circ}\)C. The glow curve data (TL-signal) area was used to determine the absorbed dose through a calibration curve.  
Figure 1: Diagram of the simulated geometry for the analytical model of the imparted dose at the surface (IDS). The theoretical phantom represents a water cylinder, the center of whose cross-section coincides with the microCT unit isocenter, and it was assumed that the radiation beam came from a point source.  
The TLDs were calibrated in terms of absorbed dose to water using a \({}^{60}\)Co external beam therapy system (Theratron Phoenix, Best Theratronics), with a dose rate (in water) of 538.2 mGy/min. Dosimeters were irradiated in a 100-500 mGy range under CPE conditions, using the cavities (with the exact dimensions of the TLD chips) of a lucite container covered with a 0.5 cm cap of the same material. Three dosimeters were used for each dose-point. A 10 \(\times\) 10 cm\({}^{2}\) field at an SSD of 80 cm was used during the irradiations, and a TMR (Tissue-Maximum Ratio) factor was used to correct dose depth distribution in TLDs. An energy dependence correction was performed, due to the TLD-100's low-energy dependence, with 35-45 keV X-rays compared with the 1.25 MeV average energy of \({}^{60}\)Co photons. The effective energy obtained from half-value layer (HVL) measurements for each X-ray beam quality was used to determine the energy dependence correction factors [21].
# Robustness Analysis of the Collective Nonlinear Dynamics of a Periodic Coupled Pendulums Chain
## 2 Mechanical Model  
Figure 1 illustrates a generic structure for \(N\) coupled pendulums of identical length \(l\), mass \(m\). and viscous damping coefficient \(c\) generated by the dissipative force acting on the supporting point of each one. The pendulums are coupled by linear springs of stiffness \(k\) and are subject to an external excitation \(f\cos(\Omega t)\) each one. The inclination angle \(\phi_{n}\). from the equilibrium position quantifies the rotational displacement of the \(n^{th}\) pendulum. Applied boundary conditions are such as the pendulum labeled \(0\). and \(N+1\) are fixed so that \(\phi_{0}=\phi_{N+1}=0\). The periodicity of the structure is broken by presence of \(p\) pendulums containing parametric uncertainties which can, for instance, be the pendulum's length \(l_{s}\) as illustrated in Figure 1.  
In perfect periodicity case, one can refer to works performed in [12; 15; 28; 29] to investigate the collective dynamics of the periodic nonlinear coupled-pendulums chain. Nevertheless, such analyzes are no longer suitable if periodicity is disturbed. The main objective of the present work is to propose a generic model which is adapted to the presence of uncertainties.  
Uncertainties are supposed to affect structural input parameters, here some pendulums' lengths, and to vary randomly. A probabilistic modeling of uncertainties, by random variables, is used and implies applying stochastic uncertainty propagation methods to evaluate the effect of the randomness in structural input parameters on the collective dynamics of the nonlinear coupled-pendulums chain.  
Developing the generic model, through which uncertainties will be propagated, is based on the fact that the pendulums behave in different ways, depending on the position of each one with respect to uncertainties localization. Indeed, the equations of motion of the system are written according to the number and positions of uncertainties in the structure.
## 5 2-Chain Based Routing
### Protocol Operations
#### 5.2.1 Formation of Chains and Election of Chain Head  
Chain creation starts from the farthest node from the sink. Every node finds its nearest node which is not already connected to chain. Sink transmits a token towards the farthest node of the chain. The farthest node transmits the token in the reverse direction towards the sink. Each node uses shortest path selection algorithm to find its nearest node in the chain formation. The primary chain head is selected on the basis of \(W\). After the chain formation, every node identifies its child and parent node. The node receiving data from the _ith_ node is parent of \(i\), whereas, the node sending data to the _ith_ node is its child node.
## 5 Case Study and Algorithm Validation
### Scenario and Parameter Settings  
It is assumed that the UAV operation area is two-dimensional, with a size of 30 km \(\times\) 20 km, as shown in Figure 1. The size of every ground grid is defined as 1 km \(\times\) 1 km. The accurate risk value of each grid on the ground is provided in advance and is presented in the form of a thermodynamic chart. The coordinates of the given starting point \(S\) and destination point \(D\) are (0.5, 0.5) km and (28, 18) km, respectively. They are kept the same in the simulation. In this way, the mission of a path-planning algorithm is to generate an optimum flying route from \(S\) to \(D\), which should also be suitable for UAVs to follow with the lowest flying cost. Meanwhile, the risk of a generated UAV path should be guaranteed to be as low as possible to prevent people from being hit by UAVs when they crash. Some other factors should be considered, which include the turning angles of the UAVs and the length of the path from \(S\) to \(D\).  
