esa-sceva/satcom-chunk-collection huggingface dataset:
# Comparative Study of Minimally Invasive Microwave Ablation Applicators
## 5 Conclusions  
This study presents the first comparative analysis of microwave ablation (MWA) applicators under standardized conditions, utilizing both homogeneous and two-compartment models. It also evaluates the safety aspects of MWA by investigating not only the temperature thresholds for effective treatment (50 \({}^{\circ}\)C and 60 \({}^{\circ}\)C) but also the isothermal contours at 42 \({}^{\circ}\)C and 38 \({}^{\circ}\)C. These contours are critical to ensuring the safety of MWA, a key factor in advancing this therapeutic modality. To achieve this, a two-compartment model, incorporating both tumor and healthy tissue, is employed. This model provides a robust framework for validating the performance of MWA applicators under these critical conditions.  
This comparative analysis was conducted between previously developed microwave ablation applicators. Initially, various applicators were modeled, including the monopole, capacitive-fed monopole antenna, floating sleeve dipole antenna, dual-slot antenna, multi-slot antenna, triaxial antenna, choke antenna, balun-free base-fed monopole antenna, balun-free helical dipole antenna, and the helical open waveguide structure.  
Following the simulation phase, our results show strong alignment with those reported in the literature. We subsequently tested the applicators using (i) a homogenous tissue and (ii) a two-compartment model, incorporating a tumor with a diameter of 3 cm or 2 cm. This model differentiated between healthy tissue and the tumor by assigning distinct electrical properties, offering a reliable approximation of anatomical geometry.  
The results revealed notable differences in the behavior of the electromagnetic field between the homogeneous model and the two-compartment model. Notably, all applicators except for helical open waveguide failed to confine the temperature field within the tumor, resulting in unintended damage to surrounding healthy tissue. In contrast, helical open waveguide demonstrated the capability to limit the ablation zone effect to the tumor region, thereby enhancing the safety and precision of the microwave ablation process. However, its current size is unsuitable for an immediate exploitation as a minimally invasive microwave ablation (MWA) applicator, even though a plethora of folding techniques exist that may devise a mechanism to compress the spirals inside the catheter. These can be unfolded to form the proper shape after the insertion of the catheter inside the tumor.  
**Supplementary Materials:** The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app15042142/s1](https://www.mdpi.com/article/10.3390/app15042142/s1)]([https://www.mdpi.com/article/10.3390/app15042142/s1](https://www.mdpi.com/article/10.3390/app15042142/s1)), The flow diagram illustrating the PRISMA screening and selection process is available as Supplementary Figure S1 online.  
**Funding:** This research received no external funding.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
# A Trajectory Optimisation-Based Incremental Learning Strategy for Learning from Demonstration
## 3 Research Methodology
### Extended Demonstration via G-PRM
#### 3.2.2 Bias Optimisation
\begin{cases}Tra_{s\_i_x}=x_{s}+scale_{factor\_x}\cdot(Tra_{gen\_i_x}-x_{s})\\ Tra_{s\_i_y}=y_{s}+scale_{factor\_y}\cdot(Tra_{gen\_i_y}-y_{s})\,,i=(1,2\dots,k) \\ Tra_{s\_i_z}=z_{s}+scale_{factor\_z}\cdot(Tra_{gen\_i_z}-z_{s})\end{cases}  \tag{7}\]  
where \(Tra_{s\_i_x}\), \(Tra_{s\_i_y}\), and \(Tra_{s\_i_z}\) represent the x, y, and z coordinates of the ith point in \(Tra_{s}\). \(Tra_{gen\_i_x}\), \(Tra_{gen\_i_y}\), and \(Tra_{gen\_i_z}\) refer to the x, y, and z coordinates of the ith point in \(Tra_{gen}\). Finally, the bias \(\mathbf{bv}=\{bv_{1},bv_{2},\dots,bv_{k}\}\) for updating \(G\mathbf{SP}\) is calculated based on the results of 1 and 2. The initial \(\mathbf{bv}\) in each dimension is zero. \(\rho\) is the learning factor, and \(iter\) represents the current number of iterations. Equation (8) shows the calculation process of \(\mathbf{bv}\):  
