esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Thermographic Techniques for Skin Cancer Diagnosis
### Dynamic Infrared Thermography
#### 3.2.1 Cold Stimuli for IR Imaging
The skin phantoms were cooled for a period of 60 s, and the successive reheating process was recorded by an IR camera and two embedded resistance temperature detectors to assess the cooling penetration over time. To compare the cooling techniques, the authors defined a decision matrix considering different criteria, such as uniform cooling, repeatability, obstructing the view, cooling efficiency, workload per patient, patient comfort, use in a clinical setting, noise exposure, consumables, additional equipment, and price. The most suitable cooling method for skin cancer diagnosis was the technique that reached the highest weighted score. The authors concluded that convective cooling techniques create a more uniform cooling effect than conductive techniques, which are effective only for cooling flat objects; indeed, when they are applied to bulging skin, uniform cooling is more challenging to obtain. According to the authors, cooling methods involving ice or alcohol are unsuitable when using infrared thermography because they can compromise the accuracy of the temperature readings as they affect both the infrared radiation and the thermal camera's view. Based on their decision matrix, [PERSON] et al. stated that the Zimmer Cryo 6 cooler is the best cooling method thanks to its ability to uniformly cool the skin, the consistent conditioning of cold air, high cooling efficiency at the airflow temperature of \(-30\)\({}^{\circ}\)C, and unnecessary consumables; on the other hand, the disadvantage of this cooling device is its cost. Cryogenic cooling of the skin was also applied in the thermographic study, aided by numerical simulations and recently published by [PERSON] et al. [36]. The authors showed that the choice of an appropriate cooling method, a cooling sequence, and an optimized experimental set-up leads to an improvement in thermal contrast.
## 4 Experiments
### Case Western Reserve University (CWRU) Bearing Experiment Data Analysis
#### 4.1.2 Impact of FC Layers on Experimental Test Results  
Throughout the model, besides the feature extractor, the performance of the algorithm is also affected by the parameters in the fault identification module and the domain discrimination module. To investigate the effect of fully connected layers on the algorithm's performance, experiments were conducted to assess the classification accuracy with different quantities of fully connected layers over a certain number of iterations. As shown in Table 5, it was found that the classification accuracy was higher in the structure that used two fully connected (FC) layers, a dropout layer, and an output layer. This is because a lower number of fully connected layers in the classifier reduces the model's expressive ability, adversely affecting its performance and failing to achieve the desired classification effect. On the other hand, a higher number of layers increases the model's computational cost and can easily lead to overfitting, negatively impacting the classifier's effectiveness. Based on this, the fault recognition module and domain discrimination module are structured with two fully connected layers, a dropout layer, and an output layer.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Task** & **No DA** & **No CRU** & **Proposed Method** \\ \hline A-B & 82.5\% & 97.2\% & 99.1\% \\ A-C & 87.1\% & 97.3\% & 99.4\% \\ B-A & 85.3\% & 98.3\% & 99.6\% \\ B-C & 81.7\% & 94.58\% & 98.6\% \\ C-A & 85.4\% & 96.4\% & 99.1\% \\ C-B & 83.2\% & 95.8\% & 99\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Test results of each method for each task.  
Figure 5: Visualization of t-SNE for each experimental result.  
Figure 6: Confusion matrix for each experimental result.
## 4 Proposed PSO Variant
### Ic-Vaindiwposo Algorithm
#### 4.2.3 Logistic Chaos Map  
The chaotic sequence can be generated using five sorts of chaotic maps, such as the logistic map [42], the lorenz map [43], the piecewise linear chaotic map [44], the tent map [45], and the Henon map [46]. Among them, logistic mapping is a relatively easy mapping technique in terms of its mathematical shape and is broadly used. We apply the improved logistic chaotic map to deal with the optimal particle of the population.  
