esa-sceva/satcom-chunk-collection huggingface dataset:
# Real-Time Flood Monitoring with Computer Vision through Edge Computing-Based Internet of Things
## 5 Results and Analysis
### Scenario 1: Consistent Weather  
Scenario 1 showcases the performance of the system under consistent weather conditions during each testing period, i.e., rainy during the night and cloudy during the day. The weather remaining consistent allowed the system readings to be stable as there was relatively little fluctuation in the readings across both periods. In this scenario, the day RMSE was 0.23 m while the night RMSE was slightly lower at 0.2 m.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Cloudy** & **Fair** & **Rain** \\ \hline RMSE (day) & 0.39 m & 0.66 m & 0.23 m \\ RMSE (night) & 0.16 m & 0.23 m & 0.2 m \\ Data points (day) & 57.84\% & 14.71\% & 27.45\% \\ Data points (night) & 38.6\% & 28.65\% & 32.75\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Collective RMSE error under all weather conditions.
## 3 Methodology
### Random Forest and Ridge Regression Ensemble Algorithm (RFRR)
#### 3.4.3 Ensemble Algorithm  
Ensemble algorithms are a machine learning paradigm that improve prediction accuracy and robustness by combining multiple models. In this approach, several different models are trained and applied to the same task, with the final output typically combined in some way (such as averaging or voting) with the individual model results. Random Forest, as a key component of ensemble learning, excels in classification and regression tasks, particularly when dealing with data that have complex structures and high-dimensional features. It improves prediction accuracy and generalization ability by constructing multiple decision trees and aggregating their predictions. Ridge regression, as another key component, robustly handles highly correlated features through the L2 regularization mechanism, effectively preventing overfitting, especially when the number of features exceeds the number of samples.  
This ensemble algorithm typically involves combining the prediction results of Random Forest and Ridge Regression. The combination method can be simple, such as averaging, weighted averaging, or more complex techniques like stacking. In the stacking method, the prediction results of Random Forest and Ridge Regression are used as inputs for another model to learn and optimize the best way to combine these predictions.
# Research on Communication Stability of Inter-Cannonball Network Based on OPNET
## 4 Modelling Scene Setup  
### Introduction to OPNET  
OPNET Network simulator is a tool to simulate the behaviour and performance of any type of network [35; 36; 37]. Originating from Massachusetts Institute of Technology (MIT), it enables predictive network performance management and simulation, thanks to its accurate analysis of the performance of complex networks.  
OPNET consists of three modules, that is, ITDecisionGuru, Modeler, and Modeler/Radio, which are nested in layers. Among them, ITDecisionGuru has only the function of simulation and analysis; Modeler adds the function of library building based on the former; and Modeler/Radio adds support for mobile satellite and satellite communication. Modeler, the first commercial product of OPNET, is also the most widely used one. Its main features are as follows:  
1. Hierarchical modelling mechanism.  
The three-layer modelling mechanism of OPNET is shown as Figure 7, which al-lows multiple scenarios to be set up in the network layer for comparing the simulation results of different design solutions.  
2. FSM (finite state machine) programming.  
The process layer combines FSM and C/C++ to model the behaviour and functionality of processes. The relevant execution actions are performed by specific trigger conditions. Users can also rewrite the process module to customize its functionality according to their needs.  
1. Flexible statistical tools.  
In terms of data collection, OPNET has its own information collection process module that provides common statistics. If users have other requirements, they can customize the probe into any position in the network to collect the required data. OPNET can graphically  
Figure 6: The AODV routing protocol route discovery process, where circles indicate nodes and numbers represent node indices.  
Figure 7: OPNET three-layer modelling mechanism.  
organize the collected data and correlated data in a planar rectangular coordinate system for easy observation by the user if there are other needs.  
Full support for protocol programming.  
OPNET comes with many network protocol models and library functions. For existing models, you can directly set external variables and then simulate them. If you want to change the protocol, you can develop and improve it based on the original model.
## 4 Simulation and Analysis  
To evaluate the IMM-SCKF algorithm's effectiveness in vehicle state estimation, we built a co-simulation platform for distributed-drive electric vehicles, using industry standard simulation software Matlab (version 2018a) and Carsim (version 2019.0), and set up a distributed-drive electric vehicle model in the Carsim platform with model selection A-class, Hatchback. In this paper the sampling time \(T\) was set to 0.001 s. The parameters of the whole vehicle model are shown in Table 1, and the IMM-SCKF state observer was built in Matlab/Simulink platform. The IMM-SCKF state observer was compared to the SCKF observer in simulation experiments by using the double-lane change condition and sinusoidal steering conditions as vehicle operating conditions and keeping the input parameters the same as the vehicle model to evaluate the effect of both observers under the same operating conditions.  
