esa-sceva/satcom-chunk-collection huggingface dataset:
# CAA-RF: An Anomaly Detection Algorithm for Computing Power Blockchain Networks
## 1 Introduction
### Contributions  
In this study, we make the following substantial contributions to anomaly detection in computational blockchain networks:  
* To address the issue of dataset scarcity, we designed and implemented a distributed, lightweight proof-of-stake blockchain model tailored for computational networks. This model not only simulates the operation, communication, and consensus processes of blockchain in a computational network environment but also enables the collection of key performance indicators and instrumental data during the normal operation of the blockchain system through the simulation process.
* In the research field of computational blockchain technology, the absence of publicly available datasets remains a fundamental issue. To tackle this, we designed and carried out a series of security attack experiments on computational blockchain networks, including Sybil attacks and denial-of-service (DoS) attacks. These experiments were designed to simulate various real-world attack scenarios, thereby compensating for the lack of data in existing studies. Through these experiments, we successfully captured and recorded a large volume of anomalous behavioral data from blockchain systems under attack. The collected data span multiple dimensions, including abnormal error rates and packet sizes, and provide detailed information on the system's data reception during attack periods.
* We propose an adaptive anomaly detection method that integrates attention mechanisms, random forests, and convolutional neural networks. This method is trained on the dataset collected from the aforementioned distributed computational blockchain model. Furthermore, we designed a series of comparative experiments to evaluate the performance of our model against traditional machine learning methods and six deep learning approaches. The results show that our proposed method achieves more balanced performance in terms of both accuracy and F1-score.
# Research on Tbps and Kilometer-Range Transmission of Terahertz Signals
## 3 Enabling Technologies for THz Communication Systems
### Selecting Appropriate Modulation Formats  
In THz communication systems, achieving both long wireless transmission distance and high spectral efficiency presents a fundamental trade-off. While utilizing higher-order QAM signals rationally when extending transmission distance can yield extremely high spectral efficiency, the inherent burdens on the THz system cannot be ignored. For the same transmit power, higher-order QAM signals exhibit a smaller Euclidean distance between adjacent constellation points. Figure 6 shows the constellation diagrams of QPSK, 16 QAM, and 64 QAM under normalized power. Consequently, they require either higher receiver sensitivity, increased input power, or a higher signal-to-noise ratio (SNR) at the receiver side [64; 65; 66; 67; 68; 69; 70; 71]. Furthermore, these signals are significantly constrained by the THz channel's path loss and nonlinear effects, necessitating the integration of advanced DSP techniques and system optimization strategies [72; 73; 19; 74].  
Given our primary focus on achieving longer wireless transmission distances, we predominantly employ lower-order QAM formats, such as QPSK and 16 QAM. Using 16 QAM modulation, we achieved 920 Gbps-km, and using QPSK, we demonstrated transmissions spanning 30 km in the sub-THz band [75; 76]. While higher-order modulation significantly boosts spectral efficiency by carrying more bits per symbol, its application is typically restricted to short-range scenarios for achieving ultra-high data rates due to the limiting characteristics of the THz channel.
# Reliable and Energy-Efficient LEO Satellite Communication with IR-HARQ via Power Allocation
## 2 Preliminaries
### Energy Efficiency in Different IR-HARQ Protocols
#### 2.2.2 IR-HARQ with Variable Power Transmission  
Different from the above fixed power transmission scheme, we adjust the transmission power \(P_{i}\) (\(P_{i}\
eq P_{j},i\
eq j\)) in each HARQ round to improve the energy efficiency under the constraint of a desired outage probability \(\epsilon_{0}\). In the IR-HARQ-VPA transmission scheme, the mutual information \(I_{k}^{epd}\) accumulated after \(k\) rounds is:  
\[I_{k}^{ppa}=\sum\limits_{i=1}^{k}\log_{2}\biggl{(}1+\frac{P_{i}h^{2}\gamma_{i} }{\sigma_{n}^{2}}\biggr{)}. \tag{22}\]  
The average total energy used for the IR-HARQ-VPA transmission scheme is:  
\[\mathcal{E}^{epa}=N_{s}\sum\limits_{i=1}^{L}\Biggl{(}\sum\limits_{j=1}^{i}P_{ j}\Biggr{)}\Pr\{\mathcal{T}^{epa}=i\}. \tag{23}\]  
The probability of decoding failure after \(k\) transmission rounds \(\epsilon_{k}^{epa}\) is:  
