esa-sceva/satcom-chunk-collection huggingface dataset:
# Influence of Pre-Radiation and Photo-Bleaching on the Yb-Doped Fiber Laser Radiated with Gamma-ray
## 4 Results and Discussions of Photo-Bleaching  
### Output Power  
In material science, it is usually necessary to study and observe the morphology and chemical composition of samples at the micrometer scale. Radiation resistance of Yb-doped fibers is closely related to the fibers' doping elements. The type of commercial Yb-doped fiber in this research is Nufern-976-20/400, presenting a stronger radiation resistance than the Yb-doped aluminophosphosilicate ternary fiber in our previous work [41]. Therefore, an EPMA was applied to identify the crucial doping elements in this commercial Yb-doped fiber core. EPMA is a relatively mature testing instrument, which uses an electron beam to excite the intersecting surface of Yb-doped fiber samples and perform feature analysis on various signals generated. Carbon needs to be sprayed on the intersecting surface of fiber samples to make it conductive.  
The radial elemental concentration of Al2O3, P2O5, and Yb2O3 was measured to be 1.13 mol%, 2.20 mol%, and 0.15 mol%, respectively, as shown in Figure 7. With the molar ratio of (Al + P)/Yb being more than 22:1, Yb ions could be sufficiently dissolved in the fiber core area, which would prevent Yb ions from clustering and increase the fluorescence lifetime [42; 43; 44]. The concentration of P is approximately twice that of Al, which is helpful to improve the radiation resistance, due to the formation of [AlPO4] units with a large charge-compensating effect, and excess P formed a solvation shell surrounding Yb3+ ions [22].  
Due to the ability to convert Yb2+ ions into Yb3+ ions and simultaneously eliminate the hole-related defect [34; 38], the 532 nm laser was suitable as a photo-bleaching source for the radiated Yb2O3-Al2O3-P2O5 ternary fiber. To investigate the bleaching effect, 532 nm light was fed into the pre-radiated fiber laser with 10 krad(Si) radiation, as shown in Figure 8a. The power of 532 nm light was 5 W, and the photo-bleaching duration lasted 2 h. After the bleaching treatment, we measured the output power of the fiber laser, as shown in Figure 8b. The output power of the pre-radiated fiber laser partly recovered with an increase of 26 W, and the slope efficiency also recovered to 74.8%. The research indicated that part of the radiation-induced defect was bleached by 532 nm light, and the output  
Figure 7: Radial elemental concentration profiles in the Yb-doped commercial fiber core, (**a**) Al2O3 and P2O5, (**b**) Yb2O3.  
power has been correspondingly increased while the radiation-induced thermal load partly decreased. Time and frequency domain signals of the output laser were also investigated by the combination of the above-mentioned highspeed photodetector and oscilloscope, which proved no MI effect existed at this pump power. In this research, the MI threshold raised approximately 227 W through the photo-bleaching treatment of 532 nm light.
## 3 Summary of the Considered Mobility Models and Routing Protocols
### The Routing Protocols
#### 3.2.2 Geographic Routing Protocol (GRP)  
The geographic routing protocol (GRP) [14] is a position-based routing protocol based on two assumptions: (1) each node is aware of its own geographic location and the location of its immediate neighbors; and (2) the source node is aware of the position of the destination node. Immediate neighbors' locations are updated periodically by means of HELLO messages. The data packets are routed through the network using the geographic location of the destination. GRP operates without routing tables, and routing to the destination relies on the information each node has about its neighbors. The most commonly-used geographic routing algorithms are greedy forwarding and face routing. In greedy forwarding, the data packet is brought closer to the destination in each step by the holding node forwarding it to the neighbor that reduces the distance to the destination. Greedy forwarding fails if there is no next hop among the neighbors that is closer to the destination. When this happens, the greedy forwarding switches over to perimeter mode, where the next hop is selected to traverse the perimeter of the region where greedy forwarding fails. Perimeter mode forwarding continues as long as there is no better greedy next-hop neighbor. In face routing, the regions are considered to be separated by the edges of a planar graph. The algorithm takes a way around the face; it returns to the point closest to the destination and explores the next face closer to the destination. Face routing always finds a path to the destination.
## 3 Results
### Temporal Evolution of Polarimetric Observables
#### 3.1.1 SAR Backscattering
The range of variation was small; the value from mid-July to late September was larger and more stable, which can be distinguished from other crops. When winter wheat biomass peaked in late June, the response of HV increased, so the HV/VV also reached the peak. Other crops peaked mainly from July to September, such as corn, which peaked in mid-August. The peaks for forage, tobacco and watermelon all occurred in early September. Tobacco showed a steady state of HV/VV (about \(-10\) dB) from mid-July to late September. The variation of watermelon was the clearest, with a continuous upward trend from May to September, reaching a very high peak (\(-4\) dB), and then decreased from September to October.