In the IMM-SCKF fusion algorithm in this paper, the Markov state transfer matrix is set to  
\[p=\left[\begin{array}{cccc}0.95&0.025&0.025\\ 0.025&0.95&0.025\\ 0.025&0.025&0.95\end{array}\right] \tag{50}\]  
Because of the system statistical noise uncertainty, different noise is set for each of the three SCKF sub-filters in the interacting multiple model algorithm, and the settings based on experience are as follows:  
\[\begin{array}{l}R_{1}=\text{diag}([0.01,0.01,0.01])\\ Q_{1}=\text{diag}([0.01,0.01,0.01])\\ R_{2}=10R_{1};Q_{2}=10Q_{1}\\ R_{3}=10R_{2};Q_{3}=10Q_{2}\end{array} \tag{51}\]  
The next sub-process model and measurement noise magnitudes are each ten times larger than the previous model. As a comparison, the simulation results of adding a single SCKF with the UKF algorithm with settings such as noise parameters remain the same as Submodel1 of the IMM-SCKF algorithm, the system equations and the measurement equations in the design process are consistent with the above. The corresponding simulation results are shown below.
# Implementation of Fault-Tolerant Control for
## 3 Fault Estimation Using an Extended State Observer
\right].\]  
It is easy to see that \(A\) is Hurwitz, so there is a symmetric positive definite matrix \(Q\) satisfying the Lyapunov equation  
\[A^{T}P+PA=-Q \tag{14}\]  
Substituting Equations (13) and (14) into Equation (12), we have  
\[\dot{V} =\dot{\epsilon}\dot{\tilde{\epsilon}}^{T}P\tilde{\epsilon}+ \varepsilon\tilde{\epsilon}^{T}P\dot{\tilde{\epsilon}} \tag{15}\] \[=\left(A\tilde{\epsilon}+\varepsilon B\dot{\phi}\right)^{T}P \tilde{\epsilon}+\tilde{\epsilon}^{T}P(A\tilde{\epsilon}+\varepsilon B\dot{ \phi})\] \[=\tilde{\epsilon}^{T}A^{T}P\tilde{\epsilon}+\varepsilon\left(B \dot{\phi}\right)^{T}P\tilde{\epsilon}+\tilde{\epsilon}^{T}PA\tilde{ \epsilon}+\varepsilon\tilde{\epsilon}^{T}PB\dot{\phi}\] \[=\tilde{\epsilon}^{T}(A^{T}P+PA)\tilde{\epsilon}+2\varepsilon \tilde{\epsilon}^{T}PB\dot{\phi}\] \[\leq-\tilde{\epsilon}^{T}Q\tilde{\epsilon}+2\varepsilon\|PB\| \cdot\|\dot{\epsilon}\|\dot{\phi}\Big{|}\]  
where \(L\) is the positive constant that satisfies \(\left|\dot{\phi}\right|\leq L\). Therefore,  
\[\dot{V}\leq-\lambda_{\min}(Q)\|\tilde{\epsilon}\|^{2}+2\varepsilon L\|PB\|\| \tilde{\epsilon}\| \tag{16}\]  
To guaranty stability of the system, we impose \(\dot{V}\leq 0\), so the observer error convergence is given as  
\[\|\tilde{\epsilon}\|\leq\frac{2\varepsilon L\|PB\|}{\lambda_{\min}(Q)} \tag{17}\]  
\(\sqcap\)
# A Robust Multi-Watermarking Algorithm for Medical Images Based on DTCWT-DCT and Henon Map
## 4 Experiment and Analysis
### Algorithms Comparison  
We compared the average NC values of the watermarks using our proposed algorithm under different attacks with [PERSON] et al. [18], [PERSON] et al. [19], [PERSON] et al. [21] and [PERSON] et al. [22]. It can be clearly seen from Figure 23 and Table 11 that under conventional attacks, the NC values of the proposed algorithm are significantly higher than the those of the other four algorithms. Figure 24 and Table 12 show the performance of the watermarks under geometric attacks. Observing these data, we can see that with the increase of the attack intensity, the NC values of all of them showed a downward trend. And under cropping attacks, left translation attacks and down translation attacks, our algorithm has obvious advantages over other algorithms in terms of NC values. Figure 24 illustrates that compared with the multi-watermarking scheme based on FDCuT-DCT proposed by [PERSON] et al. [19], the proposed algorithm performs slightly worse in rotation (clockwise) attacks. But their NC values are not much different and performance in other geometric attacks was significantly better than the latter. The watermark extraction effect under geometric attacks is generally better than that of [PERSON] et al. [19]. When performing scaling attacks, as shown in the radar chart in Figure 24, [PERSON] et al. [22] proposed the use of a spread spectrum multi-watermark in the wavelet transform domain. The NC values of this algorithm are basically equal to the NC values of our proposed algorithm under scaling attacks; they are only differ by 0.01. However, it is