esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Data and Preprocessing
### Feature Selection  
It was previously shown that feature selection (FS) can improve prediction performance in some areas [21]. We applied feature selection to find the optimal subset of features for better classification between healthy and pathological samples. We selected simple filter FS to get \(k\) best features. Filter FS is a computationally effective approach that provides results competitive with more complex methods. Mutual information for discrete target variables was used to estimate the score of features. This function is based on entropy estimation from \(k\)-nearest neighbor distances. Mutual information between two random variables is a non-negative value, which measures the dependency between variables. If the variable is independent, it is zero, and the higher the value, the greater the dependence.
## 5 Conclusions  
_G. bontenense_ infection can be detected at an early stage even when no physical signs are present by using SVM with the optimal number of wavelengths. For single-band reflectance, models developed using a linear kernel demonstrated the best performance among other kernels since it obtained a low CV score which has thus shown a high consistency in the results and excellent scores in accuracy, sensitivity, and specificity with the percentage value above 90%, respectively. Linear SVM, which was developed usingsingle-band reflectance frequencies taken from 934 nm, was identified as the best model for detection since it demonstrated a high score in accuracy (94.80%), sensitivity (97.60%), specificity (92.50%), and AUC (0.95). The linear SVM also performing well when using SR and NDVI as datasets among the other ML models. However, both models only obtained 57.30% accuracy. This research has also demonstrated that in the early stage of _G. boninense_ infection, where the exterior information is invisible, selection of a single-band reflection from the NIR regions should be used as a dataset due to its capability to obtain information from the interior tissue. It is also shown that a simple linear separation between two classes is more suitable to classify between infected and healthy seedlings. The findings of this research could provide a huge contribution to the oil palm industry since it could help prevent the spread of the disease by successfully detecting the asymptomatic infection which cannot be done by manual inspection through human vision. Better management could produce higher yield and improve the sustainability of palm oil production which would thus protect the environment and communities in palm oil-producing regions.  
Conceptualization, S.K.-B. and M.S.S.; Methodology, S.K.-B., M.S.S. and A.N.N.A.; Software, M.S.S. and S.K.-B.; Validation, M.S.S. and S.K.-B.; Formal analysis, M.S.S. and S.K.-B.; Investigation, M.S.S. and S.K.-B.; Resources, S.K.-B. and M.J.; Data curation, M.S.S. and A.N.N.A.; Writing--original draft preparation, M.S.S.; Writing--review and editing, S.K.-B.; Visualization, M.S.S., A.N.N.A. and M.J.; Supervision, S.K.-B.; Project administration, S.K.-B.; Funding acquisition, S.K.-B. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Malaysian Ministry of Higher Education (MOHE), Universiti Putra Malaysia (UPM) under grant number FRGS/1/2018/TK04/UPM/02/4 and LRGS-NANOMITE/5526305.  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to restrictions.  
The authors declare no conflict of interest.
# 6G Use Cases and Scenarios: A Comparison Analysis Between ITU and Other Initiatives
## 6 Next-Generation Mobile Network (NGMN)
\begin{table}
\begin{tabular}{c c} \hline \hline
**NGMN Use Cases** & **Examples** \\ \hline Enhanced Human Communication & XR Immersive Holographic Telepresence Communication \\ Multimodal Communication for Teleoperation & Intelligent Interaction Sharing of Sensation, Skills Thoughts \\ \hline Enhanced Machine Communication & Robot Network Fabric \\ Interacting Cobots \\ \hline Enhanced Machine Communication & Interacting Cobots \\ \hline \multirow{5}{*}{Enabling Services} & 3D Hyper-Accurate Positioning, Localisation, and Tracking \\  & Interactive Mapping \\ \cline{1-1}  & Digital Healthcare \\ \cline{1-1}  & Automatic Detection, Recognition and Inspection \\ \cline{1-1}  & Smart Industry \\ \cline{1-1}  & Trusted Composition of Services \\ \hline \multirow{5}{*}{Network Evolution} & Native Trusted AI (AlaaS) \\ \cline{1-1}  & Coverage Expansion \\ \cline{1-1}  & Autonomous System for Energy Efficiency \\ \hline \hline \end{tabular}
\end{table} 
Table 5: List of use cases proposed by NGMN [20; 21]. healthcare, automatic detection, recognition and inspection, smart industry, and the trusted composition of services. The first example is on the high-accuracy 3D localization and advanced tracking capabilities, particularly in indoor environments (e.g., smart factories, warehouses, hospitals), while the second one is on the interactive maps created starting from the representations of physical assets to enable efficient large-scale system management and real-time environmental representation. Digital healthcare is another important use case to be considered in the case of the advanced 24/7 monitoring of vital parameters through wearable devices. The automatic detection, recognition, and inspection analysis of the usage of 6G features in different applications, including security screening at airports, smart hospitals, and intelligent factories. The smart industry objectives focus on reducing carbon footprints and resource circularity and reducing daily commutes due to COVID-19 concerns. Finally, the trusted composition of the service use case is centered on the network convergence for enhancing human and machine communication and enabling a trusted composition of services for dynamic and complex use cases.
