esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Results and Discussion
### Supervised Analyses of Cinnamon VOCs Dataset by SPME/GC-MS
#### 2.3.2 OPLS-DA Analysis for Adulteration and Markers Detection  
Authentication and QC of official _C. verum_ via its VOCs profile were assessed using OPLS-DA, where it is usually adulterated with inferior grades consisting of other species, particularly _C. cassia_[42]. The models' validation parameters were also calculated, including the R\({}^{2}\) and Q\({}^{2}\) of calibration.  
Firstly, CV and CVM were modeled as one class group against CA as a second class, Supplementary Figure S4A-C. The score plot resulted in R\({}^{2}\) = 99.9%, Q\({}^{2}\) = 99.3%, and _p_-value = 0.0199, Supplementary Figure S4A suggestive for strong model with no over fit based on permutation plot, Supplementary Figure S4B. (_E_)-cinnamaldehyde and coumarin were also strong determinant factors associated with CV/CVM and CA, respectively, Supplementary Figure S4C.  
Secondly, CI in one class group was modeled vs. other cinnamon drugs in a second group to aid in identifying markers for CI, Supplementary Figure S4D-F. The score plot produced strong validation parameters R\({}^{2}\) = 99.4%, Q\({}^{2}\) = 96.9% and _p_-value = 9.31 \(\times\) 10\({}^{-5}\), confirming that CI exhibited a unique aroma profile from other species Supplementary Figure S4D. Several markers appeared to be associated with such discrimination, includingunknown aromatic hydrocarbons (P72 and P89), eugenol (P41), (_E_)-calamenene (P76), \(\alpha\)-selinene (P70), and (_E_)-cinnamaldehyde (P28) Supplementary Figure S4F. The presence of all these markers is the first time to be identified in _C. iners_ to the best of our knowledge.
# Detection and Classification of Bearing Surface Defects Based on Machine Vision
## 3 Materials and Methods
### Defect Detection
#### 3.2.2 Defect Detection Algorithm  
The structural characteristics of the bearing determine the high level of gray-scale consistency of the images along the \(x\)-axis, but the appearance of defects often disrupts this consistency. Based on this, we designed the following algorithm:  
* Perform a Gaussian filter on the image to eliminate the influence of noise, such as fingerprints on the bearing surface, and generate a Gaussian-filtered image.
* Calculate the average value of each row of pixels along the \(x\)-axis to generate an average filtered image.
* Form a difference value image using the difference between the Gaussian-filtered image and the mean filtered image.
* Identify the defective region, which is the region in the difference image where the amplitude is greater than the preset threshold.  
Figure 4 shows the detection results of images with a certain level of noise.  
Figure 3: Feature location region-of-interest (ROI) extraction process: (**a**) original image; (**b**) filtered image; (**c**) region after conversion into binary; (**d**) dark background region; (**e**) ROI extraction result.
# A Blockchain and IoT-Enabled Framework for Ethical and Secure Coffee Supply Chains
## 3 Methodology
### System Architecture and Dataset Design for IoT-Supported Blockchain Integration
#### 3.1.5 Mobile Validation  
At final checkpoints such as roasters and retail outlets, mobile validation can be used to confirm the physical handover of coffee batches for ownership confirmation and transaction finalization [46]. Android or iOS NFC-enabled smartphones [47] running blockchain-connected scanning applications can be used to scan RFID tags, validate delivery, and trigger smart contract events. These scans complete the traceability cycle by updating batch statuses in the blockchain ledger, finalizing ownership transfers, and providing verified proof of product authenticity to consumers and businesses alike.