###### Abstract  
This study focused on improving the forecasting of the afternoon thunderstorm (AT) event on 5 August 2018 near Pingtung Airport in southern Taiwan through a three-dimensional variational data assimilation system using Doppler lidar-based wind profiler data from the Weather and Research Forecast model. The assimilation of lidar wind profiler data had a positive impact on predicting the occurrence and development of ATs and wind fields associated with the local circulations of the sea-land breeze and the mountains. Evaluation of the model quantitative precipitation forecast by using root-mean-square error analysis, Pearson product-moment correlation coefficient analysis, Spearman rank correlation coefficient analysis, and threat and bias scores revealed that experiments using data assimilation performed much better than those not using data assimilation. Among the experiments using data assimilation, when the implementation time of assimilation of the wind profiler data in the model was closer to the occurrence time of the observed ATs, the forecast performance greatly improved. Overall, our assimilation strategy has crucial implications for the prediction of short-duration intense rainfall caused by ATs with small temporal and spatial scales of few hours and a few tens of kilometers. Our strategy can help guarantee the flight safety of aircraft.  
a _Atmosphere_2022, 13, 1341. [[https://doi.org/10.3390/atmos13091341](https://doi.org/10.3390/atmos13091341)]([https://doi.org/10.3390/atmos13091341](https://doi.org/10.3390/atmos13091341))
## 2 Study Area and Data  
### Study Area  
Located at the eastern edge of Qinghai-Tibet plateau, the Zoige Plateau is covered by the largest area of peat wetlands in the world [29]. It extends from 102.00\({}^{\circ}\)E to 103.50\({}^{\circ}\)E and from 33.00\({}^{\circ}\)N to 34.35\({}^{\circ}\)N, with the average altitude of 3500 m. The climate in the Zoige plateau is cold and wet, with an annual mean temperature of 0.7 \({}^{\circ}\)C-3.3 \({}^{\circ}\)C. The mean annual precipitation recorded by the nearby Zoige meteorological station (at 3439 m a.s.l) is 648.5 mm during the period 1971-2000. Most of the precipitation falls as rain during the summer months (June-September), owing to the influence of Asian summer monsoons [30]. Because of the concentrated precipitation during this period, it is difficult to acquire the clear-sky satellite imagery, especially for the infrequent revisit satellites such as Landsat. Besides, the water levels of wetland area on the Zoige Plateau manifest substantial seasonal variations, primarily in response to precipitation events. The seasonal variation of surface water aggravates the complexity of the land surface of this area. Because of the difficulty to obtain clear-sky satellite imagery and the highly heterogeneous land surface on the Zoige Plateau, it is typical and suitable to test the quality of the composited product. An area of about 22,500 km\({}^{2}\) (150 km \(\times\) 150 km) in the Zoige plateau is selected as the case study area (Figure 1). The study area is mainly covered by grassland, meadow, swamp meadow and peat land [31].
# Multi-Sensor Photoelectric Fire Alarm Device Implementation for Early Fire Detection in Campsites
## 3 Multi-Sensor Photoelectric Fire Detection System
### Relationship Between Weather and Fire Occurrence  
#### 3.1.1 Data for Analysis  
This study's dataset comprises hourly meteorological and fire data recorded in 2021 from various regions, including Seoul, Daegu, North Gyeongsang, Gyeonggi, and Daejeon, in South Korea [13; 14]. Meteorological data, including temperature, humidity, wind direction, wind speed, and precipitation, were collected at minute intervals from Automatic Weather Stations (AWS) situated at key observation stations in areas corresponding to the 17 fire headquarters. This dataset encompasses a total of 1,185,864 individual cases, with this study focusing primarily on temperature and humidity data, as illustrated in Table 1, considering their influence on fire ignition at campsite tents.  
