esa-sceva/satcom-chunk-collection huggingface dataset:
# Image Watermarking Scheme Using LSB and Image Gradient
## 4 Experimental Results
### Perceptual Quality Measures  
To calculate the watermarked image perceptual quality, two performance metrics were calculated, i.e., PSNR and structural similarity (SSIM). PSNR measures the visual eminence between the original and watermarked image. A larger value of PSNR shows the visual equivalence of the real and watermarked image. The mathematical formula to measure peak signal to noise ratio is  
\[\text{PSNR}=10\log_{10}\left(\frac{\left(255\right)^{2}}{\left(\text{MSE} \right)}\right), \tag{4}\]  
Figure 4: Watermark extraction flow diagram.  
where the value 255 is the extreme image pixel strength. The term \(MSE\) stands for the mean squared error of image.  
\[MSE=\frac{1}{M\times N}\sum_{m=1}^{M}\sum_{n=1}^{N}e(m,n)^{2} \tag{5}\]  
where \(M\times N\) is the size of the image, and \(e\) (\(m\), \(n\))\({}^{2}\) is the difference between the watermarked and real image.  
SSIM measures the similarity between two images on the basis of luminance, contrast, and structure. The mathematical formula of similarity measure is  
\[\text{SSIM }(x,\ y)=\frac{(2\mu_{x}\mu_{y}+C_{1})(2\sigma_{x}\sigma_{y}+C_{2})}{( \mu_{x}^{2}\mu_{y}^{2}+C_{1})(\sigma_{x}^{2}\sigma_{y}^{2}+C_{2})} \tag{6}\]  
where \(\mu_{x}\)\(\mu_{y}\) are the averages of \(x\) and \(y\), are the variances of \(x\) and \(y\), and is the covariance of \(x\) and \(y\). Figure 5 reveals various images i.e., actual image and watermark inserted image.  
The PSNR and SSIM of the Lena and Baboon watermarked images without any attack are shown in Table 3.
## 3 Experiment
### Remote Control  
Flight control that controls rotors is managed by the flight controller Pixhawk 4 with firmware ArduCopter V4.5.1 Quad, with the frame type of Tilt-X being set to quadcopter with X-configuration. The translation experiment and rotation experiment in Section 3.3 are conducted under ArduPilot's altitude hold mode (AltHold). This mode maintains the body's attitude level of Tilt-X without GPS, keeping the roll angle, \(\phi\), and pitch angle, \(\theta\), small, thus enhancing stability. Additionally, the Pixhawk 4's internal IMU can obtain real-time attitude angles of Tilt-X. The pulse width (PW) values of each rotor and the PW values of each servo motor can also be obtained through Pixhawk 4. This allows calculating the propeller tilt angles and the theoretical values of horizontal pulling force and yaw moment.  
Propeller tilt control that control servo motors is also managed by the flight controller Pixhawk 4. The propellers' rotational speeds and tilt angles are manually controlled via a remote controller (RC), whose model is [FrSky] Taranis X9D Plus 2019. Both propeller tilting ways shown in Figure 2b,c are operated by the RC. Figure 3 shows the channel (CH) mapping of the RC. In the translation experiment and rotation experiment mentioned in Section 3.3, the propellers' rotational speeds are controlled by the throtttic stick (CH 1), the propellers' tilt angles are controlled by the servos control rotary knob (CH 5), and the flight mode is switched to AltHold mode using the flight mode button (CH 7). The motor emergency stop button (CH 6) is used for emergency braking. Firmware installation and setup are conducted in Mission Planner. It is worth noting that the RC needs to maintain a distance of 1 m or more from the receiver to prevent signal interruption. The control implement for the translation experiment and rotation experiment is as follows:  
1. In AltHold mode, the throttle stick is raised to 50% and maintained.
2. The servos control rotary knob is rotated to a certain position to tilt the propellers to a certain angle.
3. The servos control rotary knob is rotated to a larger position to tilt the propellers to a greater angle.  
The two different degrees of propeller tilt will generate different magnitudes of horizontal pulling force and yaw moment. In Section 3.2, the theoretical calculation methods for horizontal pulling force and yaw moment are presented based on the rigid body transformation and dynamic model from Section 2. In Section 3.3, the experimental values of horizontal pulling force and yaw moment are presented and compared with the theoretical values.
