esa-sceva/satcom-chunk-collection huggingface dataset:
# Optical Element Surface Defect Size Recognition Based on Decision Regression Tree
## 3 The Principle of Defect Size Recognition
### Defect Size Recognition Based on DRT
#### 3.2.2 The Pruning of DRT  
As can be seen in Equation (10), the generation of DRT \(T\) is based on the rule of minimizing the prediction error. However, like the high order polynomial fitting will lead to the overfitting, the continuous split of the set \(\mathbf{R}\) will also cause the same situation. The extreme situation is, each light intensity distribution curve is a subset of \(\mathbf{R}\) (i.e., \(n=m\)). Thus, the prediction error \(\varepsilon\left(T\right)\) will be 0. This result is meaningless. Although the split termination condition (\(\tau<6\) or \(S^{2}<0.01\)) in Figure 4 will limit this extreme situation to some extent, \(\varepsilon\left(T\right)\) will still be very close to 0. Hence, the DRT pruning must be taken to prevent this overfitting situation.  
The pruning core strategy is to balance the prediction error \(\varepsilon\left(T\right)\) in a small range. As discussed above, the small prediction error \(\varepsilon\left(T\right)\) is what we expect, but the too-small value is not what we want. Since \(\varepsilon\left(T\right)\) decreases with the increasing of the subset number \(m\) (when \(m\) increases to the total number of simulation curves \(n\), \(\varepsilon\left(T\right)\) will be 0), decreasing the value of \(m\) would be a good choice. It can be achieved by stopping the split of some subsets in \(\mathbf{R}\), which makes some original split subsets be discarded (namely be pruned). The key question is how to decrease \(m\) so that the prediction error won't increase a lot. The solution has already been put forward in DRT pruning by solving an optimization problem as follows [20],  
\[T_{\alpha}=\text{arg}\min_{T}\left(\varepsilon(T)+\alpha m\right) \tag{11}\]  
where \(\alpha(\alpha\geq 0)\) is a penalty factor. In Equation (11), we can see that \(\varepsilon\left(T\right)\) reaches the minimum and is not the optimized result since \(m\) will reach the maximum meanwhile. The optimization in Equation (11) is also called regularization in mathematics for preventing overfitting. Different values of \(\alpha\) will obtain different DRT optimized result \(T_{\alpha}\). The equation of \(\alpha\) can be given as follows [20],  
\[\alpha=\frac{\varepsilon(t)-\varepsilon(t_{\text{sub}})}{m(t_{\text{sub}})-m(t)} \tag{12}\]  
where \(t\) is a subtree of \(T\) (namely a subset of \(\mathbf{R}\)). When subtree \(t\) is selected to be pruned, all its subsets are discarded, so its subset number \(m(t)=1\). \(t_{\text{sub}}\) is a subtree of \(t\), and its subset number is remarked as \(m(t_{\text{sub}})\). Since the size of the original DRT \(T\), which is not be pruned, is finite, the number of values of \(\alpha\) is also finite. From the Equation (11), we can get a series of finite optimized DRT \(\left\{T_{\alpha_{0}},T_{\alpha_{1}},\ldots,T_{\alpha_{c}}\right\}\), where \(\varsigma\) is the number of the values of \(\alpha\). To get the final optimized DRT, the cross-validation is carried out among these DRT.
# Innovative Integration of Severe Weather Forecasts into an Extended Arrival Manager
## 5 Case Studies
### Traffic Scenario Definition  
The simulations involved three traffic scenarios for the TMA at MXP, which were based on FlightRadar24 datasets of the three days (11 May 2019, 7 July 2019, and 6 August 2019). There were two days when weather conditions did not cause airport closure (7 July 2019 and 6 August 2019) at MXP, which were used as baseline scenarios, and one day with an airport closure for some period of time (11 May 2019), which was used as the disturbed scenario.
