esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Theoretical Background
### Modified Technology Acceptance Model  
The TAM was developed by [PERSON] in order to explain the effect of a user's beliefs on their attitude and behaviour regarding a new technology [16]. Specifically, the TAM explains that a user's perceived usefulness and ease of use of a new technology affects their attitude toward its use, which then affects their behavioural intention and actual usage behaviour.  
TAM is one of the most powerful and commonly used theoretical models for explaining the adoption of new technologies. It has been utilized to explain and predict the adoption behaviour of e-commerce, ride-sharing services, and mobile-learning [17; 18; 19]. It has also been revised and extended in follow-up studies, leading to the development of modified models such as TAM2 and TAM3, which are intended to explain technology acceptance more effectively by adding factors such as subjective norm and self-efficacy [20; 21]. Despite these moderations, perceived usefulness, perceived ease of use, behavioural intention, and acceptance/use behaviour remain the key constructs.  
TAM itself, however, fails to consider the distinctive features of specific technologies and contexts. To overcome this shortcoming, researchers have extended the TAM by adding technological or usage-specific factors that affect the user's belief. For example, to study the acceptance of VR in aeronautical assembly tasks, [PERSON] et al. [22] analysed the effect of technology-specific factors, such as pragmatic quality and hedonic quality stimulation. Similarly, to explain the use of ICT among senior citizens, [PERSON] and [PERSON] [23] utilized social influence, anxiety, and self-satisfaction as external variables affecting the user's perceived usefulness and perceived ease of use. Therefore, in the current study, we consider both technology-specific and usage-specific external variables that reflect the distinctive features of VR construction safety training.  
Another shortcoming of the TAM is that in cases where technology usage is mandatory, such as a system mandated by company policy, predicting acceptance through usage intention is rather inadequate. In such cases where users were mandated to adopt a certain system, satisfaction was found to be an effective predictor of user acceptance or performance [24]. Therefore, because the adoption VR construction safety training is mandatory, we adopted training satisfaction as a predictor of user acceptance.
## 3 Results
### Effect of Sample Material on Beam Profile and Decay  
The effect of various materials on beam width was investigated in Figure 3, where the only changing variable between simulations was the sample material used. Beam width was represented by R\({}_{0.68}\); the perpendicular distance within which 68% of the electrons detected were located was calculated using each electron's horizontal spread from the center, which was then plotted at each thickness. At 10 \(\upmu\)m, this width was found to be 27.0 nm at 10 \(\upmu\)m for amorphous ice, 35.5 nm for amorphous carbon, 55.7 nm for silicon dioxide, and 60.0 nm for crystalline silicon. Note that the R\({}_{0.68}\) measurement of beam width in this simulation only accounts for electrons detected within the specified collection angle, which was set to 10 mrad in Figure 3.  
The number of electrons detected at a certain depth is used to quantify electron beam intensity. Table 1 shows beam intensity for the same simulations plotted in Figure 3 in the form of a percentage of the incident beam, calculated for the bright field imaging angular range (0-10 mrad) and the annular dark field imaging range (10-50 mrad). Any remaining electrons are scattered outside 50 mrad, in the high-angle annular dark field imaging range that was not considered in this study. Materials with increasing beam width in the bright field range shown in Figure 3 display fewer electrons detected in that angular range. This  
Figure 3: Electron beam width R\({}_{0.68}\) as a function of sample depth. Geometric beam broadening is shown as a dashed line for reference. A 10 mrad collection angle, a 1 mrad convergence semi-angle, and a bottom-focused beam were used in simulations.  
Figure 2: Relation between log ratio of intensities and thickness in silicon dioxide collected in the 0–8.8 mrad range: (**A**) 300 keV incident beam top-focused, to 1.2 \(\upmu\)m thickness; (**B**) 3 MeV incident beam top-focused, to 6 \(\upmu\)m thickness. The vertical axis is unitless.  
indicates the viability of bright field amplitude contrast imaging, motivating much of the analysis in this study to be performed with a collection angle of 10 mrad.
