esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Overview of the Selected Techniques
### Global Adaptive Observer (GAO)
#### 3.1.1 Development of Phase Voltage Dynamical Model  
Let us first consider the phase \(a\). Models of phase \(b\) and \(c\) are the same as phase \(a\). To develop the state-space model, let us first consider the phase voltage \(U_{a}\) and its derivative as the following state variables:  
\[\xi_{1} = U_{a}=A^{+}\sin\bigl{(}\omega t+\phi^{+}\bigr{)}+A^{-}\sin\bigl{(} \omega t+\phi^{-}\bigr{)}+A^{0}\sin\Bigl{(}\omega t+\phi^{0}\Bigr{)},\] \[\xi_{2} = \dot{\xi}_{1}=\dot{U}_{a}=\omega\Bigl{\{}A^{+}\cos\bigl{(}\omega t +\phi^{+}\bigr{)}+A^{-}\cos\bigl{(}\omega t+\phi^{-}\bigr{)}+A^{0}\cos\Bigl{(} \omega t+\phi^{0}\Bigr{)}\Bigr{\}}.\]  
By considering the state vector, \(\xi=\bigl{[}\begin{array}{cc}\xi_{1}&\xi_{2}\end{array}\bigr{]}^{T}\), the state-space model of the phase voltage dynamics can be written as:  
\[\dot{\xi} = \mathcal{A}\xi, \tag{10}\] \[U_{a} = \mathcal{C}\xi, \tag{11}\]  
where  
\[\mathcal{A}=\biggl{[}\begin{array}{cc}0&1\\ -\omega^{2}&0\end{array}\biggr{]},\mathcal{C}=\bigl{[}\begin{array}{cc}1&0 \end{array}\bigr{]}.\]  
State matrix \(\mathcal{A}\) contains the unknown grid frequency \(\omega\). To facilitate the frequency adaptation law development for \(\omega\), let us rewrite the frequency in terms of the known nominal frequency as, \(\omega^{2}=\eta\omega_{n}^{2},\eta>0\). Observability matrix of system (10) and (11) is given by:  
\[\mathcal{O}=\biggl{[}\begin{array}{c}\mathcal{C}\\ \mathcal{CA}\end{array}\biggr{]}=\biggl{[}\begin{array}{cc}1&0\\ 0&1\end{array}\biggr{]}. \tag{12}\]  
Since the observability matrix \(\mathcal{O}\) is of rank 2 which is the same as the rank of the state matrix \(\mathcal{A}\), the system (10) and (11) is observable. Observer design for this system is described in the following section.
# Performance Optimization of Underwater Communication Links at Different Ranges for AIS Relay to AUV
## 7 Underwater AIS Relay  
### Automatic Identification System  
Automatic Identification System (AIS) is a tracking system based on reports provided by the ships carrying an AIS transponder. This navigation aid system is originally designed for maritime safety (collision avoidance) and communications between ship and shore, or between different ships. Even only giving indications on a portion of maritime traffic, this self-reporting system is a worldwide standard and, therefore, a coherent source of information for global traffic analysis. More precisely, this broadcasting system can receive the navigation information form other ships or vessels and meanwhile broadcasts own ship's states at a certain time interval. This time interval varies with types of the AIS and navigation states of ships. The ships regularly exchange information, such as their positions, identity, and navigation status.  
Differently than other operational coastal active systems for maritime surveillance, AIS is characterized by considerable coverage (VHF propagation) together with a relatively accurate positioning performance [28; 29]. AIS standard is defined by International Association of Marine Aids to Navigation and Lighthouse Authorities (IALA) and International Telecommunication Union (ITU) [30].  
According to AIS standard, ships have to send their positions at a rate according to their kinematic from one packet every 2-10 s while moving to 3 min at anchor. Ships will also send other information, such as identity or shipment's type.  
There are currently 27 different existing messages types among the 64 possibilities. A time slot is 26.67 ms long with 256 bits meaning that the raw bit rate (BR) of AIS is 9600 bps. One message can use one up to five time slots to be sent. An AIS time slot is shown in Figure 11. The size of the data field can vary depending on the number of times slots used. In Figure 11, the structure is faithful for a message using one time slot.  
Figure 10: General process for allocating the spreading sequence in the time and frequency domain.
## 2 Materials and Methods
### Statistical Methods
#### 2.3.3 Cross-Correlation Analysis  
Cross-correlation analysis is an efficient way to detect a time lag between two signals. In this study, we used this method to detect if forest growth during the growing season had a legacy effect on the SPEI. For example, we first ran Pearson correlation analysis between the EVI during May-September from 2001 to 2018 and the SPEI during the same period, which implied a 0-month lag, or no legacy effect. Secondly, we moved the SPEI series one month earlier, or April-August from 2001 to 2018, and analyzed its correlation with the EVI during May-September from 2001 to 2018, which implied a 1-month lag of the forest growth (Figure 2). Since studies have demonstrated that global forests generally have 1-4 years of lag to droughs, here, we set the shifted lag step from 0 months to 60 months (i.e., 5 years) and calculated the correlation coefficient (\(r\)) for each valid pixel. After that, we detected the \(r\) value of the largest absolute value. The corresponding lag step corresponded to the length of the legacy effect for each forest pixel.