Fire occurrence data, as presented in Table 2, were obtained from the National Fire Information System [13], which contains fire investigation reports documented by 183 Fire stations across South Korea. This dataset contains 184 distinct attributes that were catego  
\begin{table}
\begin{tabular}{l l l l l} \hline
**Location** & **Location Name** & **Day** & **Temperature (\({}^{\circ}\)C)** & **Humidity (\%)** \\ \hline
116 & Gwanak-gu & 2021-01-01 1:00 & \(-\)12.3 & 74 \\ \hline
116 & Gwanak-gu & 2021-01-01 2:00 & \(-\)11.6 & 75 \\ \hline
116 & Gwanak-gu & 2021-01-01 3:00 & \(-\)12 & 74 \\ \hline
116 & Gwanak-gu & 2021-01-01 4:00 & \(-\)11.6 & 76 \\ \hline
116 & Gwanak-gu & 2021-01-01 5:00 & \(-\)10.8 & 60 \\ \hline
116 & Gwanak-gu & 2021-01-01 6:00 & \(-\)10.5 & 54 \\ \hline
116 & Gwanak-gu & 2021-01-01 7:00 & \(-\)9.8 & 37 \\ \hline
116 & Gwanak-gu & 2021-01-01 8:00 & \(-\)9.9 & 52 \\ \hline
116 & Gwanak-gu & 2021-01-01 9:00 & \(-\)9.4 & 62 \\ \hline
116 & Gwanak-gu & 2021-01-01 10:00 & \(-\)7.9 & 67 \\ \hline \end{tabular}
\end{table}
Table 1: Sample data extracted from AWS dataset.  
rized under the National Fire Information System and comprised a total of 36,268 instances of fire occurrences.  
In this study, we merged data from these two datasets based on the fire occurrence date and the corresponding province, district, or city within the regions of Seoul, Daegu, North Gyeongsang, Gyeonggi, and Daejeon in South Korea. Initially, as indicated in Table 3, a total of 24,412 cases were extracted from the data. After excluding records with ambiguous regional names or missing humidity data, the final dataset used for the analysis consisted of 15,946 cases.
## 2 Related Works
The competency of the classifier is measured by considering the neighborhood of each sample augmenting with selection criteria (with a threshold option) for a classifier corresponding to the minority class in this neighborhood. The authors in [46], proposed the Bayesian learning probabilistic model to improve the performance of Bayesian classification using the combination of a Kalman filter and K-means. The method is applied to a small dataset just for establishing the fact that the proposed algorithm can reduce the time for computing the clusters from the data. The authors in [47] proposed a deep image analysis-based model for glaucoma diagnosis that uses several features to detect the formation of glaucoma in the retinal fundus. The proposed model is combined with SVM, KNN, and NB to investigate the various aspects related to the prediction of glaucoma in retinal fundus images that help the ophthalmologist make better decisions for the human eye. Some of the prominent existing techniques reported in the literature are listed in Table 1. \begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt} p{142.3 pt}} \hline \hline
**Solutions** & **Strength** & **Weakness** \\ \hline Decomposition strategies [45] & Transform imbalance multi-class problems into several classes and design a separate classifier for each class. & In the case of multiple classes, handling individual classifiers for each class is time consuming. \\ \hline Optimization classification model (OCM) using evolutionary computation (EC) techniques, Self-adaptive Fireworks Algori3m (SaFWA) based on swarm intelligence [44] & OCM deals with classification problems, and SaFWA deals with optimization problems. To increase the diversity/range of the solutions, four candidate solution generation strategies (CSGSs) merged with SaFWA. & Both in the oversampling and undersampling, the samples in the classes were discarded or increased, while our focus was on increasing the samples in the underlying dataset to minimize visibility. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Weaknesses and Strengths of Existing techniques in the literature.
# Modeling and Compensation of Motion Errors for 6-DOF Robotic Manipulators
## 4 Simulation Studies for a 6-DOF Manipulator
### Errors Caused by Connecting Rod Deformation  
A mechanical analysis model of the manipulator was established, and a stress analysis was carried out to compensate for the errors caused by connecting rod deformation. To facilitate the stress analysis of the manipulator, the model of the manipulator was simplified and constructed in ANSYS software, and the basic configuration and rigid body characteristics of the manipulator with six degrees of freedom were retained. The'material' was set as'structural steel' and the'mesh' was set as'medium'. Under the experimental conditions, it was difficult to keep the generalized force constant on the manipulator arm, so the direction of the generalized force was set as straight down, which is also helpful to design experiments. Figure 4 shows the deformation of the manipulator when the manipulator arm was in a posture with different loads of 0 N, 10 N, and 20 N. The maximum displacement of the manipulator itself was basically maintained at approximately 0.03 mm. However, it can also be seen that errors caused by the stiffness of the connecting rod of the manipulator do exist and will affect the positioning accuracy of its end. Considering that the stiffness model of the joint link was very difficult to calculate, a finite element analysis was carried out for the different postures of the manipulator to better compensate for the error. The displacement results of the finite element analysis were added to the final error compensation model as the additional generalized errors of the manipulator.