## 4 Conclusions  
In this paper, thermal-mechanical properties of a Fe-based shape memory alloy (Fe-SMA) were investigated for use as strengthening material for civil structures. Based on results from material tests, conclusions can be drawn as follows:  
1. The Fe-SMA shows sufficient potential to be applied as strengthening material when retrofitting damaged structures or strengthening civil structures. By applying a electric resistive heating temperature of 160 \({}^{\circ}\)C, the 4% pre-strained Fe-SMA embedded inside the concrete would show the shape memory effect (SME), which induces a compressive force on concrete. This compressive force plays an important role as the pre-stressing force on concrete structures.
2. From the direct tensile tests of the Fe-SMA, the limit of proportionality stress, the 0.2% offset stress, and the modulus of elasticity were 463 MPa, 863 MPa, and 133 GPa, respectively. Based on these mechanical properties, the recovery stresses of the Fe-SMA by the SME activation at recovery temperature from 100 \({}^{\circ}\)C to 220 \({}^{\circ}\)C range linearly from 207.59 MPa to 438.61 MPa, which is transferred to the surrounding concrete as the pre-stressing force.
3. The bonding strength and feasibility index of the Fe-SMAs were improved by an average of 8.8% with an increased embedding length from 250 mm to 450 mm. The regression analysis based on the feasibility index and bonding length of the Fe-SMA indicates that the pre-stressing force can be fully developed when the embedded length is over 600 mm.
4. Based on the recovery stress transfer tests, the amount of pre-stressing force applied on strengthened concrete structures could be estimated by the recovery stress of the Fe-SMA.  
[PERSON] conducted the experiments and [PERSON] wrote the initial draft of the manuscript. [PERSON], [PERSON], and [PERSON] analyzed the data and wrote the final manuscript. All authors contributed to the analysis of the data and read the final paper.  
This work is supported by the Korea Agency for Infrastructure Technology Advancement (KAIA) grant funded by the Ministry of Land, Infrastructure and Transport (Grant #115171).  
This work is supported by the Korea Agency for Infrastructure Technology Advancement (KALA) grant funded by the Ministry of Land, Infrastructure and Transport (Grant #115171). In addition, authors would like to acknowledge the support from the re-fer AG Company in Switzerland for providing the Fe-SMA material for this research.  
The authors declare no conflict of interest.
## 4 Groundwater Level Prediction Techniques
### Genetic Programming (GP)  
A general genetic algorithm (GA) was developed called genetic programming (GP) [69]. Darwinian theories of evolution are used for genetic programming and ecological choice as the GA. The author in [70] developed a GP-based model to predict the GWL changes and calculate the vagueness in the forecasting. The paper used Indian monthly rainfall data to predict the GWL. The GP model proposed by the author could successfully predict variations by using only hydrometeorological parameters for GWL, i.e., the model predicts without knowing the physical characteristics of the wells. GP has been mostly affected for feature selection work and optimization. Furthermore, because of its flexibility and intelligible tree structure it is more used in GW modeling. The author in [71] proposed GWL for the next day and prediction intervals of up to 7 days and applied SVM, GP, ANN, and ANFIS. All of these algorithms have prediction capabilities to predict GWL. There are several GWL combinations, including evapotranspiration and rainfall data, which are used as input to the prediction model, using data gathered from Republic of Korean, Hongcheon well station. After making a model, the autoregressive moving average (ARMA) model is used for comparison to validate the accuracy. The final conclusions proved that the ARMA methodology performed well compared to other ML methods, which is therefore the most effective with the GP model.  
Figure 5: A basic architecture of ANFIS model.