## 2 Proposed Method and Environment
### Proposed Method
#### 2.2.1. STFT (Short-Time Fourier Transform)  
With respect to the method for converting time-amplitude data to 2D images, spectrograms were used after performing discrete STFT. Discrete STFT is a method of partitioning continuous signals over a long period into shorter segments at short time intervals and applying a Fourier transform to each signal segment. This technique allows researchers to observe how the vibrations of signals change with time. These changes in vibrations can be expressed as Equation (1) [44; 45]:  
\[X(k,n)=\sum_{m=0}^{L-1}w[m]x[m+nH]exp(-2\pi k/N)m \tag{1}\]  
\(w[m]\) was assumed to be a non-zero window function in the interval \(m=0,1,\cdots,L-1\), and \(L\) is the window length, and a smaller signal than the signal \(x[m]\). In this experiment, the Han window was applied as the window function [45]. \(w[m]x[n+nH]\) is a non-zero signal in \(m=0,1,\ldots,L-1\). The signal \(x[m]\) is a form that undergoes \(N\) point DFT (Discrete Fourier Transform) according to the hop size of \(H\)(=512). The hop size H is specified in samples and determines the step size moving through the window in the overall signal [45]. Therefore, FFT was calculated according to the size of \(m\). Because a signal generated through this process constitutes a different spectrum with time, it cannot be represented as a spectrum. Therefore, it was represented by taking \(|X(k,n)|\) and applying a color map (spectrogram), as shown in Figure 6.  
Figure 4: Process comparison between the traditional machine learning method (**Up**) and the proposed deep learning method (**Down**). The traditional machine learning method consists of a total of 5 steps, and the Deep Neural Network (DNN) method consists of 3 steps: Feature Engineering, Extraction, and Classification at once.
# A Thermal Discomfort Index for Demand Response Control in Residential Water Heaters
## 2 Demand Management and Direct Control of Electric Water Heaters
### Demand Management and Direct Load Control  
The traditional model of an electrical system supplies electricity to end consumers through a unidirectional flow of energy, which is delivered by centrally controlled generators. However, in the last thirty years, power grids all over the world became decentralized systems, thus resulting in distributed energy resources that have fostered new business models and specific transformations of energy markets. The concept of energy demand management emerged within these new business models.  
Energy demand management involves a set of techniques oriented to modify the energy demand of consumers of an electric grid to fulfill specific goals [8]. The subset of techniques oriented to reduce the energy demand of consumers in the short term are known as demand response methods. A specific technique within demand response methods is _direct load control_. The main idea of direct load control is to provide the energy company the permission to control (i.e., switch off) the devices of end-users, which is usually obtained via specific agreements that grant users a monetary incentive. Load control is considered an effective technique to achieve immediate power reduction in a very short time and is very useful to deal with peak reduction needs [9] in order to obtain a more stable grid operation. Another situation where load control methods are very useful is to provide frequency regulation services [10], with the main goal of maintaining the system frequency close to the utility frequency (i.e., the nominal oscillation frequency of alternating current, 60 Hz in the Americas and Asia, and 50 Hz in other sites), preventing deviations that affect generators and also make the grid unstable. This article addresses some aspects related to the direct load control of electric water heaters, mainly focusing on the impact of using direct load control in the thermal comfort of end-users.
## 3 Performance Analysis
### Characteristics of the Hotspot  
The hotspot implemented within the parish of Checa possesses the following attributes:  
* Brand: Huawei.