# Discrimination of Spatial Distribution of Aquatic Organisms in a Coastal Ecosystem Using eDNA
## 4 Discussion
### Ecological Values of eDNA Monitoring  
Our results showed that _Acropora_ sp. was dominant in terms of abundance of OTU sequences across all study sites, even though it is not a planktonic species. The Acropora colonies post-recruitment by larval recruitment demonstrates higher efficiency for survival than coral-colony growth [43]. It was concluded that larval recruitment largely determines species composition, and that reduced larval recruitment is responsible for the sparse distribution of fragmenting species [44]. Therefore, the larval stage could be easily detected among the study sites by our eDNA monitoring approach. We also found that nutrient-related factors (Chl-a, TN, and TP) were significantly correlated with _Acropora_ sp. and _Megabalanus_ sp. (Figure 2). It is therefore possible that the strong positive correlation between the two species could be the result of coral-inhibiting barnacles [45].  
The comprehensive understanding of feeding characteristics of aquatic organisms has not been well elucidated in comparison to their importance [46; 47; 48; 49]. Our results showed that composition of aquatic organisms have different patterns of feeding habits among the three different zone divisions designated by [PERSON] et al. [25] (Figure 4), and we also found similar patterns using CCA based on genus-level identification resulting from our division of Zone III (Figure 2). However, previous research findings were attributed to the absence of adequate information for analysis obtained from the field sites due to difficulties in culture, handling, and identification of eDNA samples derived from bulk sediment and filtered water. In this sense, our results overcame the aforementioned limitations by using a broader detection range for aquatic organisms.  
Although, [PERSON] et al. [50] has described the increased sensitivity of meta-barcoding for NIS, its application for monitoring biological invasion has only recently been demonstrated [51]. In the present work, we identified the utility of meta-barcoding for detection of NIS and their spatial distribution patterns (Figure 4). The reason behind this is the capacity to detect the presence of individuals at early life stages, such as eggs or nauplius larvae, whose identification is difficult with traditional methods [52]. The sensitivity of meta-barcoding, combined with the relatively low time and cost associated with this technique [53], makes it a promising alternative approach for the rapid and accurate detection of biodiversity shifts in aquatic organisms, allowing its potential implementation in environmental policies.
## 4 Experiment and Results
### Evaluation Criteria  
Six metrics, including the mean Dice similarity coefficient and the mean intersection over union (IoU) correlation, which operate at the regional level, were employed to quantitatively evaluate the data. The weighted Dice metric (\(F_{\beta}^{\omega}\)) was utilized to rectify the issue of \"equally important defects\" in the Dice calculations. The mean absolute error (MAE) measured the pixel-level discrepancies between the predicted and globally predicted maps, making it a valuable tool for assessing the pixel-level accuracy. Additionally, the enhanced  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **Marked Content** & **Modal** & **Size** & **Test/Train** \\ \hline ETIS-LaribPolypDB & Primary lesion of colon cancer. & CT & 196 & 196/0 \\ CVC-ClinicDB & A total of 612 public images were cut out from 31 colonoscopy images. & CT & 612 & 62/550 \\ CVC-ColonDB & Contains 380 images of polyps from 15 short colon video sequences. & CT & 380 & 380/0 \\ EndoScene & A total of 912 images of polyps combined by CVC-ClinicDB and & CT & 912 & 60/0 \\ CVC-300, and 60 images of CVC-300 were selected for testing. & CT & 1000 & 100/900 \\ Kvasir & Selected 1000 polyp images from the subcategory. & CT & 1000 & 100/900 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of published data on intestinal polyps.  
alignment metric (\(E_{\varphi}^{max}\)) was utilized to assess both the pixel-level and global-level similarities. As \(E_{\beta}^{\omega}\) and MAE are pixel-level evaluation metrics that overlook the similarity of the target structures, and thus, we incorporated the structural index (\(S_{\alpha}\)) into our evaluation. \(S_{\alpha}\) aligns more closely with the human visual system and is used to evaluate the similarity between the predictions and the ground truth maps.  
Simultaneously, this study considered eleven comparative models from the realm of intestinal polyp segmentation, namely, U-Net [20], U-Net++ [21], PraNet [25], SANet [43], SFA [24], ACSNet [44], SAM [45], CaraNet [3], RFPA [27], CFA-Net [28], and BoxPolyp [26]. To ensure a fair comparison, we executed open-source code using the same dataset and experimental environment.
