esa-sceva/satcom-chunk-collection huggingface dataset:
# Ultra-Reliable Link Adaptation for Downlink MISO Transmission in 5G Cellular Networks
## Appendix A  
The probability density function of \(\sum_{i=1}^{n}\alpha_{i}\left(Z_{i}+\delta_{i}\right)^{2}\) where \(Z_{1},Z_{2},...,Z_{n}\) are mutually independent standard normal random variables, is given by [25]  
\[f_{n}(\mathbf{\alpha},\delta,y)=\sum_{k=0}^{\infty}a_{k}\beta^{-1}\chi^{2}\left( \frac{y}{\beta};n+2k;\Delta\right) \tag{21}\]  
where \(\chi^{2}(y/\beta;n+2k;\Delta)\) is a non-central chi-square distribution with \(n+2k\) DoF and non-centrality parameter \(\Delta=\sum_{i=1}^{n}\delta_{i}^{2}\). The parameter \(\beta>0\) have to be chosen appropriately to guarantee convergence. The coefficients \(a_{k}\) follow from a recursive formula [26];  
\[a_{0}=\prod_{j=1}^{n}\left(\beta/\alpha_{i}\right)^{\frac{1}{2}}\] \[a_{k}=k^{-1}\sum_{r=0}^{k-1}b_{k-r}a_{r},\;\;k\geq 1\]  
with  
\[b_{1}=\frac{1}{2}\sum_{j=1}^{n}(1-\delta_{j})\theta_{j}\] \[b_{k}=\frac{1}{2}\sum_{j=1}^{n}\theta_{j}^{k-1}\left(k\delta_{j }+(1-k\delta_{j})\theta_{j}\right)\;\;k\geq 2.\]  
Here \(\theta_{j}=1-\beta/\alpha_{j}\). If any of the \(|\theta_{j}|>1\), the ratios \(b_{k}/k\) grow without bound when \(k\rightarrow\infty\). Accordingly, the ratio of the absolute values of two consecutive \(a_{k}\) is non-zero, and convergence in Equation (21) is either slow or absent. To guarantee convergence, one should select a suitable value for \(\beta\) to keep \(-1<\theta_{j}<1\). A simple choice is to select \(\beta\) so that \(\max_{j}|\theta_{j}|\) is minimized. By selecting \(\beta=2\alpha_{\min}\alpha_{\max}/(\alpha_{\min}+\alpha_{\max})\), the extrema of \(\theta_{j}\) become \(\theta_{\max}=(\alpha_{\max}-\alpha_{\min})/(\alpha_{\max}+\alpha_{\min})\) and \(\theta_{\min}=-\theta_{\max}\) and all \(|\theta_{j}|<1\).
## 5 Photovoltaic Power Prediction Based on Objective Optimization IHO-ELM
### Example Analysis  
To verify the validity of the proposed method, the PV power data from Yulara Solar System 38.3 kW, mono-Si, Roof, 2016, Sails in the Desert-2, from 1 April 23:45 to 6 April 3:35 in 2016, were selected for verification (Data Download | DKA Solar Centre). The sampling interval was 5 min, had a total of 1200 sampling points, and was combined with the corresponding meteorological data, including temperature, radiation, humidity, and other indicators, were combined to form the example data set.
