esa-sceva/satcom-chunk-collection huggingface dataset:
# Joint Communication-Motion Planning in Networked Robotic Systems
###### Abstract  
In the recent decade, many research efforts in robotic society have considered motion planning for maintaining connectivity in networked robotic system (NRS) by exploiting robotic autonomous mobility. On the other hand, cognitive radio (CR) in the communication society aims at fully exploiting the spectrum in a wireless network, while the motion planning is seldom considered, as a wireless device itself may not decide where to go. In this article, joint communication-motion planning (JCMP) is proposed to boost the capability of NRS by exploiting both the adaptive communications and mobility control of autonomous robots. Specifically, we propose a JCMP framework for NRS, which aims at jointly exploiting the degree-of-freedom in mobility, space, time, frequency and power dimensions from both the motion and communication components. Afterward, we design and evaluate JCMP in a conventional and a CR-relay-assisted robot system, which shows the capability of JCMP in improving the performance of NRS. Finally, we summarize the proposed JCMP-enabled NRS framework and provide a series of future research directions.  
joint communication-motion planning; cognitive radio; networked robotic systems; robotics networking +
Footnote †: journal: Applied
# A Systematic Review of Radio Frequency Threats in IoMT
## 3 Results and Discussion
### Cyber Impacts and Layered Attacks
#### 3.1.2 Cyber Security Framework  
A security framework assessment matrix compares various cybersecurity framework implementation trends [29]. Authors performed through literature review and qualitative document analysis. The cybersecurity framework's assessment matrix helps identify how many items are covered. Besides, three frameworks from three countries are aligned to their country profile and risk management strategies. According to the investigation, their analysis benefits policymakers and executives doing business in three states by improving their framework strength and understanding of necessary improvements. In addition, country-specific cybersecurity implementation frameworks (CIFs) were implemented across regions, and business values were shared. Hence, evidence-based insights are developed for decision-makers from business regions to improve their existing cybersecurity frameworks. However, most action items are derived from the NIST framework except for risk governance, which had substantial quantitative empirical support. NIST's limitations are prioritized in this paper, but most action items are still inherited from NIST.  
Moreover, the author used the old policy-2014 instead of the amended policy-2018 for the Australia protective security policy framework (PSPF) assessment. To improve cybersecurity framework implementation, the authors analyzed the assessment matrix and used pattern-matching [30]. On the other hand, there is a potential gap in enhancement to understand the effectiveness of adopting and utilizing cybersecurity implementation frameworks, though adopted by businesses having branch offices across those regions (the UK, Australia, and the USA).
# The Effect of Cu (II) on Swelling and Shrinkage Characteristics of Sodium Bentonite in Landfills
## 5 Conclusions  
To evaluate the performance of the bentonite liner as a chemical barrier, comprehensive experimental studies are conducted aiming at the properties of swell, shrinkage, and mineral change, the conclusions were drawn as below:  
The free swell rate of bentonite was inversely proportional to the concentration of Cu (II). In the high concentration of Cu (II) solution, the FSR is low, which allows the water to permeate easily through the liner. Similar to FSR observation, the non-load swell rate and swell speed of bentonite as the indicator of the performance of structured clay decreased with the increase of Cu (II) concentration. The commonly used dose model has been proved unsuitable in the low concentration of Cu (II); however, it is applicable in the high concentration. The results of mineralogy show that the interlayer space of montmorillonite decreased with the increase of Cu (II) concentration. The Cu (II) weakens the expansibility of bentonite by changing the interlayer space of montmorillonite. Moreover, the ion exchange of Cu (II) and Na\({}^{+}\) between layers is the main reason for impairing the barrier performance of bentonite liner. The diffusive double layer formed by water molecule copper ion and other ions that were attracted to the surface of soil particles become thinner, hence the swelling performance of bentonite in the environment with Cu (II) solution decreased. Because the potential energy curves of soil particles in different concentration environments were different, the higher the concentration of Cu (II), the smaller the expansibility. The paper provides insight into the relationship between Cu (II) and the performance of bentonite linear.  
