esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions  
This study established a performance-based UAV classification system using the K-Means++ algorithm and calibrated safety intervals for low-altitude operations under random heading conditions. Key contributions include the following:  
1. A UAV clustering model that improved the silhouette coefficient, SSE, and DBI by 53.9%, 55.6%, and 46.6%, respectively, enabling precise categorization based on maximum takeoff weight, flight altitude, and speed metrics.
2. A conflict probability model that translates 3D collision risks into geometric intersections of spherical safety zones, with calibrated intervals demonstrating applicability in both single-category and mixed airspace environments.
3. Empirical validation showing that Category 1-4 UAVs require safety intervals of 10 m, 15 m, 25 m, and 33 m, respectively, balancing collision prevention (conflict probability \(<\) 5%) and airspace utilization (\(>\)70%).  
These results provide theoretical and methodological support for standardizing low-altitude airspace management, facilitating the safe integration of diverse UAV operations.  
Conceptualization, [PERSON] and J.Y.; methodology, [PERSON]; software, [PERSON]; validation, H.X. and X.J.; formal analysis, H.X. and S.D.; investigation, X.J.; resources, J.Y.; data curation, X.J. and S.D.; writing--original draft preparation, H.X.; writing--review and editing, H.X. and J.Y.; visualization, X.J.; supervision, J.Y. and Y.Z.; project administration, J.Y. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Frontier Leading Technology Basic Research Project of Jiangsu Province (No. BK20222013), and the Major Science and Technology Project of Nanjing (No. 202405026).  
Not applicable.  
Data Availability Statement: Data are contained within the article. Conflicts of Interest: The authors declare no conflicts of interest. Abbreviations The following abbreviations are used in this manuscript: UAV Unmanned Aerial Vehicle SSE Sum of Squared Errors DBI Davies-Bouldin Index Sil Silhouette Coefficient K-Means++ K-Means++ Clustering Algorithm 3D Three-Dimensional ADS-B Automatic Dependent Surveillance-Broadcast 4D Four-Dimensional
# Image Segmentation by Searching for Image Feature Density Peaks
## 3 Experiment
### Evaluation
#### 3.2.1 Effects of Parameters  
Ronneberger2015 UNetConvolutionalIn our algorithm, there are practically only two parameters to control, \(h\) (bandwidth in Equation (3)) and \(k\) (constant in Equation (6)). \(h\) is a parameter which basically determines the bandwidth of smoothing window and have a clear impact on the performance. Typically, an estimate with smaller value of \(h\) might provide a better estimate to the empirical cumulative distribution function. We performed our algorithms with varied value of \(h\), and we found that the segmentation accuracy tends to be stable when when \(h\) varied from 0.5 to 1.5. Afterwards, we compare the performance when \(k=0.5,0.6,\cdots 1.5\) under fixed value of \(k\). We observed that thesegmentation accuracy achieves the best when \(h=0.8\). Thus we empirically set \(h\) to be 0.8 in our experiment for keeping a stable performance. Moreover, we found that the accuracy tends to be stable when \(k\) takes values between 0.04 and 0.06 after running our algorithm for a certain number of times. For \(k\in(0.04,0.06)\), we found that it also has a clear impact on the segmentation performance, as shown in Figure 8, where the accuracy indicates the ratio of the total correctly classified pixels. In addition, the accuracy tends to be stable when \(k\) is 0.0462 and 0.0515, and it reaches the peak of 0.0972 when \(k=0.0501\).
# Effects of Fineness and Dosage of Fly Ash on the Fracture Properties and Strength of Concrete
## 3 Experimental Details
### Testing of Specimens  
A compression test was carried out in accordance with ASTM C39. The three-point bend test was performed on the notched beam as follows to determine the \(\mathrm{G_{F}}\) and \(\mathrm{K^{S}_{IC}}\):  
1. Test for determining \(\mathrm{G_{F}}\).  
The fracture energy test followed the guidelines established by RILEM [17] using a closed-loop testing machine. The testing arrangement is shown in Figure 1. A linear variable differential transformer (LVDT) was installed at midspan of the beam to measure the deflection. The loading velocity was chosen so that the maximum load was reached 30 s after the loading started. The loading rate selected was 0.25 mm/min. A load-deflection (F-\(\delta\)) curve was then plotted, with the energy \"W0\" representing the area under the curve. The fracture energy (\(\mathrm{G_{F}}\)) according to this work of fracture method can be calculated as [12; 18]:  
\[G_{\mathrm{F}}=\frac{W_{0}+mg\delta_{0}}{A}\ (N/m) \tag{1}\]  
where W0 = area under the load-deflection curve (N/m); mg = weight of the specimen (kg); g = acceleration due to gravity; \(\delta_{0}\) = maximum deflection recorded (m); and A = cross-sectional area of the beam above the notch (m\({}^{2}\)).  
