esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### fNIR Data Acquisition and Analysis  
A fNIR100A-2 (Biopac System Inc., Goleta, CA, USA) device was used to assess the brain activation in the prefrontal area. This particular device records at a frequency of 2 Hz with 16 recording channels with a source-detector separation of 2.5 cm. It measures oxy-Hb and deoxy-Hb (unit in \(\upmu\) mol/L) changes with two peak wavelengths at 730 nm and 850 nm.  
For data acquisition and analysis the COBI Studio (v1.2.0.111) and fNIRSoft professional (v3.3), respectively, were used (Biopac software).  
Before performing each task, participants were asked to relax and not think about anything for 10 s to collect the baseline changes in oxy-Hb and deoxy-Hb.  
First, a visual inspection to eliminate low-quality channels was performed. The raw files were filtered with a low-pass finite impulse response (FIR) filter, with an order of 20 Hamming, and a cutoff frequency set at 0.1 Hz to remove long-term drift, high-frequency noise, and cardiac and respiratory cycle effects [36, 37]. Afterwards, to remove motion artifacts the sliding-window motion artifact rejection (SMAR) algorithm was used (window size= 10 s, upper threshold = 0.025 nm, lower threshold = 0.003 nm) [36]. The changes in light absorption were converted to changes in concentration of oxy-Hb and deoxy-Hb using the modified Beer-Lambert Law concerning a 10 s local baseline recorded at the beginning of data collection and a differential pathlength factor (DPF) = 6 [18]. The total hemoglobin (total-Hb) also was assessed by [total-Hb] = [oxy-Hb] + [deoxy-Hb].
## 3 Application of Proposed RF Algorithm for Intrusion Detection
For each training set, a tree predictor \(\theta_{i}\) is generated by randomly selecting \(m\) features used for tree splitting from all features \(M\) (\(m<M\)). Generally, the splitter with the smallest Gini index that generates \(n\) classification and regression trees for classification purposes was selected through the RF approach. The Gini index is a number describing the quality of the split of a node on a feature. If data set \(D\), for example, contains samples from \(C\) classes, the Gini index is defined according to [10] as follows:  
\[\text{Gini }(D)=1-\sum_{c=1}^{C}{P_{c}}^{2}, \tag{8}\]  
where \(P_{c}\) is the relative frequency of class \(c\) in \(D\). Step RF.3--The prediction results from each DT are synthesized through a plurality vote (PV) that generally increases the classification accuracy of the overall model. The vote determines the class \(i\) that maximizes the sum based on the majority voting rule. \[\text{class}(x)=\text{Arg max}|\sum_{k}g(y_{k}(x),c_{i}], \tag{9}\]  
where \(x\) is the behavior feature of the sampling data, \(y_{k}(x)\) is the classification result of the \(k\)th DT, and \(g(y_{k}(x),c_{i})\) is a counting function defined as  
\[g(y,c)=\left\{ \begin{array}{cc}1,&y=c\\ 0,&y\
eq c\end{array} \right., \tag{10}\]  
where \(g(y_{k}(x),c_{i})\) is the prediction result of the classifier \(i\) that \(x\) belongs to class \(k\). \(y_{k}(x)\)=1 for the true class \(k\) of \(x\); otherwise \(y_{k}(x)=0\). Step RF.4--The outcome chosen by the most decision trees is the final indicator. Subsequently, the behavioral patterns of network threats are classified into those associated with normal versus abnormal connections. Thereafter, the accuracy of RF classifiers in detecting existing or identified network attacks is evaluated using Equations (8)-(10). Step 3. Performance evaluation  
This step is performed to validate the classification performance of the proposed classifier on unbalanced data. In this study, we conducted such an evaluation in termsof accuracy, precision, recall, and F1 score on the experimental data set. In particular, SMOTE with uniform distribution resampling was used in the training experiment. Finally, a cross-validation scheme was adopted to compare the predicted accuracy of the developed model with an SVC.