The effects of the swarm sizes, the individual dimensions, and the number of iterations on a planned UAV path are explored and compared. The detailed scenario parameter settings can be found in Table 1.  
As illustrated in Table 1, three typical operation scenarios are considered to validate the proposed BSO method, namely Scenario 1, Scenario 2, and Scenario 3. These three scenarios would be used to test the effects of the different swarm sizes, different individual dimensions, and different numbers of iterations. Meanwhile, the smoothness of a generated UAV path is another important index to evaluate the performances of the path-planning algorithms. The smoother the path is, the easier it is for the UAVs to follow, which is more desirable. The path cost, path risk, path length, and turning angle are the four quantitative indexes to better validate the superiority of the proposed BSO method. Comparisons are made with some other heuristic algorithms based on these indexes, including PSO, GA, FA, and standard BAS.
# Cargo Terminal Intelligent-Scheduling Strategies Based on Improved Bee Colony Algorithms
## 4 Implementation and Experimental Results
### Scheduling Problem  
The cargo terminal mentioned in this section is the northern freight area in Xinzheng international airport, and there are totally 16 entrances and exits in total in the container storage area. The coordinates of the nine entrances are R\({}_{1}\)(1-1-5), R\({}_{2}\)(2-1-15), R\({}_{3}\)(1-1-20), R\({}_{4}\)(1-1-25), R\({}_{5}\)(1-1-30), R\({}_{6}\)(1-1-35), R\({}_{7}\)(1-1-40), R\({}_{8}\)(1-1-50), and R\({}_{9}\)(1-1-60), and the coordinates of exits are C\({}_{1}\)(1-1-8), C\({}_{2}\)(1-1-18), C\({}_{3}\)(1-1-28), C\({}_{4}\)(1-1-38), C\({}_{5}\)(1-1-48), C\({}_{6}\)(2-1-53), and C\({}_{7}\)(1-1-58) respectively, where the first value in the bracket represents the row number of shelf, the second value indicates the number of layers, and the third value is the number of columns. Sixty tasks are waiting to be scheduled, where the first thirty tasks are inbound and the later thirty tasks are outbound. The assigned positions are listed as Table 5.  
For solving the optimal scheduled sequence, a sort mapping coding (SMC) strategy is introduced to establish the mapping relationship between the solution and optimization algorithm. It assigns a random number to each dimension of the solution, then sorts the numbers in ascending order and assigns index values based on their sequence. The resulting sequence represented by the index values is the scheduling scheme. The values obtained with SMC could be updated based on Equation (7) with the help of ABC, where the number of dimensions to be updated is determined by different algorithms: ABC algorithm only randomly selects one dimension, all dimensions can be updated in PfdABC, and RmdABC randomly selects certain dimensions. For the above scheduling problem, comparative studies among five algorithms, including PSO, ABC, PfdABC, and RmdABC, are executed. The scheduling program ran 20 times, and Tables 6-9 show the optimization results, including the convergence iterations and task execution time corresponding to the optimal solution, the resulting sequences, and the allocation plans of entrances and exits. Obviously, all swarm intelligent algorithms could solve the complicated cargo scheduling problem in certain iterations. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** & **10** \\ \hline Iteration & 889 & 654 & 805 & 841 & 912 & 735 & 889 & 903 & 836 & 749 \\ \hline Result/s & 6841.9 & 6811.9 & 6828.4 & 6779.6 & 6854.9 & 6819.5 & 6841.9 & 6866.1 & 6826.1 & 6811.9 \\ \hline  & 11 & 12 & 13 & 14 & 15 & 16 & 17 & 18 & 19 & 20 \\ \hline Iteration & 889 & 654 & 901 & 912 & 912 & 905 & 697 & 903 & 827 & 749 \\ \hline Result/s & 6841.9 & 6811.9 & 6828.4 & 6854.9 & 6854.9 & 6819.5 & 6757.4 & 6866.1 & 6826.1 & 6828.4 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: (**a**) The scheduling results with PSO. (**b**) Entrances and exits allocation scheme with PSO.