# A Survey of Information Extraction Based on Deep Learning
## 3 Event Extraction Based on Deep Learning
### Single-Task or Multitask Event Extraction
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{3}{c}{**NER**} & \multicolumn{3}{c}{**Event Extraction**} \\ \cline{2-7}  & **P/\%** & **R/\%** & **F1/\%** & **PI\%** & **R/\%** & **F1/\%** \\ \hline Layered-BiLSTM-CRF & 74.20 & 70.30 & 72.20 & - & - & - \\ \hline GEANN & 77.10 & 73.30 & 75.20 & - & - & - \\ \hline BiFlaG & 75.00 & 75.20 & 75.10 & - & - & - \\ \hline Merge and Label [ELMO] & 79.70 & 78.00 & 78.90 & - & - & - \\ \hline Merge and Label [BERT] & 82.70 & 82.10 & 82.40 & - & - & - \\ \hline JOINTEVENTENTITY & - & - & - & 75.10 & 63.30 & 68.70 \\ \hline DMCNN & - & - & - & 75.60 & 63.60 & 69.10 \\ \hline FN-ANN & - & - & - & 79.50 & 60.70 & 68.80 \\ \hline BDLSTM-TNNs & - & - & - & 75.30 & 63.40 & 68.90 \\ \hline JRNN & - & - & - & 66.00 & 73.00 & 69.30 \\ \hline TD-DMN & - & - & - & 65.80 & 65.90 & 65.60 \\ \hline RNN-AL & - & - & - & 77.40 & 61.30 & 67.80 \\ \hline GAIL & - & - & - & 74.20 & 65.30 & 69.50 \\ \hline Conv-BiLSTM & - & - & - & 74.70 & 64.90 & 69.50 \\ \hline ANN-Gold2 & - & - & - & 81.40 & 66.90 & 73.40 \\ \hline HNN-EE & 84.00 & 82.50 & 83.20 & 74.40 & 67.30 & 70.60 \\ \hline Single task NER (MDL-J3E) & 83.86 & 84.10 & 83.98 & - & - & - \\ \hline Single task ED (MDL-J3E) & - & - & - & 66.67 & 74.25 & 70.25 \\ \hline MDL-J3E & 83.48 & 84.83 & 84.15 & 69.16 & 72.85 & 70.96 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The effects of DL models. In specific fields, researchers have mostly used the joint extraction method to perform event extraction. For example, in the field of TCM, [PERSON] et al. used the joint event extraction model to divide the model into four layers: (1) a BERT layer representing text, (2) a BiLSTM layer learning an input vector, (3) a self-attention layer mainly used to capture the dependence between words, and (4) a CRF layer that output the results [81]. [PERSON] et al. proposed a joint financial event extraction method integrating the pre-trained model and multi-layer convolutional neural network (BERT-MultiCNN), which captures the semantic information in events and further improves the effect of event extraction in this field [70]. [PERSON] et al. extracted the event information contained in ancient texts, selected Zuo Zhuan as the data set, and used the RoBERT-CRF model. However, the subject category could not be balanced owing to a small data size [82]. Event extraction requires high-quality expert human annotations, which are usually expensive. [PERSON], [PERSON] et al. focus on low-resource end-to-end event extraction and propose DEGREE, a data-efficient model that formulates event extraction as a conditional generation problem. Given a passage and a manually designed prompt, DEGREE learns to summarize the events mentioned in the passage into a natural sentence that follows a pre-defined pattern [83].
###### Abstract  
The aim of this paper is to investigate the vibration suppression effect of a nonlinear energy sink (NES) on the wind-vortex-induced vibration of deep-water jacket pipes. Based on nonlinear dynamical theories, the coupled dynamic equation of a deep-water jacket pipe with NES was established, and the nonlinear response characteristics and motion stability of the system were studied by using the multi-scale mixed harmonic balance method and Routh-Hurwitz criterion. The paper also analyzed the influence of the nonlinear stiffness and damping parameters of an NES on the dynamic response of the pipe. It is concluded that when the nonlinear stiffness of an NES increases to a certain extent, the motion response of the pipe appeared as a saddle-node and Hopf bifurcation, and the vibration amplitude of the pipe is greatly suppressed. For a system with nonlinear motion, NES damping mainly affects the range of the unstable region. For the system without nonlinear motion, the increases in NES damping will reduce the amplitude near the resonance region. Selecting proper parameters of an NES nonlinear stiffness and damping can effectively suppress the vortex-induced vibration of pipes.
