esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Numerical Solution and Model Validity
### Numerical Solution of the System Model
In the global optimization method, the sum of the square of \(3Z\) mathematical formulas at the left of the equilibrium equations Equation (13) of all ball elements in the \(k\)th ball bearing is the objective function, and the aim is to find the correct \(X_{1},X_{2}\) and \(\mu\) that make the objective function almost zero (global minimum). The scatter search [19] algorithm is used to generate trial points (initial estimates of independent variables \(X_{1},X_{2}\) and \(\mu\)). The two filter conditions, the distance filter and merit filter [20], are used to examine trial points to ensure that the trial points that do not actually contribute to finding the local minimum of the objective function are excluded. After examination, the correct \(X_{1},X_{2}\) and \(\mu\) of ball elements can be calculated, which make the objective function nearly zero (global minimum). The calculation shows that the global optimization method is very fast and reliable, and the computational efficiency of the system model is improved significantly.
## 3 Results and Discussion
### Thermal Characterization of Original and IL-Cations Doped Membranes  
TG-DSC analysis in N\({}_{2}\) and air indicates the relatively high stability of the samples Nafion112/BMIM\({}^{+}\) and Nafion112/TMPA\({}^{+}\) in comparison with the Nafion112 membrane. The doped membranes are stable up to 300 \({}^{\circ}\)C and 325 \({}^{\circ}\)C in the cases of Nafion112/TMPA\({}^{+}\) and Nafion112/BMIM\({}^{+}\), respectively. Figure 3 shows the TG curves in N\({}_{2}\) atmosphere as an illustrative example. DSC curves show two marked exothermal effects between 450 and 600 \({}^{\circ}\)C for Nafion112/BMIM\({}^{+}\) and Nafion112/TMPA\({}^{+}\) doped membranes due to the pyrolysis of the ionic liquid. A weak exothermal effect is observed at a much higher temperature range in the case of Nafion112 (550-650 \({}^{\circ}\)C).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Membrane** & **Average contact angle (\({}^{\circ}\))** \\ \hline Nafion112 & \(91\pm 2\) \\ Nafion112/TMPA\({}^{+}\) & \(74\pm 8\) \\ Nafion112/BMIM\({}^{+}\) & \(72\pm 6\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Average values of the contact angles for original and IL-modified membranes.  
Figure 2: Comparison of the O 1\(s\) core level spectra obtained for Nafion112, Nafion112/BMIM\({}^{+}\) and Nafion112/TMPA\({}^{+}\) doped membranes.
# How 5G Wireless (and Concomitant Technologies) Will Revolutionize Healthcare?
## 6 Technology for Healthcare: Challenges/Pitfalls
### Will Computers Replace Doctors?  
Today's diagnoses and treatments are partially performed by patients' medical histories and symptoms. A study [80] found that each year, 40,500 deaths are caused by misdiagnosis in the USA. Similarly, the system-related factors, e.g., poor processes, lack of teamwork and inadequate communications, incorporated 65% of diagnostic error [81]. These kinds of diagnostic errors are adding to the rise in healthcare expenditures [82]. Most of the physicians perform check-ups and testing to suggest prescription and behaviour modification that can be done better by data-driven analytics. Computers can perform better in much of the diagnosis and treatments by utilizing more complex physiological and sensor data than a practitioner could comprehend. It has even been argued that computers will replace 80% of what doctors do in the future while providing quicker, more accurate and fact-based clinical performances [83].  
Notwithstanding the hype, it is safe to assume that computers will play a much larger complementary role in clinical settings in the future through the automation of the various routine matters that doctors must do, thereby creating more space for doctors to engage in non-routine health decisions that humans can manage better than algorithms. Human doctors will likely warm up to the use of information technology in the long run due to the benefits it affords. Human doctors can use their intuition that is trained over years of experience to oversee the working of algorithms and AI. Furthermore, human doctors are better equipped to manage personalized care than a machine.  
The transition to automation in health will not be easy due to the sensitive nature of the domain and the high cost associated with any kinds of errors. Furthermore, there are numerous technical challenges associated with the collection and processing of enormous amounts of data to comprehend the patients' problems and thereafter perform diagnostics through sophisticated AI and ML algorithms.