# A Study on the Roof-Cutting and Pressure Releasing Technology of Roof Blasting
## 2 Theoretical Analysis
### Theoretical Analysis of Rock Blasting  
#### 2.2.1 Radius Calculation of Crush Zone  
According to the von [PERSON] yield criterion [17], the three principal stresses of the rock fracture zone are \(\sigma\), \(\sigma_{\theta}\), and the stress in the \(z\)-axis \(\sigma_{z}\). The rock begins to break when the equivalent stress \(\sigma_{s}\) is greater than the dynamic compressive strength of the rock \(\sigma_{cd}\). At this time, the initial impact pressure acting on the hole wall is \(P_{0}\)[18]:  
\[P_{0}=\frac{1}{8}\rho_{0}D^{2}\bigg{(}\frac{r_{0}}{r_{1}}\bigg{)}^{6}n \tag{1}\]where \(r_{0}\) is the radius of the cartridge radius, m; \(r_{1}\) is the radius of the borehole; \(n\) is the pressure increase parameter when the detonation product collides with the borehole wall, \(n=8\)-\(11\); \(D\) is the detonation wave velocity, m\(\cdot\)s\({}^{-1}\); \(\rho_{0}\) is the density of explosive, kg\(\cdot\)m\({}^{-3}\).  
Due to the rock impedance, the shock wave attenuates into a stress wave. The propagation velocity \(D\) decreases to the sound velocity \(C_{1}\) of the rock mass, and the compressive stress generated by the stress wave is \(P_{1}\):  
\[P_{1}=\rho C_{1}u_{1} \tag{2}\]  
where \(\rho\) is the density of rock, kg\(\cdot\)m\({}^{-3}\); \(u_{1}\) is the movement speed of rock, m\(\cdot\)s\({}^{-1}\).  
For columnar charge, the relationship between the radial compressive stress \(\sigma\) and the radius \(r\) of stress wave propagation can be expressed as:  
\[\sigma=P_{0}(t)\left(\frac{r_{0}}{r}\right)^{1/2}e^{\alpha(r_{0}-r)} \tag{3}\]  
where \(P_{0}(t)\) is the initiation pressure exerted on the rock by an explosive; \(\alpha\) is the shock wave attenuation index.  
The equivalent stress \(\sigma_{s}\) when damage occurs can be expressed as [19]:  
\[\sigma_{s}=\frac{\sigma}{2(1-\mu)}\sqrt{t^{4}+6\mu^{3}+10\mu^{2}-3\mu+1}\geq \sigma_{cd}=\sigma_{c}\varepsilon^{1/3} \tag{4}\]  
where \(\mu\) is the dynamic Poisson's ratio of the rock; \(\sigma_{c}\) is the static compressive strength of the rock, MPa; \(\dot{\varepsilon}\) is the loaded strain rate.  
Based on the above formulas, the radius \(r_{1}\)\({}^{\prime\prime}\) of the crushing area is given by:  
\[\rho C_{1}u_{1}\left(\frac{r_{0}}{r_{1}^{\prime\prime}}\right)^{1/2}e^{(r_{0} -r^{\prime\prime})\alpha}=\frac{2\sigma_{c}\varepsilon^{1/3}(1-\mu)}{\sqrt{ \mu^{4}+6\mu^{3}+10\mu^{3}-3\mu+1}} \tag{5}\]  
Many theoretical and semi-empirical formulas have been proposed to estimate the relationship between the shock wave propagation radius \(r^{\prime\prime}\) and \(r_{0}\). Under the uncoupled charge, the relationship proposed to estimate the radius ratio is as follows:  
\[\left(\frac{r^{\prime\prime}}{r_{0}}\right)^{\alpha}=\frac{\rho_{0}D^{2}n}{8 \rho C_{1}u_{1}}\left(\frac{r_{0}}{r_{1}}\right)^{6} \tag{6}\]  
The calculation formula of the radius of the crushing area \(R_{1}\) is given as follows:  
\[R_{1}=r_{1}{}^{\prime\prime}+r^{\prime\prime} \tag{7}\]  
Figure 1: Schematic diagram of crush zone and stress state of rock blasting.
