esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Relationship between Changes in Distance and Angulation  
The data obtained were analyzed to determine whether there was a direct relationship between changes in angulation and the amount of prosthetic space gained.  
The scatter plot below represents the space gained in relation to reductions in angulation (Figure 4).  
It is clear from the scatter graph that the greater the reduction in angulation was (points towards the left on the horizontal axis), the greater the prosthetic space gained was (points higher up in vertical direction).  
[PERSON]'s correlation coefficient was r = \(-\)0.93 and was statistically significant (\(p\) < 0.001), pointing to a very strong correlation.  
As this was a case series, quadratic adjustment was performed, as this would provide a fairly precise adjustment. The adjustment showed significance (\(p\) < 0.05) applying the following equation to obtain an approximate estimate:  
Gain in space = 8.14 + 0.60 change in angulation + 0.02 (change in angulation)  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **N** & **Mean** & **Standard Deviation** & **Minimum** & **Maximum** & **Median** & **95\% CI Median** \\ \hline
**Distance T0** & 10 & 5.98 & 3.64 & 2.40 & 15.50 & 5.50 & 3.20–6.80 \\
**Distance T1** & 10 & 14.11 & 5.03 & 8.04 & 21.43 & 12.41 & 9.18–20.6 \\
**Distance T1-T0** & 10 & 8.13 & 4.53 & 3.14 & 16.73 & 6.39 & 4.91–13.4 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Changes in prosthetic space.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **N** & **Mean** & **Standard Deviation** & **Minimum** & **Maximum** & **Median** & **Median CI 95\%** \\ \hline
**Angle T0** & 10 & 35.30 & 8.83 & 22.00 & 48.00 & 35.00 & 25–47 \\
**Angle T1** & 10 & 11.10 & 4.91 & 5.00 & 19.00 & 11.00 & 5–16 \\
**Angle T1-T0** & 10 & \(-\)24.20 & 10.11 & \(-\)36.00 & \(-\)6.00 & \(-\)24.50 & \(-\)35–16 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Changes in angulation.  
When the change in angularation was entered in the equation, this generated the expected gain in space.
## 4 Simulation Results
#### 4.1.1 Detection Performance Analysis  
The detection performance of the proposed algorithm (HDE PF-TBD) is evaluated in simulations with the probability of existence. The average of the probability of detection with different SNRs of the three algorithms is compared in Table 1. Figure 2 shows the results in terms of the probability of existence at different frame times.  
Figure 1: (**a**) Sampled image at Frame 15; (**b**) sampled image at Frame 18.  
The target exists if the probability of existence is higher than 0.6, which a tested and sensible value and is plotted as a horizontal line in Figure 2. Moreover, the asterisk signs at the bottom of the figures indicate the time of the presence of the target. It can be seen from Figure 2 that for SNR = 1, 3, 6, 9 dB, three algorithms have target presence and absence declaration delays, and each algorithm follows the true existence of the target much more closely for the higher SNR cases, with performance degrading as SNR reduces. The HDE PF-TBD algorithm provides higher target detection confidence than the other two algorithms. Furthermore, the HDE PF-TBD algorithm performs very well among the four algorithms.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Algorithm** & **9 dB** & **6 dB** & **3 dB** & **1 dB** \\ \hline MultinomialR PF-TBD & 0.770 & 0.657 & 0.423 & 0.217 \\ SystematicR PF-TBD & 0.774 & 0.682 & 0.498 & 0.300 \\ HDE PF-TBD & 0.875 & 0.765 & 0.628 & 0.395 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The average of the probability of detection for the constant velocity (CV) model. R, resampling; PF, particle filter; TBD, track-before-detect; HDE, hybrid differential evolution.  
Figure 2: **(a)** Probability of the existence at SNR = 9 dB for the CV model; **(b)** probability of existence at SNR= 6 dB for the CV model; **(c)** probability of existence at SNR = 3 dB for the CV model; **(d)** probability of existence at SNR = 1 dB for the CV model.  
SNR values, in other words with less target presence and absence declaration delays than others. From Figure 2, we also can see that the probability of the existence of the proposed algorithm has a large increase when the SNR is 3 dB and 1 dB. For clarity, the data statistics of Table 1 have proven that the HDE PF-TBD has better detection sensitivity when the SNR is lower.  