necessary to perform the spread spectrum operation on the wavelet transform, and the embedding of the watermark needs to select different transform layers according to different watermark types. Finally, multi-watermark embedding is achieved by the gain coefficient and the  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Cropping (\%) Y Direction** & **2** & **8** & **12** & **15** & **23** & **29** & **30** & **37** \\ \hline NC1 & 1.00 & 0.90 & 1.00 & 1.00 & 1.00 & 0.79 & 0.75 & 0.61 \\ NC2 & 1.00 & 0.94 & 1.00 & 1.00 & 1.00 & 0.86 & 0.82 & 0.61 \\ NC3 & 1.00 & 0.90 & 1.00 & 1.00 & 1.00 & 0.78 & 0.74 & 0.56 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Data of cropping attacks.  
Figure 22: Under cropping attacks: (**a**) Cropping 23%, Y direction; (**b**) the extracted watermark 1 under 23% cropping; (**c**) the extracted watermark 2 under 23% cropping; (**d**) the extracted watermark 3 under 23% cropping; (**e**) Cropping 37%, Y direction; (**f**) the extracted watermark 1 under 37% cropping; (**g**) the extracted watermark 2 under 37% cropping; (**h**) the extracted watermark 3 under 37% cropping.  
Therefore, the algorithm presented in this paper is outstanding in all kinds of attack situations, especially so in solving the problem that existing algorithms cannot balance resistance to geometric attacks and robustness. It has strong resistance to geometric attacks and shows a good level of robustness.
## 4 Discussion
The pulsatile and circadian pattern of GH secretion make it more difficult to interpret one timepoint measures. IGF-1 serum level is much more stable, and therefore, a better functional measurement of the GH-IFG-1 axis. IGF-1 is regulated by GH and might enhance protein synthesis, and hence, an increase in muscle CSA [31]. No change in resting IGF-1 serum levels have been reported in short term training programs [54,55]. However, long-term studies have shown elevations in resting IGF-1 levels [33,34]. Furthermore, the resting IGF-1 concentrations were also higher in trained men compared to untrained men [56]. Our study protocol consisted of a 12-week training program, which might be too short to find elevated IGF-1 serum levels, since the changes in serum IGF-1 are typically increased after 4-6 months of training and not yet after 3 months [33,34]. These unchanged levels of IGF-1 warrant the finding that we did not find an increase in muscle fiber CSA. _Limitations of the study_  
The results of this study must be interpreted carefully, since we only studied muscle fiber characteristics on a microscopical level in muscle samples taken by a fine-needle biopsy technique. Therefore, our results cannot directly affirm that there is no paraspinal muscle hypertrophy in response to a combined training protocol at all, neither at moderate intensity nor at high intensity. Since skeletal muscle hypertrophy can reside in the connective tissue (connective tissue hypertrophy), the myofibril (myofibrillar hypertrophy) or the sarcoplasm (sarcoplasmic hypertrophy), additional molecular analyses are needed to make a statement about a possible muscle hypertrophy [57]. Additionally, by using biopsy samples, there will always be some variation in the muscle fiber characteristics between different sampling sites in a muscle. If only one biopsy samples was obtained from a single muscle, at least 100 muscle fibers should be analyzed to achieve a representative sample for the entire muscle and to minimize sampling errors [58]. In our study, the mean number of fibers analyzed was \(220\pm 75\) for the ES and \(228\pm 84.51\) for the MF, which should be adequate for valid statistical conclusions. The intramuscular and interindividual variability of muscle fiber composition is relatively high, which makes it difficult to make firm conclusions only with result of muscle fiber characteristics. The heterogeneity of the persons with NSCLC in our study and the relatively small sample size make it difficult to generalize the present findings to the average person with NSCLC. Therefore, these results can be used as pilot data to think about the effect of training on paraspinal muscle fiber characteristics and optimize treatment strategy for NSCLC.