# Experimental Study on Dynamic Mechanical Properties of Sandstone Under Different Damage Degrees
## 1 Introduction
They investigated how joint angles affect the dynamic mechanical properties and the crack evolution mechanism of the sandstone. The above-mentioned researchers conducted research on the stability of the surrounding rock, the mechanisms of rock burst generation, and prevention after the excavation of tunnels and chambers. However, the opposite of these studies is how to accelerate rock damage and improve the efficiency of blast hole drilling or rock breaking in the mining process. Building on the previous research, this paper will examine the mechanical properties of surrounding rock that has been damaged during the drilling process. As shown in Figure 1, during the blast hole drilling, the impact drill bit delivers high-frequency, reciprocating strikes to the rock at the base of the blast hole. The damage to the surrounding rock is not caused by just one or two impact loads. Instead, the damage process in the surrounding rock area typically occurs as follows: the initial or first few impacts from the drill bit cause microcracks in the surrounding rock area to develop to a certain extent, and subsequent impacts promote further expansion and interconnection of these cracks, and then induce damage to the surrounding rock area. However, during the drilling operations in mining, the rock mass has already undergone the influences of geostress (tectonic stress, self-weight stress, etc.) before being subjected to impact loads [25; 26; 27]. During the drilling process, impact loads can result in a specific degree and extent of damage to the surrounding rock [28; 29]. Given this, exploring the impact of the presence or magnitude of geostress on the damaged surrounding rock has significant importance in the field of geotechnical engineering. Consequently, it is crucial to carry out experimental research and theoretical analysis on the dynamic mechanical properties of damaged surrounding rock under various confining pressures. From this perspective, this paper aims to conduct loading experiments on rock specimens drilled with impact loads under dynamic conditions. Specifically, dynamic uniaxial loading experiments will be performed to analyze the effects of impact loads on the dynamic properties of damaged surrounding rock in open-pit mining without confining pressure. Additionally, triaxial dynamic and static combined loading experiments will be conducted to investigate the influence of impact loads on the dynamic properties of damaged surrounding rock under varying geostress conditions. The experimental results will provide insights into the strength properties and energy consumption characteristics of rock specimens with drilling-induced damage in the surrounding rock under impact loads.
## 4 Influence Factors on Damage Location Identification
### Influence of Vehicle Weight on Damage Location Identification  
The weight of the inspection vehicle is an important parameter affecting vehicle response and bridge response. Considering the actual vehicle situation, the weight of the inspection vehicle was calculated by taking 1000 kg, 1500 kg, 2000 kg, and 2500 kg, respectively. Other vehicle parameters and bridge parameters were set according to Section 3.2. In order to study the influence of vehicle weight on damage recognition, we selected condition 3 in Section 3.3 (single damage; damage location: 20 m; damage degree: 30%) for calculation, and conducted discrete wavelet transform on the acceleration response difference in the vehicle body for bridge damage identification. The wavelet transform coefficients under different vehicle weights are shown in Figure 10.  
Figure 9: db20–d5 coefficient after MASS treatment.  
As can be seen from Figure 10, with the increase in vehicle weight, the bridge damage can still be accurately identified by using the wavelet transform coefficient of vehicle acceleration difference, but the coefficient peaks of different vehicle weights are slightly different at the damage location. At the same time, when the vehicle weight exceeds 1000 kg, there is more interference in the wavelet transform coefficient near 90 m, which is not conducive to the location of bridge damage. According to the finite element simulation calculation results, the vehicle weight of the test vehicle shall be 1000 kg as far as possible.
# Geographical Information System Enhancement Using Active Learning-Enhanced Semantic Segmentation
## 1 Introduction
Building detection using drone images necessitates cross-referencing with the building registration ledger [19; 20; 21; 22]. When the extracted data align with the building registration ledger, updates are in order. Any building not documented in the ledger is earmarked for special administrative consideration. In scenarios where building coordinate information is aury, a robust system is imperative to rectify these variances. It is essential that the drone images are cross-verified with the building registration ledger to ensure data integrity. Moreover, the service interface should be visually cohesive with the database, mirroring the building registration ledger records. In this research, methods that recognize buildings by earmarking roof edges, commonly integrated into many studies, were excluded. The emphasis was on data refinement. Edge detection was intentionally omitted. Moreover, it was recognized that drone images can be influenced by shadows based on the sun's orientation--a factor not considered. Hence, meticulous care is required when segmenting such regions. The primary objective was data refinement using GIS data, and there is optimism that the proposed method will elevate recognition in application development. This study increases the accuracy of map data by identifying unregistered structures and refreshing the geographic information system with data on demolished buildings. This study presents a UAV-centric semantic image segmentation methodology to pinpoint building locations using geographic information. The paper unfolds as follows: Section 2 outlines the methodologies employed, ranging from the refinement of the Geographical Information System to the detailed processes of semantic segmentation for roof detection and geographic image processing techniques. Section 3 explains our experimental results, highlighting the optimal algorithm selection, the GIS dataset refinement, and the practical application of our findings on test images. Finally, Section 4 concludes with a summary of our significant discoveries and their broader impact on geographic information management.
