esa-sceva/satcom-chunk-collection huggingface dataset:
# Compression of Multibeam Echosounders Bathymetry and Water Column Data
## 4 FAPEC Tailoring for the KMALL Data Format
### MWC Pre-Processing Stage  
In this section, we propose an algorithm to decorrelate MWC datagrams. We only focus on the BeamData subdatagram, as the others are negligible in comparison.  
If we represent the amplitude samples as a raw rectangular image (see Figure 2), we can see that correlation between beams is higher than between samples (i.e., the image is smoother from top to bottom than left to right). Although this is purely qualitative, it matches with the algorithm designed in [12], and it can be proved to be true by applying a simple differential first by rows and then by columns and finally comparing the results. In the picture, we can also see that the number of samples per beam is not constant, but it has a parabolic behavior.  
In the implementation for.wcd files in [12], given a sample \(S_{i,j}\) in the position \(i\) of the beam \(j\), the vertex coordinates \((V_{x},V_{y})\) and the number of beams \(N_{B}\), prediction errors \(E_{i,  
As the reader may have noticed, these equations are not defined for \(j=0\) (i.e., the first beam). The reason is that the system must be causal; therefore, the first beam is predicted from the previous sample in the same beam:  
\[E_{i,0}=S_{i,0}-S_{i-1,0},\quad 1\leq i<N_{S_{0}}. \tag{7}\]  
The algorithm we propose in this section consists of an adaptation from that of [12] but with some improvements. We also follow the approach of predicting samples from the previous beam, but we do not restrict it to the rectangular region. Instead, we do that for all the samples that have a sample above. Formally,  
\[E_{i,j}=S_{i,j}-S_{i,j-1},\quad 0\leq i<\min(N_{S_{j-1}},N_{S_{j}}),\quad 1\leq j <N_{B}. \tag{8}\]  
The remaining samples are predicted from the previous samples. That is,  
\[E_{i,j}=S_{i,j}-S_{i-1,j},\quad\min(N_{S_{j-1}},N_{S_{j}})\leq i<N_{S_{j}}, \quad 1\leq j<N_{B}. \tag{9}\]  
The prediction errors for the first beam are also calculated with Equation (7), with the addition that  
\[E_{0,0}=S_{0,0}+128. \tag{10}\]  
Now we are interested in observing if this algorithm also works for the phase information present in MWC datagrams. Notice that the former stage for.wcd was not designed to work with phase samples. In Figure 7, we show the phase corresponding to the amplitudes in Figure 2. As the reader can see, this information is quite stochastic and much more difficult to predict than amplitude samples. Our final decision has been to apply a simple differential between samples (i.e., Equation (9)).  
We should remark that well-known algorithms such as nearest-neighbor or a generic linear predictor of order \(Q\) have been tested, but they are slower and also have a worse compression ratio. Our approach is simple (thus fast) and already provides good ratios, also allowing us to easily introduce losses (see Section 4.5).
## Appendix A List of Acronyms  
\begin{tabular}{c c} \hline \hline
3 GPP & Third-generation partnership project \\
5G & Fifth Generation of Cellular Communications \\ BER & Bit Error Rate \\ COOP NOMA & Cooperative NOMA \\ dB & Decibel \\ DFT & Discrete Fourier Transform \\ eMBB & Enhanced Mobile Broadband \\ IDFT & Inverse Discrete Fourier Transform \\ IoT & Internet of Things \\ ISI & Intersymbol Interference \\ MFB & Match Filter Bound \\ MIMO & Multiple Input Multiple Output \\ mMTC & massive Machine-Type Communications \\ mm-wave & Millimeter waves \\ MRC & Maximum Ratio Combiner \\ MSE & Mean Squared Error \\ m-MIMO & Massive MIMO \\ NOMA & Non-Orthogonal Multiple Access \\ NR & New Radio \\ OFDM & Orthogonal Frequency Division Multiplexing \\ QPSK & Quadrature Phase Shift Keying \\ SC-FDE & Single Carrier with Frequency Domain Equalization \\ SIC & Successive Interference Cancellation \\ \hline \hline \end{tabular}
## 3 Optimization Methods
### Genetic Algorithm  
The main operations related to GA are the creation of the first generation of individuals, crossover, and mutation. In the rest of the section, the selection for each of these operations is presented based on [32].  
