esa-sceva/satcom-chunk-collection huggingface dataset:
# Genetic Algorithm-Based Intelligent Selection Method of Universal Shield Segment Assembly Points
## 2 Establishment of Assembly Point Selection Objective Function  
### Related Concepts  
#### 2.1.1 Overview of Segment Assembly  
As shown in Figure 2, the shield machine is seen as a cylindrical structure, and the thrust cylinders are installed in the shield machine. The cylinders extend on the side wall of the assembled segment ring during operation, the reaction force given by the segment pushing the shield machine forward. The shield machine will stop boring every working stroke, retract the thrust cylinders, and a new segment ring will be assembled with a reasonable assembly point under the tail shield support. This process will continue until the entire tunnel is completed.  
Figure 1: The pipeline of the work.  
Formed tunnels need to fit the DTA for which construction usually follows the following principles: the shield machine borings along the DTA and the segment assemble for the purpose of adjusting the relative location of the shield machine and segments. Meeting this principle enables both safe construction and the fitting of the actual tunnel to the DTA. The relative location of the shield machine and the segment ring is reflected in the shield tail gap and thrust cylinder stroke difference. Selecting reasonable points to assemble the segment will make the shield tail gap and thrust cylinder stroke difference improved to different degrees, which can prevent the shield machine from squeezing with the segments, and the shield machine can have a larger movement space and better deflection correction ability. The lower cylinder stroke \(U_{3}(n)\) and lower shield tail gap \(T_{3}(n)\) before segment ring assembly and the lower cylinder stroke \(U_{3}(n+1)\) and lower shield tail gap \(T_{3}(n+1)\) after segment ring assembly are shown in Figure 2.
## 2 Formulation and Model
### The Thermal Model  
The governing equation of the heat transfer model in the HTS coil is [25; 26]:  
Figure 2: Schematic diagram for the equivalent circuit model of the high-temperature superconducting (HTS) generator.  
\[dC\frac{\partial T}{\partial t}+\
abla\cdot(-k\
abla T)=Q_{su}+Q_{cu} \tag{13}\]  
where \(d\), \(C\) and \(k\) are the mass density, the heat capacity and the thermal conductivity of the materials of each layer of the HTS coil, respectively. The material properties of the Kapton layer are from [26] and other layers are from [25].  
The magnetization loss density \(Q_{su}\) is generated in the superconducting layers. The ohmic loss density \(Q_{cu}\) is generated in the copper layer, and it is caused by the shunting of the copper layer, which will occur after the total current exceeds the critical current of the HTS tapes.  
The distribution of the same kind of loss in each turn is non-uniform because, in the current distribution, the magnetic field distribution and the temperature distribution of each turn are different. The values of these heat sources for each turn need to be calculated by coupling with the field model and the circuit model introduced previously:  
\[\begin{array}{l}Q_{su}=E\big{(}J\big{)}\mathbf{J}\cdot\mathbf{J}\\ Q_{cu,k}=\frac{\sum_{cu,k}^{2}R_{cu,k}}{S_{cu,k}}\end{array} \tag{14}\]  
During the operation of the HTS machine, the cryocooler will take away a certain amount of heat in coils. However, in order to estimate the most serious consequences of short circuit faults conservatively, the adiabatic boundary condition is set on the surface of the coils.  
The coupling relationship among the field, circuit and thermal model of the HTS coils is summarized in Figure 3:  
The field model uses the temperature distribution calculated from the thermal model, and the current values of the superconducting layers and copper layers calculated from the circuit model, to calculate the magnetization loss of the superconducting layers, the magnetic field distribution, and the generated electromotive force of the HTS coils. The circuit model uses the temperature distribution calculated from the thermal model, and the magnetic field distribution calculated from the field model, to calculate the current values of the superconducting layers and copper layers and the copper layer loss.  
All these models are implemented in the COMSOL Multiphysics software.
## 1 Introduction  
Supercontinuum (SC) generation is, especially for glass fibers, uniquely positioned among broadband light sources as it provides a very high degree of spatial coherence and, at the same time, high spectral power density, allowing light to be directed into areas that are normally difficult to illuminate [1; 2; 3]. Currently, several coherent, octave-spanning, high power SC sources have been realized using photonic crystal fibers (PCFs) [4; 5]. In order to achieve broad spectral distributions with high power spectral densities, different technological approaches have been applied [6].  