\[\begin{array}{ll}\epsilon_{k}^{epa}&=\Pr\Bigl{\{}I_{k}^{epa}<R\Bigr{\}}\\ &=\Pr\biggl{\{}\sum\limits_{i=1}^{k}\log_{2}\Bigl{(}1+\frac{P_{i}h^{2}\gamma_ {i}}{\sigma_{n}^{2}}\Bigr{)}<R\biggr{\}}.\end{array} \tag{24}\]  
Similar to (20), we have \(\mathcal{K}^{epa}=N_{b}(1-\epsilon_{L}^{epa})\). The optimization problem of the IR-HARQ-VPA transmission scheme can be given as follows:  
\[\begin{array}{ll}\underset{(P_{1},\ldots,P_{L})}{\text{argmax}}&\eta_{EE}^ {epa}=\frac{R(1-\epsilon_{L}^{epa})}{\sum\limits_{i=1}^{L-1}\Bigl{(}\sum \limits_{j=1}^{L}P_{j}\Bigr{)}\Bigl{(}\epsilon_{i-1}^{epa}-\epsilon_{i}^{epa} \Bigr{)}+\epsilon_{L-1}^{epa}\sum\limits_{i=1}^{L}P_{j}}\\ &s.t.\,\left\{\begin{array}{c}\epsilon_{L}^{epa}\leq\epsilon_{0}\\ 0<P_{i}\leq P_{\max},1\leq i\leq L.\end{array}\right.\end{array} \tag{25}\]
# A Review of Fabrication Technologies for Carbon Electrode-Based Micro-Supercapacitors
## 6 Micro-Supercapacitor Challenges and Future Perspectives
Fourth, the recent research trend on novel carbon electrode materials goes into the field of hybrid micro-supercapacitors to further enhance the performance of micro-supercapacitors with higher energy density and wide voltage range. The stability of the pseudocapacitive materials can still be further improved. The potential window can be significantly widened by the use of ionic liquid electrolytes, which are also the subject of recent studies. Water-in-salt electrolytes are also investigated, and their limited ion transport, poor performance at low temperature, and the low cost water-in-salt electrolytes, such as NaNO\({}_{3}\), NaClO\({}_{4}\), and LiCl, still need further research. Fifth, the high interface resistance in solid-state micro-supercapacitors is another challenge to solve in future studies. Long-term integrity between the electrode materials and substrates needs to be developed to reduce internal resistance, which could lead to power failure. More focus is also needed on matching the electrolyte to the electrodes, and optimizing the interface, for example, in gel polymer electrolytes. A stable gel electrolyte needs to be developed with excellent ionic conductivity and thermal stability, as well as higher cycling stability of flexible micro-supercapacitors. The electrolyte is important, not only for the energy and power density of the micro-supercapacitors but also for the stability of the electrode and the device, and a better understanding is needed on charge mechanisms acting at the electrode/electrolyte interface. Finally, sustainability is an increasingly important aspect in materials studies. For micro-supercapacitors, recent research involves the synthesis of biomass-derived porous carbon materials. Systematic studies on the correlation of precursor biomass resources, derived carbon materials with micro- and nanostructures, their physical-chemical characteristics, and the electrochemical performance are needed. New synthetic methods need to be developed to produce the carbon materials from biomass and then, use those in electrodes for micro-supercapacitors. Eco-friendly, low-cost manufacturing methods that can be up-scaled are required for mass production. Research studies on biomass-derived carbon materials for micro-supercapacitors also involve heteroatom doping.
## 3 Results  
### Soil Water Content and Soil Temperature  
The soil water contents in the 0-20 cm soil layer tended to decrease during the wheat growth process. The application of biochar affected soil water content. With B0 as the control, the increasing range of soil water content in different months varied from \(-14.06\%\) to \(25.18\%\) and from \(-30.66\%\) to \(85.26\%\) under B10 and B20, respectively (Figure 3). Finally, the application of biochar significantly increased the average soil water content during the wheat growth period (\(p<0.05\)). Compared with B0, the average soil water contents were \(3.10\)-\(5.64\%\) and \(3.24\)-\(12.23\%\) higher under B10 and B20, respectively (Figure 3).  
The soil temperature at a depth of 10 cm decreased initially and then increased under each treatment during the wheat growth process, with a trough period in December-January (Figure 4). The application of biochar affected soil temperature. With B0 as the control, the increasing range of soil temperature in different months varied from \(-6.67\%\) to \(8.57\%\) and from \(-3.64\%\) to \(16.67\%\) under B10 and B20, respectively (Figure 4). Overall, the application of biochar reduced the variations in the soil temperature range to some extent and increased the average soil temperature during the wheat growth period. Compared with B0, the average soil temperatures were \(1.96\)-\(3.53\%\) and \(0.98\)-\(2.35\%\) higher under B10 and B20, respectively (Figure 4).  