When most particles acquire approximate solutions or the identical solution, the population falls into a local optimum and loses diversity. So it is imperative to mutate the optimal particles so that the rest of the particles can rapidly leap out of the local optimum state. This is why the logistic chaotic map was introduced into the improved algorithm [32]. The logistic chaotic map can additionally generate a logistic of a certain length, and its randomness and ergodicity can be used to enhance searchability. Chaos is an unstable phenomenon springing up from a deterministic system, indicating that its movement state is random. The ergodicity of chaotic motion can traverse all the states without repetition of its laws. Therefore, a purposeful search for chaotic mutation will yield better outcomes than a random search. The adopted logistic chaotic map is shown in (22):  
\[z_{i}(k)=\mu z_{i}(k-1)(1-z_{i}(k-1)) \tag{22}\]  
Among them, \(\mu\) is the control parameter of the chaotic state, \(\mu\in(0,4]\), when \(\mu=4\), the control system is in a completely chaotic state, which is conducive to leaping out of the local optimum. When using logistic chaotic mapping, the position of the particle must be mapped in the range of \([0,1]\), that is, \(0\leq z_{i}(k)\leq 1\).  
When using chaos theory for local search, the particle position is shown in (23):  
\[x_{i}(k)=x_{i}(k-1)+\eta z_{i}(k-1) \tag{23}\]  
Here \(\eta\) is the adjustment coefficient. In the search process, setting \(\eta\) slightly larger in the early stage is beneficial to the algorithm leaping out of the local optimum. When the optimal solution is approached, the value \(\eta\) step-by-step turns smaller. So \(\eta\) is adaptive as the algorithm iterates, as shown in (24):  
\[\eta=\gamma[(k_{\max}-k+1)/T_{max}]^{2}x_{i}(k-1) \tag{24}\]  
Among them, \(\gamma\) is the neighborhood radius, \(\gamma=0.1\).
# Changes of Gait Patterns after Correction of Refractive Error in the Elderly with Cataracts
## 2 Materials and Methods
### Experimental Procedures  
An objective refraction was performed on the subjects using a retinoscope (Retinoscope, WelchAllyn, Auburn, NY, USA) and a 6-m LCD chart (LUCID'LC, Everview, Seoul, Republic of Korea), and a subjective refraction test was then performed using a manual phoropter (Ultramatic RX Master, Reichert, Depew, NY, USA). The endpoint of refractive correction was determined by MPMVA (Maximum to Plus Maximum Visual Acuity) [21], and the visual acuity was recorded as the corresponding decimal visual acuity when reading more than four numbers in each decimal visual acuity line, wherein there are five numbers in one line in order to represent each decimal visual acuity level. The tests were performed by a skilled examiner, and the measurement of each subject's corrective power for refractive errors was performed. Based on the measured correction powers, each subject received glasses made for the correction of distance. In many previous studies, the paths of walking test was performed at 5 m or less, and its effectiveness was also proven [22,23]. In addition, the reproducibility of the Optogair system has been confirmed by previous studies [24]. In this experiment, the actual walking distance was 7 m in total (the start  
Figure 1: Factors affecting gait patterns used in this study.  
and end points were placed at a 1-m distance in order to reduce the effects of acceleration/deceleration) and the evaluation walking distance was 5 m. For the measurement of changes in gait patterns before and after participants wore correction glasses, two Optogait measuring bars were extended to 5 m and installed facing each other on both sides of the walking path. Each subject were instructed to walk in their normal manner while focusing on a target located ahead of them. A randomized order of measurement before and after the participants wore correction glasses was utilized for each individual in order to avoid effects caused by familiarity with the use of the devices. Each subject was allowed a rest period of 5 min in order to avoid fatigue before the second measurement. An analysis of the collected data was performed to determine whether wearing the correction glasses had resulted in significant changes in gait patterns among the elderly subjects.
# A Fast and Simple Method for Absolute Orientation Estimation Using a Single Vanishing Point
## 2 Materials and Methods
### Orientation Refining  
In this section, the coarse camera orientation \(\left(a_{x}\quad a_{z}\right)\) obtained in Section 2.1 is used as the initial solution \(\left(a_{x0}\quad a_{z0}\right)\) to refine the camera orientation. Three cost functions can be found, from Equations (8) and (9), as follows.  
\[f_{1} =\cos a_{z}\cdot d_{cx}-\sin a_{z}\cdot\cos a_{x}\cdot d_{cy}+ \sin a_{z}\cdot\sin a_{x}\cdot d_{cz}-d_{wx} \tag{13}\] \[f_{2} =\sin a_{z}\cdot d_{cx}+\cos a_{z}\cdot\cos a_{x}\cdot d_{cy}- \cos a_{z}\cdot\sin a_{x}\cdot d_{cz}-d_{wy}\] \[f_{3} =0\cdot d_{cx}+\sin a_{x}\cdot d_{cy}+\cos a_{x}\cdot d_{cz}-d_{ wz}\]  
The goal of orientation refining is to minimize \(f_{i}(i=1,2,3)\) with the initial solution \(\left(a_{x0}\quad a_{z0}\right)\). Here, the Gauss-Newton method [52] was used and the processing was as follows.  