For the creation of the first generation of individuals, there is no initial guess for the amplitudes or phases, since there is no notion of where the optimal solution may be. The diversity of the initial generation was desired; thus, the amplitude and phase values were obtained from a uniform distribution. However, a constant power for the antenna array was desired; thus, the amplitudes must be normalized by the total power, from which the final values for the amplitudes in the first generation are given by:  
\[\bar{A}_{j}(0)=A_{j}(0)\frac{P^{2}}{\sum_{i=1}^{k}A_{i}(0)^{2}}, \tag{8}\]  
where \(\bar{A}_{j}(0)\) is the value for the amplitude of the \(j\)th antenna obtained in the first generation, \(A_{j}(0)\) is the value for the amplitude of the \(j\)th antenna resulting from the first generation before normalization, and \(k\) is the number of antennas in the array. The antenna array as a whole has a constant output power due to a fixed amount of power being provided to the array by the source. This power is denoted as \(P\).  
For the crossover operation, the single point crossover [32] was employed over the array of values for the antenna array. The only special consideration when doing the crossover was that the crossover points for the phase and amplitude were used separately; thus, the crossover point for both values may be different. When using crossover schemes, there is a chance that the total power of the antenna array is not \(P\); thus, Equation (7) must be used once again to normalize the total power.  
For the mutation process, a Gaussian distribution was used for every phase and the amplitude values [33]. This is typically used for mutation when the values are not binary coded, as was the case, since floating values were used. This means that the values for an amplitude and a phase and in each iteration are updated as follows:  
\[\phi_{j}^{mut}=\phi_{j}+\mathcal{N}(0,\sigma_{\phi}), \tag{9}\]  
\[A_{j}^{mut}=A_{j}+\mathcal{N}(0,\sigma_{A}), \tag{10}\]  
where \(\phi_{j}^{mut}\) and \(A_{j}^{mut}\) are the mutated values for both the phase and amplitude and \(\sigma_{\phi}\) and \(\sigma_{A}\) are the standard deviation for both values. Once again, the mutation may result in a total power different than \(P\); thus, Equation (7) must be used to normalize the total power.  
A final consideration is that elitism is used in training, meaning that a small part of the best performing individuals is copied into the next generation unchanged. This process improves performance since the GA does not need to find previously discarded solutions once again.
## 4 Threading Statistical Disclosure Attack Algorithm with EM (TSDA-EM)
### Rows Permutations  
To obtain feasible tables, three approaches have been proposed [16]:  
* Uniform generation of table cell values.
* Successive random rearrangement of rows and columns.
* Equal feasible tables are discarded.  
Previous works [16; 17] are based on the second approach, successive random rearrangement of rows and columns. That is, for every round, there is an initial order for sender and recipients. After generating a few feasible tables, there is a possibility to obtain a table that has already been generated. In order to avoid this repetition, the senders' and recipients' order needs to be changed. Elements from the subset of senders \(S^{\prime}\) are randomly picked, one by one, until we get a new order. The same process is applied for subset \(R^{\prime}\). After obtaining feasible tables with a new order, it is important to restore these feasible tables' order to its original.  
A truly random reorder may produce and use a previous sequence, which is inefficient. Therefore, all possible orders are generated sequentially for each round; this is the same as a permutation of each sender in the round:  
\[Numberof\ permutations=n_{s}! \tag{1}\]  
where \(n_{s}\) is the number of senders in the round.  
Equation (1) generates all possible permutations of the senders. At the beginning, \(n\) senders are available and 1 of \(n\) has to be selected. At the second position, the available senders is reduced by 1.  
The main limitation of this formula is that factorial numbers increase at a greater than exponential rate. Thus, if a round has 8 senders, there would be 8! = 40,320 permutations. This will rapidly increase computing time. The solution to avoid a large number of permutations is to leave some fixed senders. The number of fixed senders is defined as:  
\[n_{fs}=n_{s}-max_{s} \tag{2}\]  
where \(max_{s}\) is the number of senders that will be processed. For example, for the values \(n_{s}=5\), \(max_{s}=3\), some of the permutations could be:  
* 1,2,5,3\(A\)
* 1,2,4,5,3  
Notice that the first two senders do not change their position while the last senders are rearranged. The recommended parameter for the dataset under study is \(max_{s}=6\). If there is a round with fewer senders than \(max_{s}\), all senders must be processed.  