\[ \begin{cases}bv_{i_x}\getsbv_{i_x}+(1-Fresim)\cdot\rho^{iter}\cdot(Tra_{ imi\_i_x}-Tra_{s\_i_z})\\ bv_{i_y}\getsbv_{i_y}+(1-Fresim)\cdot\rho^{iter}\cdot(Tra_{imi\_i_y}-Tra_{s \_i_y})\\ bv_{i_z}\getsbv_{i_z}+(1-Fresim)\cdot\rho^{iter}\cdot(Tra_{imi\_i_z}-Tra_{ s\_i_z})\end{cases} \,i=(1,2\dots,k) \tag{8}\]  
In Equation (8), \(bv_{i_x}\), \(bv_{i_y}\), and \(bv_{i_z}\) represent coordinate differences between \(Tra_{s}\) and \(Tra_{imi}\) in the x, y, and z dimensions, respectively. \(Tra_{imi\_i_y}\), \(Tra_{imi\_i_y}\), and \(Tra_{imi\_i_z}\) are the x, y, and z coordinates of the \(i\)th point in \(Tra_{imi}\). To update \(G\mathbf{SP}\) based on the \(\mathbf{bv}\), interpolation is a necessary method to align the number of elements in \(\mathbf{bv}\) with \(G\mathbf{SP}\). After interpolation, the \(\mathbf{bv}\) is renamed and reformulated as \(\mathbf{lbv}=\Big{\{}lbv_{1},lbv_{2},\dots,lbv_{sample}\Big{\}}\). Subsequently, updated \(G\mathbf{SP}\) (\(G\mathbf{SPnew}\)) is generated through Equation (9), where each coordinate is calculated accordingly:  
\[ \begin{cases}GSPnew_{i_x}=GSP_{i_x}+lbv_{i_x}\\ GSPnew_{i_y}=GSP_{i_y}+lbv_{i_y}\,,i=0,1,\dots,sample\\ GSPnew_{i_z}=GSP_{i_z}+lbv_{i_z}\end{cases}  \tag{9}\]  
The \(G\mathbf{SPnew}\) is used to generate updated \(Tra_{gen}\) via G-PRM. \(G\mathbf{SP}\) is regenerated based on Equations (1) and (2) during each iteration. Subsequently, \(Fresim(\cdot)\) needs to be recalculated based on updated \(Tra_{gen}\) and \(Tra_{imi}\). Additionally, Equations (6)-(9) are applied iteratively to continue updating \(Tra_{gen}\) and \(G\mathbf{SPnew}\). Through this process, the similarity between \(Tra_{gen}\) and \(Tra_{imi}\) gradually increases. After reaching the pre-set number of iterations, bias optimisation is completed.
## 4 Results and Discussion
### Policy Implications  
Even though the Chinese government has supported the development of digital technologies in the agricultural sector through various policies, a significant share of farmers is yet to adopt any of these technologies due to varying concerns. This review reveals key motivating factors that, if addressed, could assist agencies and the government in overcoming adoption constraints. By formulating more targeted strategies according to the farmers' characteristics and the features of the technology, public efforts could help transform the numerous trial examples of digital agriculture into viable industries and spread benefits to a larger group of farmers. Potential interventions could involve offering knowledge training and extension services, establishing farm cooperatives, and implementing environmental regulations.  
Adoption may not occur if DATs are not demonstrated to be more cost-effective than traditional methods, given that the farming business is profit-orientated. Financial initiatives such as capital subsidies, cuts in interest rates, and enhanced financial services can play an important role in motivating the adoption of digital technologies, especially among small-scale farmers. These incentives need to be performance-oriented and farm-specific instead of being uniformly applied across all farmers and regions.  
Additional challenges for the wide adoption of DATs include insufficient information and communication technology (ICT) literacy, inadequate supporting services and infrastructure, and an unreliable supply of electricity and Internet. Public entities can participate in supplying digital services and infrastructure, establishing open standards and protocols, and creating platforms for communication and information sharing. By providing access to agroeconomic data, connectivity, and environmental outcomes of production decisions, public action can harness the opportunities created by digital technologies and facilitate the digital transformation in agricultural production.  