## 5 Results and Evaluation
### Personal Preferences  
The static clues were on average ranked better than the dynamic clues, as shown in Table 4. The participants preferred the color clue the most, followed by the size clue and the font clue. The size animation clue was the best animated clue followed by the color animation clue and the wiggle animation clue. It is interesting to see that, despite having the best input speed, the size animation clue is nearly a whole rank behind the color clue concerning personal preferences. The color clue and the size clue appear to be preferred by the participants but do not display this in the experiment concerning typing performance. Generally, it is interesting to see that the animated clues were not as appealing to the user as the static clues. This may indicate that the animated clues may have overwhelmed some participants. Now, while, for example, the size animation clue performed very well in the experiment, it did not do so well in the questionnaires, displaying some sort of trade-off between performance and usability. The wiggle animation clue confirmed its worst performance in the experiment among the visual clues with also the worst average ranking.  
\begin{table}
\begin{tabular}{|l|l|l|l|l|l|l|l|} \hline  & **Opti** & **SC** & **CC** & **FC** & **SAC** & **CAC** & **WAC** \\ \hline Opti & 1 & 6.46 \(\times\)\(10^{-8}\) & 4.08 \(\times\)\(10^{-8}\) & 3.75 \(\times\)\(10^{-9}\) & 1.94 \(\times\)\(10^{-10}\) & 2.75 \(\times\)\(10^{-6}\) & 4.58 \(\times\)\(10^{-8}\) \\ \hline SC & 6.45 \(\times\)\(10^{-8}\) & 1 & 0.90 & 0.22 & 0.17 & 0.95 & 0.07 \\ \hline CC & 4.08 \(\times\)\(10^{-8}\) & 0.90 & 1 & 0.03 & 0.10 & 0.85 & 0.03 \\ \hline FC & 3.75 \(\times\)\(10^{-9}\) & 0.22 & 0.03 & 1 & 0.88 & 0.14 & 0.00 \\ \hline SAC & 1.94 \(\times\)\(10^{-10}\) & 0.17 & 0.10 & 0.88 & 1 & 0.10 & 0.00 \\ \hline CAC & 2.75 \(\times\)\(10^{-6}\) & 0.96 & 0.85 & 0.14 & 0.10 & 1 & 0.04 \\ \hline WAC & 4.58 \(\times\)\(10^{-8}\) & 0.07 & 0.03 & 0.00 & 0.00 & 0.04 & 1 \\ \hline \end{tabular}
\end{table}
Table 2: \(T\)-test concerning typing speed.  
\begin{table}
\begin{tabular}{|l|l|l|l|l|l|l|l|} \hline  & **Opti** & **SC** & **CC** & **FC** & **SAC** & **CAC** & **WAC** \\ \hline Mental Demand & 12.96 & 8.63 & 8.26 & 7.59 & 8.33 & 9.07 & 9.44 \\ \hline Physical Demand & 7.11 & 5.07 & 5.52 & 4.33 & 5.11 & 5.30 & 5.59 \\ \hline Temporal Demand & 9.63 & 7.56 & 7.52 & 7.48 & 8.37 & 8.70 & 7.37 \\ \hline Performance & 9.78 & 7.85 & 6.85 & 7.40 & 7.22 & 8.52 & 8.44 \\ \hline Effort & 11.37 & 8.11 & 8 & 7.63 & 8.11 & 8.19 & 8.96 \\ \hline Frustration & 7.07 & 5.63 & 4.52 & 4.63 & 5.19 & 5.96 & 6.30 \\ \hline Mean & 9.65 & 7.141 & 6.78 & 6.51 & 7.06 & 7.62 & 7.69 \\ \hline \end{tabular}
\end{table}
Table 3: NASA Task Load Index results.
# Assessing Evolution of Microservices Using Static Analysis
## 3 The Proposed Centric Metrics
### Intermediate Representation Construction
#### 3.2.3 Intermediate Representation Formalization  
The two IRs constructed from both views encompass vital attributes for depicting the system's inter-connections and data dependencies. These attributes can be expressed using the following mathematical notations, which provide a precise and formal rep  
Figure 2: Service view and data model intermediate representation construction process.  
