esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Theoretical Framework and Hypothesis Development
### Hypotheses  
The literature review on empirical evidence across different technological, organisational, and educational domains, including application within the maritime sector, has consistently confirmed the TAM's robustness. In particular, the model core constructs, PEU and PU, have been shown to form stable direct and indirect predictive capacities in explaining users' behavioural intention to use innovative technology or systems. Reviewed cross-sectional meta-analyses, systematic reviews, and VR-context-specific studies consistently identified PU as the dominant predictor of BI. The direct relationship between PEU and BI is generally modest, primarily influencing behavioural intention indirectly through  
Software  
The HMD-VR engine room simulator was developed using Blender 4.2 software for 3D modelling and using Unreal Engine 5.4 as the primary game development platform. The virtual engine room environment was designed using an authentic technical blueprint sourced from a roll-on/roll-off (RO-RO) vessel and designed as a double-deck spatial configuration with dimensions of 16.1 metres by 19.6 metres.  
The lower deck houses two main four-stroke diesel engines, each connected to a shaft generator and propeller shaft, with three auxiliary diesel generators, a range of cooling systems, valve networks, pipelines, pumps, and other essential engine room apparatus. The upper deck includes two starting air compressors, a service air compressor, two Heavy Fuel Oil (HFO) purifiers, a diesel oil purifier, and associated systems such as lubricating and fuel oil pipelines, tanks, and control valves (Figure 2). The simulator was developed for compatibility with the Windows-based operating system and designed to support both wired and wireless VR headset connectivity, allowing interoperability with various HMD platforms. Key functional features, including actions and scenarios, are depicted in Figure 3.
# Bioreactors: Applications and Innovations for a Sustainable and Healthy Future--A Critical Review
## 6 Recent Advances in Bioreactor Applications
### Production of Biofuels  
Biofuel production has gained significant attention as a sustainable alternative to fossil fuels. Microalgae cultivation is been widely studied as third-generation biofuel production, mainly for biodiesel [74]. The cultivation in open raceway ponds is cheaper to build and operate but lacks contamination control and is susceptible to environmental influence [74; 75]. Photobioreactors provide higher process control, leading to higher product yield and purity, but they present higher operation and build costs [76]. Many cost-reduction alternatives are being developed, such as the use of aquaculture water for microalgae cultivation in open ponds [77], as well as the coproduction of high-value-added products in photobioreactors, such as astaxanthin, antioxidants, hormones, pigments, etc. [75; 78]. Biojet fuels may also be produced on bioreactors using genetically modified microorganisms [79]. The use of lignocellulosic biomass as a carbon source for the fermentation process requires pretreatments and sequential saccharification [80], increasing the operational cost and hindering the scale-up process [81]. Consolidated bioprocessing is a promising alternative, creating a single system in which cellulase production, as well as saccharification and fermentation, may occur simultaneously [79; 82].
## 4 Statistical Testing of ENMRA Algorithm
### System Model
#### 5.2.2 FEC Schemes  
In comparison to the automatic repeat request (ARQ), when addressing channel coding systems, FEC improved the reliability of transmission lines without adding overhead or retransmission cost. Furthermore, compared to the energy-efficient convolutional codes, the multilevel cyclic BCH code for WSNs achieved roughly 15% better [56]. Thus, it was necessary to strike a balance between transmission quality and energy consumption, and the presented cross-layer approach enabled such a compromise for maximum energy-throughput efficiency.
# Research of Enzyme-Induced Carbonate Precipitation on Strength Behavior of Reinforced Sand
## 3 Results and Discussion
### Micro-Mechanism of EICP-Reinforced Sand
#### 3.2.4 Influence of EICP on Fractal Dimension of Soil  
The fractal dimension reflects the complexity, shape intricacy, and surface roughness of the pore structure. Based on micro-CT scan results, the fractal dimension (\(D\)) of EICP-reinforced sand at different \(C\) was calculated using the box-counting method [32], as shown in Figure 13. The ratios of small pore volumes and the strength of different specimens are also presented. As \(C\) increases, the fractal dimension of the soil also increases, indicating that CaCO\({}_{3}\) precipitation contributes to greater pore surface roughness and more heterogeneous pore structures. The trends in the proportion of small pores and fractal dimension are similar, due to the larger specific surface area of small pores. The fractal dimension reflects the soil's microstructure, which plays a key role in its mechanical properties [33]. As shown in Figure 13, the changes at the microscale align with macroscopic observations: both strength and fractal dimension initially improve significantly (from 0.5 to 1.5 mol/L) and then stabilize (from 1.5 to 2 mol/L). This pattern suggests that the increase in fractal dimension--indicating more roughness and irregularity in pore surfaces--enhances interparticle friction, thereby strengthening the soil through both cohesion and friction.  
