esa-sceva/satcom-chunk-collection huggingface dataset:
# Stretched Exponential Survival Analysis for South Korean Females
## 1 Introduction  
Statistical analysis of human lifespan is a central topic in demography and gerontology. Life expectancy of humans has risen steadily and rapidly over the past 150 years in most countries and keeps increasing in the 21 st century [1; 2; 3]. A recent study of 35 industrialized countries shows an interesting projection: there is more than 50% probability that by 2030, female life expectancy is likely to break the 90-year barrier [4]. Interestingly, South Korea is predicted to have the highest female life expectancy at birth by 2030 [4]. This prediction is achieved by applying a mathematical ensemble of 21 forecasting models to reliable statistics data about life tables for humans [4]. South Korean females are an important model to understand current trends in human lifespan and a better survival analysis is required for a better understanding of human lifespan.  
To achieve a more reliable prediction, a mathematical model of human survival curves is helpful in quantifying current trends in human lifespan for human aging and demography research [5; 6; 7; 8]. In fact, many mathematical models for survival curves have been proposed, including the Gompertz, Weibull, Heligman-Pollard, Kannisto, quadratic, and logistic models [5]. In a recent work, a mathematical model has been suggested owing to a usefulness to describe survival curves with complexity in shape by adopting a modified function from the stretched exponential (Weibull or Kohlrausch-Williams-Watts) function [9; 10; 11; 12; 13; 14]. Flexibility of a modified stretched exponential function, originating from anage-dependent stretched exponent, enables us to mathematically describe plasticity and rectangularity of survival curves [5; 13]. In this work, by incorporating this mathematical model to the reliable life table datasets taken from the Korean Statistical Information Service (KOSIS, [[http://kosis.kr/eng](http://kosis.kr/eng)]([http://kosis.kr/eng](http://kosis.kr/eng))), a practical application is demonstrated to quantify current trends in female lifespan for South Korea over three recent decades, from 1987 to 2016. This analysis for South Korea is compared with that for Japan, France, Australia, Switzerland, UK, Sweden, and USA, which are the representative leading countries in longevity [4].  
In this study, a mathematical analysis with a modified stretched exponential model is demonstrated to interpret the recent survival curves for South Korean female, taken from the Korean Statistical Information Service. The current trends for 1987-2016 are estimated from the female survival curves in South Korea based on the estimates of the characteristic life and the maximum lifespan.
# Management Platforms and Protocols for Internet of Things: A Survey
## 8 IoT Device Management Platforms  
### Management for the IoT (ManIoT)  
ManIoT platform allows managing devices that make up the IoT environments [93]. Figure 21 shows the applications and sensors installed physically on IoT management environment.  
The ManIoT platform takes into account the devices heterogeneity or \"things\". Therefore, ManIoT does not require modifications or installations of additional software on devices or applications in user devices. ManIoT accesses the applications through a Web user interface.  
Figure 20: Illustration of the CWMP Protocol messages.  
The ManIoT standardizes the data model and format used to applications, services, and devices communications. The device's status (on/off) and the Id (identification device) are characteristics used to model information. To integration with external systems, the platform uses popular protocols and data models of the industry, e.g., XML and RESTful API.  
The ManIoT project has two management scopes, Local and Global/Remote. The Local Manager acts to control events performed by a user or application devices that make up a particular scenario, for example, turning a water valve on or off. The remote manager standardizes the actions by users in different scenarios, consumption rates in various areas defined by the water utility.
## 5 Classification Performed by Machine Learning Algorithms
### Comparison with the Listening Test Results  
According to the obtained results presented earlier in Figure 7, for the BRIR matched test scenario, the machine learning algorithms markedly outperformed human listeners. The difference between the best performing algorithm (SVM) and the accuracy reached by the listeners who passed the post-screening test is equal to 24%. This difference is statistically significant at \(p=1.2\times 10^{-47}\) level. For the worst performing algorithm (Logit), this difference is slightly reduced, amounting to 16%, but it is still statistically significant (\(p=1.6\times 10^{-19}\)). However, when the algorithms are tested under the  
Figure 10: Classification accuracy obtained using the machine learning algorithms: (**a**) BRIR matched scenario; (**b**) BRIR mismatched scenario. Red line denotes no-information rate.  