# Plant Design for the Conversion of Plastic Waste into Valuable Chemicals (Alkyl Aromatics)
## 1 Introduction
### Upcycling  
In the past 70 years, plastics made from synthetic petroleum have seen a dramatic increase in production around the world. In 2020, it was expected that 367 million tons of plastic waste would be produced globally--up from less than 2 million tons in 1950--and by 2030, that number is expected to increase by a factor of 19 [12]. Roughly 40% of all plastics are single-use items that cannot be recycled. They typically wind up in dumps and similar locations, where they can remain for countless generations. However, the amount of energy gained from burning this trash is less than what was used during production if it is  
Figure 2: Process flow diagram for fuel production from plastic waste.  
converted into fuel first. Recovering the plastic's original monomer through a process called depolymerization, also known as chemical or material recycling, then repolymerizing it can result in a product with the same qualities as the original polymer, but a large amount of energy is needed for this procedure when working with polyethylene and PP. Nevertheless, plastic trash can be recycled into useful chemicals through a process called controlled partial depolymerization. Increasing the cycle is referred to as upcycling.  
HDPE products, such as bottles and cans and other types of plastics, make up a significant portion of plastic trash. LDPE products, such as polyethylene bags, comprise a considerable portion of this trash as well. Above 400 \({}^{\circ}\)C, pyrolysis can depolymerize them, but the resulting gas, crude oils, and char are of little value and can be difficult to use. This can be accomplished with or without the use of a catalyst. At lower temperatures, catalytic hydrogenolysis or double catalytic alkane metathesis can achieve somewhat more selective disassembly; however, the products of these reactions are all low-value alkanes, making it unlikely that their production costs can be recouped. Partial depolymerization can obtain more valuable aromatics, and naphtha reforming is the standard method of producing aromatics [13]. This process consumes a considerable amount energy and generates a mixture known as benzene-toluene-xylenes (BTX) at temperatures of 500-600 \({}^{\circ}\)C. It is alkylated to produce linear alkyl-benzenes. Manufacturing of BTX by linear alkanes is a reaction that requires harsh conditions and high temperatures of up to 800 \({}^{\circ}\)C. Under these harsh conditions, the catalyst tends to deactivate rapidly. New catalysts have been developed, which convert certain materials into BTX at lower temperatures, but they are not cost-effective, with slow rates, low yields, and high hydrogen requirements.
## 4 Applications of GenAI-Driven CV in Smart Agriculture
### Fruit and Vegetable Maturity Detection and Harvesting Automation
The Ocluder-Occludee Relational Network (O2 RNet) enhanced apple detection in clustered orchard environments, achieving a detection accuracy of 94% and an F1-score of 0.88, thereby improving localisation precision in occluded clusters [148], while soft pneumatic grippers minimised mechanical damage during cucumber harvesting [149]. Real-timedefect detection systems, exemplified by finger-vision sensors [150], further ensured selective picking of market-ready produce. A critical component for the safe and efficient operation of such autonomous harvesting systems is robust field obstacle detection, for which improved algorithms based on models like YOLOv8 have been developed [151]. Beyond direct harvesting tasks, specialised robotic units like RoMu4o contribute to orchard automation by performing tasks such as proximal hyperspectral leaf sensing [152]. However, common operational constraints, including limited battery endurance and mechanical agility, broadly hinder the scalability of such robotic systems. To enhance real-time adaptability, temporal analysis and edge AI frameworks prioritised low-latency processing and decentralised computation. A video-based tracking system integrating MangoYOLO detection, Kalman filtering, and an enhanced Hungarian algorithm effectively monitored mango fruit across growth stages, reducing redundant detections by approximately 9.9% [153], while federated learning allowed models to be trained across distributed farms without compromising data privacy [154]. Smartphone-integrated CNNs democratised access to quality assessment, as evidenced by portable systems for Zanthoxylum fruit evaluation [155]. However, seasonal and regional domain shifts necessitated frequent model retraining, exacerbating computational overheads [156]. In addition, cross-modal VLMs and physics-informed synthetic data generation represent transformative frontiers. Vision-language systems like AgroGPT empower farmers to obtain expert agronomic insights from images through intuitive natural language queries, bridging the gap between AI capabilities and field-level decision-making [123], while synthetic data generation frameworks replicate the variability of agricultural environments to create realistic, specific training datasets for farming vision models [157]. Ethical considerations, including labour displacement and algorithmic bias, demand rigorous governance frameworks to ensure equitable technology deployment [158]. Addressing these challenges will require interdisciplinary collaboration to harmonise technical innovation with socio-economic sustainability in global agriculture. Table 2 provides a comparison of different approaches discussed in this subsection.