Moreover, it can be seen from Figure 3 that for SNR = 0, \(-\)1 dB, all three algorithms suffer a poor detection performance. However, compared to SystematicR PF-TBD and the MultinomialR PF-TBD, the HDE PF-TBD algorithm can detect the target at the end of the frames where the target exists. It is demonstrated that the proposed approach has a larger detectable and traceable SNR range and is capable of resolving large noise in the target. Meanwhile, it also proves that the improvements in the proposed algorithm can effectively enhance the detection sensitivity and that the proposed algorithm is more suitable for detecting weak targets with low SNR.
# High-Precision Ranging Method of 5G NR Co-Band PRS in Industrial Internet Scenarios
### Related Work
(3) Practicality: It adheres to the 3 GPP PRS standard, can be easily expanded within the framework of commercial mobile communication networks, and does not require high sampling rates from the terminal.
# Design and Application of Daylight-Based Lighting Controller on LED Luminaire
## 3 The Control Circuit and Test Setup  
### Test Setup  
A test setup was built to evaluate the performance of the LED daylight control circuit in place of an actual field test as shown in Figure 4a. The test setup was a wooden room designed with dimensions of a typical office room: 2 m in width, 1m in length, and 1.7 m in height. The reason for 1.7m height is that the floor of the experimental setup represents the working plane. The height of the actual floor in the building that was modelled for the experimental setup is 3 m with working plane height measure from floor is 0.8 m and the mounting height of the luminaire is 2.5 m. This results in the distance from the luminaire to the working plane being 1.7 m using the experimental setup. The height of the LED luminaire from the working plane was 1.5 m. The front panel of the test setup, shown in Figure 4b, consisted of control switches for two sets of the LED luminaire, power meter, and two lux meters for illuminance measurement. The interior of the test setup, shown in Figure 4c, consisted of two sets of LED luminaire indicated by 1, a louver for letting daylight shining in indicated by 2, and a light sensor for illuminance data feedback to the control circuit indicated by 3. Layouts show the exact locations of the LED luminaire and the photosensor is shown in Figure 5a,b respectively. The testing of daylight control circuit prototypes using experimental setup on a laboratory level has been divided into two parts. The first part is the effect of louver angles on daylight control circuit performance with louver in the experimental setup can be adjusted by 9 levels as follows 5, 10, 20, 30, 40, 50, 60, 70, and 80 degrees. The second part is the performance of the daylight control circuit for one day by obtaining data from 9.30 A.M. to 4.00 P.M. with a 10-min interval.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Luminaire** & **Surface** & **Average Light** & **Minimum Light** & **Maximum Light** & **Uniformity of** \\
**Type** & **Surface** & **Intensity (lux)** & **Intensity (lux)** & **Intensity (lux)** & **Light** \\ \hline LED & Floor & 294 & 139 & 473 & 0.453 \\  & Ceiling & 42 & 33 & 48 & 0.785 \\  & Wall & 85 & 24 & 149 & / \\ \hline Fluorescent & Floor & 202 & 152 & 245 & 0.749 \\  & Ceiling & 216 & 88 & 390 & 0.406 \\  & Wall & 254 & 103 & 724 & / \\ \hline \hline \end{tabular}
\end{table}
Table 3: The lighting characteristic from simulation on experimental setup.  
Figure 4: The field test experimental unit: (**a**) layout of exterior; (**b**) Front panel; (**c**) Interior.
## 3 Experiments and Performance Assessment
### Quantitative Evaluation
In general, the proposed _LSI_ algorithm delivers higher values of the nonshadow producer's accuracy (over 95%), the nonshadow user's accuracy (about 94%) and the overall accuracy (over 92%), stable values of the shadow producer's accuracy (about 83%) and the shadow user's accuracy (over 87%), and lower committed error (less than 5%), which reveals the excellent shadow detection performance and robustness of the proposed _LSI_ algorithm for the test image Tripoli-1. Similarly, for the test image Tripoli-2, the proposed _LSI_ approach also achieves a higher overall accuracy value than that of the other five contrast methods (i.e., MC3 [18], NSVDI [25], LSRI [24], SDI [26] and SRI [20]). Additionally, relatively low values of the overall accuracy are obtained by MC3, NSVDI and SDI, although the corresponding omitted error values are relatively low, which indicates the poor performance of MC3, NSVDI and SDI for shadow detection of the test image Tripoli-2. Relatively low values of the overall accuracy and the user's accuracy are also acquired by SRI, revealing that great room for improvement remains for SRI for shadow detection of the test image Tripoli-2. In contrast, better performance is shown in the result by LSRI with relatively high values of the overall accuracy (close to 89%) and the user's accuracy as well as low omitted error (about 5%), even though various shadow detection accuracy measurements are slightly inferior to those of the proposed _LSI_ approach. Consequently, the proposed _LSI_ algorithm presents a better performance for the test image Tripoli-2. Through comparing shadow detection results of test images Tripoli-1 and Tripoli-2 by the proposed _LSI_ approach and other five methods (i.e., MC3 [18], NSVDI [25], LSRI [24], SDI [26] and SRI [20]) both qualitatively and quantitatively, a conclusion can be drawn that the proposed _LSI_ shadow detection approach further settles typical shadow detection problems of high shadow omission and typical nonshadow misclassification (like bluish and greenish nonshadow misclassification, and large dark nonshadow misclassification), and delivers a relatively excellent, robustness and timesaving performance for shadow detection of HSR satellite images.