Digital technologies are characterized by aggregating high-resolution data from multiple sources, including privately owned data, remote sensor data, and public data. Concerns about data ownership, privacy, and confidentiality can pose additional barriers to adoption, as farmers are likely to perceive less control over their farm operations. Accordingly, appropriate policy should shape regulatory conditions concerning how and by whom the collected data is maintained and controlled. By offering clear guidelines and security on data-related issues, public initiatives can reduce the riskiness perceived by technology adopters.
## 3 Results and Discussions
### Optimal Laser Fluence Range
A similar trend was observed for the MRR--it increases with the increase in peak fluence and the increase is quite significant in the lower fluence range as shown in Figure 9d. Based on this study, the optimal peak fluence range for fabrication of microchannels was found to be 125-130 J/cm\({}^{2}\). This peak fluence range resulted in microchannels with low dimensional error and medium depth of cut of about 3-5 \(\upmu\)m.
## 2 Related Work
For the maximum comfort and cost of both the utility and end user, the dual decomposition-based approach is used. User's comfort has been modelled by using a utility function that maps the total energy consumption up to the satisfaction level and decreases the energy variations at a desired point [32]. Similarly, in [33], user's comfort has been modelled by using a sigmoid function, where comfort is achieved at the cost of extra energy. Another scheme has been proposed in which users specify their frustration level to change the length of operation and the start time of the appliance without taking into account the effects on the user's lifestyle [34]. In [35], thermostatically-controlled appliances are used to enhance the maximum user comfort. Users only specify their preferred upper and lower temperature limits and the deviation point. Possible trade-offs between user's comfort and electricity cost are studied in [36]. An optimization problem is proposed that adjusts the knob parameter to achieve minimized appliance waiting time or electricity cost. However, this technique is not precise and does not provide any idea of the obtained trade-offs. Moreover, the waiting time is associated with electricity cost. Here, waiting time cost increases as more energy is consumed in the later time slots. On the other hand, if high energy is consumed in the first hour(s) and very low energy in later hour(s), the waiting time cost would be less as compared to the former case. To sum up, all of the user's comfort-based DSM models discussed above have trade-offs between appliance waiting time and electricity cost. Moreover, these models fail to satisfy the requirements of middle class and low class users. The proposed DSM technique based on the WDO algorithm is fully decentralized. Based on the energy price signal obtained form the utility, the appliance start time and the length of operation time, EMC performs its own calculations locally and makes energy-efficient schedules. These schedules are not shared with other users in order to reduce the complexity and communication delay between appliances, EMC and the utility. Moreover, in the proposed model, we balance the appliance waiting time and electricity cost to provide benefits to both end users and the utility company rather, than to provide benefits to a single party. For more cost reduction, the min-max regret knapsack problem formulation technique is used and compared to that of simple optimization algorithms.
## 2 Modeling and Implementation
### Numerical Implementation and Code Description  
The method described in the previous section is now to be implemented as a function called RooTri(arg1, arg2, arg3, arg4, arg5) in the Matlab(r) software environment. A total of five arguments are provided as input variables to the function and are listed in Table 1. In the first step, the input variables are read to define the plane and extract the \(x\) and \(y\) components of the elements of \(\mathcal{P}\). After that, Delaunay triangulation is performed using the function delaunay() provided in Matlab(r). This produces a matrix \(\mathbf{K}\in\mathbb{R}^{m\times 3}\) containing the indices of those points from \(\mathcal{P}\) that form a triangle. Subsequently, the vectors and intersection points are determined for all elements of the matrix, which are also checked to verify whether they are elements of the interval defined by the vector in question using Equation (4). If this is true, these intersection points are stored in an output matrix \(\mathbf{P}\in\mathbb{R}^{p\times 3}\), which contains the components of all valid intersection points. This process is repeated until all triangles of matrix \(\mathbf{K}\) have been checked. In case no intersection  
Figure 3: Origin planes with constant offset and corresponding plane parameters.  
point is found, the matrix contains no element, i.e., \(\mathbf{P}=\emptyset\). Here, Figure 4 summarizes the general procedure of the algorithm. In the course of increasing the performance of the algorithm, we refrain from using for loops in the implementation but instead build the code vectorized. This has the advantage that the code can be executed faster, which is especially important if the function has to be called frequently. However, the general principle of operation based on Figure 4 remains the same. The entire code can be found in Appendix A.
