esa-sceva/satcom-chunk-collection huggingface dataset:
# WiFi Positioning in 3 GPP Indoor Office with Modified Particle Swarm Optimization
## 4 Proposed Indoor Positioning  
Figure 2 shows the block diagram of the proposed scheme in this paper. The proposed scheme sequentially applies the fingerprinting scheme, the WFM algorithm, the initial search region limitation, and the PSO. First, the fingerprinting scheme is performed in an offline step, and the RSSI value for each AP is measured at a SP. A fingerprinting database is built based on the measured RSSI values. In the online step, the RSSI value of the actual user is measured from the AP. The measured RSSI value of the user performs a WFM algorithm with the value of the fingerprinting database. When the WFM algorithm is applied, the closest SP can be derived based on the degree of correlation between the user and the SP [26; 27].  
Figure 1: Indoor environment suggested by 3 GPP.  
Figure 2: Proposed scheme with modified particle swarm optimization.  
The core idea of the proposed scheme is to limit the initial search region of the PSO to the closest SPs derived above. When the initial search region is limited, the probability that the user exists within the limited region can be increased. It is possible to increase the probability that intelligent particles converge to the global optimum (i.e., the user's position) in the PSO process and shorten the convergence time for achieving the target positioning accuracy.  
The PSO, which is then performed in a limited region, is an intelligent evolutionary computation algorithm that uses intelligent particles to find the optimal location of the user. The PSO has many advantages, such as high location accuracy, few parameters, and simple implementation [21,28]. During the search, all particles within the cluster share their optimal position. Each particle determines its own direction of movement based on shared information. Therefore, all particles must be periodically updated not only to the optimal position of the individual but also to the optimal position of the cluster. If the information of each particle is not shared or updated, all particles converge to the wrong position, which causes a serious position error. Each scheme is analyzed in detail through the following subsections.
## 2 Research Methodology
### Transmissibility as Damage Sensitive Feature  
The local transmissibility between the two coordinates \(i\) and \(j\), with reference to input location \(k\), can be defined in the frequency domain as the ratio of two responses \(X_{i}\) and \(X_{j}\), directly as follows:  
\[T_{ij}^{k}(\omega)=\frac{X_{i}(\omega)}{X_{j}(\omega)} \tag{1}\]  
Due to its inherent relationship with structural properties, changes in these properties are reflected in the TF, making it a salient damage-sensitive feature for monitoring structural health [43]. Furthermore, because it characterizes the vibration response across small sections of the structure, the TF is expected to be particularly sensitive to localized damage. The local TF, obtained using Equation (1), eliminates the need for direct measurement of the input; however, it requires impact testing across multiple locations to generate a comprehensive set of TFs.  
Among possible loading (unmeasured) conditions that can be implemented for damage detection purposes, a potential alternative with practical significance is to use a single input moved to different points on the structure. For each excitation location, a transmissibility matrix of size \(N\times N\), elements of which are functions of frequency can be formed for all combinations of outputs between location pairs. The selection of appropriate ones as damage detection features is essential not only for reducing the dimensionality of the original dataset but for eliminating the unrelated variables to ensure the success of the proposed methodology. To 'close-in' the damaged region, typically the two successive pairs of coordinates are selected and the transmissibility corresponding to the two adjacent coordinates along the structure are interrogated. This corresponds to the first-upper diagonal of the matrix with entries of \(T_{12},\ T_{23},\ldots T_{k,k+1}\). Furthermore, to limit the dependence of the TF outside the domain enclosed by the output coordinates, the input location is also arranged such that it is collocated with one of the output coordinates. Hence, with these two criteria imposed on the selection, the number of TFs retained as damage detection features reduces down to \(N-1\) to for an N-DOF system. With input location indicated as the superscript, the vector of selected features for damage detection as a function of frequency are defined as \(T_{12}^{2},T_{23}^{3},\ \ldots T_{k,k+1}^{k+1},\ldots\ T_{N-1,N}^{N}\).
# IAQ Prediction in Apartments Using Machine Learning Techniques and Sensor Data
## 6 Conclusions  
The results of this study allow for several conclusions to be drawn.  
In apartments, there exists a co-dependency between indoor air parameters in the kitchen and living room. The individual air parameters in the two locations display similar patterns of temporal variation.  
The relationship may be exploited by MLTs and utilized for IAQ prediction in the living room. As multiple indoor air parameters shall be determined to attain a comprehensive representation of indoor conditions, the multiple input-multiple output approach is favored to limit the number of applied models.  
