esa-sceva/satcom-chunk-collection huggingface dataset:
# Iterative MLFMA-MADBT Technique for Analysis of Antenna Mounted on Large Platforms
## 4 Numerical Results
### Patch Antenna Array Installed on an Airplane  
An airplane model which has a complex patch antenna array with 16 \(\times\) 18 (288) elements installed above the top is studied. The layout and feeding structure of the patch antenna array is shown in Figure 8a-c. The length of the airplane is 17.7 m, the wingspan is 15.6 m and the height is 4.9 m, as illustrated in Figure 9a. The dimension of the patch antenna array is 0.74 m \(\times\) 1.17 m \(\times\) 0.004 m and the magnitude of each element is 1 \(Idl(Am)\). The work frequency of the antenna is 3 GHz.  
As the antenna is placed about 0.2 m (2\(\lambda\)) above the airplane, there is no need to partition a transition region between the airplane (BT region) and antenna (MLFMA region). The mesh size is about 1\(\lambda\) for the BT region in the iterative MLFMA-MADBT method.  
The far-field radiation patterns of the model in XOZ plane are shown in Figure 9b. According to the radiation patterns and the comparison mean, standard deviation, the result of the proposed iterative MLFMA-MADBT method agrees well with the result based on the MLFMA method, which suggests that the proposed method is correct and valid even for platforms with curved surfaces and modeled by coarse mesh.  
Figure 8: (**a**) The layout of the patch antenna array. (**b**) The plan view of a single patch antenna. (**c**) The 3D view of a single patch antenna.  
Figure 9: The model and far-field radiation patterns in XOZ plane of the patch antenna array installed above the top of an airplane. (**a**) Model (**b**) Comparison result.
## 5 Discussion  
### About the Model  
The efficacy of extracting water bodies is intrinsically linked to the quality of feature information fed into the network. Historically, both optical and SAR imagery have been leveraged for this task. Nonetheless, SAR imagery often falls prey to noise interference, hampering the clarity of water body delineations. Concurrently, optical imagery acquisition becomes problematic during cloudy conditions, and the extraction process is often confounded by the'same spectrum, different objects' dilemma. One strategy for amalgamating optical and SAR images involves their concatenation within the channel dimension. While this methodology endows the network with a richer feature set, the simplistic nature of pixel-level fusion inadvertently ushers in substantial noise during feature extraction, given the unselective sharing of weight information. In contrast, feature-level fusion operates on a multimodal platform, treating different satellite data individually based on their inherent properties. This approach involves a comparative analysis of labels to discern the contribution of each pixel to varying branches, thereby optimizing parameters for a more effective water bodies extraction process.  
The efficacy of water bodies extraction is intricately tied to the network architecture employed. This study utilizes a hybrid network, drawing upon the strengths of both Transformer and CNNs, structured around the U-Net framework for enhanced multimodal water bodies extraction. During the single-modal feature encoding phase, an innovative connection is forged between the DCB-ConvNet and Transformer branches through systematic feature linkage in the channel dimension at each level. This structural confluence facilitates a robust extraction of global semantic information, augmented by detailed local features. An advanced D-CBAM module is integrated to steer the DCB-ConvNet branch, emulating human perceptual capabilities by automatically modulating the receptive field size in response to the diverse morphologies of complex water bodies, thereby bolstering local feature amplification. Within the Transformer branch, the self-attention mechanism's inclusion significantly mitigates the limitations traditionally associated with convolutional operations' ability to model long-range dependencies. This strategic amalgamation effectively addresses the global context insufficiency and extended semantic interactions typically lacking in CNNs. Furthermore, the introduction of a FPA module enhances the network's proficiency in discerning water bodies across various scales. Feature-level fusion is meticulously achieved by concatenating the features distilled from both the Transformer and DCB-ConvNet branches within the channel dimension. Progressing to the decoding stage, the study employs U-Net architecture's skip connection concept, leveraging high-level feature insights to navigate lower-level feature synthesis, culminating in a more nuanced extraction of edge detail features.