# Beam Training for Millimeter-Wave Communication Based on Tabu Table Enhanced Rosenbrock Algorithm
## 6 Experimental and Result Evaluations  
In this section, we will evaluate the performance of our proposed algorithm by simulations. The mm Wave channel model by [26] is used. For analysis convenience, the 1-D uniformly spaced antenna array is used during the simulation experiments. The antenna array element number \(M\) is assumed to be 64 (i.e., \(M_{t}=M_{r}=M\)).
# Non-Uniform-Illumination Image Enhancement Algorithm Based on Retinex Theory
## 2 Proposed Method
### Estimation of the Illumination Components  
The Retinex theoretical model means that the image is composed of two parts: the light image and the reflection image, which is expressed as follows:  
\[F(x,y)=V(x,y)\times R(x,y) \tag{6}\]  
where \(V(x,y)\) represents the incident light component of the image, \(R(x,y)\) represents the reflection component of the object surface and \(F(x,y)\) is the luminance of the current \((x,y)\) coordinate point of the image. This formula demonstrates that accurate enhancement of the illumination component can lead to reduced impact on the reflection component after enhancement, consequently minimizing the impact on the color. As a result, this paper primarily focuses on image brightness adjustment through gamma correction applied to the illumination component. The resulting illumination component image can be observed in the flow chart depicted in Figure 2.  
The multi-scale Gaussian function offers a significant advantage by effectively capturing more precise lighting components through dynamic range compression of the image. The method in this paper constructs a multi-scale Gaussian function to extract the light components, and the decomposition model of the image using the Gaussian function is shown in Figure 2. The Gaussian function is expressed as follows:  
\[G(x,y)=\alpha e^{-\frac{x^{2}+y^{2}}{\delta^{2}}} \tag{7}\]  
Convolving the image with a Gaussian function can estimate the value of the illumination component, which is expressed as follows:  
\[V(x,y)=F(x,y)G(x,y) \tag{8}\]  
where \(\delta\) denotes the scale factor and is inversely related to the dynamic range of the image; the smaller the \(\delta\), the clearer the image. \(\alpha\) is the parameter that satisfies the Gaussian function \(\iint G(x,y)dxdy=1\). Convolving the image with a Gaussian function provides an estimate of the value of the illumination component.  
To balance the integrity and difference of the extracted illumination components, an \(N\)-dimensional Gaussian function with weighted functions is employed to extract the illumination components of the image, and finally, the estimated value of the illumination components is obtained. The expression is as follows:  
\[I_{V}(x,y)=\frac{c}{N}\sum_{i=1}^{N}F(x,y)G(x,y) \tag{9}\]  
Figure 2: Image decomposition model.  
where \(\frac{c}{N}\) denotes the weight coefficient for extracting the illumination component under the \(N\)-dimensional scale, \(N\) denotes the dimension number of the Gaussian function, and \(c\) denotes the balance parameter. To achieve a dynamic balance between the amount of calculation and the acquisition of illumination components, the values taken in this paper are \(N=4\), \(c=1\).
# Minimizing Interference-to-Signal Ratios in Multi-Cell Telecommunication Networks
## 4 Experiments  
We compared the performance of DP-Similarity Clustering (Algorithm 2) and Spectral Clustering [3] in several scenarios.  