## 1 Introduction
In the context of CFAR target detection, performance degradation caused by abrupt changes and non-uniformity in the clutter background represents a typical manifestation of such fuzziness. Therefore, an improved CFAR detector is proposed, which improves the OSTA detection method by integrating fuzzy theory and designing an adaptive FOSTA detection algorithm. In the FOSTA algorithm, the mean ratio of the leading and lagging windows is calculated, and the difference between this ratio and the preset threshold is then used as input to the fuzzy inference machine. This allows for the dynamic adjustment of the background clutter estimation range, effectively enhancing the adaptability and robustness of the OSTA algorithm under varying environmental conditions. Meanwhile, KLQ is adopted to improve the detection performance in a multiple-target environment. Thus, the overall detection performance of the system can be enhanced effectively. Compared with the traditional VI-class CFAR detectors, the proposed CFAR detector adopts an improved detection algorithm in a clutter edge environment. This algorithm can adaptively adjust its own threshold to adapt to a variety of environmental conditions and improve the detection performance of the detector in the case of environmental misjudgment, thereby enhancing the robustness of the VI class CFAR detectors.
## 2 Description of Experiments
### Reuse of Struvite Decomposition Residues  
A first set of experiments was carried out with the aim of identifying the quantity of struvite decomposition residues required to efficiently remove the ammonium load. In this regard, wet decomposition products of 7.5%, 8.5%, 10%, 20%, 30% (\(w/v\)) were tested for the treatment of 200 mL of raw landfill leachate. The tests were performed using beakers of 400 mL equipped with magnetic stirrers. In each test, the struvite residues were mixed with leachate before the pH was set to about 9 using NaOH. A reaction time of 2 h was adopted. The process performance was investigated by analyzing the supernatant withdrawn after a settling phase of 1 h.  
For multiple recycling of decomposition residues, the following sequential batch operating mode was adopted, using the same equipment just abovementioned: (1) struvite was produced from the treatment of raw leachate according to the optimal conditions identified by means of the experiments described in Section 2.1; (2) the supernatant was withdrawn; (3) an appropriate amount of settled solid was directly decomposed in the reactor through the identified optimal procedure; (4) after a settling step, the liquid phase was taken; (5) the raw landfill leachate was fed into the beaker containing the decomposed solids; (6) the pH was set to 9 and a reaction phase was conducted by stirring the mixture (300 rpm) for 2 h; steps 2-6 were repeated several times. In order to identify the more favorable conditions for phase 6, three treatment methods were tested. For two of these, the treatment cycles for ammonia removal were conducted by exclusively exploiting the struvite decomposition products, without any supply of Mg and P reactants. For the first method, the process pH was set by means of NaOH, but for the second method air insufflation (flowrate of 10 L/min) was used. In the third method, MgCl\({}_{2}\).6H\({}_{2}\)O and H\({}_{3}\)PO\({}_{4}\) were supplied at a molar ratio of _n_(N):_n_(Mg):_n_(P) 1:0.05:0.05 (5% of magnesium and phosphorus molar amounts required for struvite production from raw leachate); the pH was set by means of chemical addition (NaOH, 40% \(w/v\)). The supernatant formed at the end of each cycle was collected for chemical characterization.
# Risk Perceptions on Social Media Use in Norway
## 3 Method
### Data Analysis  
The compared groups in this study are the sexes (male/female), age and hacked groups. We have also split the collected ages into digital natives and non-digital natives; as there might be a difference in how the digital natives perceive risk on social media. [PERSON] et al. (2019) defined digital natives as people born between 1987 and 1997. We classified participants younger than 31 as digital natives, and those above as non-natives, to comply with this definition as much as possible for the available sample. When analysing differences between the sexes, we left the group that had chosen \"prefer not to answer\" out because of the low number of respondents that chose this option, with only five people being in the group. We sorted the hacked/not hacked group using question 21 in the survey.  