## 4 Sample Preparation
### Sample Construction
#### 4.3.2 Sample Construction in the Time-Frequency Domain  
The CWT is employed on the raw signal to obtain the results in the time-frequency domain, where the Morlet wavelet is chosen as the mother wavelet. In the frequency dimension, we set the scale to be 256, which represents the frequency domain is divided into 256 sub-bands; in time dimension the size matches the length of the input signal. By performing CWT on the raw signal, the wavelet coefficients in the form of \(256\times 256\) matrix are obtained. Each row of the matrix contains the coefficients for one scale. Finally, the coefficients are converted into an image by gray processing likewise.
# A Soft Computing Framework to Support Consumers in Obtaining Sustainable Appliances from the Market
## 2 Literature Review and Paper's Contribution
### Paper's Contribution  
Based on the literature discussed above, there is a gap regarding sustainable measures for buildings, involving household appliances, that allow supporting a household consumer in choosing a set of sustainable solutions from the market.  
Therefore, the main contribution of this paper is the design of an approach to support a consumer to identify sustainable options for household appliances that exist on the market that attends to their needs, as well as a set of requirements, namely:  
1. Maximization of a consumer's economic wellbeing (water and energy consumption savings, investment savings, etc.);
2. Maximization of a consumer's social wellbeing through their preferences (e.g., design, quality perceived, noise, and number of functions, among others);
3. Maximizing the consumer's environmental wellbeing (avoidance of CO\({}_{2}\) emissions, water savings);
4. Providing a methodology that allows obtaining several alternative sustainable solutions, which allow tackling some contingencies that eventually may occur (e.g., an out-of-stock electrical appliance initially recommended by the method).  
In order to fulfill the previously identified gap, this work presents a decision support approach that provides the consumer (decision-agent) with a set of household appliances obtained from the market according to their preferences and needs.  
The method presented here also promotes the circular economy by promoting sustainable options that exist on the market.  
The presented approach also includes economic (e.g., budget), social (e.g., minimum value of air conditioner heat capacity), and environmental (e.g., CO\({}_{2}\)) restrictions related to each energy service (household appliance type) considered in this study.
## 2 System Architecture
### Receiver  
This subsection introduces the demodulation principle of the TDOM system. As carriers are not orthogonal any more, band-pass filters or fast Fourier Transformation (FFT) in traditional multi-carrier communication systems can no longer work. Therefore, in the proposed TDOM, a system of linear equations for all information \(a_{n}\) is established to demodulate the information.  