2. Test for determining \(\mathrm{K^{S}_{IC}}\).  
The test for the determination of \(\mathrm{K^{S}_{IC}}\) followed the test method suggested by [PERSON] and [PERSON] [18] using a closed-loop testing machine under the crack mouth opening displacement (CMOD) control. The test set-up is shown in Figure 2. A clip gauge was used to measure the CMOD. The CMOD and the applied load were recorded continuously during the test. The test procedure included loading and unloading. The test was started from loading until the peak load was reached, and then the applied load was manually reduced (also termed unloading) when the load passed the maximum load and was at about 95% of the peak load. When the applied load was reduced to zero, the specimen was reloaded. Each loading and unloading cycle was finished in about one min. Only one cycle of loading-unloading was required for the test. The \(\mathrm{K^{S}_{IC}}\) was calculated using the following Equation:  
\[K^{S}_{IC}=3(P_{max}+0.5W)\frac{S(\pi a_{c})^{1/2}F(\alpha)}{2d^{2}b}\Big{(}Nm^ {-3/2}\Big{)} \tag{2}\]  
in which  
\[W=W^{\prime}\times\frac{S}{L} \tag{3}\]  
\[F(\alpha)=\frac{1.99-\alpha(1-\alpha)\Big{(}2.15-3.93\alpha+2.7\alpha^{2} \Big{)}}{\sqrt{\pi}(1+2\alpha)(1-\alpha)^{3/2}} \tag{4}\]  
where \(P_{\mathrm{max}}\) = the measured maximum load (N); S = the span of the beam (m); b = beam width (m); d = beam depth (m); L = beam length (m); W\({}^{\prime}\) = self-weight of the beam (N); \(\alpha_{\mathrm{c}}\) = critical effective crack length (m); and \(\alpha=\alpha_{c}\)/d.
## 5 Results and Discussions
### Short-Run Analysis Results  
Table 6 depicted the short-run analysis results. Among the connection of variables, the cointegration presence requires an error correction model (ECM) to imprison the dynamics of the short-run relation with its coefficient, which measures the adjustment speed.  
The estimated value of R-squared is 0.996705 in the dynamics of the short-run relation, which demonstrates that about 99% variation in the economic growth was described in the model by the independent variables. The joint significance regarding the independent variables confirmed the F-statistic at a 1% level of significance. The value of the Durbin-Watson (DW) statistic was 2.575, which was not equal to the standard DW value for the nonappearance of resistance of any autocorrelation. However, this is good enough to expose whether any autocorrelation exists in the model.  
Diagnostic and stability tests results are presented in Table 7.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{4}{c}{**Short-Run Analysis**} \\ \hline
**Variables** & **Coefficient** & **Std. Error** & **t-Statistic** & **Prob.\({}^{\star}\)** \\ \hline LNGDPFC(-1) & \(-\)0.031504 & 0.233261 & \(-\)0.135061 & 0.8950 \\ LNEAP & \(-\)1.828790 & 1.992169 & \(-\)0.917989 & 0.3783 \\ LNEAP(-1) & 3.180164 & 2.076449 & 1.531540 & 0.1539 \\ LNEARP & 1.179909 & 1.159803 & 1.017336 & 0.3308 \\ LNEARP(-1) & \(-\)2.099826 & 1.266322 & \(-\)1.658209 & 0.1255 \\ LNEAUP & 0.184181 & 1.907163 & 0.096573 & 0.9248 \\ LNEAUP(-1) & 2.992745 & 1.832034 & 1.633564 & 0.1306 \\ LNEN & 1.549670 & 0.727377 & 2.130490 & 0.0565 \\ LNEN(-1) & 0.810703 & 0.732127 & 1.107325 & 0.2918 \\ LNPG & 6.825561 & 3.067669 & 2.224999 & 0.0479 \\ LNRPG & \(-\)6.964634 & 3.127366 & \(-\)2.226997 & 0.0478 \\ LNRPG(-1) & 2.850917 & 2.873678 & 0.992048 & 0.3425 \\ LNUPG & 10.11440 & 6.157831 & 1.642527 & 0.1287 \\ LNUPG(-1) & \(-\)9.796348 & 6.585777 & \(-\)1.487501 & 0.1650 \\ C & \(-\)27.93622 & 11.64342 & \(-\)2.399314 & 0.0353 \\ R-squared & 0.996705 & Mean dependent var. & 6.569321 \\ Adjusted R-squared & 0.992510 & S.D. dependent var. & 0.448658 \\ S.E. of regression & 0.038828 & Akaike info criterion & \(-\)3.365683 \\ Sum squared resid. & 0.016584 & Schwarz criterion & \(-\)2.639858 \\ Log likelihood & 58.75388 & Hannan-Quinn criter. & \(-\)3.156672 \\ F-statistic & 237.6347 & Durbin-Watson stat & 2.575936 \\ Prob(F-statistic) & 0.000000 & & & \\ \hline \hline \end{tabular}
\end{table}
Table 6: Short-Run Analysis.