## 2 Methodology
### Data Filtering Based on Multi-Source Semantic Information
#### 2.3.1 Word Vector Generation  
Before the similarity calculation, the data should be transformed into vector representation. In this study, we used the word2 vec model, a neural network model that represents words in the corpus as vectors with contextual understanding [41]. In vector space, the closer the distance between two vectors is, the more similar the two words are. The results of word2 vec depend on two user-defined parameters: the dimension of vector space (i.e., size) and the maximum distance between a word and the surrounding words in a sentence (i.e., window). Word2 vec can be configured in two ways: skip-gram and the continuous bag of words (CBOW). The main difference is that skip-gram aims to predict the surrounding words by entering reference words, while CBOW uses the surrounding words to predict the current words. In this study, we set the dimension and window of vector space to 100 and 5, respectively, and selected CBOW as the configuration mode. Finally, we obtained the n-dimensional vector space and calculated the similarity between words using the distance function, which, in turn, calculates the similarity between multi-source semantic information and websites.
# Privacy-Preserving Handover Optimization Using Federated Learning and LSTM Networks
## 3 Method
### Dynamic Handover Algorithm
This adjustment aims to make the handover decision process more stringent in environments prone to frequent, unnecessary handovers, thereby enhancing network stability and reducing rapid, back-and-forth handovers. The core of the algorithm is a loop that iterates over each pair of predicted RSRP and NR-RSRP values with their respective index i. For each iteration, the algorithm calculates the previous average RSRP and NR-RSRP. If i is greater than 0, it computes these averages over the past three values or fewer if i is less than 3. If i is 0, the previous averages are set to the current predicted values. Next, the algorithm adjusts the dynamic_threshold, which is computed based on the base_dynamic_threshold and the deviation of the current predicted RSRP from the previous average RSRP, normalized by mae_rsrp. The handover conditions are then evaluated: if the current predicted RSRP exceeds the previous average RSRP minus the dynamic threshold and the current predicted NR-RSRP is greater than the previous average NR-RSRP, the continuity_count is incremented. If the continuity_count meets or exceeds the adjusted TTT threshold, optimized_handover_predictions[i] is set to 1. If the conditions are not met, the continuity_count is reset to 0. This loop continues until all predicted values are processed. In summary, the Algorithm 1 leverages real-time signal strength predictions to make more informed and optimized handover decisions, effectively minimizing unnecessary handovers and enhancing the overall efficiency of the wireless network. By utilizing Figure 4, the identification and calculation of ping-pong effects during handovers in mobile networks can be achieved. Ping-pong effects occur when a mobile device rapidly switches back and forth between two base stations within a short period. This can be further validated to ensure that the algorithm meets expected performance standards. Figure 5 is divided into three parts, they are Data Preprocessing, F-LSTM and Dynamic Handover Decision. Data Preprocessing prepares the dataset by handling missing data, normalizing values, and removing sensitive information. F-LSTM explains, trains and  
Figure 4: Explanation of the Process for Checking Ping-Pong Conditions. aggregates local models in a federated learning framework, predicting RSRP and NRxRSRP values. The Dynamic Handover Decision statement uses predicted and historical data to make intelligent, real-time handover decisions, adjusting thresholds based on detected network conditions like the ping-pong effect, and the upper right corner is annotations for relevant parameters.
## 4 Results and Discussion
### Compaction Test Results  
The compaction test results are shown in Figure 4 in the form of dry density versus water content. Generally, the compaction curves of the contaminated soil and both treated soils moved below the natural soil curve. The MDD of the contaminated soil substantially decreased with a 14% oil content to a low value of MDD (1.625 g/cm\({}^{3}\)) due to the oil content in contaminated soils. This reduction is attributed to the effect of the specific gravity value of crude oil on the soil. Moreover, with silty sandy soils polluted with crude oil, the particles separated as the voids filled with the oil and coated the granules. Therefore, a decline in dry density was observed as the soil transited into a loose material state. Similar results are reported by [PERSON] et al. [40], [PERSON] et al. [7], and [PERSON] [31]. Nevertheless, these findings differ from other studies by [PERSON] et al. [9], [PERSON] et al. [32], and [PERSON] et al. [17]. At the same time, no discernible change in the OMC was noticed between the natural soils and polluted soil by crude oil.  