Conceptualization, G.X. ([PERSON]) and G.X. ([PERSON]); writing--original draft preparation, G.X. ([PERSON]) and T.W.; writing--review and editing, W.L. and L.Z.; project administration, J.Z.; All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The study did not involve humans or animals.  
The study did not involve humans.  
The date presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.  
Figure 10: Relation between potential energy and particle spacing.
# Isocyanide Multicomponent Reactions on Solid Phase: State of the Art and Future Application
## 7 Other Post-Ugi Transformations
### Spontaneous Transformations  
Spontaneous transformations proceed simultaneously with the cleavage of the acyclic Ugi products from the resin. The first example is the formation of ketopiperazines by tandem U-4 CC followed by \(N\)-acyliminium ion cyclization [34]. Wang resin-bound isocyanide (**20**, R\({}^{3}\) = \(p\)-CH\({}_{2}\)-C\({}_{6}\)H\({}_{4}\)-O, Scheme 54) entered the Ugi reaction with a carboxylic acid, aldehyde, and aminoaldehyde diethyl acetal. Acidic treatment of polymer-supported Ugi product **21** cleaved the acetal, led to the formation of \(N\)-acyliminium ion, followed by the loss of proton upon neutralization, and resulted in the target unsaturated heterocyclic product **22**. Note, HCl treatment prior to TFA cleavage was not necessary. The synthesis was reported in the solution phase as well.  
2,4,5-Trisubstituted oxazoles, which are known pesticides, were synthesized via \(\alpha\)-acylamino ketones **329** subjected to the Robinson-Gabriel intramolecular cyclization by using TFAA in DME, where TFAA served as both a cleavage and cyclodehydration agent (Scheme 55) [70]. In addition to affording \(\alpha\)-acylamino ketones by U-4 CC, five more synthetic strategies were investigated. Despite low yields (39% and 40% crude, 14%, and 15% after passage through a silica gel plug), the Ugi reaction was beneficial due to the single step-formation of \(\alpha\)-acylamino ketones. Only two examples were given.  
The Diels-Alder reaction is another example of a post-Ugi transformation. Resin-bound amine **331**, which was prepared via two steps from ArgoGel Rink resin, furalaldehyde **332**, benzylisocyanide **179**, and activated dienophile acids **333** (such as maleic and fumaric acid derivatives), led to the formation of triene **334**, which immediately underwent an acid-mediated intramolecular Diels-Alder reaction to afford highly functionalized rigid tricyclic lactams **335** (Scheme 56) [96].  
The influence of electronic and steric effects was investigated. While condensation of unsubstituted furaldehyde (**332**, R\({}^{1}\) = H) led to products in high yields and good diastereoselectivities (the ratio of the two isomers ranged from 91:9 to 88:12), 5-methylfuraldehyde (R\({}^{1}\) = CH\({}_{3}\)) afforded products with moderate diastereoselectivity (the ratio of the two isomers ranged from 88:12 to 77:23). The synthesis was initially carried out in the solution phase.
# Machine-Learning Applications in Oral Cancer: A Systematic Review
## 3 Results

Table 1: _Cont._In concordance with the four application areas found, the diagnosis and prevention [19; 24; 25; 29; 31; 33; 35; 36; 38; 39; 40; 41; 42; 45; 46; 47; 49; 56; 58; 63; 65; 67; 69; 70; 71] register the largest number of articles with 45.61%, followed by prognosis [17; 18; 23; 26; 37; 43; 48; 50; 51; 52; 54; 57; 62; 68; 72; 68; 73] with 28.07%, and potentially malignant oral lesions (pre-cancer) [20; 21; 26; 30; 34; 44; 55; 59; 61; 64; 66] with 21.05%, as shown in Figure 2. On the other hand, 5.26% of the articles focus on therapy and quality of life [22; 27; 53]. Furthermore, from 2018, a sustained increase in the number of publications that address the prognosis and diagnosis of oral cancer pathology can be observed. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{1}{c}{**Ref. **} & \multicolumn{1}{c}{**Year**} & \multicolumn{1}{c}{**Clinical**} & \multicolumn{1}{c}{**Applied**} & \multicolumn{1}{c}{**Input**} & \multicolumn{1}{c}{**\#**} & \multicolumn{1}{c}{**\#**} & \multicolumn{1}{c}{**Concluding**} \\  & **Context** & **Algorithm** & **Features** & **Samples** & **ROB** & **Remarks** \\ \hline \multirow{4}{*}{[65]} & \multirow{4}{*}{2005} & \multirow{4}{*}{2005} & Diagnosis and & & & & The Bayesian framework of RVM \\  & & & & & & formulation makes it possible to predict the posterior probability of class membership in \\  & & & & & & discriminating early SCC from the normal \\  & & & & & & squamous tissue sites of the oral cavity in \\  & & & & & & & & \\  & & & & & & & provided by the non-Bayesian SVM \\ \hline \multirow{4}{*}{[66]} & \multirow{4}{*}{2012} & \multirow{4}{*}{2012} & Potentially & \multirow{4}{*}{BC, SVM} & Images of normal & \multirow{4}{*}{119} & \multirow{4}{*}{+} & Bayesian classification and support vector machines (SVM) to classify normal and OSF \\  & & & & & & & image classifier (SSE-BL) to categorize \\  & & & & & microscopic images & & & \\  & & & &  