resentation of the relationships within the system, along with the formulas for metrics calculation accordingly:  
\[S =\{ms_{1},ms_{2},\cdots,ms_{n}\};ms_{i}=\langle E_{i},C_{i},D_{p_{i}},D_{t_{i}}\rangle\] \[E_{i}=\{\langle u_{j},r_{j},P_{j}\rangle,\cdots\};C_{i}=\{ \langle ms_{j},u_{k},r_{k},P_{k}\rangle,\cdots\};\] \[D_{p_{i}}=\{\langle F_{j},R_{ij}\rangle,\cdots\};D_{t_{i}}=\{ \langle F_{j},R_{ij}\rangle,\cdots\}\] \[P=\{\langle t,n\rangle,\cdots\};F_{i}=\{\langle t,n\rangle,\cdots\}\]  
\[S\]  
- Microservice system \[ms_{i}\]  
- Single microservice. \[E_{i}\]  
- Endpoints defined in the microservice \[ms_{i}\]  
- Calls made from the microservice \[ms_{i}\]  
to all other microservices. \[D_{p_{i}}\]  
- Persistent data entities in the microservice \[ms_{i}\]  
- Transient data entities in the microservice \[ms_{i}\]  
- Relationships starting from a data entity \[j\]  
to other entities. \[F_{j}\]  
- Fields in a data entity \[j\]  
- Parameters in an endpoint or in a call. \[u\]  
- URL path. \[r\]  
- Return type. \[t\]  
- Field data type. \[n\]  
- Field name.  
The constructed views and their properties can be elucidated by categorizing the five main sets of extracted attributes. The process begins with a microservice system as input and subsequently analyzes the system to yield the following sets of attributes per each microservice. Firstly, a set of endpoints (\(E\)) is introduced within each microservice. Secondly, a set of request calls (\(C\)) is initiated from each microservice towards endpoints in other microservices. Lastly, the system yields two sets of data entities: one for the persistent data entities (\(D_{p}\)) and the other for the transient data entities (\(D_{t}\)).
## 3 Current State of Object Detection Algorithms
### Yolo  
The YOLO model, introduced by [PERSON] [24], is a state-of-the-art object detection algorithm that offers real-time object detection capabilities. Unlike traditional object detection methods that require multiple scans of an image, YOLO detects objects in a single forward pass through a neural network. In YOLO, the input image is divided into a grid, and a network of bounding boxes is generated for each grid cell to predict the presence and location of objects within the grid. Each bounding box is associated with a probability score indicating the likelihood of detecting a specific object class and the location information encoded in the bounding box. The final predictions are made by selecting bounding boxes with a probability score higher than a predefined threshold. The YOLO framework comprises three main components:  
* Backbone extracts high-level features from the input image;  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Key Features** & **Improvements** & **Issues** \\ \hline Fast R-CNN & Utilizes selective search for object proposals. & Improved detection accuracy and reduced computation time. & Reliant on the quality of the object proposals. \\ Faster R-CNN & Integrates object proposal generation into the network. & Increased speed and accuracy compared to Fast R-CNN. & Limited to fixed-sized object proposals. \\ Mask R-CNN & Adds object mask output to the bounding box and class predictions. & Improved object detection with the more precise spatial & Requires additional computation for the mask output. \\ Cascade R-CNN & Employs a series of detection heads with increasing IoU thresholds. & Improved accuracy by addressing the issue of missed detections. & Increased computation time compared to Faster R-CNN. \\ Sparse R-CNN & Uses predefined, learnable proposal boxes. & Improved detection accuracy and reduced computation time compared to other & Limited to the number of proposal boxes defined. \\  & & R-CNN models. & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of R-CNN models.  
* Neck generates feature pyramids to enable multi-scale object detection;
* Head performs the final detection by applying anchor frames and generating the output vectors with class probabilities, scores, and bounding boxes. YOLO's single-pass object detection strategy results in faster inference times compared to traditional object detection algorithms such as R-CNN. However, one of the limitations of YOLO is its difficulty in detecting smaller objects in an image. Over the years, several variants of YOLO have been proposed, including YOLOv3 [39], YOLOv4 [40], YOLOv5 [41], YOLOv6 [42], and YOLOv7 [43], which differ in the backbone, neck, head, and loss function used, as shown in Table 3. These variants offer improved performance and accuracy, making YOLO a powerful tool for object detection in various applications.