Figure 13: Variation in fractal dimension and strength of EICP-reinforced sand at \(D_{\rm r}=0.7\).
## 4 The Improved NSGA-II
### Reinforcement-Learning Algorithm
#### 4.1.1 Design Q Table  
SARSA and Q-learning are two main algorithms in reinforcement learning used to train the value function Q [27]. The Q-table is initialized as a matrix with a number of rows and columns equal to the number of states and actions, respectively, and all its entries are set to zero. As shown in Equation (13), the feedback on actions is updated based on the environment and state.  
\[Q(state,action)=\begin{array}{c}S_{1}\\ S_{2}\\ \vdots\\ S_{n}\end{array}\left[\begin{array}{cccc}a_{1}&a_{2}&\cdots&a_{n}\\ 0&0&\cdots&0\\ 0&\ddots&&0\\ \vdots&&\ddots&\vdots\\ 0&0&\cdots&0\end{array}\right] \tag{13}\]  
\[Q(state,action)=(1-\alpha)Q(state,action)+\alpha(r+\gamma\underset{a^{\prime}}{ \max}Q(state^{\prime},action^{\prime})) \tag{14}\]  
\[Q(state,action)=(1-\alpha)Q(state,action)+\alpha(r+\gamma Q(state^{\prime},action ^{\prime})) \tag{15}\]  
Q-learning is a model-free method that does not require previous state values and can be executed in a stochastic dynamic environment. It updates the Q-table based on the maximum expected reward using Equation (14). SARSA, on the other hand, is an improved version of Q-learning, which trains the action value function using Equation (15). Both algorithms have their own advantages, and the experimental results presented by [PERSON] and [PERSON] [28] showed that SARSA has a faster convergence speed, while Q-learning achieves better performance in the end. Furthermore, paper [29] has shown that combining SARSA and Q-learning can improve the convergence performance of the single objective genetic algorithm. In this paper, we adopt the SARSA algorithm in the initial phase and switch to the Q-learning algorithm in the subsequent phase, as shown in Equation (16):  
\[\begin{cases}SARSA&gen\leq gen_{\max}/2\\ Q-learning&gen>gen_{\max}/2\end{cases} \tag{16}\]
# Achieving Low Latency Communications in Smart Industrial Networks with Programmable Data Planes
## 4 Design of the INC-Based System
### Proposed Architecture  
Industrial networks rely both on cloud and edge computing approaches, each one being optimal for different goals. These two approaches can coexist in an industrial network, but frequently this requires full duplication of all transmitted packets and duplicated processing in both end-sites. However, we designed a distinct paradigm for IIoT scenarios, which takes advantage of the strengths of each solution depending on the exact communicated packet. In other words, our architecture manages each message depending on the purpose of that transmission. In this way, a \"hybrid\" operation mode is implemented, improving response times for time constrained communications, while keeping track of all IIoT data in a centralized site.  
Figure 4a shows the communication with the traditional cloud and edge computing, while Figure 4b shows our design including the programmable network device. The main difference between both network topologies is the introduction of a programmable switch. This network device processes the information transmitted through the network and decides the appropriate forwarding that should be given to each packet, implemented by the INC application that is explained in the next section.
# Detection and Localization of Overlapped Fruits Application in an Apple Harvesting Robot
## 2 Images Acquisition and Preprocessing
### Color Space Selected  
To a large extent, the choice of color space limits the image segmentation effect. The commonly used color spaces include the following: Red-green-blue (RGB), Lab, hue-saturation-intensity (HSI), XYZ, and so on. The RGB color space is defined based on three primary colors. It can represent almost colors, but due to the high correlation among the components, it cannot be used to directly segment images. The lab is a color standard model assigned by the International Commission on Lighting (CIE). It has higher uniformity than XYZ and forms a 3D space based on chroma and brightness. Therefore, in this paper, Lab color space is selected for overlapping image segmentation. Therefore, in this paper, the Lab color space is selected for overlapped images segmentation.  
In the color space Lab, L represents luminosity, and the value is 0-100 (black to white). a represents the range is from red to green, the value is \(-\)128\(-\)+127 (green to red); b represents the range is from yellow to blue, the range is \(-\)128\(-\)+127 (blue to yellow). A Lab color space diagram is shown in Figure 2.  