Figure 9: Examples of confusion matrices obtained under BRIR mismatched conditions: (**a**) CNN, BRIR sets 1-11; (**b**) XGBoost, BRIR sets 1-11; (**c**) SVM, BRIR sets 1-11; (**d**) Logit, BRIR sets 1-11; (**e**) SVM BRIR set 9; (**f**) Logit, BRIR set 9. Vertical axis represents the classification results obtained from the classification algorithms.  
BRIR mismatched scenario, the performance of the classification algorithms is similar or even slightly worse than that exhibited by the listeners who passed the post-screening test. For the Logit algorithm, the classification accuracy is equal to 56.7%, which constitutes a 3% drop in the accuracy compared to the level attained by the listeners who passed the post-screening test. Nevertheless, this difference is not statistically different (\(p=0.11\)), and therefore the Logit algorithm cannot be regarded as performing worse than the listeners who passed the post-screening test. For the remaining three algorithms, this difference is greater (up to 6%) and statistically significant (\(p<0.011\)). Hence, it could be concluded that the CNN, XGBoost, and SVM algorithms perform slightly worse than the listeners who passed the post-screening test under the BRIR mismatched test scenario.
## 4 Results
### Experimental Results and Analysis
#### 4.3.4 Example Images and Analysis of Test Results  
Figure 8 presents a collection of images containing orthopedic medical devices along with their detection results generated by different methods. The improved DETR model's strong performance in handling issues such as occlusion and background interference is illustrated through these examples. This can be attributed to the series of optimization measures introduced in the model, which allow the enhanced DETR model to adapt to a  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**MLP-Mixer Backbone** & **Improved Transformer** & **Optimized Loss Function** & **MAP** & **FPS** \\ \hline \(\surd\) & & & 0.718 & 20 \\  & & & 0.703 & 24 \\  & \(\surd\) & & 0.750 & 23 \\  & & \(\surd\) & 0.739 & 19 \\ \(\surd\) & \(\surd\) & \(\surd\) & 0.756 & 26 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Results of ablation experiments on OrthoDETR model components.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Model** & **Average Precision** \\ \hline Baseline Model & 80.0\% \\ Contrast Enhancement & 82.5\% \\ Noise Addition & 81.3\% \\ Brightness Adjustment & 83.2\% \\ Flipping & 82.4\% \\ Rotation & 82.1\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: The impact of different data augmentation strategies on model performance. The baseline model refers to the original model without data augmentation, while the models for various augmentation strategies are based on the baseline model, with each respective data augmentation strategy applied individually.  
wide range of challenging visual environments. As a result, the improved DETR model demonstrates superior performance in practical applications.  
In summary, our method excels in the precise recognition task of orthopedic medical devices. Compared to techniques such as Faster R-CNN, YOLOv8, SSD, and RetinaNet, our improved DETR model achieves enhanced Average Precision (AP) and Average Recall (AR), demonstrating its superiority in terms of recognition accuracy. Simultaneously, our approach also exhibits commendable performance in inference speed, particularly when compared to two-stage methods like Faster R-CNN. Although slightly inferior in inference speed to single-stage methods like YOLOv8 and SSD, the improved DETR compensates for this shortcoming with its advantages in accuracy and robustness.
## 4 Algorithm Overview
### Upper-Level Decision  
To optimize signal time and determine EV route priorities, TMC uses input data, including traffic conditions, EV locations, and RV locations. Based on current traffic conditions, the estimated travel time for EVs and RVs is calculated. Once the priority level for each segment of the route is determined, the system gives priority to those who minimize the time of EV travel. To ensure smoothness and speed, traffic signals along the EV route are adjusted to create a \"green wave\" effect. Therefore, traffic signals and priority routes are updated accordingly, allowing EVs to move faster and more efficiently.  
_Objective for TMC_: Maximize the combined objective function using Equation (1).  
\[U_{\text{TMC}}=\beta\times(\text{Priority EV Success Rate})-\alpha\times( \text{Total Delay})-\gamma\times(\text{Fairness Penalty}) \tag{1}\]  
where: \(\alpha,\beta,\gamma\) are the weights assigned to different objectives (minimizing delay, maximizing EV success, and ensuring fairness).  