First, according to the cost functions (Equation (13)), the Jacobian matrix \(J\) is given by  
\[J^{T}=\begin{bmatrix}\frac{\partial f_{1}}{\partial a_{x}}&\frac{\partial f_{ 2}}{\partial a_{x}}&\frac{\partial f_{3}}{\partial a_{x}}\\ \frac{\partial f_{1}}{\partial a_{z}}&\frac{\partial f_{2}}{\partial a_{z}}& \frac{\partial f_{3}}{\partial a_{z}}\end{bmatrix} \tag{14}\]  
Then, we refine the orientation using  
\[\begin{bmatrix}a_{xk+1}\\ a_{zk+1}\end{bmatrix}=\begin{bmatrix}a_{xk}\\ a_{zk}\end{bmatrix}-\left(J^{T}J\right)^{-1}J^{T}\begin{bmatrix}f_{1}\\ f_{2}\\ f_{3}\end{bmatrix} \tag{15}\]  
Here, \(J^{T}J\) is the Hessian matrix. This is an iterative algorithm and a parameter will be given to stop the iteration  
\[t=\sqrt{\left(a_{xk+1}-a_{xk}\right)^{2}+\left(a_{zk+1}-a_{zk}\right)^{2}} \tag{16}\]  
If \(t\) is below the threshold we set, the iteration stops and the orientation refining is finished.
# Biological Decontamination by Microplasma
## 2 Materials and Methods  
### Experimental Setup  
Figure 1a shows the schematic view of the experimental setup developed for the bacteria treatment using microplasma. The microplasma electrode is placed on a support that has electrical connections and also provides the gas flow to the electrode through a gas inlet, as shown in Figure 1b. During experiments, the electrode was placed at a distance of 2 mm above the surface of the agar, containing bacteria.  
A pump (Mitsumi, Tokyo, Japan, R14-1604) was used to disperse the air. It has a miniature size and it can be powered with voltages in range of 4.5-12 V, thus making it suitable for battery use and furthermore for integration in a portable system. Room air with a relative humidity RH at 39-41% was used, and the air-flow rate was set at 0.6 L/min. An ozone monitor (NORM, Changzhou, China, ADKS-1 O\({}_{3}\) Ozon) was placed 2 mm under the microplasma electrode to measure the ozone concentration. The discharge voltage was increased from \(-1.2\) kV to \(-1.7\) kV with 0.1 kV incremental steps, and the ozone concentration was measured at each voltage value and at an air-flow rate of 0.6 L/min.  
A Marx Generator (laboratory manufactured) with MOSFET switches was developed to be used as the pulse power supply to energize the microplasma electrodes. The output of Marx Generator is a negative pulse with voltage peak values up to \(-3\) kV. In our experiments, the discharge voltages were set at \(-1.5\) kV and \(-1.7\) kV. The frequency was 500 Hz. A pulse wave was chosen to energize the electrodes, since it has the advantage of producing less ozone than an AC excitation [16]; thus, it is less harmful to humans. The energy efficiency is higher than the sinusoidal microplasma for pulsed microplasma because of a longer microplasma idle time period, resulting in a lower power consumption [34]. The discharge voltage and corresponding discharge current were measured using a high voltage probe (Tektronix, Beaverton, OR, USA, Tek P5100), and a current probe (Tektronix, A622), respectively. The probes were connected to a digital oscilloscope (Tektronix, MSO 4104).
# Converging Telco-Grade Solutions 5G and beyond to Support Production in Industry 4.0
## 5 The Telco Cloud Enabled 5G Applications
### Telco Cloud Industrial Applications
#### 5.5.5 Localization and Tracking  
The collaboration of the Internet of Things and Telco Cloud is a critical factor in localization and traceability, as it is possible to achieve very high accuracy in localization and real-time tracking of targets and their identifications [208]. Localization is important in knowing the location of robots or industrial equipment in the event of natural disasters or theft, as well as in smart driving operations in self-driving mechanisms [209].