## 3 Materials and Methods
### Matrix of Variability of DLAs Sensors Data for Each Monitored Subject  
The matrix of variability \(M\) has the form:  
\[M=\begin{bmatrix}a_{1,1}&...&a_{1,N_{DLAs}}\\...&...&...\\ a_{D,1}&...&a_{D,N_{DLAs}}\end{bmatrix} \tag{1}\]  
where \(D\) is the number of features of the data generated by the monitoring sensors, which is also the dimension of the search space in the adapted version of BPSO, and \(N_{DLAs}\) is the number of daily living activities. Each line \(i\) of the matrix \(M\), where \(i=1,...,D\), is a permutation of the set \(\{1,2,...,N_{DLAs}\}\), such that the order of the elements of that set is given by the increasing order of the ranges of variability of each monitored DLA. The range of variability for a DLA \(a\) and the corresponding feature \(f\) is given by the formula:  
\[r\_var_{a,f}=v\_max_{a,f}-v\_min_{a,f} \tag{2}\]  
where \(r\_var_{a,f}\) is the range of variability for the DLA \(a\) and the feature \(f\), \(v\_max_{a,f}\) is the maximum value monitored by the feature \(f\) for the DLA \(a\) and \(v\_min_{a,f}\) is the minimum value monitored by the feature \(f\) for the DLA \(a\).  
Metric for the Evaluation of the Matrix of Variability of DLAs Sensors Data for Each Monitored Subject  
The value of the metric \(M_{\Delta}\) used in the evaluation of the matrix of variability \(M\) of DLAs sensors data is given by:  
\[M_{\Delta}=\sum_{i=1}^{N_{DLAs}}{{{\left({\left|{max\{j|j\in\mathbb{N},1\leq j \leq N_{DLAs}\wedge\exists k\in\mathbb{N},1\leq k\leq D,M_{k,j}=i}}\right.} \right.}\right.}}\\ -min\{j|j\in\mathbb{N},1\leq j\leq N_{DLAs}\wedge\exists k\in \mathbb{N},1\leq k\leq D,M_{k,j}=i\}+1\Big{|}\Big{)}. \tag{3}\]  
Heuristic for Features Ranking in the Optimal Solution Returned by the Adapted BPSO Algorithm for Each Monitored Subject  
In order to restrict the number of selected features to a maximum threshold, a matrix \(R\) of variability is proposed. The matrix \(R\) is defined by the set of all \(r\_var_{a,f}\) such that \(a\) takes values from the set \(\{1,...,N_{DLAs}\}\) and \(f\) takes values from the set \(\{1,...,D\}\):  
\[R=\begin{bmatrix}r\_var_{1,1}&...&r\_var_{1,D}\\...&...&...\\ r\_var_{N_{DLAs},1}&...&r\_var_{N_{DLAs},D}.\end{bmatrix} \tag{4}\]  
The feature \(f_{1}\) is considered better than the feature \(f_{2}\) if the value returned by the \(Comparator(f_{1},f_{2})\) described next is greater than or equal to 0:  
\[Comparator(f_{1},f_{2})=\sum_{i=1}^{N_{DLAs}}{{s(r\_var_{i,f_{1}}-r\_var_{i,f_{2 }})}}, \tag{5}\]such that for any real number \(x\) the value of \(s(x)\) is \(1\) if \(x\geq 0\) or \(-1\) otherwise.
# Finite Element Analysis and Optimization of Hydrogen Fuel Cell City Bus Body Frame Structure
## 2 Model Establishment and Simulation Test Design
### Design of Static Structural Mechanics Simulation Tests
#### 2.2.4 Emergency Cornering Condition  
The emergency cornering condition mainly simulates the situation when the bus needs to make an emergency turn during its operation. When the hydrogen fuel cell city bus performs an emergency turn, the body frame is subjected not only to its own weight but also to the centrifugal force in the opposite direction of the turn, resulting in a lateral force acting on the overall body. When the lateral acceleration exceeds a certain value, there is even a risk of rollover, making emergency cornering a highly dangerous condition. In the simulation analysis of this condition, a Y-axis 0.3 g lateral acceleration is applied to the entire bus to simulate the loading condition during an emergency right turn [32]. The boundary conditions are set as follows: the left front wheel's Z-direction translational degree of freedom is constrained, the right front wheel's Y and Z-direction translational degrees of freedom are constrained, the left rear wheel's X and Z-direction translational degrees of freedom are constrained, and the right rear wheel's X, Y, and Z-direction translational degrees of freedom are constrained.