Various machine-learning algorithms were used in the investigations. The first study was conducted in 1996, using the performance of a computer-generated neural network trained to identify normal, premalignant, and malignant oral smears using an artificial neural network [56]. Among the most frequently applied algorithms in oral cancer pathology (Figure 3) is the support vector machine (SVM) with 42.10%, the artificial neural network (ANN) with 24.56%, and the logistic regression (LR) with 21.05%. On the other hand, in the deep-learning subarea, the application of the convolutional neural network (CNN) was 12.28%. Studies of machine-learning applications in oral cancer analyzed different types of data (Table 1), for example: genomic data [17; 19; 22; 47; 48; 50; 53; 54; 72], histopathological data [49; 56; 64], image data [20; 21; 26; 28; 29; 30; 31; 38; 32; 33; 34; 40; 42; 44; 55; 60; 61; 62; 66; 67], medical history/clinical data [18; 23; 25; 37; 43; 51; 57; 59; 68; 70; 71; 73], spectral data [24; 34; 36; 41; 45; 46; 52; 58; 63; 65; 69], and speech data [27].
# A Roadmap to Integrate Digital Twins for Small and Medium-Sized Enterprises
## 4 Discussion
### Future Research  
Despite its effectiveness, this DT model and communication framework has some limitations with respect to industrial-scale systems. For example, the simplified 3D design does not reflect the true characteristics of PLM and IoT systems that have multiple stakeholders, users and many components, devices, and data types. Such characteristics require a comprehensive analysis of communication protocol, digital transformation and larger efforts in system integration. As such, we note the following areas for future research:  
This article has focused primarily on building the connection between the physical asset, the digital asset and the database while giving priority to components with in-built security. SMEs and industry users may consider implementing internal safety control measures to ensure data security and intellectual property protection.  
We noted throughout the development of this article that different platforms communicate data with their proprietary format such as 3 DExperience's 3 DXML file format for the systems model. There are the many commercial simulation platforms available, each with their own proprietary data formats; however, this is not limited to modelling and simulation platforms but rather covers many hardware and software that utilise data for their operations. Due to the number of platforms and data types, we observe that the standardisation of data and models is a potential field of study for future research.  
Regarding the physical assets, the data sources and the data types, this article has focused on a singular source of structured data; however, unstructured data may generate business value if utilised correctly. Future DT developers may look to implement methods of utilising unstructured data into the DTH. As different sources of data are integrated into the DTH, users will observe the increase in traffic and therefore optimise and synchronise the communication systems such that data silos and communication interruptions are avoided and collaboration is improved.  
We faced a challenge in integrating the individual components that make up the communication framework and found one of many viable solutions. This solution was suitable for the purposes of this article. In the industry setting, a smoother integration between I4.0 and DT components and their reliability and resilience will require further dedicated analysis and evaluation. Collaboration between those representing the various hardware and software developers would aid significantly in creating a unified platform of the digital twin.