# Improving the Robustness of DTW to Global Time Warping Conditions in Audio Synchronization
## 4 Discussion
It is important to point out the limitations of our study. The audio-audio alignment task we have selected has several distinctive characteristics that may limit the generalizability of our findings: (a) sequence elements (e.g., chroma features) have strong correlations over time, (b) sequence elements are continuous valued rather than discrete symbols, and (c) the true alignment path is (roughly) monotonically increasing, meaning that the alignment is a one-to-one mapping (rather than a many-to-one mapping). We cannot say if our conclusions and findings will necessarily generalize to settings in which these characteristics are different. For example, if the sequences consist of discrete tokens with no temporal correlation, then downsampling may have a very different effect. Nonetheless, we are eager to study to what extent these conclusions may generalize to other domains in future work. Furthermore, we also point out that the sequence normalization method has two restrictions. First, it requires knowing the average global time warping factor a priori, usually through a boundary assumption, in which we assume that both sequences begin and end together. For situations like subsequence DTW, in which the boundary conditions are not known a priori, sequence normalization methods cannot be applied. Second, it only provides benefit in handling global (rather than local) time warping conditions. For example, if the true alignment path has an average global time warping factor close to one but contains a local section with extreme time warping, the sequence normalization method will not provide any benefit. This is also an area to explore in future work.
## 2 Related Work
### Cooperative Multi-Agent  
Cooperative multi-agent systems can be modeled as distributed partially observable Markov decision processes [23], defined by the tuple \(\langle N,S,A,P,R,O,\gamma,\tau\rangle\), where \(N=\{1,\cdots,n\}\) represents the set of n agents, \(S\) denotes the global state space, \(A=\{A_{1},A_{2},\cdots,A_{n}\}\) represents the action space of agents, \(P\big{(}s^{t+1}|s^{t},a^{t}\big{)}:S\times A\times S\rightarrow[0,1]\) is the global state transition function guiding all agents to the subsequent global state \(s_{t+1}\) after executing a joint action at time \(t\), \(R\big{(}s^{t},a^{t}\big{)}:S\times A\to R\) signifies the reward from the environment acquired by the team at time \(t\) subsequent to the joint action in state \(s\), and \(\gamma\in[0,1]\) represents the discount factor. In the context of partially observable environments, \(O=\{O_{1},O_{2},\cdots,O_{n}\}\) denotes the observation space of agents, with \(o_{t}=\big{\{}o_{t}^{j}\big{\}}_{i=1}^{n}\) indicating the portion of the space observed by agent \(i\) at time \(t\). Similarly, \(a^{t}=\big{\{}a_{i}^{t}\big{\}}_{i=1}^{n}\) characterizes the action space at time \(t\), where \(a_{i}^{t}\in A_{i}\) represents the action taken by agent \(i\) at time \(t\). The local state transition mechanism \(\big{(}o_{t}^{t},a_{i}^{t}\big{)}\to o_{i}^{t+1}\) is similar to the global state transition above, guiding agent \(i\) to the subsequent local state \(o_{i}^{t+1}\) after executing its action \(a_{i}^{t}\) at time \(t\). Owing to the inherent partial observability, each agent maintains its observation history \(\tau_{i}\) and adjusts its strategy \(\pi_{i}(a_{i}|\tau_{i})\) accordingly. The primary objective for each agent is to determine a joint strategy \(\pi=\langle\pi_{1},\ldots,\pi_{n}\rangle\) that maximizes either the joint value function \(V_{tot}(s)=E\big{[}\sum_{t=0}^{\infty}\gamma^{t}r^{t}\big{|}s=s_{0},\pi\big{]}\) or the joint action value function \(Q_{tot}(s,a)=r(s,a)+\gamma E_{s^{\prime}}[V^{\pi}(s^{\prime})]\).