The rise in the ubiquity of these SC light sources circa 2010 [7] is due, in large part, to advances in the fabrication of specially designed optical fibers, known as PCFs. By tuning the properties of these PCFs, such as their core- and air-hole diameter, spacing and arrangement, the guiding properties of the fiber can be adapted for a myriad of applications such as a zero-dispersion fiber [8] or an endlessly single-mode fiber (ESF). An ESF contains only one core-guided mode over an extremely broad wavelength range [9]. In fact, it has been shown that the glass between the air holes in the cladding of an ESF can also guide light [10; 11]. These modes, which are guided outside of the core, are known as ESF cladding modes. A study of these cladding modes [12] indicated that their dispersion characteristics are well suited to SC generation, while also maintaining a large cross-sectional area. These cladding modes provide coupling of radiation between individual cladding modes because of similarly tailored dispersion properties. Previous research has shown that, during supercontinuum generation in a polarization-maintaining PCF, the part of the pump laser light guided in the cladding contributes to generation of the supercontinuum [13]. The investigations carried out for the present work verified both experimentally and numerically that these ESF cladding modes had ideal dispersion characteristics for SC generation. Experiments conclusively demonstrated that it was feasible to couple enough power into these modes such that they did, in fact, efficiently generate an SC. Somewhat more interestingly, they did so with a higher efficiency in the visible spectrum than the standard core-pumped scenario.
# UV System Architecture: Challenges and Evolution
## 5 Conclusions and Future Work  
Different proofs of concept have been shown for different UV system architectures and different types of UAVs, more specifically UGVs (ground vehicles) and UAVs (air vehicles).  
Figure 6: Architecture V3b.  
Finally, a complete system and proof of concept of a search and rescue application were developed following the proposed architecture.  
Accurate measurement of the different components of the architecture not only allows for the calculation of operation time but also helps in device comparison analysis to improve the performance of the UAV. By clarifying the different parts or subsystems involved in a UV application architecture, such as the one presented, the implementation of the architecture can be adapted to the application to be carried out. The most direct and clear example is changing the type of UV according to the medium in which the vehicle must move, such as on land (UGVs), in the air (UAVs), on the surface of water (USVs), or underwater (UUVs). In the same way, we can change other parts of the system, such as the power required, identification of hard real-time tasks, etc.  
The use of a heterogeneous architecture is not only viable but also provides flexibility in the implementation of applications. A specific case could be the capturing and processing of scalar or multimedia data without having to \"sacrifice\" the stability or operability of the UAV. In all cases, we must consider that more devices also lead to higher energy consumption and additional weight.  
This paper shows the general architecture of the system and possible adaptations for different types of processing and unmanned vehicles. In addition, the system manages to combine a command-and-control system at the base station with developed software for control, image processing, and system interface.  
The lines of research remain open for the future. We recommend that future research explores whether increasing the number of UVs is viable, wherein not only is the extension of the operating limit obtained but also other data transmission times. Also, UGVs were used in the proof of concept, but other types of UVs such as UGMs or UMVs could be experimented with, as well as heterogeneous combinations of UVs (e.g., UGV + UAV or UAV + USV). This will allow the proposal of new general or specific architectures for each type of vehicle.  
Conceptualization, O.C.V.-R. and F.G.T.; Methodology, O.C.V.-R. and F.G.T.; Software, O.C.V.-R. and F.G.T.; Validation, O.C.V.-R. and F.G.T. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data Availability StatementData is contained within the article.  
The APC was funded by Universidad Cooperativa de Colombia, Colombia.  
The authors declare no conflict of interest.