We studied and compared our algorithm to a representative of the existing algorithms in two main scenarios that model different distributions of the users. In both scenarios, users are randomly and uniformly distributed, and independently placed into or \(\left[0,3000\right]^{2}\) (squares with an area of 1 km\({}^{2}\) and 9 km\({}^{2}\), respectively). The weight (or signal strength) between a base station (BS) and a user \(b_{i},u_{j}\in\mathbb{R}^{2}\) is determined by  
\[w_{ij}=\left\{\begin{array}{ll}\|\text{dist}_{\min}\|^{-\alpha}&\text{if }\|b_{i}-u_{j}\|\leq \text{dist}_{\min},\\ \|b_{i}-u_{j}\|^{-\alpha}&\text{if }\text{dist}_{\min}\leq\|b_{i}-u_{j}\|\leq \text{dist}_{\max},\\ 0&\text{if }\text{dist}_{\max}<\|b_{i}-u_{j}\|,\end{array}\right.\]  
where \(\text{dist}_{\min}=1\), \(\text{dist}_{\max}=200\), and the _path attenuation (path loss) exponent_\(\alpha\) is set according to the modeled scenario (the value of \(\alpha\) depends heavily on the environment; for further details, see ([1], Section 2)). In other words, a user cannot connect to a base station that is farther away than \(\text{dist}_{\max}\). We assume that in real-world applications, the signal strength values are readily available.  
* Scenario 1 (urban environment): the first scenario models a city center as a space \(\left[0,1000\right]^{2}\), where base stations are also randomly and uniformly distributed, and independently placed (see Figure 1a); we assume that the path loss attenuation factor is \(\alpha=2\) as suggested by ([1], Section 2).
* Scenario 2 (rural environment): the second scenario models an unevenly occupied rural environment, where the placement of base stations is less restricted: the BSs are placed into \(\left[0,3000\right]^{2}\) with density function proportional to the sum of the signal strengths of the users, where the path loss attenuation factor is \(\alpha=3\), again, as suggested by ([1], Section 2).  
DPH-clustering (Algorithm 1) is guaranteed to return an \(M\)-part clustering of the base stations (if \(M\leq b\)). Consequently, the output of DP-Similarity Clustering (Algorithm 2) is always an IF-cluster system (Definition 1) because it assigns each user to the cluster with the largest weight to the user; consequently, every user is served by at least one base station. However, this is not the case for Spectral Clustering, which, in certain scenarios, is very likely to create a cluster with some users but zero base stations; see Figures 2a and 3a.
# Point-Sim: A Lightweight Network for 3D Point Cloud Classification
## 3 Methods
### Overall Structure  
The nonparametric modeling of point cloud classification method known as Point-Sim is shown in Figure 1. In the classification model, nonparametric feature embedding is first performed using trigonometric functions(the Trigo block). Subsequently, in the hierarchical feature extraction stage, the centroids are selected using FPS, and from these centroids, the point clouds are grouped using k-NN. We apply trigonometric functions to map the local geometric coordinate. In order to better match the feature naive similarity, the geometric and local features are added and fed into the Squash block, the features are squeezed to make them smoother, and then the smoothed features are fed into the ResSimAM block so that the model can pay better attention to the features with higher energy; this improves the classification ability of the encoder, and then finally the global features are obtained by using the pooling operation.  
The nonparametric point cloud classification model has been extended by integrating neural network layers at various stages within Point-Sim. The constructed Point-SimP network, outlined in Figure 2, introduces a lightweight framework. To enhance the model, the raw embedding layer within the nonparametric network was substituted with an MLP. Furthermore, MLP layers were incorporated post the Feature Expansion and Geometry Extraction phases during feature extraction and applied to the ultimate global feature to obtain the classification outcomes.
# Machine Learning Techniques in Structural Wind Engineering: A State-of-the-Art Review
## 3 Prior Studies on Applying ML Techniques in Structural Wind Engineering
### Prediction of Wind-Induced Pressure
Four hyperparameters were considered in developing the ML model, two hyperparameters for CART (i.e., maximum depth, d, for each decision tree, and a minimum number of samples to split an internal node), and two hyperparameters for a gradient boosting approach, i.e., learning rate (Lr) and number of CART models. The developed method was shown to be a robust and accurate method to predict the wind-induced pressure on structures under the interference effects of neighboring structures. [PERSON] et al. [116] predicted the typhoon-induced response (TIR) on long-span bridges using quantile random forest (QRF) with bayesian optimization instead of the traditional FE analysis. The QRF with bayesian optimization was able to provide adequate probabilistic estimations to quantify the uncertainty in predictions.
## 2 WBANs for Life and Safety Critical Applications
### Applications of WBANs
#### 2.2.1 WBANs for Rescue and Emergency Management  
The growing numbers of disasters and accidents, in terms of frequency and intensity, has a significant impact on humans' living conditions, asset safety, as well as the economy. These disasters might be caused by a combination of either natural, man-made or unexpected factors and their negative impact on human beings is amplified due to the increased population densities in cities, public areas, or buildings. For instance, fire is responsible for the largest number of deaths in the US in comparison with all other natural disasters combined. Indeed, more than 1 million fires were reported in the US in 2012 [22], and which caused 2,855 civilian deaths, 16,500 civilian injuries and $12.4 billion in property damage. In this context, it is envisioned that WBAN technology will enable humans to respond to these unexpected events in a timely and efficient manner in order to reduce injuries, deaths, asset and economic losses [10; 11; 12; 13; 14].  