# Guidelines towards Information-Driven Mobility Management
## 4 Mobility in ICN
### Architectural Design Advantages
Thirdly, the pull-based communication model of NDN, where data are only sent if Interest packets are first transmitted, allows for a binding signalling reduction during the handover process. A fourth advantage of the architectural design proposed in ICN concerns the flexible forwarding strategies and the routing, which is data-oriented. Such approach provides better support for multi-homing environments, as well as for the support of frequent movement by devices.
# The Relationship between Radon and Geology: Sources, Transport and Indoor Accumulation
## 4 Risk Assessment and Mitigation  
### Techniques Used to Measure Radon Concentration in Rocks and Soils  
#### 4.1.1 Solid State Nuclear Track Detectors (SSNTD)  
Solid state trace detectors, such as alpha track detectors, are a common and low-cost technique for measuring radon concentration [164]. These devices contain a sensitive material that records the damage caused by the alpha particles emitted by radon and its radioactive decay products [165]. After a period of exposure, the detector is analysed in the laboratory to determine the radon concentration. This technique is suitable for long-term measurements and provides an average of the radon concentration during the exposure period. The alpha track detectors are small and can be easily placed in various locations within indoor environments to obtain a representative sample of the radon concentration [166]. The detectors are sensitive enough to measure low levels of radon, making them a useful tool for assessing indoor air quality and identifying areas with elevated radon levels. Solid state trace detectors are widely used in radon measurement programs and are recommended by various international organizations as a reliable and cost-effective method for measuring radon concentration in indoor environments [167]. However, the accuracy of the measurements can be influenced by factors such as humidity, temperature, and air circulation, and periodic calibration of the detectors is necessary to ensure their reliability.
# Parameter Optimization of Orbital TIG Welding on Stainless Steel Pipe
## 4 Conclusions  
This study investigated the influences of orbital welding parameters such as voltage, current, travel speed, and filler wire diameters on the tensile strength of 304 steel pipes. The results showed that the best sample had a UTS value of 562 MPa with V\({}_{\text{f}}\) of 3.8 mm/s, HI 0.32 kJ/mm, and D\({}_{\text{w}}\) 1 mm. Moreover, high tensile values were obtained at HI levels of 0.32 kJ/mm, V\({}_{\text{f}}\) from 3.8 mm/s to 4.5 mm/s, and D\({}_{\text{w}}\) from 0.8 mm to 1 mm. When HI increased, the tensile strength increased. However, if HI rose too high, the weld will overheat and the tensile strength will decrease. An HI value of 0.32 kJ/mm or less is unsuitable for the 1.2 mm filler wire diameter to create a good welding joint. The welding current has the highest impact rank, followed by the wire feeding diameter. The welding speed ranks in the lowest position compared to other parameters. The direct impact of the welding current on the HI value could explain the high-ranking effect of the welding current on the tensile strength. The optimal results are D\({}_{\text{w}}\) of 1.0 mm, welding current of 110 A, and welding speed of 4.5 mm/s, corresponding to a predicted tensile strength of 432.4 MPa.  
Conceptualization, T.T.N. and T.T.D.; formal analysis, T.T.N., V.H.H. and D.N.H.; funding acquisition, T.T.N. and T.T.D.; investigation, T.T.N., V.-T.N., T.T.D., H.T.P. and V.T.T.N.; project administration, V.-T.N., D.N.H. and H.T.P.; visualization, T.T.N., V.H.H. and D.N.H.; writing--original draft, T.T.N., V.-T.N. and T.T.D.; writing--review and editing, T.T.N., V.-T.N., T.T.D. and V.T.T.N. All authors have read and agreed to the published version of the manuscript.  
This research was funded by HCMC University of Technology and Education (HCMUTE) via grant T2025-31.  