## 6 Discussion
### Mobile Measurement  
Mobile mapping is performed from aerial or ground-based mobile vehicles. Aerial platforms enable us to collect 3D topographic data of large areas in a very short time. They are also widely used in 3D modeling of urban areas. Reducing the size and weight of LiDAR sensors has made the use of UAV LiDAR widespread. Another positive effect of the small-sized LiDAR is the spread of terrestrial hand-held and back-pack mobile scanning.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Brand/Model**} & **LW** & **Pixel** & **FoV** & **Range** & **Imaging** & **Max.** & **Frame** & **Range** & **PC** \\  & **(nm)** & **Array** & (H\({}^{\circ}\)\(\times\) V\({}^{\circ}\)) & **Principle** & **Technique** & **Range** & **Rate** & **Accur.** & **PC** \\ \hline ASC/CSFL-16K & 1570 & 128 \(\times\) 128 & 3 \(\times\) 60 & Puls-time & LMAPD & 1000 m & 20 Hz & 10 cm & 30 W \\ Ball Aerospace/ & 1000 & 128 \(\times\) 32 & Na & Puls-time & GMAPD & 6 km & 90 kHz & 7.5 cm & 18 W \\ Ouster/ES2 & 880 & Na & 26 \(\times\) 13 & Puls-time & SPAD & 450 m & 30 Hz & 5 cm & 18 W \\ Leica/SPL100 & 532 & 10 \(\times\) 10 & beamlet & 20 \(\times\) 30 & Puls-time & SPL & 4500 m & 25 Hz & 10 cm & 5 W \\ Leddar Tech/ & 905 & Na & 177.5 \(\times\) 16 & Puls-time & LMAPD & 56 m & 20 Hz & \textless{}3\) cm & 20 W \\ Leddar Pixel & & & & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 7: Technical specifications of flash LiDAR cameras. (LW: Laser wavelength, PC: Power consumption).  
Figure 8: Schematic visualization of flash LiDAR camera measurement.  
Thus, indoor areas and narrow urban areas that cannot be reached by vehicle can be viewed. LiDAR sensors are used to integrate with the other related instruments in mobile mapping. Although the energy requirement is directly provided in static measurements, batteries are used in mobile devices. The low energy consumption reduces the measurement cost. In particular, robots and unmanned vehicles require low energy consumption in LiDAR devices.  
Mobile scanning requires LiDAR devices that do not contain mechanical parts affected by vibrations. For this reason, solid-state LiDAR, MBL, ToF, and 3D flash LiDAR cameras are used in mobile scanning. Robot navigation, driver assistance, and 3D mapping are common in mobile LiDAR (Table 8).  
The speed of the vehicle providing mobility directly affects the density and accuracy of a point cloud. When the speed of the vehicle is increased, the point density and accuracy are decreased. The measuring speed of LiDAR instruments is in accordance with the average city vehicle speed (50 km/h). There are also LiDAR devices that can measure at high navigation speeds. For example, LSLIDAR/HS device [52] can perform mobile mapping while the vehicle is traveling at 120 km/h.
# Concert Halls as Nearly Adaptive Spaces
## 2 Main Adaptive Factors for Room Acoustic Quality in Concert Halls
### Temperature and Humidity  
Thermo-hygrometric conditions play an important role in the variation in indoor acoustic quality. Some acoustic parameters have, in fact, been demonstrated to take influence from temperature, relative humidity, and air velocity (e.g., strength; clarity C\({}_{50}\) and C\({}_{80}\), T\({}_{30}\)) [7].  
Thermo-hygrometric conditions are considered rather infrequently in acoustic analysis. Only a few standards include such conditions in their acoustic measurements. For example, the standard ISO 9613-1 [9] considers the effect of air absorption on the propagation of sound waves and allows for a direct correlation with temperature, relative humidity, and static pressure.  
The influences of temperature and humidity have been considered in the field of voice alarm (VA) systems; [PERSON] et al. [10] as well as [PERSON] and [PERSON] [11] have investigated their effects in voice reproduction systems. Research work have = observed that reverberation time increases at high frequencies when temperature and humidity increase.  
Changes in air temperature affect the propagation speed of a sound wave, modifying the Sabine equation for reverberation time and having consequences on other correlated acoustic parameters when considering their theoretical basis [12]. One approach has applied the correlation between reverberation time and sound strength by using the method of least squares [13]. Another example is the statistical relationship of reverberation time with the speech transmission index (STI) [14; 15].  