## 5 Experimental Results  
### Evaluation Measures  
To evaluate the results of the VO algorithm based on feature-based methods, we use several evaluation metrics as follows:  
* The absolute trajectory error (\(ATE\)) [12] is the distance error between the ground-truth \(\hat{AT}_{i}\) and the estimated motion \(AT_{i}\) trajectory, aligned with an optimal \(SE(3)\) pose **T**. \(ATE\) is calculated according to Equation (14): \[ATE=\min_{T\in SE(3)}\sqrt{\frac{1}{l_{g^{t}}}\sum_{i\in l_{gt}}||TAT_{i}-\hat{ AT}_{i}||^{2}}\] (14) where \(t_{rel}\) is the average translations \(RMSE\) drift (%) on a length of 100-800 m. \(r_{rel}\) is the average rotational RMSE drift (\({}^{\circ}\)/100 m) on a length of 100-800 m.
* We calculate trajectory error (\(Err_{d}\)), being the distance error between the ground-truth \(\hat{AT}_{i}\) and the estimated motion \(AT_{i}\) trajectory. \(Err_{d}\) is calculated according to Equation (15): \[Err_{d}=\frac{1}{N}\sqrt{||AT_{i}-\hat{AT}_{i}||^{2}}\] (15) where \(N\) is the frame number of the frame sequence used to estimate the camera's motion trajectory.
* In addition, we also calculate the ratio of the number of frames with detected keypoints (\(r_{d}\)(%)).
## 6 Experimental Analysis
### Simulation Verification
#### 6.1.2 Controller Disturbance Rejection Performance Analysis  
In order to verify the attitude tracking performance and disturbance rejection performance of the optimized ADRC controller, the attitude tracking experiment is designed, and the turbulence wind field disturbance is added to verify the attitude tracking performance and disturbance rejection performance of the controller, respectively. The experimental results are compared with unoptimized ADRC. The parameters of the unoptimized ADRC controller are shown in Table 4. The optimized controller parameters come from the optimized results of ADRC by FOFPSO algorithm mentioned in Section 6.1.1.  
Figure 8: Iterative curve of global optimal value.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Value** & **Parameter** & **Value** \\ \hline \(\beta_{01}\) & 81 & \(c\) & 225 \\ \(\beta_{03}\) & 1695 & \(h_{1}\) & 210 \\ \(\beta_{04}\) & 6709 & \(b_{0}\) & 48 \\ \(r\) & 215 & \(h_{0}\) & 0.004 \\ \(r_{0}\) & 20 & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Optimized parameters of ADRC.  
In the disturbance rejection performance analysis experiment, the sine signal is used as the expected signal, the amplitude is 0.31 rad and the simulation time is 10 s. During the period of 2 s to 6 s, the turbulent wind field is added. The turbulent wind field is shown in Figure 9, and the experimental results are shown in Figure 10.  
The attitude response curve fluctuates greatly near 2 s and 5 s due to the interference of turbulent wind field. Among them, for attitude tracking, both ADRC and FOFPSO-ADRC can retain the stability of attitude tracking under the interference of the wind field. However, because ADRC is optimized by FOFPSO, its overshoot is smaller than that of non-optimized ADRC.  
For comparison, the same method is used to test the optimum performance of PSO, FPSO, AGAPSO. The comparison results are shown in Table 5. It can be seen that the ADRC optimized by FOFPSO has less tracking deviation and RMSE.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Value** & **Parameter** & **Value** \\ \hline \(\beta_{01}\) & 250 & \(c\) & 5 \\ \(\beta_{02}\) & 2000 & \(h_{1}\) & 200 \\ \(\beta_{03}\) & 3000 & \(b_{0}\) & 47.4 \\ \(r\) & 25 & \(h_{0}\) & 0.004 \\ \(r_{0}\) & 20 & & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Unoptimized parameters of ADRC.  
Figure 10: Attitude tracking curve.  
Figure 9: Turbulence.