The fairness penalty measures the inequality in the distribution of road resources (lanes and signal priority) between emergency and regular vehicles.  
The TMC's utility function \(U_{\text{TMC}}\) aggregates rewards (EV success) and penalties (delays, fairness violations) to be maximized.
# Independent Random Recurrent Neural Networks for Infrared Spatial Point Targets Classification
## 2 Infrared Radiation Sequence Model  
### Radiation Intensity Analysis  
The emitted radiation is the main part of external radiation on the surface of the targets in outer space. The emitted radiation is determined by temperature, infrared emissivity, projection area, observing angle, etc. [21]. If the target surface is assumed to have gray body radiation and diffuse reflection characteristics, according to [PERSON]'s law, the infrared radiation intensity received by the detector focal plane in the band can be approximated as  
\[I(\lambda_{1}-\lambda_{2})=A_{0}A_{proj}/\left(\pi R^{2}\right)\cdot\int_{ \lambda_{1}}^{\lambda_{2}}M_{T}(\lambda)d\lambda\cdot\Delta t. \tag{1}\]  
Suppose \(A_{0}\) is the entrance pupil area, \(M_{T}(\lambda)\) is the radiation value at temperature of \(T\), and \(\Delta t\) is integration time of observing. If all available parameters are attributed to \(\kappa\), then Equation (1) can be further expressed as  
\[I(\lambda_{1}-\lambda_{2})=\kappa\cdot A_{proj}\cdot 1/R^{2}\cdot u(T). \tag{2}\]  
It can be seen from the formula that the infrared radiation intensity is mainly determined by the target surface temperature \(T\), the detection distance \(R\) (i.e., the linear distance between the detector and the target), and the geometric projection area of the target in the Line of Sight (LOS).  
The heat transfer method in the outer space environment is mainly heat radiation, and the changes of \(T\) of various targets in the middle stage are only maintained in a small range [22]. In the situation of long-distance detection, there is often a dense point target group, and the spatial distance is close. The changes of \(R\) are basically the same, so these two variables are difficult to provide important information for the classification of ballistic targets [23; 24]. In addition, \(A_{proj}\) depends on the target nutation and geometric parameters, which affects the wave structure of the data and is an important parameter for target recognition research.
## 3 AI/ML in the Standards and Industry
### Data Analytics and AI/ML in 3 GPP
#### NWDAF in Release 17
However, the number of model parameters, i.e., the number of NN weights, can also become large in the case of DNNs, not to mention the possibly many iterations required until convergence in federated learning. To further improve learning efficiency, federated distillation shares only the model output, i.e., the last layer of the DNN instead of the complete model, yielding, for example, 26 times less communication overhead at the cost of negligible performance loss in [72]. Other methods to reduce the signalling overhead involve model compression or model updates at longer intervals. The study in [73] considers federated learning at the RAN, where the clients are UEs and the server is a gNB. To reduce the number of errors in the received model parameters, the gNB optimises the allocation of RBs to a selected subset of UEs that experiences favourable channel conditions. For federated learning at the RAN, analogue transmissions of the model parameters from the learners to the server can also be helpful. In the analogue domain, every learner can participate in the model updates, utilising the full available bandwidth to communicate with the server, and thus, the model convergence time decreases. Recall that, in federated learning, the server is not interested in the individual parameters calculated from each learner, but only in their summation, which is equal to the superposition of the received analogue signals at the server [74]. An extensive review of federated learning for MEC can be found in [75], while the study in [76] presents open research topics and future directions for federated learning in 6G communication networks.
## 4 Discussion  
Prior to these experiments, the hypothesis was that the speed of the drone and the number of passes has an influence on the reading of unique or new tags. Results from our experiments suggest that only the number of passes will affect the number of new tags read. At 2.2 m/s, the label scanning is between 0.037 and 0.146 spt. At 1.1 m/s the label scanning is between 0.048 and 0.580 spt. The result also shows that the total time required  
Figure 13: The distance covered to read unique tag for each pass at three different speeds.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Source of Variation** & **SS** & **df** & **MS** & **F** & \(p\)**-Value** & **F Crit** \\ \hline No. of Passes & 81.3 & 7 & 11.6 & 9.3 & 0.000126 & 2.7 \\ Drone Speeds & 0.6 & 2 & 0.3 & 0.1 & 0.941182 & 3.5 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Anova results for scanning a new/unique tag.  
to collect tag information is determined by the drone's speed and the length of the flight path [20; 21]. In addition, at speeds of 1.1 to 2.2 m/s, the scan time ranged from 0.01 to 0.15 has a common overlap. Based on our results, the effect of the number of readings when the drone changes speed is not significant.  