For the cross-validation procedure, where training and test apartments are distinct, MLTs outperform the naive approach. Hence, the relationship between the living room and kitchen IAQ has a component that is common for multiple apartments despite their versatility and allows for prediction model transfer between apartments.  
Better tuning of MLT models may be attained by applying apartment categorization. In particular, open-kitchen apartments could be the category of choice for the practical application of our approach as the most accurate prediction was demonstrated for such apartments.  
Based on our study, linear MLTs, such as MLR and MLP with linear transfer functions, performed best. They were featured by the highest coefficients of determination, mostly exceeding an R\({}^{2}\) of 0.8, and they offered the lowest prediction errors compared to nonlinear models, such as DT, RFR, KNN, and MLP with nonlinear transfer functions.  
The prediction of distinct indoor air parameters was not equally effective. Based on the lowest percentage error, reasonable predictions were attained for indoor air temperature (MAPE = 1.57%), relative humidity (MAPE = 2.97%RH), and TVOC content (MAPE = 0.41%), while CO\({}_{2}\) prediction was loaded with a high error (MAPE = 20.83%).  
This work successfully exploits the relationship between IAQ in the kitchen and living room, encouraging future research endeavors aimed at refining predictive models and improving IAQ management strategies in apartments.  
Conceptualization, M.M., A.A. and A.S.; methodology, M.M., A.A. and A.S.; software, A.A. and M.M.; validation, M.M., A.A. and A.S.; formal analysis, M.M.; investigation, A.A.; resources, A.S.; data curation, A.A.; writing--original draft preparation, A.S., M.M. and A.A.; writing--review and editing, M.M., A.A. and A.S.; visualization, M.M. and A.A.; supervision, A.S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data availability restrictionts apply due to privacy issues.  
The authors declare no conflicts of interest.
## 3 Artificial Intelligence Approach
### Supervised ML
#### 3.1.6 Support Vector Machine (SVM)  
The Support Vector Machine is a powerful supervised learning algorithm with broad applications in the evaluation of food quality, mainly in combination with HSI. It is particularly capable of dealing with high-dimensional data, which comprehensively makes SVM suited for a wide variety of food attribute classification and contaminant detection cases. SVM has extensive potential for the capture of very subtle spectral variations reflecting changes in food composition, freshness, or contamination. Its strength lies in handling complex datasets where similar visual features mask significant spectral differences [16].  
With the ability to capture complicated nonlinear relationships between spectral data and chemical compositions, a widely conducted model in food analysis includes the Support Vector Machine-based model, namely Support Vector Machine Regression--SVMR. For example, it has been used to predict protein content in chickpeas [122] and to detect Robusta adulteration in Arabica coffee [123]. In addition, deep learning-integrated SVM models further improved their performance, such as in convolutional neural networks. The studies that combined CNNs with SVMR showed success regarding adulterants in processed meat. Thus, this hybrid approach represents a reliable and non-destructive method of food safety monitoring. Although SVMR has some shortcomings, for example, its careful parameter tuning and high computational burden, this technique possesses great generalization abilities; therefore, it is very useful in food science [124].
## 2 Literature Review
### Summary  
1. Existing signal timing methods often optimize for one or several objectives and construct optimization functions, primarily focusing on delay as the target, with relatively few studies considering both delay and fairness.
2. Information entropy is widely used in various fields, but there are relatively few studies applying it to signal timing optimization.
3. Currently, research on fairness in the transportation field is relatively broad, with increasing attention being paid to fairness in signal control. However, there are relatively few studies on fairness regarding delay fairness for each phase.  
In order to comprehensively consider the various objectives in signal timing optimization for low-saturation intersections and to reflect fairness, this paper proposes incorporating the differences in phase delay fairness into the optimization objectives. It utilizes information entropy and the Webster delay model as the basis to construct a fairness evaluation function, further establishing a signal timing optimization model considering both delay and fairness. The results validate the feasibility of sacrificing delay for fairness in this paper's approach. Finally, a multi-objective model incorporating delay, fairness, and emissions is constructed, and the model is verified through case studies, with sensitivity analysis conducted on intersection saturation.