# An Overview of CubeSat Missions and Applications
## 9 Review Conclusions and Discussion
### The Utilization of CubeSats in Deep-Space Exploration Missions  
For lunar exploration, CubeSat missions such as CAPSTONE have successfully demonstrated technologies which have paved the way for the manned exploration of the moon through the Artemis program and the upcoming assembly of the Lunar Gateway space station. Furthermore, the partial successes of certain Artemis I missions such as LunIR and LunAH-Map have demonstrated the successful integration and operation of cutting-edge scientific instruments on CubeSat platforms proving that CubeSats can be used as cost-effective lunar exploration platforms that are capable of incorporating state-of-the-art sensory instruments. Moreover, with humanity's renewed interest in the exploration of the Moon, the continuation of the Artemis program as well as the Chinese lunar exploration program, the number of lunar exploration CubeSat missions should be expected to rise exponentially over the next decade. However, it should be noted that the considerably high number of failed and partially successful lunar exploration missions that have been undertaken to this day all but illustrates the difficulties and dangers associated with deep space exploration missions. But, even in this way, it provides the space science and engineering communities with important lessons that will undoubtedly lead to design refinement and improvements that may elevate the success prospects of future lunar and other deep space exploration missions.  
For the exploration of other planets of our solar system, only a single mission has so far been undertaken, MarCO. Its noteworthy success has, however, clearly demonstrated that CubeSat platforms have the potential to perform important tasks as parts of large and complex space exploration missions as well as effectively communicate with the Earth from distances of tens of millions of kilometers despite their compact size and low power. This success will most certainly pave the way for a variety of other planetary exploration CubeSat missions over the next years as a variety of mission concepts involving the use of CubeSats for planetary exploration are, as of 2025, under design or development.  
Similarly, regarding asteroid exploration missions, the inclusion of CubeSat platforms in two of the most prominent planetary defense missions that have ever been undertaken, namely NASA's DART and ESA's HERA, along with the results of the first, signify that CubeSats are capable of being effectively used as distributed nodes of complex, cutting-edge space exploration missions and efficiently perform crucial scientific tasks, gathering never-before acquired scientific data from objects located millions of kilometers away from the Earth. These successes, along with opportunities such as the expected close approach of the asteroid 99942 Apophis to the Earth on 13 April 2029 are expected to facilitate the development of a large number of new asteroid exploration CubeSat missions over the next decade and beyond.
# TARS: A Novel Mechanism for Truly Autonomous Resource Selection in LTE-V2V Mode 4
## 3 Preliminaries of D2D Communications
### Resources in LTE-V2V Mode 4  
#### 3.3.1 Anatomy of RB in Mode 4  
The smallest block of resources represented in the time and frequency domain is referred to as an RB. RBs are used for DL/UL transmission. A group of 12 contiguous subcarriers, having a subcarrier spacing of 15 kHz, over a single time slot comprise an RB, as shown in Figure 3.  
One RB corresponds to 180 kHz (i.e., 15 \(\times\) 12 = 180) in the frequency domain over one slot (1 slot = 0.5 ms). There are seven Orthogonal Frequency Division Multiplexing (OFDM) symbols in each time slot [29]. Transmissions are allotted in units of RBs. In [30], the number of RBs corresponding to their respective channel bandwidths was presented, i.e., channels with bandwidths of 1.4 MHz, 3 MHz, 5 MHz, 10 MHz, 15 MHz, and 20 MHz correspond to 6 RBs, 15 RBs, 25 RBs, 50 RBs, 75 RBs, and 100 RBs, respectively.  
Figure 3: A Resource Block (RB).
## 3 Results and Discussion
### Source Analysis
#### 3.2.2 Composition Source  
The overuse of antibiotics, hormones, and other relevant drugs causes a rapid increase in PhACs in the environment. Determining the occurrence of PhACs in snow may help distinguish their possible sources. Multivariate linear regression (MLR) analysis together with principal component analysis (PCA) can be used to quantitatively evaluate the relative contribution of each PhAC from different sources to the total pollutants [58; 59; 60; 61]. Therefore, PCA-MLR was adopted to assess the similarities and differences in PhAC distribution forms to deduce the possible sources of PhACs in snow. The concentrations of 11 principal PhACs (frequency of detection >50%) were taken as the active variables, and 19 samples were taken as the subjects. Three principal components (PC1, PC2, and PC3) were extracted with the eigenvalues >1 by PCA-MLR model, accounting for 63.89% of the total variance (Table 1).  