Theoretical foundations of how air temperature influences the propagation speed of a sound wave, modifying the reverberation time Sabine equation, follow:  
\[\text{RT}=\frac{55.3\text{ V}}{\text{C}*(\text{A}+4\text{ mV})} \tag{1}\]  
where V [m\({}^{3}\)] is the hall volume, the term C is the variable sound speed, and the term (A + 4 mV) is the acoustic absorption of the hall in which the term 4 mV indicates the sound attenuation by the air.  
The influence of temperature and relative humidity is included in the formula for the sound speed C and in the m term, and the power attenuation coefficient is calculated according to ISO 9613-1: 1993 [9]. The changes in the air sound attenuation as a function of frequency can be found in the literature [16].  
The speed of sound is calculated as follows:  
\[\text{C}=331.4*\sqrt{1+\frac{\text{T}}{273}} \tag{2}\]
## 1 Introduction
This method simultaneously transmitted two chirp signals in one symbol duration and demodulated the received frequency-hopped signals in the FrFD (fractional Fourier domain) of the FrFT spectrum. As the up-slope chirp signal and the down-slope chirp signal were applied in one symbol duration, a parallel receiver should have retrieved the different transform orders. In this paper, we propose the single chirp-based FHSS method demodulated by the non-coherent FrFT method. In the proposed method, either the up-slope chirp signal or the down-slope chirp signal is transmitted within one symbol duration, and does not require the parallel demodulator. There is also a difference from the conventional method in symbol demapping using the FrFT spectrum. The conventional method was independently demodulated using different orders for the up-slope chirp and the down-slope chirp without considering the negative frequency domain in the fractional frequency domain. On the other hand, since the proposed method uses the symmetrical characteristic of the frequency (having the positive frequency and negative frequency), there is a difference in the form represented by the FrFT due to the slope inversion of the positive and the negative frequency domains. In addition, as only a single transform order is required, a simpler receiver structure can be used. Due to the movement, a Doppler shift can occur to the received signal, and the Doppler frequency will be relatively large due to the slow acoustic velocity in water compared to the propagation velocity on the ground [27]. Since the received signal is expanded and compressed by the Doppler shift, a method of implementing symbol synchronization recursively using an autocorrelation function of the estimated chirp signal should be employed. To consider the performance of the proposed method, we conducted simulations based on an underwater channel propagation model and conducted a lake trial to verify its validity. The remainder of this paper is organized as follows: In the next section, the principle of the single chirp-based FHSS scheme is presented. Section 3 describes the receiver based on the FrFT. Sections 4 and 5 then show the performance of the proposed method as evaluated via simulations and lake trials. Section 6 provides the conclusion.
## 3 Instrument Characteristics and Tests
### Residual noise  
The linear extrapolation of the relation \(T_{\rm RM,\;in}(U_{\rm RM})\) to the value \(U_{\rm RM}\) = 0 V yields the radiometer residual noise temperature:  
\[T_{\rm RM,\;0}=\frac{\left\langle U_{\rm RS}\right\rangle}{G_{\rm RM}}-T_{\rm RS} \tag{11}\]  
As outlined in Section 2.1.1, \(T_{\rm RM,\;0}\) is mainly due to the noise of the first amplifier (AMP1) in the MA and to the loss along the front-end. As these microwave components are common for the two channels (Figure 2), the \(T_{\rm RM,\;0}\) for the LSB and the USB channels tend to be similar. However, \(T_{\rm RM,0}\approx\) 153 K given in Table 2 is larger than the value \(T_{\rm RM,0}\approx\) 125 K estimated from the component specifications, and also larger than \(T_{\rm RM,0}\approx\) 108 K estimated from the measured front-end loss. This is most likely due to small mismatches between the front-end components (SW, ISO1, BP1) causing reflections not considered in the estimation of \(T_{\rm RM,0}\). Furthermore, a higher noise figure for the amplifier as a result of the higher internal physical temperature could explain the difference.
# Environment-Aware Adaptive Reinforcement Learning-Based Routing for Vehicular Ad Hoc Networks
## 5 Simulation Results and Analysis
### Simulation Results and Analysis
#### 5.2.3 CBR Flows  
In this simulation, the performance of the routing under different CBR flows was analyzed. Figure 20 illustrates the packet delivery ratio (PDR) under different traffic loads.  
