esa-sceva/satcom-chunk-collection huggingface dataset:
# A Hybrid SWIM Data Naming Scheme Based on TLC Structure
## 4 Experiment and Result Analysis
### Analysis of Search Efficiency and Scalability Based on Hybrid Naming  
The naming scheme based on the TLC hybrid structure proposed in this paper improves the search efficiency mainly in the following two aspects:  
1. Compared with a single TLC naming method, the naming mechanism based on \"layer-plane\" can effectively control the length of resource naming.
2. After route aggregation, you can use Bloom Filter to quickly find the content of plane components.  
To check the search efficiency of this naming method, different TLC layers were used to analyze the impact, and the query time of TLC structures with different layers was obtained, as shown in Figure 10.  
It can be concluded from Figure 10 that the more layers of names, the longer the average query time, and the mixed naming scheme can control the number of names in a certain range. Therefore, compared with a single hierarchical structure, this solution can effectively reduce the query time of the name.  
The comparison of query time can indirectly reflect the scalability of naming. The time comparison for a large-scale name search is shown in Figure 11.  
It can be seen from Figure 11 that when the number of query levels of the name is greater than 5, and the number of queries is greater than 160,000, the time used increases sharply, and the search work cannot be carried out normally, which explains that the scalability is low. However, when the number of layers is limited, the search time increases slightly, but it is generally controllable. Through the analysis of this experiment, it can be concluded that the naming and searching method of the mixed method can control the overall length of the resource name and improve the scalability.  
Figure 10: Comparison of query time of different layers in the mixed naming method.
# Synchronous Generator Stability Characterization for Gas Power Plants Using Load Rejection Tests
## 1 Introduction
The full models that combine GAST, SEXS and GENSAL are already validated models that can be found in the literature [20; 21; 22; 23; 24] and they are widely used by PSOs in simulation programs like PSS/E(r). With the research motivations explained, the main contributions of this study are summed up as follows:  
* Performing load rejection tests is proposed in order to study the power plant's and synchronous generator's transient response. This test allows us to provoke realistic fast transients emulating real faults where the AVR and the speed governor dynamics are not fast enough. * With the load rejection tests' data, well-known models are fitted to satisfy the dynamic response in order to provide this information to the corresponding PSO. * To corroborate the dynamic transient response characterization, load rejection tests have been carried out over more than 60 real power plants of the Spanish power grid. In this manuscript, three of them are shown as examples. The paper is structured as follows: first, Section 2 describes the theoretical model used for the transient stability parameters acquisition and fitting. Secondly, Section 3 shows the experimental tests carried out in three real gas power plants. Afterwards, Section 4 is focused on the simulation results of parameters fitting. Finally, Section 5 concludes the paper with the main ideas obtained during the research.
## 5 Research on the Frequency-Domain Characteristics of AE Signals
### Wavelet Packet Energy Analysis of AE Signals
#### 5.2.1 Wavelet Packet Frequency Band Decomposition of the P-Signals  
The AE signals generated in the loading process of a rock sample are abruptly occurring non-stationary signals with complex characteristics. Wavelet packet analysis can  
Figure 10: Frequency domain waveform of the P-signals of each sample. (**a**) A1.No.17715, (**b**) B1.No.8200, (**c**) C1.No.6375.  
Figure 9: Time domain waveform of the P-signals of each sample. (**a**) A1.No.17715, (**b**) B1.No.8200, (**c**) C1.No.6375.  
effectively identify these signals and further decompose their high-frequency and low-frequency components. Then, the corresponding frequency band is adaptively selected according to the characteristics of the decomposed signals. According to the sampling law, if the sampling frequency of the AE system is 1 MHz, the Nyquist frequency is 500 kHz. Rock is a type of anisotropic and heterogeneous material, with random microstructural planes. Different wavelet bases provide different results. Various wavelet bases have been applied to wavelet packet analysis, such as Symlets basis, Morlet basis, and Meyer basis [38; 39; 40; 41; 42]. In view of the complexity and diversity of AE signals, wavelet bases with symmetry and tight support should be selected [43; 44]. In this paper, the db3 wavelet basis function of Daubechies wavelet series was selected and the AE signals were decomposed to the fourth layer. A total of 16 sub-bands (labeled 1-16) with a band length of 31.25 kHz were obtained. The specific range of each frequency band is shown in Table 2.