# Determination of Clamping Force Using Bolt Vibration Responses during the Tightening Process
## 3 Estimation of Clamping Force from Vibration Measured in the Clamped Parts  
### Natural Frequency of a Bolt Specim  
The bolt acts as a transmission path for vibration transfer from the nutrunner into the clamped parts. In the process of bolt fastening, the boundary stiffness and tensile force of the bolt increase. This tension in the bolt increases the natural frequency for flexural vibrations. The excitation of the bolt and its transfer to the clamped structure is inevitably generated by the nutrunner that fastens the bolt. While the vibration properties of the clamped structure are stationary and do not change, the frequency response of the bolts changes due to increasing preload. The vibration modal component continuously changes in a short fastening time and is an important indicator of the clamping force. To measure the modal properties, both ends of the bolt were fixed to a hydraulic vise. The thickness of the bolt was 10 mm. The grip length was 48.2 mm, which is the same for the load cell experiments shown in the following section. As shown in Figure 1, the accelerometers (PCB, 352A21/NC) were attached to the bolt surface. An impact hammer (PCB, 086C02) was used to excite the bolt. Two accelerometers were attached in the longitudinal and the transverse directions to confirm the occurrence of the signal in a similar band to the frequency component of the responses generated in the bolt fastening process. The actual responses during the clamping process and the impact-generated transverse flexural vibrations occurred in similar frequency bands.  
The frequency response of the measured bolts is shown in Figure 2. This figure shows the bolt vibration characteristics when excited by a point force. This provides important information about the modal properties of the testing bolt. When analyzed as the beams of simply supported boundary conditions with Young's modulus of 207 GPa, density of 7600 kg/m\({}^{3}\), and 10 mm diameter, the first natural frequency of the bolt was calculated as 8452 Hz according to the Euler-Bernoulli beam theory [44]. The value was similar to the measured value of 8388 Hz (an error of less than 1%). The resonance in the longitudinal direction was not observed.
# Deep Learning-Based Flap Detection System Using Thermographic Images in Plastic Surgery
## 2 Materials and Methods
### Training Data  
Each image included as training data for the neural network was individually edited by the same plastic surgeon, who attended all the interventions, using Adobe Photoshop 2022 and a graphics tablet (Wacom Intuos). The editing procedure and color palette used were consistent across all images.  
The editing process was laborious, not due to technological issues but rather because of decision-making concerns. When it comes to pedicled or free flaps, the boundary of the flap is clearly indicated by the suture line. However, in the case of local flaps, defining this boundary is challenging. For this reason, we have arbitrarily decided to mark the border of the flap with a line that corresponds to the subcutaneous dissection line of the flap. This is primarily why the person who is responsible for editing the images must possess knowledge of plastic surgery techniques and actively participate in the surgical procedure. Once the flap has been identified on the thermographic image, a matrix is created of the area corresponding to the flap, down to the pixel level. This matrix is colored green with a transparency of 80%, to be easily identified. Based on this, a black and white matrix is created where the area of the flap is represented by the white area and the surrounding area by the black color, as shown in Figure 1. The data input to the ML algorithm is represented by the original thermographic image, and the output is the binary black-and-white mask corresponding to the segmented flap. The meticulous editing of these images is perhaps the  
Figure 1: An example of training data that includes the original, thermographic, segmented, and binary mask images. The first row of images shows a rhomboid advancement flap (Limberg) on the left thigh, a defect resulting from tumor formation excision at this level. The second row of images shows a latissimus dorsi flap performed for delayed breast reconstruction following a right mastectomy.  
most crucial stage, as the data obtained will serve as input on which the learning process will be based.
# Rotational Speed Control Using ANN-Based MPPT for OWC Based on Surface Elevation Measurements
## 2 Theoretical Considerations
### Artificial Neural Networks  
Artificial Neural Networks are rather simple electronic representations based on the biological neural structure of the human brain. ANNs were designed to mimic the behavior of the human neural system without the specific knowledge of the relationships between input data and the target data [33]. This feature made ANNs sought for solving many complex problems where specific knowledge of functions is absent or hard to determine [34; 35]. A biological neural network consists of many interconnected neurons whereas ANNs are formed by simple processing units, called artificial neurons.  
Artificial Neural Networks are formed of neurons in the input, output and hidden layers that are related with weighted signals (\(w_{jl}\)). A neuron consists of a bias \(b_{j}\), a sum function \(S_{j}\) associated with an activation function \(\varphi_{j}\) as depicted in Figure 7.  
The sum function of the \(j\)th neuron connected to \(N\) neurons from the previous layer is:  
\[S_{j}=\sum_{j=1}^{N}\left(w_{ij}y_{i}\right)+b_{j} \tag{25}\]  
where \(S_{j}\) is the sum from the \(j\)th neuron from the current layer, \(b_{j}\) is the bias, \(N\) is the total number of neurons, \(w_{ji}\) is the weight of the signal connecting the \(j\)th neuron from the current layer with the \(i\)th neuron from the previous layer and \(y_{i}\) is the input signal from the neuron of the previous layer.  