# Multi-Objective Optimization for Outer Rotor Low-Speed Permanent Magnet Motor
## 4 Multiple Objectives Comprehensive Optimization
### Optimization
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Parameters** & **Point 1** & **Point 2** & **Point 3** \\ \hline \multirow{4}{*}{Design parameters} & Pole arc coefficient & 0.92 & 0.86 & 0.89 \\  & PM thickness (mm) & 13 & 14 & 14 \\  & Stator outer diameter (mm) & 878 & 877 & 877 \\  & Core length (mm) & 922 & 936 & 932 \\  & Conductors per slot & 10 & 10 & 10 \\  & Air gap length (mm) & 2.1 & 2 & 2.3 \\ \hline \multirow{4}{*}{Performance} & Efficiency (\%) & 94.61 & 94.65 & 94.66 \\  & Thermal load (A\({}^{2}\)/mm\({}^{3}\)) & 158.6 & 156.9 & 157.6 \\  & Power factor & 0.977 & 0.983 & 0.982 \\  & Core loss (W) & 1274.6 & 1258.4 & 1247.6 \\  & Copper loss (W) & 10,684.6 & 10,763 & 10,708.1 \\  & Torque (Nm) & 34,117.1 & 34,118.5 & 34,117.8 \\  & No-load back EMF (V) & 213 & 217.4 & 216.2 \\  & Output power (W) & 200,094 & 200,125 & 200,123 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of design parameters and performance parameters of three candidate points. Figure 12: Water-cooling channel model diagram. Figure 14 shows the temperature comparison of the three candidate points. As shown, the winding temperature, rotor temperature, stator temperature and magnet temperature are compared, respectively. In terms of the winding temperature, point 1 is similar to point 2, and is higher than point 3. At the same time, the lowest temperature is 106 \({}^{\circ}\)C in point 3, and the highest temperature is 107.1 \({}^{\circ}\)C in point 2. In terms of the stator temperature, the minimum temperature at point 1 is 90.6 \({}^{\circ}\)C, while the maximum temperature at point 2 is 91.2 \({}^{\circ}\)C. In addition, the rotor temperature and PM temperature are almost the same in the three points. Figure 13: Temperature distribution map of candidate points. (**a**) Candidate point 1; (**b**) candidate point 2; (**c**) candidate point 3.
## 3 Results and Discussion
### Synthesis of Core-Shell Nanoparticles  
This section deals with the first step of the reactive gelation protocol, the emulsion polymerization towards polymeric nanoparticles with core-shell architecture, exhibiting a hard, highly crosslinked, core on which a softer and less cross-linked shell is grown. Table 2 discloses the polymerization, which is carried out in a time frame of 6 h in total. The first three hours are dedicated to the core synthesis, revealing a low polydispersity index during the whole synthesis as the emulsion polymerization is done under monomer starved conditions. As one can see, the formed copolymer contains 80 wt % styrene (St) and 20 wt % divinyl-benzene (DVB). The high amount of used crosslinker DVB is crucial to achieve a very compact core that prevents approaching particles from coagulation. After three hours, the core had a particle size of 30 nm and a high conversion of 99%. Subsequently, the monomer feed was changed to start with the shell synthesis around the previously formed seed particles. The shell monomer mixture contained 59 wt % St, 2 wt % DVB and 39 wt % vinyl benzene azide (VBA) and was fed with 0.25 mL/min. When the total particle size of 38 nm, or the ratio in diameter for the core and the total particle size of 0.79, was achieved, the synthesis was stopped and a yellowish dispersion was obtained. The produced polymer contained 3.4 wt % nitrogen as revealed by elemental analysis (Table 3), which reflects an azide content of 0.3 mmol\({}_{\text{azide}}\)/gpolymer.
# Characteristics of the Deformation and Fracture of Overlaying Slopes in Roof Cutting
## 5 Analysis of the Influence of Height of Roof Cutting on Crack Development
### The Influence of the Height of Roof Cutting on Stress and Fracture Development  
During mining, the tensile stress at the slope bottom changed, as shown in Figure 11. The change in tensile stress was divided into three stages: the growth stage, the stress release stage and the stabilization stage. In the early stage of mining, the tensile stress continued to increase until it reached the tensile strength at the slope bottom. Then, the rupture R1 appeared at the slope bottom to release the stress which was reduced to a minimum. As the mining continued, the tensile stress tended to stabilize.  