* Model: AirEngine 6760R-6760R-51 51E.
* Transmission power: 1000 mW.
* Frequency: 2.4 GHz.  
The hotspot radiation patterns can be seen in Figure 3. Here, we observe that the horizontal radiation patterns of the hotspot antennas show good beamwidth for 2.4 GHz and 5 GHz. However, in terms of directivity, the vertical radiation patterns indicate that the antennas are more directive at 2.4 GHz than at 5 GHz. Therefore, we can identify and deduce that the communication link could be more efficient at 2.4 GHz than at 5 GHz.
## 2 Materials and Methods
### Characterization of PES and sPES Membranes
#### 2.3.2 Ion-Exchange Capacity (IEC) Measurements  
Ion-exchange capacity (_IEC_) quantifies a material's ability to exchange ions embedded within its structure. For this measurement, the membranes were first immersed in 1 M HCl for 24 h, followed by rinsing with deionized water to eliminate surface acid residues. They were then transferred to a 2 M NaCl solution for another 24 h. Afterward, the solution was titrated with NaOH, using phenolphthalein as an indicator. The _IEC_, expressed in milliequivalents per gram (meq/g), was determined using the formula provided in Equation (2) [24; 25]:  
\[IEC=\frac{C_{\mathrm{NaOH}}\times V_{\mathrm{NaOH}}}{W_{\mathrm{Dry}}} \tag{2}\]  
where \(C_{\mathrm{NaOH}}\), \(V_{\mathrm{NaOH}}\), and \(W_{\mathrm{Dry}}\) denote the concentration (eq/L) of the standard NaOH solution, the volume (mL) of NaOH solution utilized for neutralization, and the weight (g) of the dry ion exchange membranes (IEMs), respectively.
## 2 Materials and Methods
### Calculation of the Percentage of Extracted Ions  
The procedure for calculating the real percentage of extracted ions was based on comparing the total amount (abundance) of ions in the dried tea, as measured on the spectrometer, and comparing it with the measured amounts of extracted ions after the traditional and ultrasound-assisted brewing procedures. The calculations made according to the formula: ((milligrams of ions after extraction)/(gram of tea leaves using for extraction))/((milligrams of ions after mineralization)/(gram of tea leaves using for mineralization)).  
\begin{table}
\begin{tabular}{l l l l} \hline
**Element** & **Measurement Line, nm** & **Recovery According to CRM, \%** & **Recovery According to Known Addition Method, \%** \\ \hline Na & 589.5 & 98 & 101 \\ Ca & 317.9 & 101 & 99 \\ Cu & 324.7 & 98 & 99 \\ K & 766.4 & 102 & 98 \\ Mg & 279.5 & 102 & 101 \\ P & 177.4 & 101 & 99 \\ S & 180.7 & 97 & 100 \\ Zn & 213.8 & 99 & 97 \\ Fe & 259.9 & 98 & 99 \\ Mn & 257.6 & 97 & 100 \\ Mo & 202.0 & 101 & 101 \\ Sr & 407.7 & 100 & 99 \\ As & 189.0 & 99 & 99 \\ Cd & 228.8 & 98 & 97 \\ Pb & 220.3 & 98 & 101 \\ \hline \end{tabular}
\end{table}
Table 2: The recovery obtained for specific elements.  
Each time, the calculations were reduced to the standardized amounts of the tea dried weight both during the mineralization process and during the extraction process.