Figure 3: Changes in soil water content in the 0–20 cm soil layer under different amounts of added biochar during the winter wheat growing seasons in 2018–2019 and 2020–2021. Note: SWC represents soil water content. Both the size and color of the circle represent the level of soil water content. B20, B10, and B0 represent 20, 10, and 0 Mg biochar ha\({}^{-1}\), respectively.
## 3 Data and Methods
### Model Optimization
#### 3.4.1 Grid Search Method to Optimize the Support Vector Regression Model  
The GS method is one of the easiest methods in the global optimization algorithm, which is a learning method to optimize the model performance by traversing the given parameter combinations. Its principle is to determine the search range of the model, separate the possible values of the parameters to be searched in accordance with a certain step length, and generate a \"grid\". Then, it searches for each node of the grid one by one, uses the objective function to make judgments, and adjusts the search range and the step length according to the value of the objective function until it reaches the optimal solution.  
The optimization flow chart of the GS method is shown in Figure 3.  
From Table 2, it is evident that the MRE between GS-SVR and the actual cementing quality is 8.46%, and the _RMSE_ is 2.653, which suggests a certain optimization effect.  
Figure 3: GS flow chart.
## 4 Discussion
### Effects of Including Laboratory Spectral Indices  
Due to the complex nature of soils, their Vis-NIR spectra aggregate multiple soil chromophore spectral signals [122]. Influenced by many other confounding factors, Sentinel-2 soil reflectance predicted SOC less well than spectra acquired under laboratory conditions [28; 33; 61]. Thus, as suggested by [PERSON] et al. [10] and confirmed by [PERSON] et al. [32], the predictability of a given soil property using remotely sensed data does not depend solely on the property itself or its spectral behavior as a chromophore. When comparing the performance of Sentinel-2A-derived models for two contrasting agroecosystems, [PERSON] et al. [32] found that loading values of the Sentinel-2 bands for a given soil property varied among study areas. Consequently, we included laboratory-acquired soil spectral information, assuming that doing so would provide models with more spectral information about soil properties themselves. In addition, [PERSON] et al. [39] improved maps of predicted SOC content by including geospatial estimates of laboratory soil spectra at 1930 nm. Since spectral indices highlight certain spectral features better, we calculated equivalents of the S2-indices using the soil spectra obtained under laboratory conditions. Similarly, [PERSON] et al. [61] found that these bare soil indices were more accurate in predicting SOC densities than those derived from Sentinel-2 imagery; however, their study relied on a single-date Sentinel-2 image acquired in early winter without describing soil surface conditions. Our results showed strong and significant correlations of these indices with the measured SOC content (Table 5). Furthermore, including these indices sequentially in our models improved the prediction of SOC content. With only two indices, we achieved good performance for both RF and DNN models (mean R\({}^{2}\) = 0.70 (\(\pm\)0.15) and 0.91 (\(\pm\)0.04) and mean RPIQ = 2.01 (\(\pm\)1.13), and 1.77 (\(\pm\)0.23), respectively). However, the results were less stable for RF than for DNN, even when including five indices. This is consistent with the idea that RF classifiers can be used to classify multisource remote sensing data, mainly due to their computational speed [115], but the fact that they optimize models using only the input datasets identified as important made them unstable in our study. These results suggest that considering the same spectral information acquired in the laboratory for all dates instead of the Sentinel-2 spectral information, which varies as a function of surface conditions, minimizes the effects of surface conditions and highlights the spectral behavior of the soil property itself.
## 3 Related Work on Correcting Typographical Errors  
A typographical mistake is an error that occurs while printing the material. Historically, this was due to errors in the setup of the manual type-setting. The term includes errors caused by mechanical failure or the slipping of the arm (or finger), but does not include errors caused by ignorance, such as spelling errors. However, typos are the subset of a bigger category of misspelling errors. These are of the same importance and are solved with the same methods. The only difference is that typographical errors are easier to model, as they depend only on the keyboard (we discuss it more in Section 5.2) and not the language. The most classical spelling error correction systems follow these steps:  
1. Error detection;
2. Candidate generation;
3. Error correction.  
We will cover separate methods constituting this pipeline below.