As shown in Figure 2, during a disaster response scenario, field personnel (_i.e._, first responders [14], emergency personnel [10], firefighters [13], _etc._) can face various uncertainties and hazards on their operational theaters, such as no _a-priori_ knowledge of the people or assets to be rescued, limited or zero visibility, the unavailability of communication infrastructure, very high ambient temperatures, the emanation of toxic gases, and many other risks.  
By fitting field personnel with wearable WBANs technology, decision makers and corresponding authorities will be able to gather accurate and real-time information from these field personnel in order to better anticipate and manage rescue and life-critical operations. The gathered information can be related to the field personnel's health status (e.g., heart pulse rate, stress level, blood pressure, _etc._), their location and movement inside harsh or damaged environments (e.g., absolute and/or relative location, body posture and orientation, _etc._), the environmental conditions (e.g., heat, toxic gases, lightening, fire, smoke, _etc._) and the status of their ongoing tasks (e.g., search, recovery, evacuation, assistance, _etc._). The efficient coordination, remote monitoring, and communication with/between these teams and their command centers are thus one major objective yet to be achieved. Moreover, it is also envisioned that WBANs will also enable emergency personnel to better manage the safety and health of rescued patients, for example, by enabling in-field patient triage and health monitoring prior to their arrival at the hospital [10].
## 4 Results and Discussion
### Performance Evaluation of Customized CNNs
The results are tabulated in Table 4. We observed that our model outperforms the current literature in all performance metrics across the classification tasks. The customized sequential CNN demonstrates higher values for recall in: (i) classifying normal and pneumonia; and, (ii) identical recall measures to the customized VGG16 model in classifying bacterial and viral pneumonia. However, considering the balance between precision and recall as demonstrated by the F-Score and MCC, the customized VGG16 model outperforms the other CNNs and the state-of-the-art across the classification tasks. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Task** & **Model** & **Accuracy** & **AUC** & **Precision** & **Recall** & **Specificity** & **F-Score** & **MCC** \\ \hline \multirow{3}{*}{Normal v. Pneumonia} & Customized & \multirow{3}{*}{**0.962**} & \multirow{3}{*}{**0.993**} & \multirow{3}{*}{**0.977**} & \multirow{3}{*}{**0.962**} & \multirow{3}{*}{**0.962**} & \multirow{3}{*}{**0.970**} & \multirow{3}{*}{**0.918**} \\  & VGG16 & & & & & & & \\ \cline{1-1}  & [PERSON] et al. & 0.928 & 0.968 & - & 0.932 & 0.901 & - & - \\ \hline \multirow{3}{*}{Bacterial v. Viral Pneumonia} & Customized & \multirow{3}{*}{**0.936**} & \multirow{3}{*}{**0.962**} & \multirow{3}{*}{**0.920**} & \multirow{3}{*}{**0.984**} & \multirow{3}{*}{**0.860**} & \multirow{3}{*}{**0.951**} & \multirow{3}{*}{**0.862**} \\  & [PERSON] et al. & 0.907 & & & & & & \\ \hline \multirow{3}{*}{Normal v. Bacterial v. Viral Pneumonia} & Customized & \multirow{3}{*}{**0.918**} & \multirow{3}{*}{**0.939**} & \multirow{3}{*}{**0.920**} & \multirow{3}{*}{**0.900**} & \multirow{3}{*}{**0.960**} & \multirow{3}{*}{**0.910**} & \multirow{3}{*}{**0.876**} \\  & VGG16 & & & & & & & \\ \cline{1-1}  & [PERSON] et al. & - & - & - & - & - & - & - \\ \hline \hline \end{tabular}  
* Bold numbers indicate superior performance.  
\end{table} 
Table 4: Comparing the performance of the customized VGG16 model with the state-of-the-art. Figure 8: Confusion matrices for the performance of the customized VGG16 model: (**a**) Normal v. Pneumonia; (**b**) Bacterial v. Viral Pneumonia. Figure 9: ROC curves demonstrating the performance of the customized VGG16 model: (**a**) Normal v. Pneumonia; (**b**) Bacterial v. Viral Pneumonia.