## 4 Discussion
### For Insights from Data Analytical Results
#### 4.1.2 For the Samples' Ranking  
As for the results upon ranking samples by CS performance, the recommendation is to use _an SR-BJPA of 5%, cure the mortar at T = 75 \({}^{\circ}\)C, and apply a W/B ratio of 0.4 as the best admixture portfolio_. This will result in the optimal full-aged CS performance for the cured cement mortar in practise (69.6 MPa).  
In addition, _all of the top five settings being ranked according to their full-aged CS can meet the standard regulation (CS > 68.5 MPa) and they are all 'acceptable'_ (see Section 3.1.2).  
Thus, _the third option that keeps SR-BJPA = 5% and the W/B ratio at 0.4 while lowering the curing temperature to T = 50 \({}^{\circ}\)C is a more energy-saving solution to be considered_ because its full-aged CS deviates from that of the optimal mixture by only \(<1\%\) (69.0 MPa vs. 69.6 MPa). This is because without-BJPA, the second option makes no sense for environmental protection or sustainable development.  
In summary, the outcome of _the ranking process has suggested the use of BJPA as a portion of cement replacement for mortar mixtures and the application of a higher curing temperature_. Moreover, this study confirmed the claim that _adding a large quantity of BJPA as a cement replacement hampers the mechanical property of concrete materials_.
## 4 Methodology
### Generating Cyclic Dynamic Time Series Data  
The combined record in dataset D is assumed as \(d_{k}=(x_{ik},y_{k})\ k=1,\ 2\ldots N,\ i\in\{1,\ 2,\ldots n\}\ y_{i}\), \(y_{i}\in\{1,\ 2..C\}\). The goal was to build a mathematical model that converts dataset \(D\) into a time series dataset \(D_{t}=(x_{it},y_{t})\ i\in\{1,\ 2,\ldots n\},\ t=1,\ 2,\ldots N\) of cyclic nature. Cyclic means that label \(y_{t}\) is repeated every time \(T\). \(x_{ik}\), is assumed to have a changing dimension, which is constrained by the condition that the dimension of \(x_{t}\) is fixed when \(y_{t}\) is fixed. The time series of cyclic dynamic is found based on the following model:  
In order to elaborate the pseudo-code that is presented in Algorithm 1, we present the equations:  
\[D=\{(x_{i},y_{i}),\ i=1,2\ldots N\}, \tag{1}\]  
\[y_{t}=\left\{\ \left[\ \frac{(y_{max}-1)}{C}sin\left(\frac{2\pi t}{T}\right) \right]+1\right\}, \tag{2}\]  
\[D_{t}=\{(y_{t,i}),\ i=1,2\ldots R,\ t=1,2,\ldots L\}, \tag{3}\]  
where:  
\(D\) denotes the original dataset;  
\(y_{max}\) denotes the maximum code of the classes in \(D\);  
\(y_{t}\) the class that is extracted from \(D\) at moment \(t\);  
Figure 2: (a) Markov model of classifier, and (b) Markov model of external memory (EM).  
\(y_{t,t}\) denotes the class \(y_{t}\) at the moment \(t\) and it is repeated for \(R\) times in the time series; and \(L\) the number of distinct samples in the time series.  
Algorithm 1 exhibits the generation pseudocode. Moreover, the nature of cyclic dynamic can be changed on the basis of value \(T\) that represents the period, and value \(R\) that represents the number of records in each class. The general form of generation is to change R randomly from one class to another.  