# An Energy-Efficient Evolutionary Clustering Technique for Disaster Management in IoT Networks
## 3 Proposed Method
### Cluster Head Selection Phase
#### 3.3.2 Cluster Closeness (CC)  
To maximize cluster quality, it is necessary to make highly compact clusters to reduce energy consumption [33]. Furthermore, a high density of nodes is a good option for selecting a candidate CH from a region [33] because the energy consumption and internal communication cost of clusters is therefore minimized [14; 27; 29]. Thus, we attempted to decrease the mean distance of the CH to its neighbors for rapid data transmission. If the density of an arbitrary node x (CH candidate) is the number of single-hop neighbors (\(nh\left(x\right)\)) and the sum of the Euclidean distance of node x from single-hop neighbors (\(h\left(x\right)\)) is _dist_\(\left(x,\ h\left(x\right)\right)\), then the cluster closeness of an arbitrary node x is calculated as follows:  
\[\mathrm{Cluster\ Closeness\ (CC_{x})}=\frac{\sum_{y\in h\left(x\right)} \mathrm{dist}\left(\mathrm{x},\mathrm{y}\right)}{\mathrm{nh}\left(\mathrm{x }\right)} \tag{16}\]  
A node will be chosen as CH which has a lower value than the other candidate CHs because increasing the \(nh\left(x\right)\)value according to [33] is effective in selecting CHs. Similarly, as in Equation (2), the mean Euclidean distance of the CMs with the CHs is minimized.
## 4 Numerical Results
### Results and Discussion
#### 4.3.3 Single Secondary User Source Device Identification Performance in an Encrypted Environment  
With the popularity of low-cost image acquisition devices and the widespread use of different devices with the same camera model, accurately distinguishing digital images captured by different devices of the same model has become critically important in the field of image forensics. Even among devices of the same model, each camera can introduce unique characteristics during the image capture process, such as variations in sensor noise, lens imperfections, or other subtle manufacturing discrepancies. These device-specific traits generate distinctive fingerprints that allow for the precise identification of the specific device used to capture a given image, rather than merely identifying the camera model.  
In our study, as illustrated in Figure 9, we used Nikon as a case study, conducting experiments with multiple devices of the same camera model to evaluate the efficacy of our proposed method for source device identification. We did not compare our results with those of [PERSON] et al., because their source camera identification method does not extend to the identification of specific devices within the same model. The Receiver Operating Characteristic (ROC) curve was employed to evaluate the performance of our approach, where a curve closer to the upper left corner indicates a higher TPR and lower FPR, corresponding to a larger AUC and better identification performance. As shown in Figure 9, the proposed method achieved high AUC values when distinguishing between different devices of the same camera model, highlighting its effectiveness in source device identification. It is worth noting that device aging and sensor degradation over time may affect the physical-layer fingerprint of a camera, and the differences between devices of the same model are typically more subtle than those between different models. Therefore, the strong identification performance across multiple devices of the same model further demonstrates the robustness and practicality of the proposed approach.  
Figure 8: ROC curves of Canon PowerShotA640 before and after encryption.
## 3 Design of AcPAS
### Optimization Design of the Coaxial to Waveguide Transition  
As shown in Figure 2, the widely used septum polarizer currently adopts a direct insertion method for the coaxial port [41]. This form of structure is simple and easy to analyze. However, the performance of the septum polarizer is sensitive to the insertion length of the coaxial probe. As shown in Figure 4, when there is a \(\pm\)0.2 mm change in the probe length, significant changes occur in the performance of the K-band septum polarizer, such as the voltage standing wave ratio (VSWR) and AR. In engineering implementations, errors due to machining or coaxial port installation may lead to changes in the length of the probe inserted into the waveguide, causing a sharp deterioration in the performance of the septum polarizer.  