Figure 16: Throughput versus packet sending rate.  
Figure 15: Packet delivery ratio versus packet sending rate.  
As the network congestion intensified with an increase in the number of flows, the PDR gradually decreased. However, the EARR consistently exhibited the highest PDR. With a flow count of 60, the EARR achieved a PDR of 69.7%, while the EARR (Fixed) maintained a PDR of 68.3%. In contrast, the GPSR and CAEB had PDRs of 35.3% and 29.2%, respectively. Throughout the simulation, the EARR maintained a high PDR in all regions, thus being unaffected by node mobility or environmental changes.  
As shown in Figure 21, the throughput increased with the rise in the number of flows. With more sending nodes, there was a rapid growth in the number of transmitted packets. Consequently, the network became more prone to congestion. The EARR is a location-based environment-aware routing protocol that utilizes the Q-learning approach to optimize bandwidth selection. By introducing the effective bandwidth factor into the  
Figure 19: Overhead ratio versus packet sending rate.  
Figure 17: Average hop count versus packet sending rate.  
Figure 18: Standardization delay versus packet sending rate.  
discount factor, the EARR minimizes the overall route bandwidth. Since Q values are updated using broadcasting, the problem of the GPSR's blind path problem is eliminated. In urban topology networks, addressing the blind path problem is challenging through perimeter forwarding. In congested network environments, the EARR exhibited a linear growth trend in throughput, thereby surpassing the other protocols significantly.  
Figure 22 illustrates the relationship between the overhead ratio and the number of flows. The overhead ratio increased as the number of packets in the network grew. This is primarily because the more packets there are to be transmitted in the network, the greater the number of failed packet transmissions caused by network congestion. In comparison, the EARR consistently maintained the lowest overhead ratio and exhibited a minimal growth rate even as the number of flows varied.  
Figure 21: Throughput versus CBR flows.  
Figure 22: Overhead ratio versus CBR flows.
### The Current State of Applicability of Metal Powder Hybrid Manufacturing Processes
The laser deposition process is the only existing process by which the existing part can be repaired or even reconfigured, allowing material compatible with the material from which the base piece is made to be added (e.g., Figure 1 where a CNC component is repaired using compatible material) [10; 11; 12]. Another advantage of laser deposition is that much more diversified cooling channels can be produced for injection moulds or moulds for pouring aluminium into their cavities, so the life of the products will be much longer. Prototyping workshops can produce metal parts much faster, replacing parts made of plastic resins by stereolithography. Using the laser deposition process, fully functional components can be produced directly from the CAD model [13]. Through the laser deposition process, surface modifications of components or even metal carbide coatings can be made, which leads to significant improvements in product durability by creating stiffening ribs (often used in strengthening castings, for example, generating ribs to stiffen gearbox cranckases), high corrosion resistance, and heat control on surfaces that must withstand high temperatures (application of carbides to brake disc surfaces to increase the thermal resistance of stressed surfaces during operation). Additive powder manufacturing produces components from materials with excellent mechanical properties, but they often require further processing. This is due to their geometry and properties. In additive powder processes, there is a partial or complete local melting and a subsequent rapid cooling caused by the high conductivity of the metal and  
Figure 1: CNC component repair by laser deposition. the large volume of material compared to the melt size. This leads, in most cases, to lower properties than those of bulk materials. Therefore, a heat or thermomechanical treatment can enhance the mechanical properties of the component and remove residual stresses from additive processes. The surface of components made by additive powder processes is usually rough, with visible layers or marks from the supporting structures needed for the geometry during additive processes. Hence, additional processing is often necessary, either for the dimensions or the surface quality of the products. When the surface has a special, decorative, or functional role, sandblasting, sanding, and polishing operations can be used to achieve the desired quality of the final surface. For high-precision parts, machining is unavoidable to obtain the required geometric tolerances. The surface after additive processes can also be uneven because of thermal stresses during the process, which can cause small distortions of component geometry [14]. For a better insight of the differences between additive manufacturing and traditional subtractive methods, Table 1 presents a SWOT (Strengths, Weaknesses, Opportunities, Threats) analysis which offers a comparative analysis of both manufacturing technologies.