Furthermore, the first pass results in the highest percentage of new tags scanned across all speeds. On the fifth pass, the scan rate begins to drop. However, 1.7 m/s scanned a total of 95 percent of new tags on the fifth pass, the highest percentage among the three speeds. The scan rate trend increases from the first to the fourth pass and then a decreasing trend from the fifth to the eighth pass. In addition, after the scan rate reaches 70% of the tags scanned, the scan rate also shows a decreasing trend. The decreasing trend was since only 30% of new tags are still available for reading.  
There were instances where readings were not reported, starting at the pass 5 onwards. In Figure 9, no readings were recorded at a drone speed of 2.2 m/s for pass 7 and 1.1 m/s for pass 8. Figure 10 also shows that at pass 5, at the speed 1.1 m/s did not register any readings, but readings were registered on passes 6 and 7. The same instances can be found in Figures 11 and 12, where no readings were registered at various speeds. This could be attributed to how the RFID-RM is responding to tag readings. Every tag read goes into an interrupt service routine where it collects all available information before the reader will be available for reading another tag. Although the tags are of the same manufacturer, some tags may respond quicker and thus create multiple instances of the interrupt, thus, delaying the reader. Moreover, the physical constraints feature of radio frequency poses a barrier to advanced anti-collision techniques for RFID chips, due to the interference from the transition signal's scattering and reflection [22].  
We presented two novel measurements on evaluating RFID reader efficiency by measuring how fast the reader can read and the shortest distance traveled of the RFID reader reading a tag. These two measurements can be used to compare other RFID readers developed by different manufacturers.
# A Novel Algorithm of Haze Identification Based on FY3D/MERSI-II Remote Sensing Data
## 2 Materials and Methods
### Data Sources
#### 2.2.2 Aqua/MODIS  
Two MODIS sensors were placed onboard the EOS Terra and EOS Aqua satellites. Terra/MODIS and Aqua/MODIS have been in orbit for more than 20 years, providing indispensable data sources for global and regional air quality monitoring, special weather events, urbanization development, assimilation systems and climate change, etc. [30; 31; 32; 33; 34]. Since FY3D is an afternoon satellite, the MYD35 cloud products with a resolution of 1 km\({}^{2}\) based on Aqua were selected for the evaluation, thus minimizing the inconsistency between the MERSI and MODIS mis-identification results over the time period. The meaning of the values in the dataset are as same as those of MERSI-II.
## 2 Materials and Methods
### Intercalation of O, L and Ln  
The intercalation compounds were obtained by ion exchange reactions achieved by equilibrating LDH-NO\({}_{3}\) with solutions containing the species of interest in anionic form. The compound containing oleate (O) was prepared by equilibrating the LDH-NO\({}_{3}\) with an aqueous solution of 0.1 M sodium oleate (0.2 g/6.4 cm\({}^{3}\) of solution; molar ratio O/NO\({}_{3}\) = 1), for 24 h at r.t. in the dark. The same experimental conditions were used for linoleate (L), linolenate (Ln) and for the mixture of the three anions (polar ratio O:L:Ln = 1:1:1). Intercalation compounds with O, L and Ln were also prepared by using a mixture of H\({}_{2}\)O/EtOH (1:1, _v/v_) as solvent. Subsequently, the intercalation compounds will be referred to as MgAl-O, MgAl-L, MgAl-Ln, and MgAl-OLLn (IFA).