## 3 Results and Discussion
### Compression Index and Swell Index  
Figure 11 illustrates the compression index (C\({}_{\rm c}\)) and swell index (C\({}_{\rm s}\)) results of the untreated soil and the fly ash-treated soils. It was seen that the fly ash content reduced the consolidation swelling behavior of the soil. This reduction could be due to the pozzolanic action of fly ash. As the fly ash content increased, the percentage of clay in the soil decreased and the percentage of silt-sized particles increased; thus, the compression index decreased. As mentioned earlier, the C\({}_{\rm c}\) indicates the amount of settlement that the soil can be expected to undergo due to primary consolidation, and high C\({}_{\rm c}\) values mean large settlements and vice versa. Both 40% and 50% fly ash contents showed the minimum C\({}_{\rm c}\) and C\({}_{\rm s}\) values (Figure 11). As a result, both 40% and 50% fly ash contents improved the consolidation swelling behavior of the soil by 52% to 74% and could be considered as viable mixtures that could be used in engineering applications. The C\({}_{\rm c}\) and C\({}_{\rm s}\) values (44% to 70%) for 60% fly ash content were also well below the C\({}_{\rm c}\) and C\({}_{\rm s}\) values for the original soil.
## 4 Experiment
### Dataset Preparation and Model Training  
The projections of 2D key joint positions were computed and collected systematically alongside their corresponding 2D images using the optimized calibration matrix discussed in Section 3.3. In addition to the key joint positions, to train the BPNET, a segmentation and boundary box were required as essential parts of the dataset. These entities were acquired by using an open-source application called Pixellib [51], which allowed for the retrieval of the segmentation and boundary box coordinates from the batched images recorded in the experiment. A sample of segmentation and boundary boxes obtained from Pixellib is demonstrated in Figure 4. Following the validation of segmentation and boundary box from the captured images, coordinates were acquired for every 2D image of the trial.  
Another critical factor to consider within the present data collection methodology was the utilization of a different camera angle during various trials--specifically, a 135 degree angle instead of the customary 90 degree angle. This was because the 90 degree view camera system presented its own set of challenges, particularly in terms of camera calibration. Subsequently, this calibration difficulty resulted in the unavailability of VICON ground truth data for the 2D images captured with this camera configuration. Moreover, these experiments were initiated prior to the currently proposed methodology, which includes commercialized camera calibration using calib.io and time synchronization, as discussed in Section 3.3.  
To acquire accurate ground truth data for these 2D images, a solution was pursued through the integration of an open-source code for human pose estimation called MediaPipe Pose [52]. The coordinates extracted from the MediaPipe Pose framework were utilized as a substitute for the absent VICON ground truth data, effectively providing an alternative reference for the 2D images captured under the 135 degree camera angle configuration. This strategic approach facilitated the continuation of the analysis despite the initial challenges posed by the camera calibration constraints.  
Figure 4: Segmentation and boundary box acquisition from Pixellib.
## 2 Segmentation Model
### Loss Function Construction
#### 2.3.2 Binary Cross-Entropy Loss  
Binary cross-entropy loss is a commonly used loss function in binary image segmentation tasks [24], and its definition is as follows:  
\[\text{Loss}_{\text{BCE}}=-\frac{1}{\text{N}}\sum_{\text{i=1}}^{\text{N}}y_{ \text{i}}\text{log}\binom{\lambda}{\hat{\mathbf{y}}_{\text{i}}}+(1-y_{\text{ i}})\text{log}\left(1\ -\ \overset{\wedge}{-}\hat{\mathbf{y}}_{\text{i}}\right) \tag{13}\]  
Here, N represents the number of samples, or the total number of pixels; \(y_{\text{i}}\) denotes the true label value for the i-th pixel, which is either 0 or 1; and \(\hat{y}_{\text{i}}\) is the predicted probability for that pixel by the model.  
As shown in the above equation, BCE loss is computed based on the true and predicted values of each pixel. Therefore, this loss function focuses more on pixel-level classification accuracy, making the model prioritize local pixel classification. This can lead to the neglect of global information when classifying building edge pixels, causing boundary blur or discontinuity. Combining it with edge consistency loss addresses this issue.
# Robust Image Watermarking Algorithm Based on ASIFT against Geometric Attacks
## 5 Experimental Results and Discussion
### Performance Comparison  
To further prove the performance of the proposed algorithm, the experiments were executed on comparing the proposed method with previous scheme [31], which applied the SIFT algorithm for distortion correction. The NC results are shown in Table 4. It can be concluded that, the ASIFT-based watermarking scheme has better robustness in scaling, translation, and rotation in small angles than the watermarking algorithm based on SIFT.