```
Inputs //dataset //number of records per sample L //Length of time series C //Number of classes T //period of the time series Outputs Dt //time series data Start D = GenerateActiveFeatures(D); //to encode the non-active features for every class t = 1 for i = 1 until L  y = sin(2*pi*t/T)  yt = Quantize(y, C) //Quantize the value y according to the number of classes in D  for j = 1 until R  xt = Extract(yt, D) //extracts random samples from D with the label y Dt(t).x = xt Dt(t).y = yt  t = t + 1 Endfor2 Endfor1
```  
**Algorithm 1** Pseudocode for converting a dataset into cyclic dynamic with a feature-adaptive time series.  
The pseudocode starts with class generation using the sin function, which is then quantized according to the number of classes in the dataset. The corresponding active features are given from the function Extract(). Finally, the record is provided to the time series data.
# Power Optimization Model for Energy Sustainability in 6G Wireless Networks
## 6 Conclusions  
A massive IoT network involves information flow between a massive number of connected devices for varied IoT application verticals. The high power consumption and huge energy overhead are the main challenges of this digital network powered by battery-limited devices. Thus, the network service providers are looking for sustainable energy solutions to meet the energy demands of these networks. This paper presents a practical power-allocation model that optimizes the power resources, resulting in increased network efficiency. Two user-scheduling algorithms are proposed which select a set of users to be served by each AP. The performance of the proposed model is evaluated through parametric variations in the number of antennas at the AP, AP deployment, CSI availability, and spatial correlation for different precoding schemes. It is observed that less densely deployed APs with multiple antennas yield more spectral efficiency. In the distributed network operation where all the signal processing occurs at the AP, the network performance degrades with spatial correlation for both MR and LPMMSE precoding. The  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Precoding Scheme** & \(\mu=0.5\), \(\vartheta=1\) & \(\mu=0.5\), \(\vartheta=0.5\) & \(\mu=-0.5\), \(\vartheta=1\) & \(\mu=-0.5\), \(\vartheta=0.5\) & \(\mu=-0.5\), \(\vartheta=0\) \\ \hline MMSE (max) & 58.2182 & 119.1856 & 154.4216 & 137.3216 & 81.6737 \\ MMSE (min) & 3.1003 & 2.0158 & 1.5627 & 3.1524 & 1.6351 \\ PMMSE (max) & 58.2706 & 119  
availability of perfect CSI suggests an improvement of 4.1% in the achieved SE by 95% of the users in the network with PMMSE precoding. Further, the performance of the system with power optimization mechanisms are carried out with the two proposed user-scheduling algorithms. Using centralized PMMSE precoding, each AP assigns a maximum power of 141.7 mW to a user with a strong channel and a minimum power of 3.1882 mW to the user with the weakest channel, with fractional power allocation for \(\mu=-0.5\), \(\vartheta=0.5\). As compared to the MDS algorithm, the MCS algorithm results in better spectral efficiency for all the users. Optimum performance is also achieved with the fractional power allocation, with which most of the users attain higher SE.  
Conceptualization, methodology: A.T., A.S. and M.A.E.; formal analysis: A.T., N.S., N.T. and A.A.; software, validation, writing--original draft: A.T., N.S., N.T., A.S. and M.A.E.; writing--review and editing: A.S., A.A., D.K. and M.A.E.; data curation: A.T., A.S., A.A., M.A.E. and D.K.; supervision, funding acquisition: A.A, A.S. and M.A.E. All authors have read and agreed to the published version of the manuscript.  
The authors are thankful to the Deanship of Scientific Research at Najran University for funding this work under the Research Collaboration Funding program, grant code (NU/RC/SERC/11/8).  
The authors declare that they have no conflicts of interest to report regarding the present study.  
The following abbreviations are used in this manuscript:
## 4 The Influence of System Parameters on Response Characteristics
### The Effect of Inerter-Mass Ratio on the Amplitude of the Primary System  
The mass ratio \(\mu=0.1\) and magnification ratio \(L=2\) are selected. The inerter-mass ratios \(\beta=0.1,0.4,0.7,1,1.3\) are selected within the working range of inerter for the two cases. The normalized displacement amplitude-frequency response curves of the primary system with different values of \(\beta\) are shown in Figure 9. We find that the larger the inerter-mass ratio is, the lower the amplitude and the better the effect are.