# Assessing the Seismic Demands on Non-Structural Components Attached to Reinforced Concrete Frames
## 6 Artificial Neural Network (ANN) Model
### Predictive Expression Using ANN Model  
In this section, the relationship between the inputs and output is developed. The weights and bias values between the inputs and output from the trained ANN models are utilized to develop the predictive expressions. Prior research has demonstrated the value of such expressions, and built comparable prediction correlations for various problems [8, 51, 53, 54]. Tables 7 and 8 provide the weights and biases of the ANN model for regular and irregular building models, respectively. Figure 18: Simulated and ANN’s predicted CDAF for an irregular building model. By inserting the weights and biases (Tables 7 and 8) into Equation (12) [8], the predictive expression for \(CDAF\) can be developed. The procedure explained in the reference study [8] to develop a predictive expression (normalized) was followed precisely in the current study to establish a proper relationship between the inputs and outputs. The resultant denormalized predictive expressions for \(CDAF\) are as shown in Equations (13) and (14) for regular and irregular building models, respectively:  
\[Y_{n}=f_{o}\Bigg{\{}b_{o}+\sum_{k=1}^{h}\Bigg{[}w_{k}\times f_{h}\Bigg{(}b_{ hk}+\sum_{i=1}^{m}w_{ik}X_{ni}\Bigg{)}\Bigg{]}\Bigg{\}} \tag{12}\] \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Hidden Neuron**} & \multicolumn{3}{c}{**Input-Hidden Weight**} & \multicolumn{3}{c}{**Hidden-Output Weight**} & \multicolumn{2}{c}{**Bias**} \\ \cline{2-7}  & \(\mathbf{T}\) & \(\mathbf{\xi_{\text{NS}}}\) & \(\mathbf{S}\) & \(\mathbf{Floor}\) & \(\mathbf{CDAF}\) & \(\mathbf{Hidden}\) & **Output** \\ \hline
1 & \(-\)68.809 & \(-\)3.090 & 46.460 & \(-\)4.962 & \(-\)0.143 & \(-\)8.113 & \\
2 & 3.427 & 0.113 & \(-\)0.063 & \(-\)0.021 & \(-\)1.425 & 0.611 & \\
3 & \(-\)68.642 & \(-\)6.979 & 11.368 & 0.189 & 0.053 & \(-\)34.320 & \\
4 & 0.137 & 4.842 & 0.009 & 0.019 & \(-\)13.283 & 7.106 & \\
5 & 22.380 & 0.396 & 8.460 & 1.107 & 0.130 & 11.461 & \\
6 & \(-\)5.362 & \(-\)0.109 & 4.665 & \(-\)1.013 & 4.460 & \(-\)3.463 & \\
7 & \(-\)32.567 & \(-\)1.630 & 28.505 & 27.228 & 0.044 & 25.297 & \\
8 & \(-\)17.193 & \(-\)0.253 & 0.019 & \(-\)0.010 & 14.987 & \(-\)7.060 & \\
9 & 17.085 & 0.202 & \(-\)0.018 & 0.015 & 15.110 & 7.019 & 10.732 \\
10 & 34.729 & 0.493 & 0.056 & \(-\)1.679 & 0.370 & 31.978 & \\
11 & 5.423 & 0.078 & \(-\)4.727 & 1.190 & \(-\)8.203 & 4.012 & \\
12 & \(-\)2.685 & 0.239 & \(-\)0.038 & 1.322 & \(-\)0.239 & \(-\)2.188 & \\
13 & \(-\)5.118 & \(-\)0.085 & 4.431 & \(-\)1.058 & \(-\)12.737 & \(-\)3.639 & \\
14 & 2.966 & 0.081 & 0.020 & 0.519 & 0.976 & 2.195 & \\
15 & \(-\)46.845 & \(-\)43.367 & \(-\)0.059 & 0.496 & 0.078 & \(-\)60.467 & \\
16 & \(-\)33.267 & \(-\)0.202 & \(-\)0.096 & \(-\)0.223 & \(-\)11.119 & \(-\)27.673 & \\
17 & 30.736 & 0.220 & 0.099 & 0.261 & \(-\)11.738 & 25.571 & \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Weights and biases of the ANN model for regular building.
# Optimizing Aircraft Routes in Dynamic Conditions Utilizing Multi-Criteria Parameters
## 4 Results  
The Results section shows the results of calculations based on the input data, in comparison with original routes and weather conditions. Weather conditions are considered for route costs calculation based on the time when the compared flights were performed.  