It is important to say that we only perform the rows rearrangement because if we repeat the same process for each column, then we will get \(n_{r}!\) for each value of \(n_{s}!\). This will increase the execution time again.
# LDPC Decoder Design Using Compensation Scheme of Group Comparison for 5G Communication Systems
## 3 LDPC Decoding Algorithms
### rExMin Algorithm  
Based on the NPMSA, the rExMin algorithm was proposed to revise the problem caused by a decline in the decoding capability due to a difference between the second minimum and the probabilistic second minimum. The rExMin algorithm uses a negative factor \(\theta\) to reduce the value of the probabilistic second minimum, which reduces the difference and recovers the decoding capability. In addition, the negative factor \(\theta\) may cause overcorrection when the new second minimum is smaller than the first minimum. Therefore, a comparator was added to determine whether overcorrection occurred. The new second minimum is the same as the probabilistic second minimum when overcorrection occurs. The new second minimum is the sum of the probabilistic second minimum and the negative factor \(\theta\). The formula for updating the check nodes with a grouping search is expressed as:  
\[new\ min^{2 nd}\ =\ \left\{\begin{array}{cc}min^{2 nd},&min^{2 nd}+\theta<min^{1 st }\\ min^{2 nd}+\theta,&min^{2 nd}+\theta\geq min^{1 st}\end{array}\right.. \tag{8}\]
## 2 Materials and Methods
### Modeling Polycarbonate  
The true stress-strain tensile behavior of polycarbonate is different from that of metals. A typical true stress-strain tensile curve of polycarbonate is shown in Figure 4. To model such a curve, three regions are identified. The first region is the linear elastic region which is governed by one material property, the modulus of elasticity, \(E\). The second region starts at the yielding points, \(\sigma_{y}\) and ends at the necking stress, \(\sigma_{ne}\). The latter is called the necking stress because it is a very distinctive level of stress at which the cross-sectional area of the tensile specimen suddenly necks at a specific location on the gauge section. This sudden change in the geometry is associated with severe localized plastic flow that results in a drop of the stress level. By observing the sample during the tests, the third region is found to be associated with the progression of the necking over the entire gauge section. As larger part of the gauge section is necking, the cross-sectional area of the gauge section is reduced further. This is associated with an increase in the stress that ultimately reaches to a critical value at which a crack is formed. Once the crack is formed, it grows quickly leading to complete fracture at \(\sigma_{f}\). It is assumed here that the part of tensile stress curve after the onset of necking is of no practical use as it corresponds to severe change in the geometry and plastic flow. As a result, failure is assumed to occur when the necking stress, \(\sigma_{ne}\), indicated in Figure 4 is reached. Hence, the strain energy density, \(W_{c}\), is calculated to the stress level \(\sigma_{ne}\) shown by the shaded area in Figure 4. Instead of using Equation (1), MATLAB's trapezoidal numerical integration function was used such that \(W_{c}=trapz(\epsilon,\sigma)\)[42].  
Figure 3: Illustration of the control volume for U-notch under mode I loading.
## 2 Modeling Approach
### Algorithm  
All numerical simulations in this study are carried out under non-steady-state conditions. FLUENT 17.0 commercial software is used to predict the distribution of the two phases. Volume fraction is discretized using the QUICK method. The First Order Upwind scheme was used for other equations. Additionally, the SIMPLE scheme was exerted for the pressure-velocity coupling. For all the simulations done in the present work, the convergence criteria for the solutions are considered when the achieved residuals are less than 1 \(\times\) 10\({}^{-3}\). One of the important parameters that affects the convergence criteria of simulation is the under-relaxation factor. The under-relaxation factor minimizes oscillations and contributes to the stability of the computation. In this study, under-relaxation factors for pressure, density, momentum, volume fraction, turbulent kinetic energy, and turbulent dissipation rates are considered 0.3, 1, 0.7, 0.2, 0.8, and 0.8, respectively.