# Modeling and Performance Analysis of Satellite Network Moving Target Defense System with Petri Nets
## 2 Background
### Stochastic Petri Nets  
Stochastic Petri Nets (SPN) [11] is extended by [PERSON] on the basis of traditional Petri Nets. By introducing the concept of time, its description ability and solving efficiency are improved, and time-dependent performance metric measures can be obtained as well. Any real-time system can be modeled as a SPN process and can be analyzed by deriving the underlying Markov Chain [12].  
Definition 2: A Stochastic Petri Net is represented by a six-tuple, \(SPN=(P,T,F,W,M_{0},\lambda)\); among them, the meaning of \(P,T,F\), \(W\), \(M_{0}\) is the same as that of traditional Petri Net, \(\lambda\) represents the set of average transition firing time rate, indicating the average number of implementations of the transition per unit time under the enforceable case, the average firing time rate corresponding to a transition \(t_{i}\), i.e., \(\lambda_{i}\). Here,  
\[\lambda\text{=}\{\lambda_{1},\lambda_{2},...\lambda_{n}\}\]  
SPN introduces a time delay \((\tau_{i})\) between the non-implementable and implementable of each transition, and the average time delay corresponding to a transition \(t_{i}\), i.e., \(\tau_{i}\). Typically, the time delay is assumed to be a continuous random variable with exponential distribution, so it can be isomorphic to a Markov Chain (MC). Furthermore, using the theory of Markov Smooth Distribution, the performance analysis of the system modeled by SPN can be executed by solving the steady-state probability of each marking. This is also the principle of SPN for performance analysis.  
The performance evaluation of a system using the SPN model is carried out in the following three steps:  
* Step1: Modeling the target system with SPN. This is the first step in conducting a performance evaluation, and the model depends on the concrete system you want to analyze.
* Step2: Constructing the Markov Chain (MC) that is isomorphic to the SPN model.
* Step3: Working on the system performance evaluation with the steady-state probability based on the MC. Specifically, we can use [PERSON]'s theory to obtain the steady-state probabilities of each marking and then obtain the relevant parameters. Here are some formulas to further get those parameters. They are as follows:  
1. Token density function: \[\begin{split} P[M(P)=i]=\sum_{j}P(M_{j})\\ \text{There, }M_{j}\in[M(P)=i],M_{j}(P)=i\end{split}\] (1)
2. Average number of tokens on a place: \[\bar{u_{i}}=\sum j\times P[M(P_{i})=j]\] (2)
3. Utilization rate of the transition: \[U(t_{i})=\sum_{M\in E}P(M)\] (3) There, \(E\) represents the set of all reachable markings that make \(t_{i}\) enforceable.
4. Token velocity of the transition: \[R(t_{i},P_{j})=W(t_{i},P_{j})\times U(t_{i})\times\lambda_{i}\] (4)  
On the basis of all the performance parameters mentioned above, we can do further research on the average time delay, throughput, and so on.
# Experimental Study on the Strength and Durability for Slag Cement Mortar with Bentonite
## 4 Results and Discussion
### Results from Rapid Chloride Permeability Testing  
A standard test based on ASTM C1202-97 using electrical indication to examine the chloride ion penetration ability was performed. Figure 7 shows the testing results, where the chloride penetration ability in terms of coulombs for specimens without bentonite was generally higher than 20 mA, and the detected current stayed at a constant level. However, when the bentonite was replaced at 0.25%, data from the electrical current indicated that the chloride penetration ability was significantly reduced, where the chloride was reduced by about 97%. The mitigation of the penetration ability of chloride from partial substitution of binders in the slag cement by bentonite is very significant and obvious.
# A Short-Term Photovoltaic Power Prediction Model Based on the Gradient Boost Decision Tree
## 5 Conclusions  
In this paper, the GBDT integrated algorithm was used to solve the problem of PV power prediction. In essence, the GBDT algorithm integrates weak learners with binary fissions by using the integrated algorithm of gradient descent. The decision tree algorithm is able to adjust its structure according to the data characteristics, and hence it is able to fit well the relationship of non-continuous functions and is suitable for the scenario of PV power prediction. The GBDT algorithm, as an integrated algorithm, inherits the advantages of the decision tree algorithm. At the same time, it can effectively avoid the over-fitting problem compared with a single decision tree algorithm.  
The case studies show that the proposed model based on the GBDT algorithm can fit well the relationship between PV power and input variables and avoid the over-fitting problem. Compared with the SVM-based PV model, the proposed PV prediction model based on the GBDT algorithm is relatively stable no matter whether on a single (sunny or rainy) day or in different seasons. Once the model has been trained, the prediction calculation is relatively simple, and good prediction results can be obtained.  