## 2 Problem Formulation
### Evolutionary Strategy  
A Multi-user Interactive Coral Reef Optimization Algorithm (MICRO) is suggested in order to solve the UA-FLP with the incorporation of several human experts' (users) preferences. The basic CRO was introduced by [PERSON] et al. [40] and its application to UA-FLP was described previously in detail by [PERSON] et al. [48]. In brief, it consists of the disposition of a number of evaluated individuals (candidate solutions to the problem) occupying a fraction \(\rho_{0}\) of the available space in a rectangular matrix of dimensions \(N\times M\) called _the reef_ (phase I: initialization). Then, the evolution starts (phase II: evolution). In this phase, a reproduction of individuals from those already settled in the reef (corals) is carried out by means of two methods: broadcast spawning (similar to the crossover in genetic algorithms) and brooding (similar to mutation in genetic algorithms). Note that all corals reproduce by either of these methods, leading to two disjoint sets whose proportion is determined by \(f_{b}\): the fraction of corals that will reproduce themselves by spawning. Afterwards, each one of the new individuals that appeared during the reproduction currently in the water (larvae) tries to take a place in the reef in a position chosen randomly (phase III: larvae setting), finding two possible situations. In the first case, the position is empty, so the larva settles freely on it; in the second one, the spot is occupied, so if the new larvae has better fitness than the one already occupying the spot, this last one is replaced; if this is not the case, it remains _in the water_ searching for another place up to three times. If after these attempts, the larva does not find a place in the reef, it is discarded. Finally, a fraction \(f_{a}\) of the individuals with better fitness values are duplicated and try to find a position in the reef in the same way formerly described (phase IV: asexual reproduction), and a fraction \(f_{d}\) of the worst individuals are candidates to be discarded with a probability \(p_{d}\) (phase V: predation). The process is repeated until a stop condition is met (number of iterations, no improvement for \(x\) iterations, etc.). See Figure 1 for a graphic representation of each of the previously described phases, and Algorithm 1 for the algorithm's pseudocode. The method has been programmed in Python 3.  
Figure 1: Phases of the basic CRO algorithm.  
```
1:procedurecro
2:\(ref\leftarrow\textbf{initialization}(n,m,\rho_{0})\)
3:repeat
4:\(spawning\_set,brooding\_set\leftarrow\textbf{partition}(ref,f_{b})\)
5:\(water\leftarrow\textbf{broadcast\_spawning}(spawning\_set)\)
6:\(water\leftarrow\{water,\textbf{brooding}(brooding\_set)\}\)
7:\(water\leftarrow\textbf{evaluation}(water)\)
8:\(ref\leftarrow\textbf{ larvae\_setting}(water,ref)\)
9:\(ref\leftarrow\textbf{ asexual\_reproduction}(ref,f_{a})\)
10:\(ref\leftarrow\textbf{predation}(ref,f_{d},p_{d})\)
11:untilstop_condition
12:return\(\textbf{best\_solution}(ref)\)
13:endprocedure
```  
**Algorithm 1** CRO algorithm pseudocode
## 3 Proposed System Model
### Selection and Application Methods of Machine Learning Algorithms
#### 3.3.4 Decision Tree Algorithm  
The Decision Tree algorithm is a supervised learning method that classifies or regresses data by creating a tree-like structure of nodes, branches, and leaves [62]. At each node, a feature is tested, directing data points down the appropriate branch until reaching a leaf node that represents a class [63]. The algorithm uses criteria like the Gini Index and Entropy to optimize splits. The Gini Index measures class purity, with values closer to 0 indicating purer subsets, while Entropy measures disorder, also aiming for low values [64]. Although Decision Trees are interpretable, they are prone to overfitting, requiring constraints to prevent excessively deep trees [65]. They can handle both numerical and categorical data, but overly complex trees may reduce model transparency [66].