## 5 Conclusions  
The present research proposed the development of two types of cathode tools for the efficient machining of difficult-to-machine Ti6 Al4V titanium alloys, with a cutting depth of 15 mm and an electrolyte outlet in the form of slits. Experiments were performed using a flow field simulation analysis and validated by the ECMG test. The major conclusions of the research are as follows:  
1. The FLUENT 17.0 flow field simulation results showed that the flow field in the cathode tool machining gap was unevenly distributed with a linear electrolyte outlet slit, and the flow field did not improve as the cathode tool rotated. The cathode tool with a spiral outlet slit provided a uniformly changing flow field with its rotation, which reduced the area repeatedly subjected to high-speed electrolyte flow.
2. According to the measurement results of the polarization curve, the dissolution potential of Ti6 Al4V in a 10% NaNO\({}_{3}\) solution at 30 \({}^{\circ}\)C was about 11 V. Thus, choosing a reasonable machining voltage can effectively improve the efficiency of rough machining and the accuracy of finish machining.
3. The cathode tool with a cutting depth of 15 mm and a spiral electrolyte outlet slit could realize the ECMG rough machining of the Ti6 Al4V titanium alloy. In addition, the flatness of the rough-processed sidewall obtained by using the proposed cathode tool was good, the processed surface had no flow marks, and the rough machining accounted for 92.37% of total removal, which realized the efficient processing of difficult-to-machine materials.
4. Under different processing parameters, the surface roughness of the titanium alloys varied greatly. At high voltage, a high amount of stray corrosion exhibited on the surface of the titanium alloy. At low voltage, the surface of the titanium alloy was not affected by stray corrosion. After finish machining, the surface roughness Ra was decreased from 5.182 \(\mu\)m to 0.760 \(\mu\)m.
5. The insoluble electrolytic products formed in rough machining were effectively eliminated by finish machining. The oxygen content decreased from 36.95 wt% to 1.13 wt%, and the carbon content decreased from 8.96 wt% to 3.25 wt%.  
Figure 12: Energy spectrum analysis of groove surface after (**a**) rough machining, (**b**) finish machining.  
**Author Contributions:** Formal analysis, J.L.; Investigation, J.L.; Project administration, J.L.; Writing--original draft, J.L.; Writing--review & editing, H.L., X.H., S.N. and G.X. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by the National Natural Science Foundation of China (Grant No. 51875286) and the Postgraduate Research & Practice Innovation Program of Jiangsu Province (Grant No. SJCX19_0012).  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Interference Alignment in Multi-Hop Cognitive Radio Networks under Interference Leakage
###### Abstract  
In this work, we examine the interference alignment (IA) performance of a multi-input multi-output (MIMO) multi-hop cognitive radio (CR) network in the presence of multiple primary users. In the proposed architecture, it is assumed that linear IA is adopted at the secondary network to alleviate the interference between primary and secondary networks. By doing so, the secondary source can communicate with the secondary destination via multiple relays without causing any interference to the primary network. Even though linear IA can suppress the interference in CR networks considerably, interference leakages may occur due to a fast fading channel. To this end, we focus on the performance of the secondary network for two different cases: (i) the interference is perfectly aligned; (ii) the impact of interference leakages. For both cases, closed-form expressions of outage probability and ergodic capacity are derived. The results, which are validated by Monte Carlo simulations, show that interference leakages can deteriorate both system performance and the diversity gains considerably.  
c 25 November 2018; Published: 4 December 2018 6 November 2018; 25 November 2018; Published: 4 December 2018