[PERSON] and [PERSON] proposed the in-line coaxial-waveguide transition structure [44]. Based on this, [PERSON] et al. proposed an in-line transition structure based on third-order impedance transformation [45], as shown in Figure 5. The coaxial probe and the waveguide are in a straight line, with the probe inserted into the bottom short-circuit plate of the waveguide and connected to the stepped structure inside the waveguide. However, when applied to a septum polarizer, due to the size limitations of K/Ka-band waveguides, the feedback structure can cause overlap between the two coaxial ports, making it difficult to fabricate. In response to this, this paper proposes a side-fed coaxial-to-waveguide transition.  
The schematic diagram of the side-fed coaxial-to-waveguide transition is shown in Figure 6. Similar to the direct insertion method, the coaxial probe is inserted into the waveguide from the side and connects to or is inserted into the stepped structure inside the waveguide. The probe inside the waveguide generates excitation and achieves  
Figure 4: The impact of probe length on the performance of the septum polarizer by direct insertion method. (**a**) VSWR. (**b**) Phase difference. (**c**) Amplitude difference.  
Figure 5: In-line coaxial-to-waveguide transition.  
the transformation from TEM mode to TE\({}_{10}\) mode, and the stepped structure realizes impedance matching.  
In AcPAS, a third-order stepped impedance matching structure is used. Typically, the length of the impedance matching step is a quarter of the wavelength, and the height conforms to the Chebyshev function [46]. Based on the Chebyshev impedance transformation, the initial step height and length are determined, a simulation model is established, and reasonable optimization is conducted. The parameters of the impedance matching steps operating in the K-band and Ka-band are shown in Table 1, and the meaning of each parameter in the table is shown in Figure 6.
## 4 Evaluation of Biological Properties
### Anticancer Activity
It also exhibited an antioxidant effect by decreasing malondialdehydehyde (MDA) levels and increasing catalase activity. In another study, it inhibited LNCaP, DU145, and PC3 prostate carcinoma cell growth and proliferation via downregulated cell cycle proteins (cdk1, cdk2, pcdk1, cyclin A and B), downregulated anti-apoptotic proteins (Akt, pAKT, and Bcl-2), and upregulated the pro-apoptotic protein Bax [114; 115; 116]. On the other hand, the in vivo anticancer effect of daucosterol on primary tumor growth and pulmonary metastasis was studied using a BALB/c mouse model of a breast tumor. After 35 days of treatment, daucosterol suppressed primary tumor growth, reduced the number of lung metastases, and delayed the trend of increasing numbers of captured CTCs in the blood circulation [117]. Using a murine H22 hepatoma allograft model in ICR mice, [PERSON] et al. [110] showed that daucosterol treatment inhibits tumor growth in vivo by inducing intracellular ROS generation and subsequent autophagic cell death. Figure 2: Anticancer mechanisms of Daucosterol. The arrow indicates increasing or decreasing.
# Intelligent Decision Forest Models for Customer Churn Prediction
## 3 Methodology
### Random Forest (RF) Algorithm  
The random forest (RF) idea is to construct binary subtrees utilizing training bootstrap samples from the learning sample \(L\) and randomly choose a portion of \(X\) at each node. The DF model selects the categorization with the most votes out of all the trees in the forest. RF is typically defined by its bootstrapping aggregation and its randomized selection concepts. If a dataset has \(N\) instances, about 2/3 of the original size is randomly determined by bootstrapping \(N\) times. The remaining occurrences have been examined as an out-of-bag set. The set of out-of-bag observations is made up of observations that were not utilized to create the subtrees. They were used to evaluate the error prediction. A random feature selection is used at each node to generate a decision node. When \(m\) is the number of features, the size of the feature evaluated at each split is generally equal to m or m/\({}^{2}\)[43]. Since no pruning is done, all the sub-trees are maximum trees. Each DT is trained using the concept of RF. Specifically, each classifier's (DT) training set is formed by randomly selecting \(N\) instances with replacement, where \(N\) is the size of the original training set. The learning system develops a classifier (DT) from the instance and combines all the classifiers (DTs) created from the various trials to build the final classifier. To classify an instance, each classifier registers a vote for the class to which it belongs, and the instance is labeled as a member of the class with the most votes. If more than one class earns the most votes, the winner is chosen randomly. Every tree in the forest is formed on an independently drawn bootstrap copy of the input data. Observations not included in this copy are \"out-of-bag\" for this tree [44]. The prediction error of the DF is determined by calculating predictions for each tree on its out-of-bag observations, averaging these predictions over the whole DF for each observation, and then comparing the expected out-of-bag response with the real value at this observation. The bootstrapping concept works by minimizing the variance of an unbiased base learner, such as a DT. The random selection of features minimizes the correlation between trees in the DF, increasing the DF's predictive power [45].  