# Molecular Identification of Keratinase DogkerA from Deinococcus gobiensis for Feather Degradation
## Materials and Methods
### Keratinolytic Activity Assay  
Keratinolytic activity was assayed with soluble keratin according to [PERSON]' s protocol [18]. The reaction mixture, which contained 20 \(\upmu\)L enzyme, 80 \(\upmu\)L buffer and 100 \(\upmu\)L soluble keratin (1%, _w/v_), was incubated at 50 \({}^{\circ}\)C for 20 min. A blank control was conducted by adding 200 \(\upmu\)L of 20% (_w/v_) trichloroacetic acid (TCA) instead of soluble keratin (Tokyo Chemical Industry Co. Ltd., Tokyo, Japan). The reaction was terminated by the addition of 200 \(\upmu\)L TCA, and centrifuged at 12,000\(\times\)_g_ for 10 min. An amount of 100 \(\upmu\)L soluble keratin was added to the control mixture. Then, 200 \(\upmu\)L supernatant was pipetted into another tube with 1 mL 0.4 mol/L Na\({}_{2}\)CO\({}_{3}\) and 200 \(\upmu\)L Folin-Phenol regent. Finally, the mixtures were incubated at 40 \({}^{\circ}\)C for 20 min to develop the color. Proteolytic products in the mixture were measured spectrophotometrically at OD\({}_{680}\). One unit (U) of keratinase activity was defined as an increase of 0.01 absorbance unit at OD\({}_{680}\) under the conditions above mentioned.  
To determine the activity of feather powder degradation, [PERSON]'s method was carried out with slight modifications [22]. The reaction system, including 4 mL buffer, 0.01 g feather powder and 200 \(\upmu\)L enzyme with 5 mmol/L dithiotheitol (DTT), was incubated at 60 \({}^{\circ}\)C for 60 min and subsequently terminated by adding 2.0 mL of 20% TCA. After centrifugation at 12,000\(\times\)_g_ for 5 min, the supernatant was gathered for measuring the absorbance at OD\({}_{280}\). TCA was added before the enzymatic reaction as the control. One unit (U) of feather powder activity was defined as an increase of 0.01 absorbance unit OD\({}_{280}\). Protein concentration was determined using the Bradford method with bovine serum albumin as a standard. All assays were performed in triple.
# A Community-Based Construct Method for an Inter-Satellite Communication Network of Satellite Swarm
## 4 Optimized Constructions of ICNS
### Dynamic-Connectivity-Oriented Construction Method of ICNS
There are two ways to avoid a satellite from constantly establishing the ISL with the same satellite in Algorithm 2. The first is to modify three fundamental matrices of \(\widetilde{G}_{k}\) and to forbid those selected CLCs from being selected again, as line 6-11 introduces; it is able to make satellites establish ISLs with different satellites during different slots. The second is to select the CCLC that is most dissimilar to those selected CCLCs and does not have any element of the blacklist, as line 12-23 introduces; it allows satellites to establish ISLs with their previous link objects but the numbers of repeats cannot exceed the limit in the blacklist, i.e., \(\kappa\) in (17). If \(|\widetilde{O}_{i}|\geq\Gamma\), the first method is applied; otherwise, both methods are used.
## 5 Experiment
### Results Analysis
#### 5.2.2 Convergence Speed and Accuracy  
In order to demonstrate the advantages of the BHFL method in terms of convergence speed and model accuracy, we use the CIFAR-10 and MNIST datasets to test the performance of the two models, CNN and MLP, under four federated learning architectures. The experiments evaluate the four algorithms in a number of dimensions, and as shown in Figure 5, the different learning methods all show a trend of increasing accuracy and eventual convergence over time. Figure 5a,b show the speed of convergence and accuracy when trained with different models. In terms of convergence speed, BHFL quickly converges to 70% in the fourth epoch in the CNN network and 70% in the seventh epoch in the MLP network, showing faster convergence speed than several other algorithms. BHFL also has a clear advantage over several other comparison methods in terms of accuracy. BHFL is able to achieve 98.3% accuracy when using the CNN model, with an accuracy 1.1% higher than the BHFL-ST algorithm and 12% and 6% higher than BHFL-SYNC and BHFL-AVG,  
Figure 4: Correlation analysis diagram of accuracy and time T.  
respectively. BHFL is able to achieve 80.3% accuracy when using the MLP model, which is 0.9%, 2.8%, and 5.6% higher than BHFL-ST, BHFL-SYNC, and BHFL-AVG, respectively.