Taking a back-to-back system as an example. When the signal is received at the receiver, the first carrier, \(\sin(2\pi f_{0}t)\), is used to perform a coherence computation with the received signal to obtain the first linear equation about all modulation amplitudes \(a_{n}\), i.e.,  
\[\begin{array}{l}G_{0}=\int_{0}^{T}sin(2\pi f_{0}t)\times S(t)dt\\ \quad=\int_{0}^{T}sin(2\pi f_{0}t)\times\{a_{0}sin(2\pi f_{0}t)\,+a_{1}sin[2\pi f _{1}(t-\tau)]+a_{2}sin[2\pi f_{2}(t-2\tau)]\\ \quad\quad+\ldots+a_{N}sin[2\pi f_{N}(t-N\tau)]\}dt\\ \quad=a_{0}r_{00}+a_{1}r_{01}+\ldots+a_{N}r_{0N},\end{array} \tag{2}\]  
where \(r_{00}=\int_{0}^{T}sin(2\pi f_{0}t)\times sin(2\pi f_{0}t)dt\), and so on. Then, the system uses the second carrier \(\sin[2\pi f_{1}(t-\tau)]\) to perform a coherent computation with the received signal to obtain the second linear equation for all modulation amplitudes \(a_{n}\), i.e.,  
\[\begin{array}{l}G_{1}=\int_{0}^{T}sin[2\pi f_{1}(t-\tau)]\times S(t)dt\\ \quad=\int_{0}^{T}sin[2\pi f_{1}(t-\tau)]\times\{a_{0}sin(2\pi f_{0}t)\,+a_{1} sin[2\pi f_{1}(t-\tau)]+a_{2}sin[2\pi f_{2}(t-2\tau)]\\ \quad\quad+\ldots+a_{N}sin[2\pi f_{N}(t-N\tau)]\}dt\\ \quad=a_{0}r_{10}+a_{1}r_{11}+\ldots+a_{N}r_{1N},\end{array} \tag{3}\]  
where \(r_{10}=\int_{0}^{T}sin[2\pi f_{0}(t-\tau)]\times sin(2\pi f_{0}t)dt\), and so on. After coherent computations with the received signal using all carriers, a system of linear equations can be obtained, i.e.,  
\[\mathbf{RA}=\mathbf{G} \tag{4}\]  
where \(\mathbf{R}=\begin{bmatrix}r_{00}&\ldots&r_{0N}\\ \vdots&\ddots&\vdots\\ r_{N1}&\ldots&r_{NN}\end{bmatrix}\), \(\mathbf{A}=\begin{bmatrix}a_{0}\\ \vdots\\ a_{N}\end{bmatrix}\), \(\mathbf{G}=\begin{bmatrix}G_{0}\\ \vdots\\ G_{N}\end{bmatrix}\). It is worth noting that the matrix \(\mathbf{R}\) is an \((N+1)\)-order full-rank Toplitz matrix. Therefore, there exists an inverse matrix, \(\mathbf{R}^{-1}\)  
Figure 2: Schematic diagram of modulation principle of time-delay overlapping modulation system.  
Then, the modulation amplitude of each carrier \(a_{n}\) can be obtained by solving the system of linear equations, i.e.,  
\[\mathbf{A}= \mathbf{R}^{-1}\mathbf{G} \tag{5}\]  
Finally, the magnitude is converted to binary bits data according to the magnitude mapping rules given in Table 1.
## 5 Performance Testing
### Torque Test  
The designed electric drive joint is characterized by high torque density, and its torque performance is specifically tested. The torque test platform, shown in Figure 35, consists of the axial flux motor, a torque sensor, a coupling, a brake, and a host computer. The brake simulates the motor load, while the axial flux motor drives this load through the coupling. The torque sensor captures the torque and current data of the electric drive joint prototype and transmits it to the host computer for real-time monitoring and recording.  
At the beginning of the test, the rotational speed of the electric drive joint prototype is stabilized at the rated speed of 1080 rpm by controlling the input frequency. Subsequently, the input current is increased in fixed increments, and the torque on the motor's output shaft is directly measured by the torque sensor. The relationship between the current and the output torque is recorded by the host computer. The results are shown in Figure 36. Under the rated current, the output torque of the electric drive joint meets the design requirements, and the output torque exhibits good linearity with the input current. This indicates that no magnetic circuit saturation occurs within the tested current range, verifying the effectiveness of the motor design.  
To validate the accuracy of the simulation results, the torque-current relationship of the electric drive joint prototype is compared with the simulation data. Since the electric drive joint incorporates a reducer with a reduction ratio of 12, the output torque of the prototype is divided by 12 before comparison. The comparison is illustrated in Figure 37.  
Figure 35: Electric joint prototype test platform.  
Figure 36: Torque–current diagram of electric drive joint prototype.  
The close agreement between the simulation data and prototype measurements confirms the accuracy of the finite element simulation.