Connected to the activation function the output \(y_{j}\) of the \(j\)th neuron is defined as:  
\[y_{j}=\varphi_{j}(S_{j})=\varphi_{j}\left(\sum_{i=1}^{N}\left(w_{ij}y_{i}+b_{ j}\right)\right) \tag{26}\]  
The neurons' activation functions at the input layer are clamped with the input vector but the activation function of the neurons in other layers may be defined as:  
\[\varphi_{j}(S_{j})=\left\{\begin{array}{ll}S_{j}&(Linear)\\ \\ \frac{1}{1+e^{-S_{j}}}&(Sigmoid)\\ \\ \frac{c_{j}^{S_{j}-e^{-S_{j}}}}{e^{S_{j}}+e^{-S_{j}}}&(Hyperbolic\:tangent) \end{array}\right. \tag{27}\]  
Among the numerous types of ANN, Multi-Layer Perceptron (MLP) is the most common type used as feed-forward ANN. Generally, MLP consists of three layers as depicted in Figure 8. The first layer is the input layer that receives measured data/information. The last layer is called the output layer where the estimated/predicted solution is obtained. The input and output layers are connected  
Figure 7: Basic structure of an artificial neuron.  
by one or more intermediate layers known as the hidden layers. Moreover, MLPs are only connected by feed-forward connections.
## 3 Baseline Model Design Based on a DQN
### Reward Function
#### 3.2.2 Operational Efficiency  
In terms of operational efficiency, the objective of the efficiency reward is to encourage the model to complete tasks as quickly as possible while maintaining safety and accuracy. Therefore, the efficiency reward function consists of time efficiency, operational accuracy, and safety rewards.  
The time efficiency reward is designed based on the time required to complete an operation. For every 5 s reduction in operational time, a reward of (+1) point is given. The specific design is as follows:  
\[R_{efficiency}=\text{max}\bigg{(}0,\frac{T_{target}-T_{actual}}{5}\bigg{)} \tag{4}\]where \(T_{\text{target}}\) is the target operational time for completing a loading or unloading task and \(T_{\text{actual}}\) is the actual time taken to complete the operation.  
Operational accuracy rewards are designed based on the distance between the ideal deviation of container placement and the actual deviation during the actual operation to encourage high accuracy upon completion of the task, and are designed as follows:  
\[r_{precision}=K_{precision}\cdot e^{-\lambda(d_{target}-d_{actual})} \tag{5}\]  
where \(K_{\text{precision}}\) is the reward coefficient for accuracy, \(\lambda\) controls the rate of the reward decay, \(d_{\text{target}}\) is the actual deviation distance from the target position, and \(d_{\text{actual}}\) is the ideal deviation for placing the container.  
To prevent compromising safety while pursuing efficiency, a safety reward is also set:  
\[r_{\text{safety}}=\left\{\begin{array}{l}K_{\text{safety}},if\text{ }\text
## 3 Performance Analysis of Bearings Based on an Analytical Method
### Computational Solution  
Using the finite difference approach to calculate Equation (1) provides the pressure within the gas film at a specific node:  
\[P_{i,j}=\sqrt{\frac{\frac{f_{i+j}+f_{i-j}}{\Delta x^{2}}+\frac{f_{ij+1}+f_{ij- 1}}{\Delta y^{2}}+\frac{Q}{2h^{2}}}{\left(\frac{1}{\Delta x^{2}}+\frac{1}{ \Delta y^{2}}\right)}} \tag{4}\]  
In applying the successive over-relaxation (SOR) technique to address this equation, the iteration ceases under the following condition:  
\[\frac{\Sigma\Sigma\left|P_{i,j}^{k+1}-P_{i,j}^{k}\right|}{\Sigma\Sigma P_{i,j }^{k}}\leq 10^{-4} \tag{5}\]  
The equation employed for determining the load capability of a bearing is:  
\begin{table}
\begin{tabular}{c c} \hline
**Dimensional Parameter** & **Numerical Value** \\ \hline Supply gas pressure \(P_{s}\) (MPa) & 0.2, 0.3, 0.4, 0.5, 0.6 \\ Bearing length \(L\) (mm) & 80 \\ Bearing width \(S\) (mm) & 40 \\ Throttle hole diameter \(d\) (mm) & 0.2, 0.4, 0.6, 0.8, 1.0 \\ Length of the rectangular throttling orifice array \(a\) (mm) & 8, 16, 24, 32, 40, 48, 56 \\ Length of the throttling orifices \(H\) (mm) & 2 \\ Gas film thickness \(h\) (\(\upmu\)m) & 8, 10, 12, 14, 16, 18, 20 \\ \hline \end{tabular}
\end{table}
Table 1: Basic parameters of the porous aerostatic rectangular thrust bearing.  