# Harnessing Digital Twins for Sustainable Agricultural Water Management: A Systematic Review
## 2 Background  
### AI and Digital Twins  
DT technology is an emerging innovation in agriculture that has the potential to revolutionize resource management, particularly when integrated with AI. DTs create digital replicas of physical agricultural systems, enabling accurate simulations and an optimized management of key parameters such as irrigation, fertilization, humidity, and soil resource availability. With AI, these simulations can be continuously updated and improved by analyzing large volumes of data, making decision-making more efficient. The integration of DTs with AI is one of the most promising advancements in modern agriculture. DTs allow for the real-time simulation of crop growth and resource optimization through the continuous monitoring and precise modeling [28] of environmental and agronomic variables. When combined with ML, DTs can significantly enhance agricultural productivity by improving decision accuracy and reducing resource wastage, including water, fertilizers, and pesticides. AI, through data from sensors, satellites, and drones, can analyze large datasets to identify complex patterns and make accurate predictions [29]. This integration enables the optimal management of variables like plant spacing, water availability, soil quality, and light intensity, ultimately improving both productivity and sustainability. AI-driven DT models can solve complex problems, such as determining optimal agricultural parameters to maximize yields while minimizing resource usage. An example of this synergy is irrigation optimization. AI combined with DT models can analyze real-time field data to monitor soil moisture levels and plant growth, allowing for dynamic irrigation adjustments that prevent water wastage and mitigate drought risks [30]. This technology is particularly beneficial in the context of increasing water scarcity. DTs also play a crucial role in predicting adverse weather events and managing crop diseases. By analyzing meteorological and biological data, DTs can forecast storms, heat waves, or humidity conditions [31] that promote disease spread, allowing for preventive measures like targeted fungicide application. As noted by [PERSON] et al. [32], this approach can save costs up to 50 percent, lowering operational costs. Moreover, DTs offer the ability to simulate future scenarios, allowing farmers to test different cultivation strategies and optimize resource use, reducing environmental impact and enhancing sustainability. This technology can reduce pesticide use, promoting more sustainable agricultural practices. DTs also optimize agricultural supply chains by synchronizing production with market demand, improving logistics, and reducing food waste [33].
## 4 Access Control Model for Preventing VM Escape Attack
### PVME Model Design  
#### 4.4.1 Access Matrix  
We put the access matrix into the Hypervisor, and store it as a binary file. For the sake of reliability, we store a backup access matrix file to the Host OS as well. The basic structure of the access matrix:  
\[[SID,OID,R,A,W,E^{*},C^{*},Flag]\]  
The symbols _SID_ and _OID_ represent the ID of the subject and object respectively, which are thirteen-bit binary numbers. Meanwhile, \(R\), \(A\), \(W\), \(E^{*}\), \(C^{*}\) respectively means read-only, write-only, write-read, execute, and control access, which are represented as a binary number (the value of 1 means permission and 0 means refuse). Thus, we need a total of five bits. The last bit of the access matrix is the Flag bit that indicates whether the rule is valid (1 means valid and 0 donates invalid). Therefore the access matrix has in total 32 (13+13+5+1=32) binary bits, namely 4 bytes.  
For example, a record of the access matrix:  
\[1001\ 0110\ 1110\ 0110\ 01100\ 1001\ 0101\ 0101\]  
It means the subject \(1001011011101\) has the \(01010\) (write-only, execute) access mentions to object \(1100110010010\) and the rule is valid.
# Malware Detection Issues, Challenges, and Future Directions: A Survey
## 5 The Taxonomy of Feature Extraction and Representation Methods
### Feature Extraction Methods
#### 5.1.2 Text Mining  
Text mining techniques are taken from the information retrieval field. Term indexing and term weighting represent the two main categories of text mining techniques. Term indexing, where a unique index is assigned to each term, while term weighting means that a specific weight is calculated and assigned to each term. In the malware detection and classification fields, term weighting methods have been widely used to extract significant features. Identifying specific importance for each word or term arranged in the analysis reports is critical to improving the proposed model's performance. There are various techniques, such as (TF-IDF) Term Frequency-Inverse Document Frequency, (IG) Information Gain, and others, to mine the texts and define their importance by assigning weights by which the analysts can know the most useful words for classification purposes.  
The authors of [43] assigned weights for each text in the analysis report using the information gain technique. Those weights were assigned to the texts that represent the performed operations and their locations based on the occurrences of those texts in malware and benign classes. In addition, TF-IDF (Term Frequency-Inverse Document Frequency) has been used by [54] as a weighting method to extract the API calls and network traffic by evaluating the appearance of each feature in both malware and benign classes to identify the uncommon features that are more significant than the common features.
# Application of Artificial Intelligence in Wind Power Systems
## 3 Results
### Application of Artificial Intelligence in the Operation of Wind Turbines
#### 3.2.3 Effective Remote Monitoring and Management  
AI allows operators to monitor the operations remotely, which is particularly useful for offshore or remote sites. This capability reduces the need for a physical presence on site, enables a faster response to problems that arise and reduces operating costs. AI systemscontinuously analyze data from various sensors installed on the wind turbines. These sensors monitor parameters such as temperature, vibration and smoke levels and can detect anomalies that could indicate a fire hazard. Conventional fire detection methods, such as smoke and flame detectors, suffer from low detection accuracy and long response times. Fire detection methods based on the use of AI are therefore being investigated. These approaches often rely on neural networks for object recognition, which leads to high false alarm rates for pseudo-fire images. [PERSON] et al. [59] proposed a hierarchical deep neural network for fire detection that reduces the false alarm rate and accurately identifies smoke and fire locations. The proposed solution first uses a neural network to classify potential fire situations into three categories: fire, smoke or normal. By analyzing all image information, false alarms are effectively reduced. The proposed approach can be applied to wind turbine nacelles.  