## 2 Evaluation Methodology
### DTMF Codes  
Most cell phones function as DTMF (Dual-Tone Multi-Frequency) generators when producing sounds from the alphanumeric keyboard. When a key is pressed, a connection is established that simultaneously generates two tones--specifically, \"row\" and \"column\" tones. These two tones identify which key has been pressed on the cell phone keyboard. When the keyboard is active, the telephony service provider can identify the dialed number based on these tones, thereby enabling the call. DTMFs can also be used to send combinations of numbers and characters, including A, B, C, D, #, and * through voice phone calls [51; 52].  
The DTMF technique, (standardized by the International Telecommunication Union ITU) [53], employs eight different frequency signals transmitted in pairs to represent 16 distinct figures and symbols, as shown in Table 6. It consists of two mutually exclusive corresponding frequency groups: the low frequency group (those less than 1 kHz) and the high frequency group (those more than 1 kHz). Four low frequency tones (697, 770, 852, and 941 Hz) are assigned to the rows, while four high frequency tones (1209, 1336, 1477, and 1633 Hz) are assigned to the columns. Consequently, each unique frequency pair corresponding to a column and a row forms a distinct DTMF tone [54].
# Resource Management in Converged Optical and Millimeter Wave Radio Networks: A Review
## 5 Delay Minimization and Resource Allocation Algorithms  
Minimizing the delay caused by congestion or increased traffic has been a common management target throughout the evolution of mobile networks. However, the emphasis on latency increased with the challenging ultra-reliable low latency communication (URLLC) requirements [48], due to the \"1 ms challenge\" of the delay sensitive applications, such as tactile internet [66] and augmented/virtual reality. In Section 5.1, we summarize the optimization methods used to reach these delay targets in converged optical and mmWave radio networks. The list of the selected algorithms with their advantages and limitations can be found in Table 4. The potential research directions for delay minimization in converged optical and radio networks with AI-ML solutions are discussed in Section 5.2.
# Urban Quarry Ground Vibration Forecasting: A Matrix Factorization Approach
## 6 Experiments and Results  
### Determination of Normalized Value Range  
To determine the normalized value range for each parameter, training was performed using an ANN. The data used were measurements from 100 sites collected at the Mikurahana Quarry in Akita Prefecture. These data were divided into training data, validation data, and test data at a ratio of 80:10:10, respectively, and learning was conducted using MATLAB 2021. During the training of the ANN, the number of hidden neurons that make up the hidden layer of the neural network was varied as 2, 4, 6, 8, 10, and 12 to compare the prediction accuracy of each model. The mean square error (MSE) and the coefficient of determination (\(R^{2}\)) were used as evaluation metrics. A prediction model with an MSE close to 0 and \(R^{2}\) close to 1 indicates a high prediction accuracy for PPV. As a result of the training, the predictive model with six hidden neurons showed the highest accuracy. Table 3 shows the learning results of the ANN. As shown in Table 3, the predictive model with six hidden neurons had an MSE of 0.0245 and \(R^{2}\) of 0.8740, showing higher accuracy compared to other models. The sum of the weights (absolute values) of each parameter extracted from the predictive model with six hidden neurons is shown in Figure 9. As a result, it became clear that the weights of \"MIC\", \"Distance\", and \"Longitude of the measurement point\" were relatively large. Furthermore, although the weight for \"Direction\" was not as large compared to the previous three parameters, it showed a larger weight than the other parameters. Based on these results, it was judged that these four parameters have a large influence on PPV. Therefore, the normalization value ranges for \"MIC\", \"Distance\", and \"Longitude of the measurement point\", which had the largest total weight value, were set to [1; 11], \"Direction\" to [1; 6], and all other parameters to [1; 2].
## 2 Materials and Methods
### Atlas-Based and DL Auto-Contouring  
The 45 CT datasets were exported to the RaySearch Laboratories AB RayStation 12A treatment planning system (with Intel Xeon W-10885M central processing unit (Santa Clara, CA, USA), 64 GB random access memory and NVIDIA Quadro RTX 5000 16 GB graphics card (Santa Clara, CA, USA)) in the Pamela Youde Netherschele Eastern Hospital. Its ANACONDA and RSL Head and Neck CT 2.0.0.47 were used for the atlas-based and DL auto-contouring, respectively. ANACONDA used the hybrid approach for auto-contouring. In the beginning of a new image set being contoured, multiple best-matching atlases from a pool of 40 nasopharyngeal cancer datasets within the hospital database were determined by rigid image registration. Contours from these best-matching atlases were subsequently mapped to the new image set using deformable registration and these contours eventually merged together as the segmentation outcome via a fusion algorithm [10]. The DL auto-contouring involved a pre-trained three-dimensional (3D) U-net CNN model to classify each voxel of the datasets into either specific tissue (i.e., an OAR) or non-specific one for generating labelled (segmented) datasets as the outcome. Only its pre-trained DL auto-contouring model without any finetuning was used for the DL auto-contouring. Details of  
Figure 1: Study design overview. ANACONDA-ANAtomically Constrained Deformation Algorithm; CT-computed tomography; DL-deep learning; DSC-Dice similarity coefficient; H&N-head and neck; HD-Hausdorff distance; HD95-HD 95 th-percentile; OARs-organs at risk.  