\[W=\iint_{S}(p-p_{a})dxdy \tag{6}\]  
Upon calculating the load capacity corresponding to a certain air film thickness \(h\), one can ascertain the air film's stiffness through application of Equation (7).  
\[K=\frac{W(h+\Delta h)-W(h)}{\Delta h} \tag{7}\]  
In the aforementioned equation, \(K\) denotes the static stiffness, \(W\) signifies the static load capacity, and \(\Delta h\) conveys the incremental change in air film thickness.  
The procedure for obtaining a numerical solution, as illustrated in Figure 2, starts with the determination of specific parameters for the bearing model and proceeds with grid generation within the scope of the model. The pressures at the bearing's inlet and outlet for the supply air are set, allowing for the calculation of mass flow rate at the entrance of the supply port. Initial pressure inputs are integrated into the discrete Reynolds equation for analysis. An iterative process is employed to refine the values within the pressure field; once the variance between two successive iterations complies with the convergence threshold, the sequence transitions to the subsequent phase. If convergence has not been achieved, the iteration cycle persists. The mass flow rate at the discharge of the bearing is then calculated. Upon achieving convergence, the established pressure distribution is treated as the conclusive outcome. This pressure profile then serves as the basis for subsequent computation of the load-carrying capacity of the bearing.
# Calibration for Sample-And-Hold Mismatches in \(M\)-Channel TIADCs Based on Statistics
## 6 Conclusions  
This paper proposes a statistics-based calibration method for S/H mismatches in \(M\)-channel TIADCs. The mismatch coefficients are identified by eliminating the statistical differences between channels using the LMS algorithm. The mismatch-induced errors are approximated using multipliers and differentiators, and are eliminated from the original output samples afterwards. Although the complexity is higher compared with the IFB-based method and the I/Q-based method, the proposed algorithm in this paper has more extensive applicability for different signals and different numbers of channels.  
There are three circumstances where our method should be given priority. When the signal's bandwidth is unknown or is narrow to some extent, it is more reliable to use our method than the  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **LSSR Improvement** & **Resource Consumption** & **Channel No.** \\ \hline This paper & 35.05 dBc & 141 & \(M\) \\ IFB-based method [23; 35] & 3.77 dBc & 42 & 2 \\ I/Q-based method [24; 25] & 26.95 dBc & 13 & 2 or 4 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Comparisons between the proposed method, the IFB-based method, and the I/Q based method.  
IFB-based method. When the signal's components are unknown or the signal is mainly composed of sinusoids, our method can provide higher calibration precision than the I/Q-based method. When the number of channels is more than four, only our method can work.  
To realize our method in a real-time way, a full-parallel structure can be employed in FPGAs. For instance in [30], fast FIR algorithm is used to parallelize the differentiators, which can make the FPGA's low clock frequency more compatible with the TIADC's high sampling rate and reduce the power consumption meanwhile. The batch size N can also be reduced since the accumulation in (23) can be shifted into the iteration process of (29) and (30) at the cost of more iterations. Furthermore, for the high sampling rate, the extra iterations do not bring much longer convergence time.  