## 3 Measurement Results of Proposed Wheatstone Bridge Readout IC
Referring to Figure 11, the conversion error boosts as the input resistance increases, which is analyzed, as the output voltage is too near to the supply voltage and the linearity of the operational amplifier is diminished. To solve this issue, a slight reduction in the gain of the Wheatstone bridge readout IC can be applied to adjust the output voltage so that it is not close to the supply voltage. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{2}{c}{**Register Settings**} & \multicolumn{2}{c}{**Gain**} \\ \hline
1 st stage & 2 nd stage & Calculated gain & Measured gain \\ \hline (MSB) L, L, L, H, (LSB) & L, L, L, H, H & 74.07 & 72.29 \\ \hline L, L, L, H, H & L, H, H, H & 62.02 & 61.17 \\ \hline L, L, L, H, H & H, L, H, H & 48.50 & 47.90 \\ \hline H, H, H, H, H, H & H, H, H, H & 38.52 & 37.53 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of the gain calculated by Equation (3) and the measured gain. Figure 10: Output of Wheatstone bridge IC based on input resistance: (**a**) Voltage output; (**b**) Digital code output. Figure 12 shows the measured input referred noise of the Wheatstone bridge readout IC. Chopping techniques are implemented twice in the LFP of the chopper-stabilized multipath operational amplifiers and fully-differential amplifiers. The measured input referred noise is 82.00 nV/\(\sqrt{\text{Hz}}\) at 1 Hz and 34.20 nV/\(\sqrt{\text{Hz}}\) at 10 Hz, and the input noise voltage density is 24.88 nV/\(\sqrt{\text{Hz}}\) over the 0.5-50 Hz range. In Table 3, the performance of the Wheatstone bridge readout IC is summarized and compared with several state-of-the-art three-operational amplifier IAs. The proposed Wheatstone bridge readout IC has the advantage of high current consumption, but low input referred noise compared with other studies. If the input referred noise is large, the noise can be amplified by the three-operational amplifier IA, which affects the precision of the output signal. Consequently, the larger the input referred noise, the greater the noise is on the output stage, which can be fatal to the linearity of the Wheatstone bridge readout IC. Generally, however, there is a trade-off between the method of reducing input referred noise and the method of decreasing current. Additionally, the chopping technique is implemented to reduce input referred noise. Compared with other studies, RRL is added to eliminate the ripple caused by the chopping technique; this is designed to adjust the gain of the three-operational amplifier IA. Figure 11: Conversion errors of the measured output voltage based on input resistance. Figure 12: Measured input referred noise of the Wheatstone bridge readout IC.
# Research on Optimization Design of Tunnel Blasting Scheme Adjacent to Buildings
## 5 Practical Application Effect on Construction Sites  
The interval tunnel from Jialingjiang Road Station to Xiangjiang Road Station of Qingdao Metro Line 13 was constructed via a combined mechanical excavation and blasting scheme, and the vibration velocity of the adjacent buildings was controlled within 0.6 cm/s. The photos of the construction sites are shown in Figure 14. Taking into account the blasting safety regulations and safety regulations on tunnel vibration velocity between Jialingjiang Road Station and Xiangjiang Road Station, the safety regulations of the project are met. With the use of the combined mechanical excavation and blasting scheme, the construction period reduced from 13 months to 10 months, shortening the construction period and reducing cost, which illustrates the feasibility of mechanical excavation combined with the blasting construction method.  
Figure 14: Picture of a construction site: (**a**) mechanical construction of upper section; (**b**) blasting construction of lower section.  
Figure 13: Peripheral hole charging structure.  
The vibration velocity monitoring of the project was completed using a TC-4850 blasting vibration meter produced by Chengdu Zhongke Measurement and Control Company (Chengdu, China). A geophone was arranged in the protected structures near the side of the blast area, and mixed lime powder was used for a rigid connection with the building foundation. The extraction of monitoring data and numerical modeling data at measurement point 1A are shown in Figure 15 together with a comparison schematic. From this figure, it can be visualized that there is not much difference among the peak vibration velocity during monitoring, the peak vibration velocity during numerical simulation and the basic variation trend, which also shows the reliability of the numerical simulation results.
# Calibration of an Adaptive Genetic Algorithm for Modeling Opinion Diffusion
## 2 Materials and Methods
### Opinion Diffusion Modeling
#### 2.1.2 Transformations  
The purpose of our method is to fit the above model, producing estimates for the parameters in the weight matrix \(W\), using observed opinions across \(T\) time steps on a network of \(N\) agents. Ideally, we would be able to observe the continuous opinions, on the interval \([0,1]\), of all agents across \(T\) time steps (\(X(0),X(1),...,X(T-1)\)). In practice, opinions are typically measured using a Likert or other ordinal scale in behavioral and social science research. As such, we assume the continuous opinions are shared with network contacts without error according to the DeGroot model, but researchers are only able to measure these opinions on an \(n\)-point ordinal scale (\(Y(0),Y(1),...,Y(T-1)\)). To be consistent with the model, we assume these ordinal data possess interval properties. A common approach when using ordinal data, this is necessary to perform any mathematical operations and is implicit in the use of a composite scale. We convert between ordinal and continuous opinions using the following process:  
Forward Transformation  
1. Begin with data on an \(n\)-point ordinal scale, converting to a \(1\) to \(n\) scale if necessary.
2. Divide the interval \([0,1]\) into \(n\) sub-intervals of equal width.
3. An opinion of \(y\) on the ordinal scale takes on the middle value, \(x\), in the \(y^{th}\) sub-interval on the continuous scale.  