## 2 Materials and Methods  
### Data Materials  
This study utilizes data from temperature measurement points on Xuefeng Lake Bridge in Anhua County, Yiyang City, Hunan Province, China. Spanning the Zhexi Reservoir, the bridge measures 1187 m in total length, with a main span of 500 m. It is a double-tower, double-cable-plane, semi-floating system, cable-stayed bridge, and it is a critical project for the Guanxin Expressway as the largest span steel-concrete composite girder cable-stayed bridge in Hunan Province. Temperature sensors installed vertically along the main girder provide surface temperature data from various positions, which we used to train models predicting the girder's temperature. By analyzing the temperature distribution's impact on the vertical deformation of the main beams during construction, we can adjust the construction elevation of the main girder. This adjustment ensures that the bridge's line shape aligns better with design specifications.  
The temperature data used in this study were collected from 23 June 2023 to 1 August 2023. Since higher temperatures have a significant impact on bridges, analyzing temperatures in spring, autumn, and winter is less meaningful. Therefore, we chose to use summer temperature data for model training and validation. Each day's data consists of the temperature readings from 40 measurement points, with temperature recorded once per minute at each point. After preprocessing the data, we obtained 57,600 data points for each measurement point. We randomly selected data from 10 out of 40 temperature measurement points for model training, and the data of the remaining 30 measurement points were used for the model compare experiments. An example of a temperature sensor installation is shown in Figure 1.  
Given the small time intervals of the selected data, it is exceptionally well-suited for validating and testing short-term prediction models, effectively meeting the demands of practical engineering applications. However, due to random sensor failures, the data obtained may contain unreasonable values. To address this issue, this study employed the multiple imputation method to handle the erroneous data [28].  
Figure 1: One of the temperature sensors and data collection devices on the main girder.
# Homomorphic Encryption Based Privacy-Preservation for IoMT
## 3 Proposed Scheme  
This section presents an overview of the healthcare environment and the components used in the proposed scheme to secure IoMT data. The environment consists of a hospital that houses patients and several IoMT devices that transmit data to the physical base station at the edge layer. Data at the edge is homomorphically encrypted, and the intelligent healthcare application sends computation queries for analysis on collected patient data. Base stations are supported by cloud computing resources for extensive computational operations and assign several virtual edge nodes with equal resources around the hospital. Virtual edge nodes are selected by the base station using secret sharing, which prevents untrusted cloud servers from learning the performed data computations. Furthermore, in the workflow subsection, we describe the particular method implemented to design virtual nodes at the edge layer, secure IoMT-edge data communication, encrypt data, and secure and preserve computation processes.
# Effects of Cement-Mineral Filler on Asphalt Mixture Performance under Different Aging Procedures
## 3 Results and Discussion  
### High-Temperature Anti-Rutting Performance  
For the modified asphalt mixture, the rutting test results under the conventional 60 \({}^{\circ}\)C test conditions tended to be high, resulting in a low degree of discrimination between the test results and high-temperature stability performance [34; 35]. However, for asphalt mixtures, aging will inevitably lead to an increase in dynamic stability and high-temperature stability. Therefore, this study used the short-time-aged test piece for the 70 \({}^{\circ}\)C rutting test for five types of filler combinations to analyze the effect of adding cement on the high-temperature stability of the asphalt mixture. The test results are summarized in Figure 3.  