###### Abstract  
As an important method for connecting structural members, anchor bolts have been installed in many situations. Therefore, accurate evaluation of the pull-out strength of anchor bolts has always been an important issue, considering the complicated actual installation conditions and the problem of aging deterioration of the structural members. In general, the patterns of pull-out failure of anchor bolts can be classified into three types: adhesion failure, cone failure, and bolt break. However, it sometimes shows a mixed fracture pattern, and it is not always easy to predict the accurate pull-out strength. In this study, we attempted to evaluate the pull-out strength of anchor bolts under various installation conditions using SPH, which can analyze the crack growth process in the concrete. In particular, the anchor bolt-concrete interface model was introduced to SPH analysis in order to consider the bond failure, and it was confirmed that various failure patterns and the load capacity could be predicted by proposed SPH method. After that, the influence of several parameters, such as bond stress limit, anchor bolt diameter, and the anchor bolt embedment depth on the failure patterns and the load capacity, were investigated by numerical calculation. Furthermore, several useful suggestions on the pull-out strength of anchor bolts under improper installation conditions, such as the ends of members for the purpose of seismic retrofitting, are presented.  
concrete; anchor bolt; pull-out strength; SPH method Article  
An Analytical Study on the Pull-Out Strength of Anchor Bolts Embedded in Concrete Members by SPH  
114 August 2021  
10 September 2021  
14 September 2021  
**Publisher's Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.**  
**Coyright:**\(\copyright\) 2021 by the authors. License MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
## 6 Development Trends and Prospects
### Intelligence  
Intelligence refers to the application of new technologies such as artificial intelligence. With improvements in big data and computing power, the application of machine learning and deep learning in fault detection is becoming increasingly widespread. Deep learning models such as convolutional neural networks and long short-term memory networks can automatically extract features from bearing signals, freeing them from the limitations of manual feature engineering. Especially in dealing with nonlinear and non-stationary signals, deep learning methods perform well. Meanwhile, the application of new technologies such as transfer learning, semi-supervised learning, and reinforcement learning is driving detection algorithms to become more adaptable and versatile.  
Figure 11: Development trends and prospects.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Techniques** & **Advantages** & **Shortcomings** & **Application Scenarios** \\ \hline \multirow{8}{*}{Vision Detection} & Surface defect detection is & & \\  & intuitive, non-contact, suitable & & \\  & for detection in complex & & \\  & environments such as high & & \\  & temperature and high speed, & & \\  & with a high degree of & & \\  & automation and rapid & & \\  & progress with technological & & \\  & development & & \\ \hline \multirow{8}{*}{Intelligent Detection} & Automatic feature extraction, & & \\  & processing multiple data & & \\  & types, high fault classification & & \\  & accuracy & & \\  & & & \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: _Cont._In 2021, Schaeffler collaborated with Siemens' Large Drive Applications Division to jointly advance the development of industrial digitalization. Through this partnership, both parties combine Schaeffler's decades of experience and expertise in bearing design, manufacturing, and maintenance with Siemens' digital platform for drive systems and solutions, Sidrive IQ. The Sidrive IQ platform integrates numerous functions into a single solution and further enhances the performance of drive systems through AI-based analytics and digital content. This solution will assist customers in making better decisions regarding the operation, maintenance, and repair measures of drive systems. The integration of Schaeffler's automatic bearing diagnostic analysis service into the Sidrive IQ platform significantly improves the accuracy and precision of bearing condition diagnostics. This collaboration marks the first instance of algorithm-based bearing diagnostics in the field of industrial IoT and serves as a successful case of traditional large technology companies collaborating in new domains.
## 3 Results and Discussion
### Destructive Tests
#### 3.3.2 Compression Test  
For example, in the flexural test procedure, when the collapse of the specimen occurs, the maximum load is registered. The compression strength is given by the following equation:  
\[f_{ct,c}=F/d^{2} \tag{8}\]  
where \(f_{ct,c}\) is the compression strength, \(F\) is the maximum load registered, and \(d\) is the piston's square section steel plate dimension. The specimens' maximum load and compression strength values are shown in Table 7.