Conceptualization, X.L., H.X.; Methodology, X.L., Y.W.; Software, Y.D., N.L.; Validation, G.L.; Formal Analysis, X.L.; Investigation, X.L., Y.W.; Resources, X.L.; Data Curation, X.L., N.L.; Writing--Original Draft Preparation, X.L.; Writing--Review & Editing, Y.W.; Visualization, Y.D.; Supervision, H.X.; Project Administration, H.X., Y.W.; Funding Acquisition, Y.W. This research was funded by National Natural Science Foundation of China [No. 61701509]. The authors declare no conflict of interest.
## 3 Results and Discussion
### NIRS Discrimination Samples After the Application of Different Clean Water Depuration Processes  
Given the differences observed in the NIRS spectra of the samples subjected to different washing times, an exploratory analysis using PCA was performed to determine if the samples could be differentiated. The results of the absorption signals obtained by NIRS are displayed in Figure 4. The score plot shows that principal component 1 (PC1) explained 77.8% of the cases and principal component 2 (PC2) explained 11.3%, with a total of 89.1% of the cases being explained at a 95.0% confidence level. There are five clusters, with all sample groups separated except for those washed for 72 and 96 h, which are indistinguishable in the PCA and grouped in the same cluster.  
Figure 3: NIR spectra obtained from fish samples after application of different clean water depuration processes.  
Control samples (0 h) and 12 and 24 h samples are on the positive side of PC1, while those at 48 h, 72 h, and 96 h appear on the negative side. The treatment at 48 h already exhibits notable differences in VOCs in relation to the other treatments, indicating that the profile of the samples recorded by NIRS differed with an increased washing time.  
Therefore, it can be concluded that NIRS is capable of discriminating fish samples washed extensively from those washed for a shorter period or not at all.  
[PERSON] et al. [35] previously applied the NIRS technique coupled with chemometric algorithms to predict fish freshness. Correlations greater than 95% were obtained, highlighting this technique's potential as a quality indicator in the food industry. Furthermore, chemometric models, like those developed by [PERSON] et al. [36], have shown prediction accuracies of up to 100% in some cases, demonstrating the utility of this method in combating fraud in the fish industry. Recent studies have also confirmed that this technique provides good prediction models for the control of toxic substances such as histamine in tuna fish [37].
## 4 Overview of Research Projects on AI/ML for Communications and Networking
### Europe--H2020 Research Framework
#### 4.1.5 Smart Connectivity beyond 5G  
Under this topic, there are nine funded projects, all started in January 2021 with a duration between 24 and 42 months that have received an aggregate EU contribution of approximately EUR 60 million. All projects make extensive use of AI/ML tools in their respective target areas as summarised in Table 7. The AI/ML aspects of the Hexa-X project, a flagship for B5G/6G vision and intelligent fabric of technology enablers connecting human, physical, and digital worlds are discussed next. The vision of Hexa-X is to intertwine the human, physical and digital worlds for sustainable, trustworthy, and inclusive use of communication technologies. In this direction, the project has, so far, identified five use-case families and associated them with six research challenges. On the one hand, the use cases are sustainable development, massive twinning, immersive telepresence for enhanced interaction, from robots to cobots (robots developing relations among each other), and local trust zones for humans and machines. On the other hand, the research challenges are connecting intelligence, extreme experiences, a network of networks, sustainability, trustworthiness, and global service coverage. According to [123] (Figure 4.1), connecting intelligence is required to address the first four of the above-mentioned use-case families. In addition to the identified use cases, scenarios and challenges, the project has also specified seven services harnessing new capabilities, including AI-as-a-service and AI-assisted V2X. The former applies the producer-consumer model between the end-devices and third-party AI agents that offer trained ML models for inferencing tasks. The latter generates digital twins of urban cities and adopts AI techniques to regulate the
## 2 Materials and Methods
### Transmission Phase
#### 2.3.1 Data Communication and Energy Consumption from Nodes to Cluster Head  
At first, the nodes collect the required information from the deployed field. The nodes execute minor processing and forward data to the CH utilizing the least energy as determined by the energy consumption model specified below in (4) and (5).  
\[E_{Nodes_{Tx}}=\eta\times l_{ength}\times\left(e_{t}+e_{fs}\times d^{2}\right) \tag{4}\]  
\[E_{Nodes_{Rx}}=\eta\times l_{ength}\times e_{r} \tag{5}\]  
Here, \(\eta\) shows the sum of the nodes (excluding the CH) within one cluster.