According to the component matrix, the loadings of the different PhACs on the components are shown in Table 1. The first principal component (PC1) explained 30.98% of total variance with high loadings of sulfaromonethoxine, sulfarameter, nitrazepam, nonidazole, salbutamol, desacetylcefotaxime, and ceftiofur. The results showed that PC1 was a factor of human drugs [62; 63]. The second principal component (PC2) explained 17.97% of total variance, corresponding to the high loads of the veterinary drugs of flunixin, sulfaratheboxazole, and tetracycline [64; 65]. The third principal component (PC3) explained 14.94% of total variance, corresponding to the high loads of 5-nitrobenzimidazole, oxolinic acid, 5-chloro-1-methyl-4-nitroimidazole, and roxithromycin. PC3 denotes shared human- Veterinary drugs [66; 67]. MLR was then carried out on the factor scores of three identified components and \(\sum\)PhACs [68; 69]. The regression equation is expressed as Equation (1):  
\[\sum\)PhACs = 0.691 PC1 + 0.7915 PC2 + 0.238 PC3 (R\({}^{2}\) = 0.951, \(p<0.01\)). \tag{1}\]  
The coefficients were set as Bi, and the contributions of each factor were calculated using Equation (2):  
\[\text{Mean contribution of source, Percentage = (Bi/\sum\text{Bi})\,\times\,100\%} \tag{2}\]The results showed that the contribution of PC1 to the total PhAC concentration in snow was calculated as 40.16%, while PC2 and PC3 contributed 46.00% and 13.83%, respectively. In other words, the drugs applied to humans, animals, or both humans and animals accounted for 40.16%, 46.00%, and 13.83% of the total PhACs, respectively. The proportion of medicine for humans and animals was relatively high in the snow sampled from the sampling sites near JZB. Therefore, the most effective methods to reduce PhACs in snow in JZB are to intensify the management of drugs used for humans and animals, improve the PhAC treatment capacities of sewage treatment plants, and scientifically dispose of animal waste.
## 3 Control Strategy  
### Input Stage Control Strategy  
#### 3.1.1 Mathematical Model of MMC  
The input stage adopts the MMC structure as shown in the blue part of Figure 2. It can be seen that the upper and lower arms of each phase consist of the same amount (\(N\)) of sub-modules which are connected in series, and \(N\) is determined by the voltage level. \(u_{\text{sj}}\) and \(i_{\text{sj}}\) (\(j\) = a, b, c) represent the voltage and current on the AC side, respectively. \(R_{\text{s}}\) and \(L_{\text{s}}\) represent the equivalent filter resistance and equivalent filter inductance on the AC side, respectively. \(u_{\text{pj}}\) and \(i_{\text{pj}}\) represent the voltage and current of the upper arm of phase \(j\), and \(u_{\text{nj}}\) and \(i_{\text{nj}}\) represent the voltage and current of the lower arms of phase \(j\), respectively. \(R_{0}\) and \(L_{0}\) represent the arm resistors and arm inductors, and \(U_{\text{dcp}}\), \(U_{\text{dcn}}\), and \(U_{\text{dc}}\) represent the positive electrode potential, negative electrode potential, and inter-electrode voltage on the DC side of MMC, respectively.  
Figure 2: System topology of the proposed novel PET.  