## 3 Methods
### The AQM-PROBA Algorithm
#### 3.1.4 Burned Area Classification--One-Class Support Vector Machine (OC-SVM) Classifier  
The training sample was then used as input to the One-class Support Vector Machine (OC-SVM) classifier. OC-SVM is a machine-learning algorithm derived from the standard Support Vector Machine algorithm [(29)] and designed to tackle single-class classification problems. It optimizes separation of the target class by constructing a hyperplane that best represents the multidimensional edge of the feature space. The hyperplane is obtained by minimizing the function:  
\[t(w,\xi,\rho)=\frac{1}{2}\|w\|^{2}-\rho+\frac{1}{mv}\sum_{i=1}^{m}\xi_{i} \tag{1}\]  
where \(v\) is used to control the volume of the sphere that bounds the portion of the feature space where the training samples are located. It defines an upper limit to the outliers fraction found in the data [(58)], \(w\) is the width of the region besides the decision boundary, and \(m\) is the number of training instances.  
Although developed for linear problems, different kernel functions for nonlinear problems can be used with OC-SVM [(29)]. Among the kernel functions available for computation, the most widely used are the radial basis function (RBF), the sigmoid function, and the polynomial function [(50)]. Kernel choice is based on the data type and on the \(n\)-dimensional feature distribution. In the present analysis we used the RBF kernel in the present analysis which has been extensively and successfully used in remote sensing image-processing tasks [(59; 60)].  
According to the boundaries defined by the kernel function of the OC-SVM, the classifier returns positive values for classes that are similar to the classes in the training sample and negative values otherwise. Computations were carried out using the R-Studio and the package e1071 [(61)]. The \(v\) value is defined as the inverse of the number of features, which in the present study is equal to two (NIR values and NIR differences). The value of \(w\) may vary between 0, which yields low omission error at the expense of a higher omission, and 1, which has the opposite effect. We selected this value as 0.1, after a series of tests and visual analyses of the trials outputs. Finally, a morphological opening (erosion followed by dilation) filter by a square structuring element of size three was applied to the burned area map.  
Figure 3: (**a**) Cumulative density function (CDF) and Jenks natural breaks for post-fire near-infrared reflectance (NIR) reflectance values (T2); (**b**) Cumulative density function (CDF) and Jenks natural breaks for pre-fire minus post-fire NIR reflectance differences. Red dashed lines represent thresholds for sample selection. Black arrows show values admitted for sample selection.
## 5 Experimental Study
### Analysis of Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM)
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Image Used** & **Block Length** & **PSNR** & **SSIM** \\ \hline \multirow{4}{*}{Airplane} & \(4\times 4\) & 27.66 & 0.97 \\ \cline{2-4}  & \(8\times 8\) & \(\infty\) & 1 \\ \cline{2-4}  & \(16\times 16\) & \(\infty\) & 1 \\ \cline{2-4}  & \(32\times 32\) & \(\infty\) & 1 \\ \cline{2-4}  & \(64\times 64\) & \(\infty\) & 1 \\ \hline \multirow{4}{*}{Baboon} & \(4\times 4\) & 24.02 & 0.94 \\ \cline{2-4}  & \(8\times 8\) & 43.34 & 0.99 \\ \cline{2-4}  & \(16\times 16\) & \(\infty\) & 1 \\ \cline{2-4}  & \(32\times 32\) & \(\infty\) & 1 \\ \cline{2-4}  & \(64\times 64\) & \(\infty\) & 1 \\ \hline \multirow{4}{*}{Boat} & \(4\times 4\) & 28.58 & 0.97 \\ \cline{2-4}  & \(8\times 8\) & \(\infty\) & 1 \\ \cline{2-4}  & \(16\times 16\) & \(\infty\) & 1 \\ \cline{2-4}  & \(32\times 32\) & \(\infty\) & 1 \\ \cline{2-4}  & \(64\times 64\) & \(\infty\) & 1 \\ \hline \multirow{4}{*}{Peppers} & \(4\times 4\) & 31.09 & 0.99 \\ \cline{2-4}  & \(8\times 8\) & \(\infty\) & 1 \\ \cline{1-1} \cline{2-4}  & \(16\times 16\) & \(\infty\) & 1 \\ \cline{1-1} \cline{2-4}  & \(32\times 32\) & \(\infty\) & 1 \\ \cline{1-1} \cline{2-4}  & \(64\times 64\) & \(\infty\) & 1 \\ \hline \end{tabular}
\end{table} 
Table 2: Analysis of PSNR (in dB) and SSIM.