Since the Lab color space is based on the XYZ color space, it is necessary to transform the color space to XYZ first. Therefore, the image color space can be realized from RGB to Lab space. The transformational relation of from RGB color space to XYZ color space is:  
\[\left[\begin{array}{c}X\\ Y\\ Z\end{array}\right]=\frac{1}{0.17697}\left[\begin{array}{ccc}0.49&0.31&0.20\\ 0.17697&0.81240&0.01063\\ 0.00&0.01&0.99\\ \end{array}\right]\left[\begin{array}{c}R\\ G\\ B\end{array}\right] \tag{1}\]  
The transformational relation of from XYZ color space to Lab color space is:  
\[\left\{\begin{array}{c}L=116\times f(Y)-16\\ a=500\times\left[f(\frac{X}{0.982})-f(Y)\right]\\ b=200\times\left[f(Y)-f(\frac{Z}{1.192})\right]\end{array}\right. \tag{2}\]  
Figure 2: Sketch of a Lab color model.  
where the function:  
\[f(t)=\left\{\begin{array}{cc}t^{\frac{1}{3}}&t>0.008856\\ 7.787t+0.138&t\leq 0.008856\end{array}\right. \tag{3}\]  
Equation (1) shows the transformation from RGB color space to XYZ color space; Equation (2) shows the transformation from XYZ color space to Lab color space, that is, the XYZ color space as a bridge.  
The lab color space is dedicated to reflecting the uniformity of colors. The visual perception range of the Lab model is better than computer screens, printers, even humans. It is a hardware-independent color system that can make up for the lack of RGB models that depend on hardware facilities. So, after the transformation, the next step is image segmentation.  
Two overlapped apple images in Figure 1 are transformed to Lab color space; the results are shown in Figure 3.
## 1 Introduction  
NDIR is a reliable gas sensing technology that is used for measuring gas concentration [1]. The theory behind it is when IR strikes a gas, the energy states of atoms vibrating in molecules will transform into separate steps if the wavelength meets the requirements. Therefore, we can infer gas concentrations by measuring IR absorption at the wavelengths ranging from 3 to 10 \(\upmu\)m.  
The most important component of an NDIR instrument is the IR sensor. The heating light source emits IR and the gas in the chamber absorbs certain wavelengths of lights. Then the IR sensor detects the attenuation of these wavelengths and refers to the gas concentration based on the Beer-Lambert law.  
A downside of light source operation is the searing heat of their performance over 1500 K under direct modulation that further enables massive power radiation to be released in a short-wave band. Moreover, we need to calculate complex equations to obtain the thermal behavior of the light source. Special surface structure modeling reveals a substantial dependence of emissivity on radiation wavelength. This allows the radiation spectra of the source to be adjusted to the absorption band of the gas whose concentration is being evaluated [2]. Furthermore, those studies provide an alternative technique to research modulation techniques and various sorts of light sources to sustain the operating temperature within an appropriate range [3].  
The development of simulation analysis of electronic circuits is based on the improvement of algorithms, component models, and physical characteristics. It plays an important role in studying and analyzing electrical properties in circuit design. However, there is still a lack of integrated analysis and simulation technology due to the complexity of optical, mechanical, and electronic circuit integration systems.  
Besides electric properties, the thermal effects on various semiconductor devices are considered and equivalent circuit models were developed for the analysis using SPICE circuit simulation software [4; 5; 6; 7; 8]. Comparisons of theoretical formulations have been made and electro-thermal models of temperature sensors were built by various researchers [9; 10; 11].  
In this article, we use SPICE to build a highly integrated optical-electrical-thermal modeling for NDIR. This model is based on the extraction of component parameters and the derivation of the characteristics of thermal radiation. Therefore, the proposed model can calculate and analyze the transient response of NDIR photoelectric thermal behavior under different modulation conditions. The actual experimental results [12; 13] are also in good agreement with the simulation results.