With an increase in cement content, the high-temperature stability of the modified asphalt mixture showed an overall increasing trend. That is, adding cement as a filler helped to increase the high-temperature stability of the asphalt mixture, which is consistent with previous experimental results. When the ratio of M\({}_{\text{PC}}\)/M\({}_{\text{MP}}\) was 3:1, the dynamic stability reached its highest value. When the ratio of M\({}_{\text{PC}}\)/M\({}_{\text{MP}}\) was 4:0, that is, after the cement completely replaces the mineral powder, the dynamic stability exhibited a decreasing trend. This implies that the addition of cement can significantly improve the high-temperature stability of the mixture up to a certain concentration; however, because the filler uses cement, it no longer achieves the best high-temperature stability.
## 3 Results and Discussions
### The Validation Accuracy
Last but not the least, the initial speed loss agrees well with the model test measurements as shown in Figure 7f, which demonstrates the completely equivalent and high reliability of numerical simulation for emergency rising of the nonpropelled model. Overall, for the state parameters, e.g., pitch angle, incidence angle, axial velocity, etc., which presents good repeatability among multiple repeated tests, numerical simulation and model tests both have similar trends. As time goes on, the deviation of parameter curve is further reduced, even almost synchronized in time. Therefore, the prediction deviation of the longitudinal parameters in the bow-lifting phase is acceptable, with producing no essential impact on the rising stability analysis. Similarly, for the state parameters, e.g., roll angle, drift angle, yaw angle etc., which are susceptible to disturbance and exhibit dispersion within certain ranges of angle, the predicted results and test data also have similar trends. It is even possible to find out the test case that almost coincides with the numerical results, as shown in the above instance. Obviously, the roll, yaw, and drift angle are almost coincident. In other words, the numerical simulation has a high accuracy for predicting the lateral motion parameters, and it plays an extremely important role in analyzing the roll stability. To sum up, according to the above analysis, it is demonstrated that the numerical simulation method established in this paper can substantially reproduce the model test results with high accuracy and reliability. Therefore, the numerical simulation can be used as an important auxiliary tool to study the emergency rising stability under the condition of rudder steering, which cannot be accomplished in model test up to now. Certainly, the self-propulsion simulation with rudder steering would provide theoretical basis and technical support for the further model test.
# Optimizing Reinforcement Learning Using a Generative Action-Translator Transformer
## 2 Background
### Decision Transformer (DT)  
The DT model was introduced in 2021 by [PERSON] [11] and collaborators, pioneering a novel approach to integrating NLP models with reinforcement learning. Previously, the utilization of the Transformer in reinforcement learning primarily involved substituting a portion of the structure within the reinforcement learning algorithm, while the overall training framework still adhered to the principles of MDP. The DT algorithm distinguishes itself from traditional reinforcement learning in that it employs Transformer to directly learn the actions that the intelligent agent should take to achieve the desired reward, rather than focusing on learning strategies or value functions.  
To align with the Transformer structure, DT processes the training data differently. For the traditional trajectory \((s_{0},a_{0},r_{0},s_{1},a_{1},r_{1},s_{2},\ldots,s_{t},a_{t},r_{t},s_{t+1})\), it needs to be transformed into the following format:  
\[\tau=(\tilde{R}_{0},s_{0},a_{0},\tilde{R}_{1},s_{1},a_{1},\ldots,\tilde{R}_{t },s_{t},a_{t}) \tag{7}\]  
Here, \(a\) and \(s\) represent actions and states at a given moment, as in the original definition. However, \(\tilde{R}\) is not an immediate reward; instead, it represents the cumulative reward that can be earned from the current state to the end of the trajectory (Returns-to-go). This definition enables the conversion of reinforcement learning into a supervised learning form for training. The training framework is illustrated in Figure 2.  
We input the encoded sequence data as a token (the row of circles beneath the Embedding layer in Figure 2) into our Transformer. Here, three elements of the same moment \((\tilde{R}_{t},s_{t},a_{t})\) share the same positional information since these data come from the same moment. We model the joint distribution of the total return, state, and action trajectories for subsequent trajectories through Transformer. During training, we use a mask to hide the subsequent information of the current moment. This ensures that the Transformercan only use historical trajectories to infer the current action to be executed and output it. The optimization goal is the difference between predicted actions and real actions, using cross-entropy if the action is discrete and mean square error (MSE) if the action is continuous.  