# _photonics_
## 4 Simulation Results
### Feasibility of LEO Satellite-to-Ground QKD in Daytime and Nighttime
#### 4.2.1 Distance Reach  
As we have already discussed in the introduction section, fiber QKD links are limited to few hundreds of kilometers due to the photon loss imposed by the fiber medium. When light propagates through empty space, none attenuation mechanism is present and therefore longer distances can be reached. On the other hand, other factors should be considered that lead to increased transmission losses, such as free-space loss. In order to minimize the geometrical loss, narrow beam divergences and large receiver apertures can be used. Aside from the loss mechanisms, the effect of the solar background noise during the day can significantly degrade the transmission performance. To combat this effect, ultra-narrow band-pass filters can be used to minimize the background noise to acceptable levels and isolate the quantum passband. Following the above approach and by using state-of-the-art equipment in both the satellite and the ground station, links up to GEO distances can be achieved [56]. Figure 2a shows the total downlink loss of the link including the quantum efficiency of the detectors, the setup loss of Bob station, the absorption and geometrical loss, the scintillation effect loss, pointing loss, and polarization decoherence loss. Figure 2b shows the SKR as a function of receiving telescope aperture and link distance under nighttime conditions.  
It is evident from the contour plots in Figure 2 that the total link loss for a 600 km link distance can get as low as 20 dB in total. To achieve this loss performance, a large enough telescope in the receiver is required. It should be also noted that this value would be the lowest for the link for an orbital height of 600 km that corresponds only to the point when the satellite is exactly above the station.
###### Abstract  
Many car manufacturers have recently proposed to release autonomous self-driving cars within the next few years. Information gathered by sensors (e.g., cameras, GPS, lidar, radar, ultrasonic) enable cars to drive autonomously on roads. However, in urban or high-speed traffic scenarios the information gathered by mounted sensors may not be sufficient to guarantee a smooth and safe traffic flow. Thus, information received from infrastructure and other cars or vehicles on the road is vital. Key aspects in Vehicle-To-Everything (V2X) communication are security, authenticity, and integrity which are inherently provided by Information Centric Networking (ICN). In this paper, we identify advantages and drawbacks of ICN for V2X communication. We specifically review forwarding, caching, as well as simulation aspects for V2X communication with a focus on ICN. Furthermore, we investigate existing solutions for V2X and discuss their applicability. Based on these investigations, we suggest directions for further work in context of ICN (in particular Named Data Networking) to enable V2X communication providing a secure and efficient transport platform.  
Information Centric Networking; Named Data Networking; V2X; secure communication +
Footnote †: journal: Information  
0000-  
The conclusion is that, V2X bears tremendous potential with respect to road and intersection safety. Industry recently also emphasized that road safety via V2X is a very important topic and that they are eager to pursue an inter-operable data specification [3].  
In this work, we focus on the technologies enabling V2X communication. We aim at addressing how specific data is requested, found, and delivered within V2X scenarios. In detail, we are going to investigate how suitable Information Centric Networking (ICN) [4; 5] is for V2X communication scenarios by reviewing the existing literature. We focus on Named Data Networking (NDN), which is one of the most elaborated implementations of ICN [6]. The benefits of NDN in V2X scenarios, especially for data offloading, has been shown in [7]. For V2X communication, the advantages of NDN over IP-based networking are its adaptive forwarding plane, inherent security concepts, and the seamless ad-hoc nature of the hardware to software coupling of interfaces. We are not going to propose completely new approaches in this work. Rather, we want to highlight and motivate research in NDN that is specific to V2X scenarios. We will discuss research trying to tackle issues of NDN in V2X scenarios. Furthermore, we will review single features of NDN and discuss how V2X communication can benefit from them. We also highlight which aspects of NDN need more attention of the research community in future.  
Thus, this work discusses research and problems of NDN in V2X scenarios covering following areas:  
* dynamic forwarding,
* caching of content,
* security, authenticity and integrity, and
* simulation of wireless NDN communication.