# Potential of a SAR Small-Satellite Constellation for Rapid Monitoring of Flood Extent
## Appendix A  
Appendix A describes the details of the Walker Constellation (WC) method and how it was implemented in our method. This is the most frequently used method in practice, and it enables continuous global coverage [14; 15]. Among the several possible ways of designing constellation configurations such as Street of Coverage and Flower Constellation, the WC method simultaneously offers simplicity and functionality, which is appropriate for this first attempt. It is also suitable for our objective of constructing a virtual constellation based on the orbital elements of an existing satellite. The WC method is based on the assumption that all orbits are circular and that they have a common altitude and inclination with reference to the equator [14; 15]. The right ascension of the ascending node (RAAN)--the place where the satellite crosses the equator from south to north--of each orbit plane is equally spaced on the equator plane, and all satellites are equally spaced on each orbit plane. This arrangement is defined with integer parameters \(T\), \(P\), and \(F\), where \(T\) denotes the total number of satellites, \(P\) is the number of orbit planes and \(F\) is the relative phase difference between satellites in adjacent planes. \(P\) orbit planes are positioned at \(\alpha\) = 180\({}^{\circ}/P\) intervals and each has \(S=T/P\) satellites, which are placed at \(\beta\) = 360\({}^{\circ}/S\) intervals on each orbit plane, given that \(S\) is the number of satellites per plane. When all orbit planes have the same relative phase difference, \(F\) is defined as an integer number to keep the arrangement's symmetry, and the angle difference between satellites in adjacent planes is \(\gamma\) = \(F\times\) 360\({}^{\circ}/T\). For reference, Figure A1 (left) presents an example of a WC whose parameters are \(T\) = 8, \(P\) = 2, and \(F\) = 1, where \(S\) = 8/2 = 4, \(\alpha\) = 180\({}^{\circ}/2\) = 90\({}^{\circ}\), \(\beta\) = 360\({}^{\circ}/4\) = 90\({}^{\circ}\), and \(\gamma\) = 1 \(\times\) 360\({}^{\circ}/8\) = 45\({}^{\circ}\) with an inclination 90\({}^{\circ}\).
# Remaining Useful Life Prediction of Cutting Tools Using an Inverse Gaussian Process Model
###### Abstract  
In manufacturing, cutting tools gradually wear out during the cutting process and decrease in cutting precision. A cutting tool has to be replaced if its degradation exceeds a certain threshold, which is determined by the required cutting precision. To effectively schedule production and maintenance actions, it is vital to model the wear process of cutting tools and predict their remaining useful life (RUL). However, it is difficult to determine the RUL of cutting tools with cutting precision as a failure criterion, as cutting precision is not directly measurable. This paper proposed a RUL prediction method for a cutting tool, developed based on a degradation model, with the roughness of the cutting surface as a failure criterion. The surface roughness was linked to the wearing process of a cutting tool through a random threshold, and accounts for the impact of the dynamic working environment and variable materials of working pieces. The wear process is modeled using a random-effects inverse Gaussian (IG) process. The degradation rate is assumed to be unit-specific, considering the dynamic wear mechanism and a heterogeneous population. To adaptively update the model parameters for online RUL prediction, an expectation-maximization (EM) algorithm has been developed. The proposed method is illustrated using an example study. The experiments were performed on specimens of 7109 aluminum alloy by milling in the normalized state. The results reveal that the proposed method effectively evaluates the RUL of cutting tools according to the specified surface roughness, therefore improving cutting quality and efficiency.  
tool wear; remaining useful life; inverse Gaussian process; cutting precision; variable threshold 28 May 2021
## 4 Proposed Security Algorithm for Patient Data in WBSN and IoMTs Applications
### Feature Vector Extraction and Encryption Phase  
This phase consists of several successive steps as follows:  
_Step_**1**: Normalize the amplitude of each input signal frame between 0 and 1. This step was designed to resist the amplitude scaling attack of the original signal during its transmission via IoMTs.  
_Step_**2**: For each normalized 2D frame of size \(N\times N\), use (2) to compute 2 DHMs up to the last order \((N,N)\). Finally, we obtain four 2D matrices noted \(M_{i},i=1,\ldots,4\), each of size \(N\times N\).  
In this step, the 2 DHM parameter values (see (2)) are given as a security key denoted _KEY1_.  
_Step_**3**: Compute the value \(Th\), which represents the average value of each \(M_{i}\), \(i~{}=~{}1,\ldots,4\) matrix. Then, binarize the elements of these matrices according to the following formula  
\[MB_{i}(x,y)=\left\{\begin{array}{ll}1,&if~{}M_{i}(x,y)\geq Th\\ 0&\text{otherwise}\end{array}\right.;\,x,y=1,2,\ldots,N \tag{12}\]  
_Step_**4**: Reshape \(MB_{i},i=1,\ldots,4\) matrices into \(VB_{i},i=1,\ldots,4\) vectors, each of length \(L=N\times N\), which represent the feature vectors of the selected frames.  