\(R_{0}\) is neglected due to its considerably small value, and Equations (1)-(3) can be obtained according to [PERSON]'s law:  
\[u_{\mathrm{s}j}-i_{\mathrm{s}j}R_{\mathrm{s}}-L_{\mathrm{s}}\frac{\mathrm{d}i_{ \mathrm{s}j}}{\mathrm{d}t}=-L_{0}\frac{\mathrm{d}i_{\mathrm{p}j}}{\mathrm{d}t}+ U_{\mathrm{dcp}}-u_{\mathrm{p}j} \tag{1}\]  
\[u_{\mathrm{s}j}-i_{\mathrm{s}j}R_{\mathrm{s}}-L_{\mathrm{s}}\frac{\mathrm{d}i_ {\mathrm{s}j}}{\mathrm{d}t}=L_{0}\frac{\mathrm{d}i_{\mathrm{n}j}}{\mathrm{d}t }+U_{\mathrm{dcn}}+u_{\mathrm{n}j} \tag{2}\]  
\[i_{\mathrm{s}j}=i_{\mathrm{n}j}-i_{\mathrm{p}j} \tag{3}\]  
Since \(U_{\mathrm{dcp}}+U_{\mathrm{dcn}}=0\), Equation (4) can be derived through Equations (1)-(3):  
\[(L_{\mathrm{s}}+\frac{L_{0}}{2})\frac{\mathrm{d}i_{\mathrm{s}j}}{\mathrm{d}t}= -i_{\mathrm{s}j}R_{\mathrm{s}}+u_{\mathrm{s}j}-\frac{u_{\mathrm{n}i}-u_{ \mathrm{p}i}}{2} \tag{4}\]
# Experience Replay Optimisation via ATSC and TSC for Performance Stability in Deep RL
## 2 Related Works
### Experience Retention Strategies and Algorithms  
The ER mechanism has three major stages: storing experiences in the buffer, retaining or removing experiences when the replay buffer is full, and selecting experiences to be replayed. When a deep RL agent observes a state, that state is fed into the deep neural network, and the network predicts the q-values of all possible actions in the state. Afterwards, an exploration-exploitation technique (for example, epsilon-greedy) selects an action in the observed state. A new state is observed and a feedback reward is given to determine the value of the action taken. The agent transits from one state to another until it reaches a terminal state. The transition tuple (state, action, reward, and new state) can be stored directly in the buffer or may be stored based on some threshold mechanisms. Since some actions are more rewarding than others and the agent aims to maximise its accumulated reward, it is vital to adopt efficient mechanisms to retain relevant experiences and remove less relevant ones when the buffer is full.  
An experience retention strategy may be implemented sequentially, uniformly, or prioritised. The standard ER uses the First-In-First-Out (FIFO) approach, where it sequentially overwrites old experiences with new ones when the buffer is full. The experiences are removed without considering their relevance to the agent's performance [26]. Another approach is to replace experiences in the first half of the buffer with new ones when the buffer is full. Moreover, a replay buffer that is big enough to store all experiences can be used [7]. However, this approach can be detrimental to training stability, as experiences that may hamper learning progress continue to stay in the buffer. In the reservoir approach, experiences are uniformly at random, overwritten with new ones. Furthermore, experiences can be overwritten stochastically based on their relevance to learning [26]. Table 1 presents a list of algorithms and their ER strategies.
# Communication System Based on Magnetic Coils for Underwater Vehicles
## 1 Introduction
### Optical Communication  
This type of technology uses electromagnetic waves with wavelengths between 400 nm y 500 nm. Optical signals are rapidly absorbed in water because light penetrates a few hundred meters in clearer waters and less in turbid waters, suspended particles cause considerable optical scattering, and the high level of ambient light at the top of the water column interferes with communication [10].  
An application of this technology is a system capable of transmitting at 5 Mbps using light-emitting diode (LED) sources for information transmission [11]. Another example is the implementation of an enhancement to the pulse position modulation (PPM) method to improve data transmission performance in a submarine optical communication system [12].
# Risk-Sensitive Policy with Distributional Reinforcement Learning
###### Abstract  
Classical reinforcement learning (RL) techniques are generally concerned with the design of decision-making policies driven by the maximisation of the expected outcome. Nevertheless, this approach does not take into consideration the potential risk associated with the actions taken, which may be critical in certain applications. To address that issue, the present research work introduces a novel methodology based on distributional RL to derive sequential decision-making policies that are sensitive to the risk, the latter being modelled by the tail of the return probability distribution. The core idea is to replace the \(Q\) function generally standing at the core of learning schemes in RL by another function, taking into account both the expected return and the risk. Named the _risk-based utility function_\(U\), it can be extracted from the random return distribution \(Z\) naturally learnt by any distributional RL algorithm. This enables the spanning of the complete potential trade-off between risk minimisation and expected return maximisation, in contrast to fully risk-averse methodologies. Fundamentally, this research yields a truly practical and accessible solution for learning risk-sensitive policies with minimal modification to the distributional RL algorithm, with an emphasis on the interpretability of the resulting decision-making process.  
distributional reinforcement learning; sequential decision-making; risk-sensitive policy; risk management; deep neural network Article
## 3 Radius of Plastic Zone and Peripheral Displacement
### Effect of Rock Mass Strength  
In Coulomb-Mohr strength theory, the main influencing factors of rock mass strength are cohesion and internal friction angle. Because the change of internal friction angle of different rock mass is relatively small, the strength of rock mass is usually expressed by cohesion. The variation curve  
Figure 1: (**a**) Variation curve of radius of plastic zone with burial depth; (**b**) Variation curve of peripheral displacement with burial depth.  