As shown in Figures 12 and 13, after massive mining, there is a tensile stress concentration area in the slope within the rotation area. Coupled with the analysis of Figure 11, it can be seen that with the increase in the height of roof cutting, the tensile stress concentration area expanded while the growth rate of tensile stress in the growth stage decreased. When the height of roof cutting was 60 m, as the tensile stress at the slope bottom did not reach the tensile strength after mining, rupture R1 was not generated at the slope bottom to release the stress, and the damage at the slope bottom can be controlled. When the height of the roof cutting was 80 m, the control layer was cut off, and the strength of the control layer was destroyed, which resulted in excessive damage to the high-level rock formation. The slope rupture R2 was connected with slope rupture R3, which increased the risk of landslides.  
Figure 11: Tension at slope bottom at different heights of roof cutting.  
Figure 10: Schematic diagram of fracture development in mining. (**a**) The tensile force chains; (**b**) the fractures; (**c**) the caving angle. R1 is the crack at the bottom of the slope; R2 is the slope surface crack; R3 is the crest crack.The blue lines represent the tensile force chains, the gray lines represent the fractures.
## 2 Materials and Methods
### Statistics Analysis  
In this study, subjects were divided into two groups using a simple random method (the 1:1 random ratio of the two groups). The sample size was selected using G*Power software version 3.1, as recommended by [PERSON] [28]. Following data collection, including the first five subjects who completed the study, the number of participants was determined based on the calculated sample effect size. At a mean effect size of d = 0.8, a significance level of alpha 0.05, and a power of 80%, our power analysis indicated that we would need 15 participants per group, accounting for a 10% loss to follow-up and 10% treatment non-adherence.  
All data analyses were performed using IBM SPSS Statistics 23.0 software. Data were tested for normality using the Shapiro-Wilk test. Since the distributions were significantly different from the normal distributions, the normality assumption was not met; therefore, statistical analysis of non-parametric criteria was used. The Mann-Whitney U test was used as a non-parametric statistical test for independent samples.  
To investigate the effects of home-based exercises and manual therapy or supervised exercises on pain and functional status, a two-by-two (group: ETG, MTG; time: before, post) mixed ANOVA (main effect of time) model was calculated. The data were interpreted using the Greenhouse-Geisser correction, where the sphericity assumption was violated. Statistically significant comparisons were determined by post hoc testing with Bonferroni corrections, provided that the main effect of time attained a significant level. The effect size was measured using the partial eta squared (\(\eta^{2}\)p) obtained from the mixed ANOVA output.  
Statistical analysis results were significant at \(p<0.05\).  
Minimal clinically important difference scores were determined according to the following formula [29; 30]: ((ODI raw score initial \(-\) ODI raw score final)/ODI raw score initial \(\times\) 100%) \(\geqq\) 30%. A minimal clinically relevant difference is the smallest difference that would concern patients or physicians. A minimal clinically important difference score reflects a patient's sense of improvement or recovery, which correlates with minimal changes in their outcome score [31].
# Macro-Modelling Approach for the In-Plane Cyclic Response of Cold-Formed Steel Partition Walls
###### Abstract  
Past earthquakes demonstrate that non-structural elements could be vulnerable to a relatively low intensity ground shaking which induces negligible structural damage. The study aims to improve previously developed macro-models of cold-formed steel (CFS) partition walls to properly capture their in-plane cyclic response and damage states of important components in a CFS partition wall under imposed excitation. An effective analytical modelling approach is adopted for a simple modelling procedure and less computational effort. The proposed analysis model of partition walls consisting of several lumped spring elements is verified using direct comparison with two full scale CFS partition wall tests. The analytical and experimental results are compared in terms of force-displacement relations, dissipated energy, and an influential damage mechanism of components consisting of partition walls. The comparison shows that the analytical model well captures the experimental response such as the overall strength and stiffness degradation and pinching behavior. Moreover, the damage mechanism predicted by the analytical model is in good agreement with that observed during the tests.  
cold-formed steel partition walls; in-plane deformation; macro-modelling approach; quasi-static test; damage states +
Footnote †: journal: Applied Sciences
## 6 Results and Discussion
### Incoherent Accumulation Length Design  
A \(P_{acq}\) of at least 0.9999 is required for successful communication. When SNR before despreading is 2, 3, and 5 dB, \(P_{acq}\) is simulated according to formula \(P_{acq}=(1-P_{f})^{2 NL-1}P_{d}\) (11). The image of \(P_{acq}\) changing with \(\lambda\) can be obtained as shown in Figure 13, and the simulation data under various SNRs are given in Table 2.  