_Step_**5**: To provide a better security level for our scheme, the proposed 2 DSCLM is used to encrypt \(VB_{i},i=1,\ldots,4\) vectors. To this end, two chaotic sequences denoted \(X\) and \(Y\), each of size \(L=N\times N+50\), are generated via the 2 DSCLM. Then, the first 50 values of \(X\) and \(Y\) are removed to ensure a very low correlation between the values of the produced sequences. Next, we determine the average values \(T_{1}\) and \(T_{2}\) of \(X\) and \(Y\) sequences, respectively. Finally, the \(X\) and \(Y\) sequences are binarized based on the next equations:  
\[XB(i)=\left\{\begin{array}{ll}1&if~{}X(i)\geq T_{1}\\ 0&\text{otherwise}\end{array}\right.;\,i=1,2,\ldots,N\times N \tag{13}\]  
\[YB(j)=\left\{\begin{array}{ll}1&if~{}Y(j)\geq T_{2}\\ 0&\text{otherwise}\end{array}\right.;\,j=1,2,\ldots,N\times N \tag{14}\]  
In the current stage, the parameters \(\{\alpha,\beta,\delta,\varphi,x_{0},y_{0}\}\) of the 2 DSCLM are given as a security key denoted KEY2.  
_Step_**6**: In this step, the bitwise exclusive OR (XOR) operator is applied to \(XB\) and \(YB\) for generating the following binary chaotic vector denoted \(D\):  
\[D=XB\otimes YB \tag{15}\]  
where \(\otimes\) designates the XOR operator symbol.  
_Step_**7**: In this step, the feature vectors \(VB_{i},i=1,\ldots,4\) are encrypted as follows:  
\[E_{i}=VB_{i}\otimes D\text{ with }i=1,\ldots,4 \tag{16}\]  
_Step_**8**: Regroup \(E_{i}\) binary sequences into 64-bit groups and then use the IEEE Standard format [45] to represent these groups using their integer format. Next, four sequences noted \(FV_{i},i=1,\ldots,4\) of integer values are obtained. These are then saved in a text file named \"Feature_vectors.txt\". This step was designed to optimize the length of the data to be included in the text file.
# Segmented Hybrid Impedance Control for Hyper-Redundant Space Manipulators
## 5 Object Manipulation Control
### Object Self-Collision Avoidance  
During operation, self-collision scenarios are likely to occur, especially in winding configurations. Self-collision is particularly common between the virtual end-effector (last 10 links) and the rest of the manipulator. To overcome this problem, a self-collision avoidance algorithm has been implemented.  
The algorithm calculates the distances between all the joints. If the distance drops below a certain threshold, a repulsive force is applied to both joints in opposite directions. The potential force magnitude is defined as follows:  
\[\mathrm{h}_{\mathrm{self}}=\mathrm{k}_{1}\left(\frac{1}{1+\mathrm{e}^{\mathrm{ k}_{2}(\mathrm{d}_{\mathrm{misself}}\cdot\mathrm{d}_{\mathrm{safety}}+\mathrm{d}_{0})}} \right), \tag{59}\]  
where \(\mathrm{k}_{1}\) is the proportional gain for the force calculation, \(\mathrm{k}_{2}\) is the exponential gain for the force calculation, \(\mathrm{d}_{\mathrm{misself}}\) is the distance between the closest joints and \(\mathrm{d}_{\mathrm{safety}}+\mathrm{d}_{0}\) is the safety distance threshold.  
The force vector can be computed as follows:  
\[\text{f}_{\text{self avoidance}}=\begin{bmatrix}0^{3\times 1}\\ \frac{\text{h}_{\text{self}}}{\text{d}_{\text{min self}}}\left(\text{p}_{\text{i}}- \text{p}_{\text{j}}\right)\end{bmatrix}, \tag{60}\]  
where \(\text{p}_{\text{i}}\) and \(\text{p}_{\text{j}}\) represent the two closest joints. The task of self-avoidance, although always active, is considered a secondary task. In this context the augmented Jacobian weighted pseudo inverse is used. The torque exerted is given by:  
\[\tau_{\text{self-avoidance}}=\left(\text{I}-\text{J}_{\text{aug}}^{\#}(\text{ t})\text{J}_{\text{aug}}(\text{t})\right)\text{J}_{\text{i}}^{\text{T}}(\text{t}) \text{f}_{\text{avoidance}}, \tag{61}\]