## 7 Definition of the Simulation Scenarios  
The simulation scenarios ensure a realistic approximation of future renewable energy production and its variability. This approach aligns with the PNIEC target of achieving 81% renewable generation by 2030 [55]. To facilitate this, the installed nominal PV power is set at 80% of the total synchronous generation capacity in the initial scenario, simulating various penetration levels. Consequently, the total nominal power of the four PV plants is equal to 80% of the total generation capacity of the grid. Initially, synchronous generators produce 6140.8 MW without the PV generators connected. Based on this, the nominal power of each PV plant is set at 1228.16 MW.  
The scenarios range from 80% to 20% renewable generation, aligning with the expected future conditions of the grid. The 80% renewable generation scenario is based on the targets established by the PNIEC [55], while the 20% represents a low-renewable-penetration scenario, as renewable generation on the grid is unlikely to be zero. The scenarios are structured as follows:  
* Scenario 1: PV plants generate 20% of the total grid power, with synchronous generators contributing 80%.
* Scenario 2: grid power is equally shared between PV plants and synchronous generators, each supplying 50%.
* Scenario 3: 80% of the grid generation is supplied by PV plants, while synchronous generators contribute 20%, consistent with the PNIEC target.  
Operational constraints are explicitly considered, particularly in scenarios where PV plants operate at their maximum rated capacity. Under these conditions, the saturation of the active power output can restrict the POD-P controller's ability to effectively mitigate oscillatory events due to its limited capacity for further active power adjustments. This limitation is especially critical in Scenario 3, where PV plants operate at 100% of their nominal capacity, pushing the system to its operational limits. To overcome this challenge, an anti-windup filter is integrated into the PID controller. This filter dynamically limits the controller's response during saturation, preventing excessive integral windup and ensuring stable system performance under extreme conditions. Consequently, in Scenario 3, simulations are conducted with and without the anti-windup filter to evaluate its impact on the POD-P controller's effectiveness when the PV plants operate at full capacity.  
Table 1 summarizes the three generation scenarios considered. The variation in the total power generated arises from the grid reference machine, which does not have a predefined active power setpoint but produces the amount needed for the grid to converge and operate correctly.
# Multi-Objective Optimization of a Wireless Body Area Network for Varying Body Positions
## 2 Wireless Body Area Sensor Network
Figure 3: The radiation patterns of antennas in the off-body link obtained for their initial positions (\(\phi_{2}=65^{\circ}\), \(z_{2}=0.32\) m, \(\phi_{3}=107^{\circ}\), \(z_{3}=0.21\) m), normalized to the maximum gain value equal to 6.5 dBi. In this case, there is no significant improvement in the performance because the radiation patterns strongly overlap.
## 4 FE Analysis and Result  
### Analysis Method and Modeling  
FE analysis was performed to assess the experimental results analytically.  
ABAQUS [36], which is a general analysis program capable of analyzing concrete composites and contact, was used as the FE analysis software. The explicit method was utilized to increase the convergence of the solutions for material nonlinear analysis and contact analysis. Previous studies have shown that ABAQUS/Explicit is more effective than ABAQUS/Standard in many nonlinear problems, such as concrete material cracking and failure. The explicit method achieves faster convergence than the implicit method for each incremental analysis because it is not necessary to perform inverse matrix conversions on the stiffness matrix, and the analysis time can be shortened by using mass scaling or increasing the loading rate.  
The element mesh size is very important for obtaining accurate analysis results. If the mesh size is large, the analysis time can be shortened, but the solution accuracy may be poor. Therefore, it is very important to select a suitable mesh size that can increase the degree of accuracy. To increase the solution accuracy, 3D solid elements were employed in this study. For the concrete slabs, steel beams, and shear studs, 3D eight-node elements were used to mitigate the solution convergence problem. For the rebar, T3D2 truss elements were used, and an embedded state was applied to the concrete slab element. The FE analysis model was modeled as a 1/4 model of the actual shear specimen, accounting for axial symmetry, as shown in Figure 12.  
The analysis was performed in two stages. Firstly, the bolt load was used to analyze the H beam, stud bolt, and nut models. Then, continuous analysis was performed on the model of the entire system, using the bolt stress as an initial value. For the bolt load, a load corresponding to 67% of the minimum tensile strength of each stud (95 kN) was used [37].