[PERSON] contributed to model establishing and paper writing. Many ideas on the paper are suggested by [PERSON] to support the work, and [PERSON] did the job of performing the simulations. [PERSON] and [PERSON] analyzed the data. [PERSON] reviewed the work and modified the paper. In general, all authors cooperated as much as possible during all progress of the research.  
This work was supported by the National Natural Science Foundation of China (NSFC) (51477111) and the National Key Research and Development Program of China (2016 YFB0901102).  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Season** & **Model** & **nRMSE** & **MAPE** \\ \hline \multirow{3}{*}{April (spring)} & GBDT & 0.0719 & 0.1420 \\  & SVM & 0.1072 & 0.1783 \\  & ARMA & 0.1143 & 0.1867 \\ \hline \multirow{3}{*}{July (summer)} & GBDT & 0.0772 & 0.1365 \\  & SVM & 0.0957 & 0.1541 \\  & ARMA & 0.1106 & 0.1618 \\ \hline \multirow{3}{*}{October (autumn)} & GBDT & 0.0703 & 0.1477 \\  & SVM & 0.1108 & 0.1732 \\  & ARMA & 0.1229 & 0.1894 \\ \hline \multirow{3}{*}{January (winter)} & GBDT & 0.0696 & \multirow{3}{*}{0.15270.1876} \\  & SVM & 0.0985 & \\ \cline{1-1}  & ARMA & 0.1121 & 0.2013 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of prediction accuracy monthly.
## 1 Introduction
The control structure's robustness and exceptional tracking performance are validated through MC simulations. (3) The engineering potential of the proposed control structure is established through frequency-domain analysis, considering flying quality requirements. The bandwidth principle is applied for rapid design, and flight tests confirm the effectiveness of the method. (4) Sensitivity analysis of parameter uncertainties emphasizes the importance of flight-safety-related parameters. The remainder of this paper is organized as follows: Section 2 presents the lateral-directional dynamics of the aircraft. Section 3 describes the control structure of the ESO-based INDI control. In Section 4, the time-domain simulations and frequency-domain analyses are presented, along with the flight test results. Section 5 summarizes the conclusions of this study, and Section 6 suggests future directions for research.
## 9 Future Research Directions and Challenges
### Simultaneous Localisation and Mapping  
A critical task in robotics is Simultaneous Localisation and Mapping (SLAM). As the robot moves in an unfamiliar environment, it needs to construct a map and localize itself on the map while navigating. The use of multiple robots instead of a single one, while each shares its maps with the others, is a fascinating topic that adds efficiency to the SLAM. Multi-robot SLAM (MRSLAM) is implemented in two ways. One involves using a central station to which all robots disseminate the collected data to carry out all the processing, construct the global map, and transmit this data back to each robot. In a decentralised system, each robot performs locally, and whenever they visit, other robots share their local map, based on which they update their map. Accordingly, one big challenge in centralised and distributed MRSLAM is the constraint on the communication bandwidth, limited computation power, and memory. 5G NR will circumvent these limitations by facilitating edge computing [123; 124], providing accurate RF-based measurements, and enabling relative localisation for each vehicle in the team via sidelink and V2V technologies.
## 4 Results
### Model Evaluation and Validation
#### 4.2.1 FastText Classifier Results  
In order to choose the parameters for the FastText model, we compared the \(F_{1}\) between successive experiments. Using a random search [22] strategy for hyperparameter search, the best results were obtained with 100 dimensions and a learning rate of 0.1, as shown in Table 4.  
We also tested the predictive performance of this model when using or not using pregenerated bigrams, but there was no significant impact on the results, as shown in Figure 4.  
The \(F_{1}\) achieved when using \(10^{6}\) samples (800K for training) was 0.87 (see Table 3). The Cohen's Kappa coefficient of agreement between the predicted and the true labels in the validation set was \(\kappa=0.75\), which is regarded as a substantial agreement. The results did not improve significantly when using the full dataset of \(4.1\times 10^{6}\) samples. In this case, the \(F_{1}\) achieved was 0.88 with a Cohen's Kappa coefficient \(\kappa=0.76\).  