In summary, the selected DFs (LMT, FT, and RF) goal is to guarantee that relevant attributes are chosen or retained on the resulting DT. However, other forms of DFs, such as Random Subspace (RS) and Extremely Randomized Trees (ERT), have been reported to be effective. These methods (RS and ERT) adopt the random feature weights and sub-spacing method, which creates and assigns weights at random, resulting in a mismatch in feature weights and erratic performance for low and large dimensional datasets. As a result, the assurance that relevant attributes will be chosen or maintained on an ongoing basis is not guaranteed [46; 47].
# Labeling Peptides with Radioiodine: An Overview of Traditional and Emerging Techniques
## 4 Indirect Radioiodination Methods: The Labelling of the Prosthetic Groups
### Iododesilylation  
Iododesilylation is another method for radioiodination that uses silanes as precursors for labelling molecules (Figure 11). Compared to iododestannylation, iododesilylation generally results in lower RCYs because the carbon-silicon bond is more stable. It typically involves the reaction of a silylated precursor with an electrophilic source of iodine, often in acidic media [2; 14]. The reaction is generally conducted in acidic media such as trifluoroacetic acid (TFA). To promote the formation of the reactive radioiodine species in situ, reagents such as \(N\)-chlorosuccinimide (NCS) and trifluoroperacetic acid can be employed [14]. [131]IJMIG was labelled in 85-90% RCY starting from the corresponding aryltrimethylsilane in TFA, using trifluoroperacetic acid to generate the reactive \({}^{131}\)I-species in situ [54]. [123]Iiodometomidate (IMTO), a high-affinity ligand of adrenal steroidogenic enzymes, has been radioiodinated from a polymethacrylamide-supported precursor [55]. Interestingly, solid-phase organic chemistry can be used for this reaction, enabling rapid purification of reaction products through simple filtration. In the radioiododesilylation of iodometomidate (IMTO), after the reaction, TFA was neutralized with a polymer-supported amine. The purification step of [123]IJMTO was then performed through elution on a Sep-Pak cartridge, while the unreacted polymethacrylamide-supported precursor could not be eluted. The radiotracer was obtained in 85% RCY and 94% RCP.  
In summary, while iododesilylation may not always provide the high RCYs seen in iododestannylation, it remains a useful method, particularly when combined with techniques like solid-phase chemistry to aid in purification.  
Figure 11: Iododesilylation through an S\({}_{\text{E}}\)Ar reaction.
# ISAC towards 6G Satellite-Terrestrial Communications: Principles, Status, and Prospects
## 2 The Development Status and Key Technology Of ISAC
### Evaluation Indicators
In [27], in the communication performance-centric ISAC, the achievable rate is optimized as a constraint objective to realize optimal communication performance. \[\text{R}=\log_{2}{(1+\frac{S}{N})}, \tag{5}\]  
where \(S\) is the power of signal, and \(N\) is the power of noise. (6) Energy efficiency (EE)  
With the rapid growth of energy-saving demand, EE has attracted increasing attention as an important performance metric. The authors of [28] defined the objective function of EE as  
\[\eta=\frac{E_{1}(W)}{E_{2}(W)}, \tag{6}\]  
where \(W\) is the coefficient of beamforming, \(E_{1}(W)\) is the weighted sum rate, and \(E_{2}(W)\) is total power consumption. In [29], the authors studied the EE optimization problem of rate splitting multiple access in ISTNs. By using the constrained soft actor-critic algorithm, EE is optimized to achieve the goal of energy saving.