## 2 Materials and Methods
### Testing of Activation Functions  
Activation functions are used specifically in artificial neural networks to convert an input signal into an output signal, which in turn is fed as an input to the next layer in the stack. In a neural network, we calculate the sum of the products of the inputs and their corresponding weights and finally apply an activation function to it to take the output of that layer and provide it as input to the next layer. The prediction accuracy of the neural network depends on the number of layers used and, more importantly, on the type of activation function used.  
A neural network works like a linear regression model, where the predicted output is the same as the provided input when an activation function is not defined. The same is the case when a linear activation function is used, where the output is similar to the fed input with some errors. The limit of the linear activation function is linear, and if they are used, the network can only adapt to linear changes in the input; however, in the real world, errors have nonlinear properties along with the ability of neural networks to learn from erroneous data. Therefore, in a neural network, nonlinear activation functions are preferable to linear activation functions. The most important feature of artificial neural networks is their ability to adapt their behavior to the changing characteristics of the system [35].  
The tanh and sigmoid functions are similar, but the tanh is symmetrical according to the origin. As a result, different output signals from previous layers are provided as input to the subsequent layer. With values between \(-1\) and \(1\), the tanh function is continuous and differentiable. The gradient of the tanh function is steeper compared to the sigmoid function. Tanh is preferred to the sigmoid function because it has gradients that are not limited to changing in a certain direction and is zero-centered [35]. The activation functions were calculated with Equations (9) and (10) below:  
tansig:  
\[\mathrm{f}(\mathrm{x})=\mathrm{tanh}(\mathrm{x})=(\mathrm{e}^{\mathrm{x}}- \mathrm{e}^{-\mathrm{x}})/(\mathrm{e}^{\mathrm{x}}+\mathrm{e}^{-\mathrm{x}}), \tag{9}\]  
logsig:  
\[\mathrm{f}(\mathrm{x})=1/(1+\mathrm{e}^{-\mathrm{x}}). \tag{10}\]
## 8 Security and Privacy Risk Assessment
### Security and Privacy Risk Assessment at the Requirements Analysis Phase
#### 8.3.2.5 Risk Treatment  
Risk Treatment is the process of selecting and implementing measures to address the risk. There are three options available for risk treatment which include:  
* Risk modification: A risk which requires implementation of controls to reduce the impact and/or likelihood to an acceptable level.
* Risk avoidance: A risk can be avoided by eliminating the source of the risk or the asset exposed to the risk. This is usually applied when the severity of the risk impact and/or likelihood outweighs the benefits gained from implementing the countermeasure. For example, physically moving an on-premises server to an alternative location to mitigate the risk caused by nature might be outweighed with the cost of moving the server.
* Risk sharing: A risk can be fully or partially shared or transferred to another party. If the application is using any third-party libraries or public cloud services, risk related to these can be shared or transferred to the owner of the service.  
The risk evaluation team will evaluate each unacceptable risk taking the above possible risk treatment options into account. Finally, the team will also record the list of risks that require controls, shared risks and avoided risks with rationale in the risk assessment report.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Risk Score** & **Semi-Quantitative Values** & **Description** \\ \hline – & **Scale** & **Bins** & – \\ \hline Very Low & 0–4 & 0 & \begin{tabular}{c} The risks are acceptable. Plans mitigate the risk should be included in future plans. \\ \end{tabular} \\ \hline Low & 5–20 & 2 & \begin{tabular}{c} The risk may be acceptable over the short term. Plans to mitigate risk should be included in future plans and budgets. \\ \end{tabular} \\ \hline Medium & 21–79 & 5 & \begin{tabular}{c} The risk is unacceptable. Measures to reduce and mitigate the risk should be implemented as soon as possible. \\ \end{tabular} \\ \hline High & 80–95 & 8 & \
\begin{tabular}{c} The risk is unacceptable. Immediate measures to reduce and mitigate the risk should be implemented as soon as possible. \\ \end{tabular} \\ \hline Very High & 96–100 & 10 &
\begin{tabular}{c} The risk is totally unacceptable. Immediate measures must be taken to mitigate the risk. \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 10: Risk acceptability criteria (Qualitative approach).