Let us consider the flight space of an aircraft as a three-dimensional space R3, where the \(x\), \(y\), and \(z\) coordinates determine the position of the aircraft in space. To determine the optimal flight path of an aircraft, it is necessary to minimize the cost function \(C\) as follows, which may contain several criteria:  
\[C=\omega_{1}\cdot f_{1}(x\text{,}y\text{,}z)+\omega_{2}\cdot f_{2}(x\text{,}y \text{,}z)+\omega_{n}\cdot f_{n}(x\text{,}y\text{,}z), \tag{12}\]  
where \(f_{i}(x\text{,}y\text{,}z)\)--criteria that reflect various optimization features (e.g., minimizing fuel consumption, flight duration, safety, etc.), and \(\omega_{i}\)--weighting factors that specify the importance of each criterion. The task is to find a trajectory that minimizes the cost function C while satisfying the following constraints:  
* Dynamic limitations of the aircraft (maximum speed, minimum turning radius, etc.);
* Restrictions on permissible flight areas (prohibited areas, military restricted areas, prohibited altitudes, etc.);
* Influence of external factors, such as weather conditions, wind disturbances.  
To implement an improved algorithm for building optimal aircraft routes at the mathematical level, taking into account all these factors (weather conditions, fuel consumption, flight altitude, and air traffic density), we will consider in detail the mathematical models and create tables for the results and initial data.
# Enhancing Traceability of Italian Almonds through IR Spectroscopy and Chemometric Classifiers
## 2 Materials and Methods
### Pls-Da  
Partial Least Squares Discriminant Analysis (PLS-DA) is a chemometric method used for discriminant classification [20; 21]. This approach is based on identifying and characterizing differences between samples belonging to different classes, focusing on creating distinct boundaries known as \"decision surfaces\". These decision surfaces separate the multivariate space defined by the measured variables in a way that clearly distinguishes one class space from the spaces of other classes. In this way, PLS-DA is able to uniquely assign each sample to a specific class or category.  
PLS-DA involves the transformation of a classification problem into a regression one, which is then solved using PLS. In order to make this possible, it is necessary to codify the class-membership of samples in a dummy response matrix (\(\boldsymbol{\gamma}\)), representing, in a binary codification, the class-belonging of any observation. For instance, in a three-class problem, as the one discussed in the present work, a sample belonging to class 1 will be associated with the y-vector \(\boldsymbol{y}=[1\ 0\ 0]\), an individual apptaining to class 2 will be codified as \(\boldsymbol{y}=[0\ 1\ 0]\), whereas a class 3 object is indicated as \(\boldsymbol{y}=[0\ 0\ 1]\). Once the \(Y\) dummy has been generated, a calibration model can be built by solving Equation (2):  
\[\boldsymbol{Y}=\boldsymbol{X}\boldsymbol{B}+\boldsymbol{E} \tag{2}\]  
where \(\boldsymbol{Y}\) is the dummy matrix, \(\boldsymbol{X}\) is the training data matrix, \(\boldsymbol{B}\) represents the regression coefficients, and \(\boldsymbol{E}\) represents the residuals.  
The Estimated \(\boldsymbol{B}\) can be used to predict the class membership of unknown samples. Nevertheless, the predicted \(\boldsymbol{Y}\) will be made of continuous values (i.e., it will not be binary), making the class assessment less straightforward. Consequently, to make predictions using this calibration model, different solutions can be followed [22]. In the present work, the procedure suggested by [PERSON] et al. has been exploited [23].
## 3 Results
### Summary of Latent Class Analysis
For example, for a data row that describes a permanent partial disability in chauffeurs and helpers class code, for knee as the injured body part, with cause of strain or injury by, and nature of strain or tear, the probability of belonging to latent classes 1, 2, and 3 is calculated as 0.9982, 0.0017, and almost zero, respectively. Thus, this row is labeled as latent class 1. Similarly, such process continues until all data rows are labeled as either latent class 1, 2, or 3. Then, the frequency of all the classes is calculated. Based on the results, class one includes 44.32%, class two has 34.31%, and class three counts includes 21.37% of the data rows. Furthermore, the analysis of the 3-cluster model shows that medical injuries, major permanent partial disability, minor permanent partial disability, and permanent total disability are not present in any of the three classes. However, permanent partial disability and temporary total or partial disability injuries are the most prevalent in all three classes. The only class that includes fatality with some size (0.16) is class three. Chauffeurs or helpers, grain elevator operations, gas and oil dealers, hay grain or feed dealers, grain milling, and farm machinery operations are the class codes present in all three classes with various probabilities (only those class codes with a probability higher than 0.06 are shown in the class tables). The most statistically distinctive factor is the nature of the injury, which is different in each class with a sizable probability. Injured body parts and cause of injury are also different in each class with a less significant presentation probability. The mean total costs of claims are also different, with class one having the lowest mean of $205,583 and class three having the highest mean of $374,783. The mean total cost of claims for injuries in class two is $289,086.