Not applicable.  
Figure 8: SN ratio for larger tensile strength is better.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**D\({}_{\text{w}}\) (mm)** & **I (A)** & **V\({}_{\text{f}}\) (mm/s)** & **HI (kJ/mm)** \\ \hline
1 & 110 & 4.5 & 0.32 \\ \hline \hline \end{tabular}
\end{table}
Table 9: The best parameters were obtained from the Taguchi method of analysis.  
Data Availability Statement: The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding authors.  
The authors acknowledge the support of HCMC University of Technology and Education (HCMUTE).  
The authors declare no conflicts of interest.
# Analyzing Impact and Systemwide Effects of the SlowROS Attack in an Industrial Automation Scenario
## 3 ROS Primer
### History  
In a nutshell, ROS is a set of open-source libraries and tools that provide commonly used robot control functionalities and algorithms. Although, strictly speaking, ROS is not an operating system per se because it relies on \"true\" operating systems such as Linux as a foundation, it still provides hardware abstraction and platform-independent robotic device control and access, along with a comprehensive message passing subsystem based on the publisher-subscriber communication paradigm and a modular, high-level software packaging framework.  
In embryonic form, ROS development began in late 2006 in the context of the Stanford Personal Robotics Program [44]. One of the main initial goals--maintained through the whole project history--was to facilitate code reuse so that researchers would focus more on designing and developing innovative robot control software, rather than re-implementing the underlying middleware infrastructure anew whenever they moved to a new project or new hardware.  
Even though ROS was initially geared towards robotic research and hobbyists' projects, it gained more and more industrial interest with time, fueled by initiatives like the ROS Industrial Consortium [1], whose charter is to extend ROS to work effectively with industrial relevant hardware and applications. Current consortium members include leading robot OEM manufacturers, end-users such as large international manufacturing companies, and research institutes worldwide.  
The most recent released version of ROS, Noetic Ninjemys (often abbreviated noetic), was released in mid-2020 and is meant to be the last version of ROS before the transition to ROS 2, slated for mid-2025 when noetic will reach its End-Of-Life (EOL) date. Despite this impending transition, noetic is the ROS release to be considered in this work.
## 4 SDR Implementation of FM-ACSK System Prototype  
To test out and obtain results from the proposed communication system in real-world conditions, SDR-based prototype was created. This prototype's software part consists of a transmitter and receiver that are implemented from MATLAB Simulink models employed in our previous research [41]. The hardware consists of Adalm PlutoTM SDRs connected to one or two computers. The parameters of the tested system are shown in Table 3.
# Research on the Path Planning Algorithm of a Manipulator Based on GMM/GMR-MPRM
## 2 The GMM/GMR-MPRM Algorithm Framework  
The GMM/GMR-MPRM algorithm consists of the GMM/GMR module and the MPRM module, and the block diagram of the algorithm is shown in Figure 1.  
The teacher will teach a specific task many times. After obtaining the data of the teaching path, the GMM/GMR module uses the GMM algorithm to obtain a compact data model according to the data of the teaching path, then the data model is fit into a smooth path with the mean and variance by GMR.  
The function of the MPRM module is to generate a collision-free path based on the fit smooth path. Firstly, the smooth path is divided into a collision-free path and a collision path according to the position and shape of the obstacle. Then, the sampling space for the points on the collision path are generated using the variable density resampling method according to the covariance of the collision points and the relative position of the collision points and the obstacle, and the collision points in the sampling space are removed. Finally, a roadmap for the MPRM according to the EPD is constructed. After constructing the roadmap, the Dijkstra algorithm is used to search for a feasible path, then the Shortcut is used to prune the path and delete the redundant sampling points. A smooth path is generated by smoothing the pruned path using B-spline interpolation.
# Research and Applied Perspective to Blockchain Technology: A Comprehensive Survey
## 5 Consensus Algorithms
### Consensus Algorithms Used by Various Cryptocurrencies
#### 5.1.3 Proof of Stake (PoS)  
* Original PoS: Proof of stake uses the wealth of miners to win a ticket rather than computational power. PoS was first implemented in 2012 as cryptocurrency PeerCoin. PoS is kind of a hybrid design where PoW is used in the beginning for coin minting and later PoS is used for the security of the whole network. PoS works with the concept of a coin's age which is explained by example. If there are 10 coins which are held for 10 days its age is 100 days. If these coins are spent, their age is consumed. In PoW the main chain with most work is followed, and in PoS a chain with most coin age is followed.