# Monitoring Land Cover Change on a Rapidly Urbanizing Island Using Google Earth Engine
## 3 Results
### The 30-Year Land Cover Change  
Table 4 shows the land cover area and change of Haitan Island over the 30 years. The main trend of land cover type change is that forest area has increased by 92.36 km\({}^{2}\) of the study area, followed by other land categories declining by 79.05 km\({}^{2}\) of the study area, and expansion of built-up land by 47.27 km\({}^{2}\) of the study area. Compared to the final and initial period for each land cover type, the rate of land cover change of built-up land and forest land increased by 264.43%, and 156.85%, respectively, while sandy land, other land, and agricultural land reduced by 88.07%, 65.80%, and 46.70% from 1990 to 2019. The built-up land areas and forest land areas were continuously increasing over the whole period from 1990 to 2019, while cultivated land areas were gradually decreasing. Other land and sandy land areas showed a similar trend, reducing from 1990 to 2007, but increasing between 2007 and 2019 (Figure 4). In 1990, sandy land covered 14.30 km\({}^{2}\) of Haitan Island, while this dropped to 1.07 km\({}^{2}\) in 2007 and increased in 2019 (4.89 km\({}^{2}\)). The built-up land areas expanded by 8.98 km\({}^{2}\) between 1990 and 2000 and substantially grew by 21.53 km\({}^{2}\) and 16.76 km\({}^{2}\) between the second (2000-2007) and third (2007-2019) intervals, respectively. Water areas experienced an increase of 4.54 km\({}^{2}\) between 1990 and 2000 and 1.03 km\({}^{2}\) between 2000 and 2007 and decreased between 2007 and 2019 (2.05 km\({}^{2}\)). The cultivated land continuously declined by 9.60 km\({}^{2}\) between 1990 and 2000, 13.45 km\({}^{2}\) between \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Land Cover Classes**} & \multicolumn{6}{c}{**Area (km\({}^{2}\))**} & \multicolumn{6}{c}{**Change in Area (\%)**} \\ \cline{2-9}  & **1990** & **2000** & **2007** & **2019** & **1990–2000** & **2000–2007** & **2007–2019** & **1990–2019** \\ \hline Sandy land & 14.30 & 6.66 & 1.07 & 4.89 & \(-\)53.43 & \(-\)83.85 & 354.90 & \(-\)65.80 \\ Built-up land & 17.87 & 26.86 & 48.39 & 65.14 & 50.24 & 80.17 & 34.63 & 264.43 \\ Cultivated land & 69.74 & 60.14 & 46.69 & 37.17 & \(-\)13.76 & \(-\)22.36 & \(-\)20.39 & \(-\)46.70 \\ Forest land & 58.89 & 89.80 & 146.25 & 151.25 & 52.49 & 62.86 & 3.42 & 156.85 \\ Other land & 89.76 & 62.57 & 2.60 & 10.71 & \(-\)30.29 & \(-\)95.85 & 312.37 & \(-\)88.07 \\ Water & 9.68 & 14.22 & 15.25 & 13.20 & 46.87 & 7.24 & \(-\)13.43 & 36.36 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Summary of land cover classes and change on Haitan Island, China.
# A Survey on Energy-Aware Security Mechanisms for the Internet of Things
## 3 Security Protections in IoT Environments
### Defense at the Software Level
#### 3.5.3 Access Control and Data Storage  
Strict access controls in the facility limit software and system access. The risk of unauthorized access can be avoided by implementing the principle of least privilege, user authentication, and authorization. For example, IoT systems assign user-specific access permissions based on their roles, ensuring individuals access only to job-relevant information.  