Back Transformation  
1. Begin with data on a continuous \([0,1]\) interval to be converted to an \(n\)-point ordinal scale.
2. Multiply the continuous opinion \(x\) by \(n\).
3. Round the multiplied continuous opinion up to an integer (ceiling function) to produce an opinion on the ordinal scale. (This final step does not work for the edge case where \(x=0\), so any such values are automatically converted to an ordinal value of \(1\).)  
This process is also presented graphically in Figure 1 using a 5-point ordinal scale. For example, an ordinal opinion of 4 is converted to a continuous opinion of 0.7, the center of the 4 th sub-interval or \(bin\) from 0.6 to 0.8, and any continuous opinion on that sub-interval are converted back to an ordinal opinion of 4.  
Figure 1: Transformation procedure for a 5-point ordinal scale.
# A Fabric Defect Segmentation Model Based on Improved Swin-Unet with Gabor Filter
## 3 Proposed Method
### Loss Function  
Due to the extremely uneven distribution of positive and negative samples during the training process, we use the Dice loss [38] to increase the loss weight of certain mis-classifications, increasing the loss when the network predicts positive samples as negative samples. The Dice Loss (\(L_{dice}\)) can be written as  
\[L_{dice}=1-\frac{2\sum\limits_{i=1}^{N}p_{i}g_{i}}{\sum\limits_{i=1}^{N}p_{i}+ \sum\limits_{i=1}^{N}g_{i}+\epsilon} \tag{2}\]  
where \(N\) represents the total number of pixels in the image, \(g_{i}\) belongs to \(\{0,1\}\), specifying the ground truth label of pixel \(i\), \(p_{i}\in[0,1]\) denotes the predicted output probability, and \(\epsilon\) is a term set to prevent the denominator from being 0.  
The Dice loss value decreases when there is more overlap between the actual distribution and the network's predicted distribution; thus, it is often used to push the model to learn in a direction where the predicted distribution is more similar to the real distribution. Nonetheless, the pure Dice loss is not a convex function, and may stay at the local optimal solution instead of the global optimal solution.  
When the data distribution is unbalanced, using the focal loss [39] can cause the transformer model to quickly converge, for which it is much faster than traditional Binary Cross-Entropy (BCE) loss. The focal loss is defined as  
\[L_{focal}(p_{t})=-(1-p_{t})^{\gamma}\text{log}(p_{t}) \tag{3}\]  
where \(p_{t}\) is defined as  
\[p_{t}=\begin{cases}p&\text{if }y=1\\ 1-p&\text{otherwise}\end{cases} \tag{4}\]  
where \(y\in\{\pm 1\}\) specifies the ground truth class and \(p\in[0,1]\) is the model's estimated probability for the class with label \(y=1\).  
Compared with the cross-entropy loss, the focal loss has an additional modulating factor \((1-p_{t})^{\gamma}\), with a tunable focusing parameter \(\gamma\geq 0\). For a sample with accurate classification (\(p_{t}\to 1\)), the modulating factor approaches 0. For samples with inaccurate classification (\(1-p_{t}\to 1\)), the modifying factor comes to 1. That is, compared with the cross-entropy loss, the focal loss does not change for samples with inaccurate classification, while decreasing for samples with accurate classification. Overall, it is equivalent to increasing the weight of inaccurate classification samples in the loss function.  
Therefore, we introduce a combination of the focal loss and Dice loss to fully utilize the advantage of focal loss in focusing on difficult samples while increasing the loss of difficult samples. The loss function used in this article is formulated as follows:  
\[FD\ Loss=w_{f}*L_{focal}+(1-w_{f})*L_{dice} \tag{5}\]  
where \(w_{f}\) is used to adjust the proportion between the focal loss and Dice loss. We set \(w_{f}\) to 0.01 to avoid the problem of the focal loss value being too large relative to Dice loss.