The learning curve (Figure 4) shows the evolution of the \(F_{1}\) during training when the number of training samples was increased from 800 to 800K. The curve converged with the score obtained in the training sample to a maximum of 0.88 when using the full dataset, as shown in Table 3. The validation score converges with the training score, and the estimator does not benefit much from more training data, denoting a bias error. It has been reported that the phenomenon of not being able to increase the performance with additional data can be overcome with the use of deep learning models applied to complex problems, in contrast to a fast but thin architecture such as FastText (as will be proved later when using CNNs). On the contrary, the model did not suffer from a variance error. Cross-validation  
Figure 3: Learning curves with 5-fold cross-validation on the FastText, Convolutional Neural Network (CNN), Support Vector Machine (SVM), and k-Nearest Neighbors (kNN) classifiers. Horizontal axes show the total training samples, whereas vertical axes are the \(F_{1}\)-score. The green region represents the standard deviation of the models.  
was used to assess how well the results of the model generalized to unseen datasets and obtained robust average validation results with a decreasing standard deviation over the \(k\) folds (Figure 3a).
###### Abstract  
In this paper, we experimentally illustrate the effectiveness of neural networks (NNs) as nonlinear equalisers for multilevel pulse amplitude modulation (PAM-_M_) transmission over an optical wireless communication (OWC) link. In our study, we compare the bit-error-rate (BER) performances of two decision feedback equalisers (DREs)--a multilayer-perceptron-based DFE (MLPDFE), which is the NN equaliser, and a transversal DFE (TRDFE)--under two degrees of non-linear distortion using an eye-safe 850 nm single-mode vertical-cavity surface-emitting laser (SM-VCSEL). Our results consistently show that the MLPDFE delivers superior performance in comparison to the TRDFE, particularly in scenarios involving high non-linear distortion and PAM constellations with eight or more levels. At a forward error correction (FEC) threshold BER of 0.0038, we achieve bit rates of ~28 Gbps, ~29 Gbps, ~22.5 Gbps, and ~5 Gbps using PAM schemes with 2, 4, 8, and 16 levels, respectively, with the MLPDFE. Comparably, the TRDFE yields bit rates of ~28 Gbps and ~29 Gbps with PAM-2 and PAM-4, respectively. Higher PAM levels with the TRDFE result in BERs greater than 0.0038 for bit rates above 2 Gbps. These results highlight the effectiveness of the MLPDFE in optimising the performance of SM-VCSEL-based OWC systems across different modulation schemes and non-linear distortion levels.  
optical wireless communications; vertical-cavity surface-emitting lasers; multilevel pulse amplitude modulation; digital equalisation; neural network; multilayer perceptron +
Footnote †: journal: _Photonics_
# Intelligent Detection of Rebar Size and Position Using Improved DeeplabV3+
## 2 Methodology
### Improved DeeplabV3+
#### 2.2.2 Efficient Channel Attention Module  
The efficient channel attention module (ECA) [31] is an adaptation of the SENet [32], which uses one-dimensional convolution to replace the fully connected layer, to solve the problem that the dimensionality reduction operation adversely affects the correlation between the learning channels of the network while sharing information using local cross-channel to reduce the complexity of the network. The mechanism can be implemented as follows: the feature dimensions [H, W, C] of the input feature layer are pooled using the average to obtain the aggregated features [1, 1, C], and then the correlation between channels is obtained by performing a one-dimensional convolution of size k to generate the channel weights w. Finally, the weights are multiplied by the original input feature layer to form a new feature layer. The network structure of ECA is shown in Figure 4.  
Figure 3: Schematic diagram of the residual blocks.  
The size \(k\) of the one-dimensional convolution kernel in Figure 4 was determined adaptively using the number of channels, \(C\), as follows:  
\[k=\left|t\right|_{odd}=\left|\frac{log_{2}C+b}{\gamma}\right|_{odd}, \tag{1}\]  
where \(k\) denotes the convolution kernel size, \(C\) denotes the number of channels, \(\left|t\right|_{odd}\) denotes the nearest \(odd\) number to \(t\), and \(\gamma\) and \(b\) were set to fixed values of 2 and 1, respectively. After determining \(k\), the channel weights \(w\) can be expressed as  
\[w=\sigma[C1D_{k}(y)], \tag{2}\]  
where C1D denotes a one-dimensional convolution, \(\sigma\) is the sigmoid activation function, and \(y\) is the channel aggregation feature.  
Although replacing the backbone network could effectively adjust the backbone network parameters, the sensitivity to feature information was not enhanced. To solve this problem, some improvements were made to the backbone network, as shown in Figure 5: the ECA was added to Resnet50 to enhance the sensitivity of the network to feature information and to reduce the unnecessary information processing procedure.