its 3D U-net CNN architecture, and model training, validation and testing were available elsewhere [29].
# Optimize Rotating Wind Energy Rotor Blades Using the Adjoint Approach
## 2 Optimization Method  
To design a blade using bend-twist-coupling as a passive control mechanism, a favorable deformed blade shape that ensures the optimal load reductions has to be determined. The deformed blade shape can then be integrated into the structural blade design where layer lay-up or geometrical sweep are options to achieve the favorable deformations. To find those favorable deformations, an optimization tool for the aerodynamic shape of the rotor blades based on the continuous adjoint approach is implemented in OpenFOAM. The gradients are then projected to central nodes, adopting geometry updates like in Fluid-Structure Interaction (FSI) based on a beam. In this work, the optimization is directly coupled with the CFD toolbox, allowing direct communication and data usage. This tool is used to find deformations that reduce loads under certain load conditions. Our approach is that the general geometrical shape of the 2D profiles is constant, and only elastic deformations are allowed, such as changing the twist angle along the blade. In order to test the optimization tool and its performance, the optimizations are conducted for the same blade under the same inflow conditions but with a variable movable part of the blade and different aimed values. Therefore, between 20% and 50% of the outer blade are allowed to be changed with respect to its twist distribution. This investigation allows an assessment of the effect of complex lay-up within large to short parts of the blades, respectively. The optimization in this work is based on the reduction of the thrust that effects the blade root bending, tower bending and other forces. Although no constraints were used, it is possible to include limiting ranges for other forces in further applications of this tool.  
In addition, the developed optimization tool allows other definitions of design parameters and cost functions.  
Details regarding the adjoint approach are discussed next.
## 4 Detector Prototype and Readout Electronics
### Electronics  
The 8 \(\times\) 8 SiPM array described above provided 64 fast output signals. After the encapsulation of the complete array, there were a total of 256 SiPM output signals. A large number of high-speed electronic channels were needed for the individual readout of SiPM signals.  
For the readout electronics matched with the SiPM array, a capacitive multiplexing network was adopted to reduce the readout channel number from 256 (individual readout scheme) to 4 [26; 27].  
The final output four-channel signals were amplified, shaped, and transmitted to the data acquisition system. Figure 7 shows a schematic diagram of the self-design capacitive multiplexing network. In this network, each anode of the SiPMs was connected to one or more weighted capacitors depending on the position. The anode signal from the SiPMs was split into one or more signals depending on the number of weighted capacitors that were connected to each anode.  
Figure 6: SiPM arrays. (**a**) The SiPM array of the MicroFJ-30035-TSV model manufactured by SensL Company. (**b**) The working principle of one microcell of the SiPM [25].  
Figure 7: A schema of the capacitive multiplexed network [28].  
The capacitive multiplexed networks encoded the 256 SiPM output signals into four position signals (P, Q, R, and S). The energy E of the incident fast neutron and its two-dimensional position (X, Y) was determined by the following equations  
\[\begin{split}\mathrm{E}=\mathrm{P}+\mathrm{Q}+\mathrm{R}+\mathrm{S} \\ \mathrm{X}=\frac{(\mathrm{R}+\mathrm{S})-(\mathrm{P}+\mathrm{Q})}{ \mathrm{P}+\mathrm{Q}+\mathrm{R}+\mathrm{S}}\\ \mathrm{Y}=\frac{(\mathrm{P}+\mathrm{S})-(\mathrm{Q}+\mathrm{R})}{ \mathrm{P}+\mathrm{Q}+\mathrm{R}+\mathrm{S}}\end{split} \tag{1}\]