# Application of Lignin for Slope Bioengineering: Effect on Soil Improvement and Plant Growth
## 3 Materials and Methods
### Materials Used  
Three different soil types were used: Soil 1 (black clay), Soil 2 (brown silt), and Soil 3 (sand). The rationale for this selection was to investigate the effect of lignin on the properties of high-plasticity (Soil 1), low-plasticity (Soil 2), and non-plastic (Soil 3) soils. A series of standardized laboratory tests were performed to estimate soil plasticity [52], maximum dry density and optimum water content [53], and specific gravity tests [54]. Additionally, X-ray tests were conducted to estimate the soil mineral composition. The following data were obtained:  
Soil 1: High-plasticity black clay with a liquid limit of 79.2% and a plasticity index of 46.0%, which is classified as CH, according to the Unified Soil Classification System (USCS). The optimum moisture content (OMC) was 27.0%, with a maximum dry density of 1.35 g/cm\({}^{3}\). The specific gravity was 2.71. The results of the X-ray diffraction test revealed the following dominant clay minerals: smectite, kaolinite, and illite.  
Soil 2: Low-plasticity brown silt (USCS classification is ML) had a liquid limit of 38.0% and a plasticity index of 11.1%. The specific gravity was 2.77, the maximum dry density was 1.72 g/cm\({}^{3}\) and the optimum moisture content was 21.7%. The mineral composition included quartz, kaolinite and calcite.  
Soil 3: Well-graded non-plastic sand (USCS classification was SW), which had the maximum dry density of 1.85 g/cm\({}^{3}\) with an optimum water content of 13.0%. It predominantly consisted of quartz.  
Lignin. The ammonium lignosulfonate lignin was commercially acquired from Dustex, Australia. The properties of lignin were as follows: calcium lignosulfonate lignin was a brown viscose liquid with a pH (10% solution) of 5.4 \(\pm\) 3.0, dry matter of 55.0 \(\pm\) 1.0%, and a density of 1285 kg/m\({}^{3}\). The lignin was a mixture of water (51%) and calcium lignosulfonate (49%). The lignin solutions used to treat the soils were prepared by mixing lignin with distilled water. Two solutions of lignin were prepared; that is, 1% and 3% of the lignin mass to the total mass of the dry soil. It is noted that solutions of lignin with greater concentrations (>3%) were too viscous, and it was challenging to mix such solutions with soil.  
Seeds. Commercially available Couch (_Cymodon dactylon_), Rhodes grass (_Chloris gayana_), and Forest blue grass (_Bothriochla bladhi_) [55] were utilized in this study as these native seeds have been widely used in engineering practices in Queensland, Australia.
## 3 Materials and Methods
### Long Short-Term Memory  
First introduced by [PERSON] and [PERSON] [52] in 1997, the LSTM architecture excels compared to the other recurrent structures in processing sequential data. The inclusion of the memory cell and the gating mechanism enables LSTM to address long-term dependencies efficiently. LSTM also mitigates the vanishing gradient problem, thus making the training of deep architectures on sequential data more effective. The structure of a single LSTM unit is shown in Figure 5.  
At each time step, an LSTM unit manages a hidden state and a memory state by utilizing three gating mechanisms. The input gate decides whether new information should be stored in the cell state or not. The input gate uses the current input and the previous hidden state in its computation, as expressed by the following equation:  
\[i_{t}=\sigma(W_{i}\cdot[h_{t-1},x_{t}]+b_{i}) \tag{6}\]  
The candidate memory state, \(\tilde{C}_{t}\), is also computed using the same input, and it represents new information that could be added to the cell state. The candidate cell state equation is  
\[\tilde{C}_{t}=tanh(W_{C}\cdot[h_{t-1},x_{t}]+b_{C}) \tag{7}\]  
The forget gate decides what information from the previous cell state should be discarded or retained. The input to this gate is the previous hidden state and the current input:  
\[f_{t}=\sigma(W_{f}\cdot[h_{t-1},x_{t}]+b_{f}) \tag{8}\]  
The cell state at the core of the LSTM is responsible for retaining long-term information, which is updated according to the following equation:  
\[C_{t}=f_{t}*C_{t-1}+i_{t}*\tilde{C}_{t} \tag{9}\]  
Based on the previous hidden state and the current input, the output gate controls what the output would be as the new hidden state. The output and the hidden state relations are as follows:  
\[O_{t}=\sigma(W_{0}\cdot[h_{t-1},x_{t}]+b_{0}) \tag{10}\]  
\[h_{t}=O_{t}*tanh(C_{t}) \tag{11}\]  
Figure 5: An LSTM cell.