# Effects of Gradient Warming on Carbon and Water Fluxes in Zoige Plateau Peatland
## 2 Materials and Methods  
### Study Area and Experimental Design  
The Zoige peatland, which is located in the northeastern part of the QTP at an elevation of 3400 to 3900 m, spans an area of roughly 16,000 km\({}^{2}\) (Figure 1a). This region features a cold temperate and plateau humid climate, which is characterized by an exceptionally long frost period. The average annual temperature varies between \(-0.7\)\({}^{\circ}\)C and \(1.1\)\({}^{\circ}\)C, and the average annual precipitation falls between 650 mm and 750 mm, with the majority occurring from June to September [9; 25]. The relative humidity is around 78%. The alpine meadow was dominated by _Carex meyeriana_, _C. multiensis_, _Blysmus sinocompressus_, _Carex sechrirostris_, and _C. tibetikobresia_.  
This study was conducted on a field-manipulated platform for warming treatment, which was established in June 2021 and located at Axi Ranch (33\({}^{\circ}\)47\({}^{\prime}\)56\({}^{\prime\prime}\) N, 102\({}^{\circ}\)57\({}^{\prime}\)28\({}^{\prime\prime}\) E) in Zoige National Nature Reserve. Nine plots with similar vegetation and hydrological conditions were selected, which were spaced at least 50 cm apart. The plots were dominated by _Blysmus sinocompressus_ and _Carex sechrirostris_, with the water table at approximately 50 cm below the ground surface. The temperature on the Zoige Plateau has increased by 0.6 \({}^{\circ}\)C per decade since 1990 [26]. This warming trend was modeled using the open-top chamber (OTC) method of setting two levels of warming treatments (low-level warming, TL; high-level warming, TH). The treatments were arranged in a randomized block design (Figure 1b). The plots for the control (CK) did not have an OTC. Each treatment had three replicates. The OTC devices were hexagonal structures made of six trapezoidal acrylic glass panels that were highly transparent; each had a base length of 75 cm and a thickness of 3 mm. The heights of the OTCs for the TL and TH treatments were 25 cm and 50 cm, respectively. A 50 cm \(\times\) 50 cm frame made of stainless steel was installed in the center zone of each plot and inserted 20 cm into the soil. The frames were used to fix transparent chambers for measuring NEE, RE, and ET. Additionally, a PVC ring, 20 cm in diameter, was positioned near each frame within the plots to facilitate measurements of E.
# Code Obfuscation: A Comprehensive Approach to Detection, Classification, and Ethical Challenges
## 8 Identifying Obfuscation Techniques: The Starting Point
### Advanced Techniques: Utilizing Machine Learning  
In the realm of deobfuscation, AI can be utilized to automate parts of the process that traditionally require manual effort. By analyzing the control flow and data flow of the obfuscated code, AI models can help simplify complex structures and provide insights into the code's functionality. This automation can reduce the time and effort required by human analysts to understand and deobfuscate code, making the process more efficient. Additionally, AI can adapt to new and evolving obfuscation techniques, learning from new data and continuously improving its deobfuscation capabilities. In complex cases, machine learning and AI-based approaches can be employed to analyze and deobfuscate code. By training models on large datasets of obfuscated and unobfuscated code, AI can identify patterns and help reconstruct the original code. Machine learning models, particularly those employing deep learning, can infer the logic behind obfuscated code and predict what the original code might have looked like. This approach is especially useful when traditional methods struggle to provide clear insights. Despite the promise of AI indeobfuscation, there are challenges that must be addressed. The diversity and complexity of obfuscation techniques mean that AI models need extensive and varied training data to be effective. Furthermore, AI models must be able to generalize across different types of obfuscation, which can be difficult given the lack of standardization in obfuscation methods. Nevertheless, as AI technology advances and more training data become available, the potential for AI to significantly enhance deobfuscation efforts is substantial. By improving the efficiency and accuracy of deobfuscation, AI can play a critical role in areas such as cybersecurity, software analysis, and intellectual property protection.
# Multiscale Superpixelwise Locality Preserving Projection for Hyperspectral Image Classification
## 3 Proposed Method
### Determination of Manifold Local Homogeneity with Multiscale Superpixel Segmentation  
Taking into consideration the irregular spatial dependency characteristic of HSIs, we adopt a multiscale superpixel segmentation strategy here to determine the irregular homogeneous subregions in various shapes and sizes in an HSI. ERS can achieve a natural representation of visual scenes [36; 37]. It is based on an undirected graph \(\mathcal{G}(\mathrm{V},\mathrm{E})\) where \(\mathrm{V}\) is the vertex set and \(\mathrm{E}\) is the edge set. The vertices can be the pixels of an HSI, and then, the weight of an edge measures the similarity of the connected two pixels. Thus, the image segmentation task becomes the problem of how to partition the graph properly. ERS aims to choose a subset \(\mathrm{S}\) of \(\mathrm{E}\) which is in the partition of \(K\) subgraphs using the following criterion:  
\[\max H(\mathrm{S})+\lambda B(\mathrm{S}) \tag{7}\]  
where the entropy rate term \(H(\mathrm{S})\) prefers homogeneous clusters, the regularization term \(B(\mathrm{S})\) forces the clusters to be of similar sizes, and the weight \(\lambda\) needs to be nonnegative. To solve the optimization problem in Equation (7), [PERSON] et al. gave an efficient greedy algorithm [36]. In our method, we first use PCA to obtain the first principal component of HSI and then perform ERS on the extracted principal component for convenience.  