To process the results of the questionnaire, we applied a variety of statistical data analysis methods available through the IBM SPSS software v2.0. A summary of the statistical tests used in this research is as follows:  
Firstly, each variable was analysed separately, looking at trends and distribution with histograms and descriptives. Furthermore, we performed bivariate analysis and ANOVA on age, gender and hacked groups to investigate differences. We treated \"No\" as zero and \"Yes\" as one in the analysis for binary-type questions. We also performed a Pearson correlation with the data on how much people care about IT, information security and privacy to see if this had any effect on how people perceive risk. A thing to note is that the use of ANOVA or other bivariate methods for analysing ordinal nonlinear data has been criticised for not being normally distributed. [PERSON] (2018) wrote a paper about different aspects of tension for when one can use ANOVA or other bivariate analysis, and used earlier studies to back up that there is little to no reason not to use bivariate analysis on nominal data such as Likert and rating scales, small sample sizes or data that do not follow a normal distribution. Following [PERSON], the analysis in this paper uses the mean and the ANOVA to illustrate differences between groups. However, we have also included the median as good practice.  
For analysis of the free text questions, the answers were grouped up together according to common characteristics to allow for quantification. For example, synonyms such as \"extortion\" and \"blackmail\" were grouped together in the analysis. Each answer was counted separately in the cases where a respondent gave multiple answers to the question.
# A Filter Pruning Method of CNN Models Based on Feature Maps Clustering
## 5 Experiments and Results
### Preparation in Advance
#### 5.1.3 The Selection of Convolutional Layer  
To verify the performance of the proposed filter pruning method, this paper takes the convolutional layers in the feature pyramid network as an example, because they are directly related to the output results. Therefore, this work focuses on the convolutional layers P5, P4, and P3 in FPN. To measure their effects on the performance of neural networks, this paper prunes all filters, _namely reduced related layer_, in three convolutional layers one by one and evaluates them on the validation set. Then, we can measure the importance of each convolutional layer in FPN by comparing the decline in the evaluation precision of the different pruned networks. The evaluation results are shown in Figure 12.  
When layer P5 is pruned, the precision declines from 71.75% to 38.9%, which is almost half of the original precision. In contrast, when layer P4 or P3 is pruned, the precision  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Network** & **Parameters** & **GPU Time(s)** & **CPU Time(s)** & **Precision (\%)** \\ \hline Resnet18 + FPN & 19195117 & 0.143 & 0.388 & 71.75 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The statistics for the Initial Network.  
Figure 11: Loss curves in Training Process. (**a**) Classification Loss Curve. (**b**) Regression Loss Curve.  
Figure 12: Precision Comparison of Pruning P3, P4, P5 layers.  
has almost no drop. In other words, the removal of P5 leads to the biggest decline in precision. Hence, P5 is considered the most important layer and is selected to be pruned by our method.
# A Disruption Tolerant Distributed Routing Algorithm in LEO Satellite Networks
## 3 Disruption Tolerant Distributed Routing Algorithm
### Main Path Direction Calculation
#### 3.3.1 Horizontal Hop Direction Calculation  
Let \(c_{i}\) represent the current satellite node orbit number, \(d_{i}\) represent the destination satellite node orbit number, and \(d_{h}\) represent the horizontal hop direction. According to the introduction in the constellation configuration of the previous section, the four intersatellite link interfaces of the satellites located in the western hemisphere are counted as 0, 1, 2, 3 in the counterclockwise direction from the north direction, so that \(d_{h}=3\) means forwarding in the direction of increasing orbit number, \(d_{h}=1\) means forwarding in the direction of decreasing orbit number, and \(d_{h}=-1\) means there are no horizontal hops, that is, the current satellite node and the destination satellite node are located on the same orbital plane. The calculation method of the horizontal hop direction is as follows.  
1. If \(d_{i}>c_{i}\), then \(d_{h}=3\);
2. If \(d_{i}<c_{i}\), then \(d_{h}=1\);
3. If \(d_{i}=c_{i}\), then \(d_{h}=-1\).
# TMVDPatch: A Trusted Multi-View Decision System for Security Patch Identification
## 5 Experiments and Results
### Model Comparison  
For the problem of security patch identification, TMVDPatch fully considers the relevant data submitted by the patch (including control dependency information, data dependency information and submission message). In order to evaluate the effectiveness of the TMVDPatch proposed in this paper, different classification models are selected for comparative experiments in this subsection.  
In our work, we use SV-CMS and SV-CDP to represent single-view classification models using message commits and code changes alone, respectively. Since PatchRNN uses the same dataset as this paper for security patch identification, and also considers all source code information before and after patch submission, we choose PatchRNN as the comparison model. In addition, we treat code changes as normal text, and build a security patch identification model based on BLSTM(Bi-directional Long Short-Term Memory) called SQ-CDP. On this basis, we combine the SQ-CDP with the SV-CMS(single-view classification models using message commits) to construct a new model called SQ-A.  