The MDD of the treatment soil slightly increased when the soil was solidified with CEM II and OPC, reaching a peak at 1.69 g/cm\({}^{3}\) and 1.635 g/cm\({}^{3}\). A high increase in OMC could be observed compared to the OMC of natural soil, particularly in relation to the soil treated with CEM II. In comparison, the value of MDD of the natural soil was much higher than that for treated soils. By adding the cement to the polluted soil, the MDD of the stabilized contaminated soil increased due to the specific gravity of cement (commonly 3.15) compared with the contaminated soil (2.35). Meanwhile, the OMC increased since cement has a better absorption potential for water.  
Figure 4: Compaction curves of natural, contaminated, and treated (CEM II and OPC) soils.
# Machine Learning Models for Ecofriendly Optimum Design of Reinforced Concrete Columns
## 3 Results
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **R\({}^{2}\)** & **NRMSE** & **NMAE** & **NMSE** \\ \hline \multicolumn{5}{c}{Quintuple combinations} \\ lm, dtr,ent,knr,svr & 0.718 & 184.846 & 147.726 & 86,664.231 \\ lm,ent,knr,dtr,svr & 0.719 & 185.841 & 148.288 & 87,732.871 \\ knr,lm,dtr,svr,ent & 0.7196 & 186.463 & 148.442 & 86,791.598 \\ knr,dr,svr,lm,ent & 0.724 & 185.953 & 149.100 & 86,301.562 \\ dtr,knr,lm,svr,ent & 0.7208 & 187.130 & 146.722 & 85,077.065 \\ dtr,svr,lm,ent,knr & 0.720 & 184.505 & 147.026 & 86,665.337 \\ ent,dtr,lm,knr,svr & 0.722 & 186.811 & 148.981 & 86,171.159 \\ ent,svr,dtr,lm,knr & 0.719 & 185.300 & 146.752 & 86,947.917 \\ svr,ent,dtr,lm,knr & 0.719 & 184.548 & 147.960 & 87,283.171 \\ svr,lm,ent,dtr,knr & 0.718 & 185.733 & 148.987 & 86,629.887 \\ Octal combinations & & & & \\ gbr,hgbr,rr,ent,lm,dtr,knr,svr & 0.888 & 119.027 & 93.466 & 35,191.334 \\ hgbr,rfr,lm,dtr,knr,gbr,ent,svr & 0.889 & 118.193 & 93.847 & 35,303.200 \\ svr,knr,ent,dtr,fr,gbr,lm,hgbr & 0.888 & 118.854 & 94.387 & 35,349.299 \\ \hline \multicolumn{5}{c}{Stacking} \\ Final Estimator = Gradient Boosting Regressor & & & & \\ gbr,hgbr,rfr,ent & 0.998 & 18.155 & 11.1406 & 876.063 \\ lm,dtr,ent,knr,svr & 0.996 & 30.292 & 18.912 & 2547.112 \\ hgbr,gbbr,rfr & 0.998 & 17.952 & 11.102 & 881.042 \\ gbr,hgbr,rfr,ent,lm,dtr,knr,svr & 0.998 & 18.445 & 11.616 & 895.697 \\ Final Estimator = Hist. Gradient Boosting Regressor & & & & \\ gbr,hgbr,rfr,ent & 0.997 & 18.080 & 11.1449 & 866.437 \\ lm,dtr,ent,knr,svr & 0.996 & 30.388 & 17.929 & 2840.752 \\ hgbr,gbbr,rfr,ent,lm,dtr,knr,svr & 0.997 & 19.249 & 11.326 & 1028.443 \\ gbr,hgbr,rfr,ent,lm,dtr,knr,svr & 0.997 & 18.023 & 11.443 & 1021.252 \\ Final Estimator = Random Forest Regressor & & & & \\ gbr,hgbr,rfr,ent & 0.998 & 17.589 & 11.173 & 864.799 \\ lm,dtr,ent,knr,svr & 0.996 & 30.151 & 18.697 & 2897.098 \\ hgbr,gbbr,rfr & 0.998 & 18.106 & 11.824 & 939.706 \\ gbr,hgbr,rfr,ent,lm,dtr,knr,svr & 0.998 & 17.582 & 10.921 & 912.215 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: _Cont._been obtained. The lowest R\({}^{2}\) in stacking was obtained from the quintuple combination of linear model, decision tree, elastic net, K neighbors, and support vector. Most of the modelers did well in predicting the outcomes. The average correlation coefficients (R\({}^{2}\)) between actual values and estimates range from 0.71 to 0.99. In cases where the RMSE value is close to 0, it is evident that the models predict very low error rates.