ERS tends to divide the image into subregions of similar sizes once the number of superpixels is given. However, the homogenous subregions in the HSI are in different sizes. That is to say, ERS with a single scale cannot represent the homogeneities well. Therefore, we perform the segmentation in a multiscale way to tackle this issue, where ERS is run with several scales. Specifically, if an HSI is segmented with \(2P+1\) scales, then the number of superpixels on a scale is determined by  
\[N_{s}=(\sqrt{2})^{p}N_{sf},\quad p=0,\pm 1,\pm 2,\cdots,\pm P \tag{8}\]where \(p\) is the index of scales and \(N_{sf}\) is the fundamental number of superpixels and given empirically. With a series of scales, the segmentation is expected to adapt homogeneities in various sizes in the scene and to offer the potential to further reduce the impact of an irregular spatial dependency.  
A superpixel obtained in the HSI represents spatially homogenous subregions, while different superpixels means there are spectral signature differences to some degree. Such homogenous subregions in an HSI imply that there are local homogeneities on the associated HSI manifold. Thus, we can determine local homogeneities on the connected HSI manifold by performing the multiscale superpixel segmentation.
# On Simulating the Propagation and Countermeasures of Hate Speech in Social Networks
## 5 Modelling Countermeasures
### Deferring Hateful Content  
Hate motivated content seems to spread faster and farther through social networks than content generated by normal users. Thus, it can trespass community borders and reach out to wider audiences. The root of this behaviour seems to lie in the higher virality of the hate content, which is achieved, e.g., by usage of emotion triggering images [22]. Therefore, decelerating the publication of content or its visibility without filtering it out completely might already have a positive effect to deescalate conversations on polarised topics. This deceleration (or deferring) has the advantage of not infringing the freedom of speech as filtering and deleting of content would do.  
Earlier theoretical work by [PERSON] and [PERSON] [43] proposed a utility-based model of hate speech influence that reveals that the perceived amount of hate speakers play a key role for motivating other users to join and engage in hate speech, making conversations more viral. It is also known that the lifetime of hateful conversations does not exceed a few days and has a culmination within the margins of one day [44]. This might be explained by people's fast emotional responses--triggered by some event--which settle down rapidly. Hence, we take the assumption that deferring hateful content (i.e., posts) might deescalate conversations on polarised topics due to decreased perceived amount of participants and stabilised emotional state of responders. As far as we know, such responses (i.e., reposting, replying or liking) give more weight to the content and lead to better promotion of it by internal algorithms of social networks [8; 45]. In this work, the response to such content is interpreted as reposting and, thus, the countermeasure of deferring posts and deferring reposting should contribute to decrease the participation in \"hot\" topics as well as to make hateful content less viral. We model this countermeasure by decreasing the willingness of posting/reposting hateful deferred content: The longer it was deferred, the less the willingness to post/repost.  
Deferring hateful content constitutes a short-term countermeasure that we apply during the opinion diffusion phase in our simulations (see Section 4.2). We implement this countermeasure by considering two variables:  
* We employ a variable \(p_{defer}\) that stands for the probability of deferring a hateful post at each round (in the code, probability \(p_{defer}\) appears as \(p_{defer\_hateful\_post}\)). Any hateful post can be deferred again, if it is reposted in further rounds.
* In addition to parameters in Section 4.2, a cumulative factor \(f_{repost\_deferred\_post}=0.5\) is used to decrease the probability of being reposted. This means that the probability of being reposted would diminish by a factor of 0.5 for posts deferred for one round, 0.25 for 2 rounds, 0.125 for 3 rounds and so on.