According to the simulation results, the incoherent accumulation length needs to reach 128 symbols under the 3 dB condition. Under the condition of 5 dB, the incoherent accumulation length of 64 symbols can meet \(P_{acq}\geq 0.9999\). (Results that meet \(P_{acq}\) requirement are shown in bold in the table.) Monte Carlo simulation is carried out for the acquisition with an incoherent accumulation length of 128 at 3 dB. It can be observed in Figure 14 that the Monte Carlo simulation results are basically consistent with the theoretical curve, and the maximum acquisition success probability meets \(P_{max}\geq 0.9999\).  
Figure 13: \(P_{acq}\) of different \(L\) and different SNR varying with \(\lambda\).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Eb/N0 (dB)** & \(L\) **(Symbols)** & \(\lambda_{opt}\) & \(P_{acq}\) & \(P_{d}\) & \(P_{f}\) \\ \hline  & 32 & 61.3 & 0.5586 & 0.63 & \(1.4693\times 10^{-5}\) \\
2 & 64 & 108.7 & 0.9202 & 0.9413 & \(1.3865\times 10^{-6}\) \\  & 128 & 201.3 & 0.9988 & 0.9992 & \(1.2863\times 10^{-8}\) \\ \hline  & 32 & 62.8 & 0.796 & 0.8416 & \(6.7883\times 10^{-6}\) \\
3 & 64 & 112.7 & 0.9881 & 0.9920 & \(2.3684\times 10^{-7}\) \\  & **128** & **211.4** & **1** & 1 & \(2.4605\times 10^{-10}\) \\ \hline  & 32 & 68.2 & 0.991 & 0.994 & \(3.6968\times 10^{-7}\) \\
5 & **64** & **126.2** & **1** & 1 & \(3.5931\times 10^{-10}\) \\  & **128** & **241.5** & **1** & 1 & \(3.8858\times 10^{-16}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Data of the \(P_{acq}\) maximum points when SNR before despreading is 2, 3 and 5 dB.  
However, the adaptive constant false alarm threshold [1,2] did not consider the false alarm caused by a false alarm, and only selected the threshold according to \(P_{f}\) rather than the acquisition success probability. According to this method, the threshold obtained when \(P_{f}=0.0001\) is 166. It can be observed from the above figure that the acquisition success probability is close to 0 at this time (the theoretical value is \(2.3122\times 10^{-14}\)), and the acquisition success probability of the Monde Carlo simulation results is 0. This threshold cannot achieve successful acquisition, and the false alarm value must continue to be reduced. Based on the acquisition success probability, the threshold setting method in this paper is equivalent to giving the exact index basis of false alarm setting on the basis of a constant false alarm threshold, so as to ensure the acquisition success probability of 99.99%.
## 3 Method Used
### Quality Assessment  
The efficiency of the developed models is evaluated using various statistical indexes namely Mean Absolute Error (MAE), Root Mean Squared Error (RMSE) and correlation coefficient (R). The value of R ranges from [0, 1], the higher value of R (i.e., closer to 1) indicates more successful model. On thecontrary, lower value of RMSE, MAE indicates better performance of proposed AI models. The criteria are determined by the following equations:  
\[\text{RMSE}=\sqrt{\sum\limits_{i=1}^{N}{{{{\left({{y_{0}}-{y_{p}}}\right)}^{2}}} \mathord{\left/{\vphantom{{{\left({{y_{0}}-{y_{p}}}\right)}^{2}}{N}}}\right. \kern-1.2 pt}N}}} \tag{1}\]  
\[\text{MAE}=\frac{1}{N}{\sum\limits_{i=1}^{N}{{{\left|{{y_{0}}-{y_{p}}}\right|}} \mathord{\left/{\vphantom{{{\left({{y_{0}}-{y_{p}}}\right)}^{2}}{N}}}\right. \kern-1.2 pt}N}}} \tag{2}\]  
\[\text{R}=\sqrt{1-\frac{\sum\limits_{i=1}^{N}{{{\left({{y_{0}}-{y_{p}}}\right)}^ {2}}}}}{\sum\limits_{i=1}^{N}{{{\left({{y_{0}}-{\overline{{y_{i}}}}\right)}^{2}} }}}} \tag{3}\]  
where \(N\) is defined as the number of input data, \(\overline{y}\) is the mean value of the outputs, and \(y_{0}\) and \(y_{p}\) express the actual and modeled values, respectively.