of the radius of the plastic zone with cohesion of rock mass (tunnel radius = 4 m, internal friction angle = 23.5 \({}^{\circ}\), burial depth = 300 m) is shown in Figure 2a. As can be seen from Figure 2a, the radius of the plastic zone decreases with the increase of cohesion. For example, the radius of the plastic zone of the tunnel is 20.7 m at a cohesion of 0.3 MPa without support, while at a cohesion of 1.0 MPa it decreases sharply to 7.4 m. This rule is very useful for the design of the bolt length.  
The variation curve of peripheral displacement with cohesion of rock mass (tunnel radius = 4 m, internal friction angle = 23.5\({}^{\circ}\), burial depth = 300 m) is shown in Figure 2b. It can be seen from Figure 2b that the peripheral displacement decreases sharply with the increase of cohesion. For example, the peripheral displacement of the tunnel is 26.9 cm at a cohesion of 0.2 MPa without support, while at a cohesion of 1.0 MPa it decreases sharply to 4.3 cm.
# Optical Beamforming Networks for Millimeter-Wave Wireless Communications
## 4 OBFNs with Phase Shifter Architectures  
The phase shifter architectures are built by an array of optical phase shifters which tune the phases of RF signals. Commonly, the main advantage of this kind of architecture is that the phases of RF signals are equal to phase differences between two optical carriers owing to coherent beating at PDs, as expressed by Equation (9). In other words, the phase shifts of RF signals produced by phase shifter architectures are same as the phase shifts in the optical domain. Phase shifter architectures predominantly include four subclasses: polarization-modulated phase shifter architectures, modulator-induced phase shifter architectures,  
Figure 15: Schematic diagrams of 2D OBFNs based on (**a**) delays combined with wavelength de-multiplexers and optical switches [86] (Copyright 2009, IEEE, #5491100487672), and (**b**) wavelength de-multiplexers and tunable CFBGs (reprinted with permission from [87] © The Optical Society).  
integrated phase shifter array architectures, matrix architectures (Butler matrix, Blass matrix and Nolen matrix), as shown in Figure 16[16; 91; 92; 93; 94; 95; 96].
## 3 Obtaining the Steering Vector without Prior Calibration  
For adaptive MVDR beamforming, obtaining the steering vector is the key to implementing a CRPA. However, as mentioned in the previous section, some parameters of the steering vector need to be calibrated. Direction vectors toward satellites can be obtained after positioning. A software receiver has the flexibility of implementing multiple channels to track multi-antenna signals separately. Integrated Carrier Phase (ICP) is one of the tracking outputs of the phase lock loop. The ICP is often used to smooth code pseudorange for improving accuracy of positioning. In our software receiver, ICP differences between different antenna elements are taken to build the steering vectors instead of deriving the vectors from the azimuths/elevations of the satellites and the baseline vectors of antenna elements [20]. Figure 3 shows the block diagram of the approach used to obtain the steering vector in our software receiver. This approach enables us to perform beamforming without a prior calibration.  
However, if ICP cannot be extracted from individual antenna elements due to weak signal, this technique does not work. If the ICP cannot be extracted due to interference, the steering vector is kept as previously calculated value. For our implementation, it is assumed that the receiver is initiated in interference free conditions.  
The IF data of each antenna is processed by \(K\) tracking channels (\(K=12\) in our case) in which each channel is assigned to track one satellite. Each satellite is tracked by \(N\) channels (\(N=4\) in our case) assigned to \(N\) antenna elements. Once the \(N\) channels assigned to a given satellite are simultaneously tracked, the ICPs are initialized by filtered phases of the in-phase (I) and quadrature (Q) components of correlator outputs. Then, the ICPs are continuously integrated and the phase differences between the reference antenna and other antennas are computed at runtime. The differences of ICPs between different antennas provide elements of the steering vector. This phase difference, which is the same as \(\Delta\varphi_{i1}^{l}+\Delta\gamma_{i1}\) in Equation (5), can also be used to calculate the cabling/RF chain part \(\Delta\gamma_{i1}\) because \(\Delta\varphi_{i1}^{l}\) is obtained after positioning.