* Cardano's Ouroboros: This protocol adds security measures to ensure persistence and liveness within the system. This implementation elects the stakeholders through a delegation process and takes the snapshots of current stakeholders labelled as an 'epoch'. The subset of current stakeholders randomly decide who will be the next epoch stakeholder.
# Interpolating the Directional Room Impulse Response for Dynamic Spatial Audio Reproduction
## 3 Experimental Setup
### Simulated DRIR Recordings  
In order to present additional comparative results with the real-world recording, the DRIRs were simulated (using the image method implemented in Matlab R2021a, named RIR Generator [27]) within two rooms of comparable size to the classroom and great hall at the same recording locations (see Figure 4).  
While the room geometry is mostly the same as the classroom and great hall at the University of London [21], they are simplified to be rectangular and measuring \"9 m \(\times\) 7.5 m \(\times\) 3.5 m\" and \"23 m \(\times\) 16 m \(\times\) 10 m\", respectively. The locations of the sources and microphones within the rooms also mimic the classroom and great hall arrangements, separately. The source is placed at (4.5, 0.5, 1.5) meters in the simulated classroom and at (11.5, 0, 1.5) meters in the simulated great hall, respectively. The origin of the microphone array \"00x00y\" is (1.5, 1.8, 1.5) meters in the classroom and is (5.5, 2, 1.5) meters in the great hall (the origin is assumed at the bottom left of the room). Firstly, the A-format recording is obtained four times at each of the arbitrary positions under a sampling frequency of 96 kHz, and for each of the four recordings, hypercardioid microphones pointing at specific orientations are utilized.  
Another factor that needs to be considered is the reverberation time (RT) of the two rooms. Due to irregular room shapes and variable room acoustics, the RT of the real-world and simulated rooms cannot be exactly the same. In the simulated room, the RT is configured to be 1.8 s in the classroom and 2.0 s in the great hall as a constant, which are approximately the average RT of the real-world classroom and great hall, separately [21].  
Finally, 130 sets of B-format recordings are obtained in both the real-world and the simulated classrooms, and 169 sets of DRIRs are recorded using B-format microphones in both the real-world and the simulated great halls. The proposed DRIR interpolation algorithm is then applied to these four databases for performance evaluation.
## 3 The Proposed Approach of SCDNN
### The SCDNN Algorithm
U2R and R2L attack detection results in low accuracy in all datasets except Dataset 1, probably because there are few such records in the dataset and their features are similar to those of DoS and probe attacks, which provides the DNN with less information and causes increased detection rate errors. #### 4.3.2 Results and Comparisons

In this section, the fusion matrix and evaluation criteria are calculated for the SCDNN and the four traditional detection methods for each of the six datasets. The optimal cluster number \(k\) and \(\sigma\) value are fed into each DNN described in Section 3. The DNNs have 65 input dimensions and five output dimensions, because the original dataset is a 65-dimensional vector and the goal is to distinguish five intrusion attack types. There are two hidden layers, the first has 40 neurons and the second has 20. The softmax layer has five dimensions. 10% validation is used to check for overfitting during the training process. The results of the above experiment are shown in Table 3 and Figure 5.

The records are unbalanced in each of the six datasets; Normal and DoS cases are well-represented, whereas U2R and R2L occur rarely, because the latter two cases require complex intrusion actions generally carried out by an advanced user, resulting in more covert intrusion that is difficult to detect. From Table 3 and Figure 5, considering overall accuracy, the SCDNN performs better than the other four methods and has the lowest error rates. Moreover, the proposed method shows especially good performance for the sparse U2R and R2L attack types in all datasets, and produces a higher accuracy rate than the other methods. The best accuracy rate for Dataset 1 is 98.21%, obtained by SVM. The SCDNN achieves 97.21% accuracy for normal data, proving that the SVM has better learned the