Strong data-storage practices, including encryption, access control, and regular back-ups, safeguard sensitive information, preventing breaches and minimizing the risk of theft, leaks, and associated consequences. An example is the use full-disk encryption like BitLocker [74] to encrypt the contents of the entire hard drive. For the complete security of IoT systems equipped with data storage, users should leverage robust encryption options like MySQL's AES function and SQL Server's transparent data encryption [75].
# A Survey on Monitoring Quality Assessment for Wireless Visual Sensor Networks
## 2 Proposed Classification and Comparisons
Besides sharpness and perspective, several other features are also used in the literature to determine content quality, like exposure (luminosity) and pixel ratio of region of interest;
* **Dependable quality**: the quality of a WVSN can be assessed through its dependability, i.e., its ability to deliver a service that can be justifiably trusted, avoiding service failures more frequent or more severe than is acceptable [21]. Hence, dependable quality entails all network elements that affect the system expected behavior. The quality can be addressed by quantitative dependability metrics, such as _availability_ (related to system readiness for correct service) and _reliability_ (associated with continuity of provision of correct service) [21; 22; 23]. All these major groups will be discussed in next sections.
## 3 Results and Discussion
### Experimental Results
#### 3.1.2 Optical Emission Spectroscopy  
In the plasma discharge media, the collision process excites several plasma species to upper states decaying and emitting photons with various specific wavelengths. By recording the emission spectra of the plasma discharge medium, these photons can be detected and analyzed. In this work, the OES technique was used to identify the different reactive species produced between the electrodes of the \(CO_{2}\) plasma reactor. Vibro-rotational bands of CO2, CO2+, C2, and CO radicals and atomic lines of oxygen were detected (see Figure 5 and Table 5). Vibro-rotational bands of radical \(\cdot OH\) and the \(H_{\beta}\) emission line of hydrogen, coming from small water impurities in the inlet gas, were also identified. The amount of this water was so small that it had no relevant influence on the plasma. \begin{table}
\begin{tabular}{c c c} \hline
**Technology** & **Range of CO2 Conversion (\%)** & **Range of Energy Efficiency (\%)** \\ \hline Dielectric Barrier Discharges & 0–30 & 0–15 \\ Microwave Discharges & 0–80 & 0–40 \\ Gliding Arc Discharges & 0–10 & 0–30 \\ AC-PPP Reactor * & 2.5–25 & 1–6 \\ \hline \end{tabular}
* Results of this work.  
\end{table} 
Table 4: Comparison of CO2 conversion factor and energy efficiency for different plasma technologies [7]. \begin{table}
\begin{tabular}{c c c} \hline
**Applied Voltage (kV)** & **Power (W)** & **\(\eta\) (\%)** \\ \hline
10 & 50.95 & 1.69 \\
12 & 61.60 & 2.89 \\
14 & 71.35 & 3.58 \\
16 & 82.14 & 4.23 \\
18 & 92.64 & 4.74 \\
20 & 105.49 & 5.11 \\
22 & 113.05 & 6.1 \\ \hline \end{tabular}
\end{table} 
Table 3: The average consumed power and energy efficiency for different applied voltages. A study of the dependencies of the normalized intensities of the headband of vibro-rotational bands of these formed species and the oxygen atomic line 771.1 nm on the plasma position was carried out. Figure 6 plots these dependencies for an applied voltage of 22 kV and a frequency of 50 Hz. The intensity of CO\({}_{2}\) species was higher than that of other species although it was reduced close to the electrodes. Furthermore, the intensities of CO, O, OH, and C\({}_{2}\) species increased near the electrodes but they had a minimum value at the middle of discharge. The relative intensities of Figure 6 are not directly related to the populations of these species because these are affected by many parameters, such as electron density, the cross sections of particles, etc. ; however, it is indicative of the presence of species between the reactor electrodes. The density and temperature of particles in the plasma are the most fundamental parameters in the physical characteristics of gas discharges and play a very important role in understanding the discharge physics and optimization of the operating parameters of the plasma reactor. From an experimental point of view, these parameters are commonly measured using different techniques including Langmuir probes, microwave interferometry, laser Thomson scattering, and optical emission spectroscopy (OES) techniques [34].