After training the network, we input the maximum reward value of the environment as our target reward (a priori knowledge). We then input the network and the current state, performing the derivation to obtain a trajectory that can yield the target reward. The outlined process represents the fundamental flow of the DT structure.
# Application of Image Fusion in Diagnosis and Treatment of Liver Cancer
## 4 Imaging Fusion Algorithms
### Edge-Preserving Based Methods  
The edge-preserving filter emerged is an effective tool for image processing applications. The overall grayscale of traditional image smoothing filtering tends to be consistent in the neighborhood to achieve a smoothing effect. It is useful for the image with the similar pixel values (grayscale). However, this assumption does not hold at the edge of the image, which contains key information, since in this region, the grayscale tends to vary significantly with space coordinates. These grayscale variations provide meaningful image information. Thus, in many applications, weakening or filtering of the edges of the image during the filtering process is not desirable. Bilateral filters [142; 143] can solve the problem of low-pass filtering. A grayscale difference weighting value is introduced into the original spatial low-pass filter based on the original spatial filtering weight. The grayscale phase and spatial difference distances have similar local characteristics. This suggests that when a point differs greatly from the center point in grayscale, it is considered that the point is distant from that center point. Guided filter is a similar edge-preserving smoothing filter that applies an optimal local linear approximation to achieve the edge-preserving goal. In this method, the guiding filter guides the filter with a guiding graph G, while the filter window radius, r, and the smoothing intensity, \(\epsilon\), are adjustable parameters. The linear transformation of the model ensures that the appearance of the output edge only depends on the edge of the guiding graph G. After the image is smoothed by edge preservation, the large edge structure of the image is preserved, and small fluctuations corresponding to noise are smoothed out. The above characteristics of bilateral and guided filters can optimize fusion weight to ensure that the fusion is more smoothly connected and visually natural. These features are extremely useful in medical image fusion applications, such as fusion of CT and MR images [142; 143].
## 3 Results and Discussion
### Allocation of Anticancer Drugs in the Sediment and Suspended Solid Samples  
The occurrences of anticancer drugs in the solid state samples (river sediments and suspended solids) at Suita Bridge (R5) and Hirakata Bridge (R6) are summarized in Figure 2[31]. Three anticancer drugs (BLT, TAM, and CAP) were detected at appreciably higher concentrations in the suspended solids at 628, 13-658, and 231 ug/kg, respectively, with higher levels at the Hirakata Bridge than at the Suita Bridge. In the river sediments, however, only lower levels of the same two anticancer drugs (BLT and TAM) and DFUR were detected at 391, 42-250, and 392 ng/kg, respectively, with no clear difference between both of the sites. These profiles indicate an abundance of particles which have high affinity to the anticancer drugs in the river water. However, the concentration of the suspended solids at the Hirakata Bridge (2.6 mg) was about 30% of that of the Suita Bridge (9.0 mg). Based on these results, the pollution load of the anticancer drugs originating from the suspended solids was concluded to be not very large. Localization of DFUR and CAP in the different solid samples is attributable to the difference in the affinity of these drugs to the particles present in the samples. The sorption property of the present anticancer drugs on the river sediments was further characterized by a sorption experiment [31]. The measured log\(K_{\mathrm{d}}\) values varied in a range from \(-0.4\) to \(2.1\) (Table 6). The actual log\(K_{\mathrm{d}}\) values of the individual anticancer drugs in the sediments at the Hirakata Bridge and the Suita Bridge were 0.4 and 1.4 (BLT), 2.1 and 1.6 (TAM), 0.8 and 0.6 (CP), \(-0.4\) and 0.1 (CAP), 0.9 and 1.5 (DFUR), and 1.3 and 0.4 (TGF), respectively. The concentrations of the anticancer drugs at the Suita Bridge showed a tendency to become higher than those at the Hirakata Bridge. This result is attributable to differences in the particle types in the sediment: sandy sediment at the Hirakata Bridge (moisture content of 9% with 79% sand, 3% silt, and 0% clay) and silty sediment at the Suita Bridge (moisture content of 49%, but no available data for its composition). Similar observation has also been reported in previous studies dealt with different pharmaceuticals [60; 67].