The comparative experimental results are shown in Table 3. Compared with single-view SV-CMS and SV-CDP, multi-view learning can effectively improve the identification ability of the model, which shows that the evidence between different views can be complementary. At the same time, SV-CDP can achieve a higher recall, because only using single-view learning will lead to better identification of non-security patches by the model, resulting in the phenomenon of model bias. Under the multi-view, the problem can be effectively solved based on comprehensive evidence. Compared with SQ-CDP and SQ-A, TMVDPatch can learn control dependency and data dependency information from code patches. It further illustrates the effectiveness of the representation method of security patches proposed in this paper. Moreover, compared with PatchRNN, TMVDPatch can achieve better identification effect in the same dataset.
## 2 Materials and Methods
### Methods
#### 2.2.3 Classification Model
In our work, we implemented multilayer perceptron networks (MLPs) [46], the most commonly used ANNs, that used the back-propagation algorithm in the supervised learning process. The MLP's architecture provides three neural levels: input, hidden and output layers. In the learning process a features array feeds the input layer. Then the input flows to the next hidden layers through dendrites, i.e., neural connections; during the crossing of the network, the signal can be inhibited or amplified through the connection weights and the neurons add the input signals and transform them into output signals by means of an activation function. Our MLP model was composed of one hidden layers with 3 neurons and used the sigmoid as an activation function.
## 1 Introduction
### Contributions and Research Significance  
_The research questions and corresponding answers presented in this study have significant implications for generative AI. Firstly, by investigating the requirements necessary for implementing generative AI systems, encompassing hardware, software, and user experience considerations, this research provides valuable insights for researchers, developers, and practitioners. Understanding these requirements is essential for effective planning and designing generative AI systems, ensuring their successful implementation and optimal performance. Additionally, exploring different types of generative AI models described in the literature contributes to a comprehensive understanding of the field. By presenting a taxonomy of models based on their architectural characteristics, such as VAEs, GANs, diffusion models, transformers, language models, normalizing flow models, and hybrid models, this research guides researchers and practitioners in selecting appropriate models for specific applications. This knowledge fosters advancements in generative AI by promoting informed decision-making and facilitating the development of more sophisticated and tailored models._  
_Moreover, this study delves into the specific input and output formats used for different prescribed tasks in generative AI systems. The comprehensive classification of these formats, along with the associated tasks and models utilized in the literature, is a valuable resource for researchers and practitioners. It enables them to understand and leverage the diverse input and output formats available, promoting the development of effective and customized generative AI systems. Furthermore, the proposal of a classification system based on output types and the discussion of commonly used evaluation metrics contribute to the establishment of robust evaluation frameworks. These frameworks enhance the credibility and applicability of generative AI models by providing standardized methods for assessing their quality and performance. In summary, this research offers valuable insights into generative AI systems' requirements, models, input-output formats, and evaluation metrics. These findings contribute to advancements in the field, guiding researchers, developers, and practitioners in effectively implementing and evaluating generative AI models for various applications._
# Current Status and Future Developments of Micromegas Detectors for Physics and Applications
## 8 Micromegas for UV Detection
### Reflective Photocathode  
One way of using the photoelectric effect in a Micromegas are thin layers of solid photocathode material like CsI. The photocathode material is deposited on top of the Micromegas mesh, and UV photons are guided in the detector by a transparent window [6]. Figure 24 illustrates this detection mode. The photocathode works in a reflective mode, as photoelectrons are extracted on top of the photocathode. They follow the electric field lines through the mesh holes into the amplification region.  
This method has some drawbacks and some benefits. The fabrication and handling of the photocathode is technically more challenging, especially for the cases where the mesh is attached to the anode (i.e., bulk, Microbulk, and InGrid), while woven mashes cannot be used. The second drawback is the mesh opacity. Photons passing through the holes of the mesh cannot be detected. A high-opacity mesh provides a larger active area of the photocathode but also increases the inhomogeneity of the electric field. Furthermore, photoelectrons have to follow an inhomogeneous electric field until passing through the mesh, and the actual path of each electron may vary, this being a drawback for timing applications.  
The benefits of applying the photocathode on the mesh are the shielding of the delicate photocathode material against ion-backflow and against avalanche UV-photons that create photon feedback on the photocathode. This consequently allows a higher gain of the  
Figure 23: (**a**) Design of the neutron Beam Loss Monitor detector. A cathode with a neutron-to-charge converter is the cathode. Outside the gas volume there is a HV card that powers the Micromegas and the cathode and a FEE card with the current preamplifiers. (**b**) View of both modules together, where the second one is surrounded by a moderator and neutron absorber, as explained in the text.  
amplification stage. Moroever, as most of the photons are absorbed on the first layers of the photocathode and additional material thickness does not affect the photon flux, a thicker layer of the photocathode can be applied, allowing the achievement of an optimal quantum efficiency (i.e., CsI \(\sim\)350 nm) [108]. With a reflective photocathode on the mesh, the detector can be operated at a higher gain or higher occupancy, having, in parallel, higher quantum efficiency.