Research into the application of artificial intelligence in recognizing the conditions for ice formation on wind turbine rotor blades is only just beginning, so a significant increase in research can be expected. Ice formation on wind turbine blades is influenced by a number of factors, including outside temperature, wind speed, humidity and so on. However, lesser-known factors, such as the liquid water content and the mean volume diameter of the water droplets, can also have an influence. Using sensors and/or computer vision, AI can recognize the conditions for ice formation on turbine blades and activate de-icing systems such as heaters or mechanical scrapers [60]. [PERSON] et al. [61] have shown that training stand-alone deep learning (DL) models with augmented data capturing range-invariant icing features can improve prediction performance for multiple wind farms.
## 4 Selection of HSS Model Parameters  
Through the above three parts of the test, the compression modulus \(E_{\text{s1-2}}\) of the soil sample can be obtained, as well as the small-strain moduli \(E_{\text{oed}}^{\text{ref}}\), \(E_{\text{50}}^{\text{ref}}\), \(E_{\text{ur}}^{\text{ref}}\) at the reference stress \(p^{\text{ref}}=100\) kPa, and the failure ratio \(R_{\text{f}}\). This allows us to determine the ratio of the soil sample's compression modulus to its small-strain moduli, which is \(E_{\text{s1-2}}:E_{\text{ood}}^{\text{ref}}:E_{\text{50}}^{\text{ref}}:E_{ \text{ur}}^{\text{ref}}=1:0.96:1.25:6.2\). The value of \(G_{\text{0}}^{\text{ref}}\) needs to be determined through resonant column tests; it can also be calculated using empirical formulas. The initial dynamic shear modulus of soil \(G_{0}\) is also known as the maximum shear modulus \(G_{\text{max}}\); [PERSON] [21] provided an expression for \(G_{\text{max}}\) based on the soil's initial void ratio \(e_{0}\) through extensive tests, and this is Equation (3); after summarizing the effects of \(e_{0}\) and confining pressure on \(G_{\text{max}}\), [PERSON] [22] improved this formula, thus obtaining Formula (4). \[G_{\text{max}}=33\times\frac{\left(2.97-e_{0}\right)^{2}}{1+e_{0}} \tag{3}\]  
Figure 7: Stress\(-\)strain curves of the triaxial loading and unloading test. \[G_{\max}=\frac{A(2.973-e)^{2}}{1+e}(\frac{p^{\prime}}{p_{a}})^{n} \tag{4}\]  
In the formula, \(p^{\prime}\) represents the average effective confining pressure and \(p_{a}\) is the reference confining pressure, typically equal to standard atmospheric pressure (100 kPa). After considering the effect of moisture content on the initial dynamic shear modulus of Ioess, [PERSON] et al. [23] improved [PERSON]'s empirical Formula (4), and fitted the functions of \(A\) and \(n\) with respect to water content, making it more suitable for Ioess regions, as shown in Formulas (5) and (6). \[A=129.094\times(100w)^{-0.364} \tag{5}\]  
\[n=0.301(100w)^{0.060} \tag{6}\]  
[PERSON] [17] derived the fitting functions (7) and (8) between the void ratio e and \(G_{0}^{\text{ref}}\) based on field wave-speed tests. \[G_{0}^{ref}=67.5e^{-1.57} \tag{7}\]  
\[G_{0}^{ref}=98.9e^{-0.45} \tag{8}\]  
In this context, Formula (7) is more suitable for clay, while Formula (8) is more suitable for sand. In actual engineering practice, the initial dynamic shear modulus \(G_{0}^{\text{ref}}\) can also be determined based on empirical values. As can be seen from Table 4, the value of \(G_{0}^{\text{ref}}\) in various regions generally ranges from 1.5 to 5 times that of the \(E_{\text{ur}}^{\text{ref}}\). In Ioess regions, the ratio \(G_{0}^{\text{ref}}/E_{\text{ur}}^{\text{ref}}\) typically falls between 1.5 and 3. However, since the value of \(G_{0}^{\text{ref}}\) has a significant impact on the prediction generated by the numerical simulation, this paper will further analyze the value of \(G_{0}^{\text{ref}}\) in the Ioess region. The coefficient of earth pressure at rest \(K_{0}\) can be obtained through stress-path triaxial tests, and some geological survey reports may also provide it. However, in practical
## 3 Optimization Model
### Initialization Module
#### 3.1.1. Actual Construction Time (Ta)  
Calculations  
The calculation used construction data from each activity and project calendar constraint, i.e., activity duration (\(D_{i}\)), activity sequence, and exception date (\(e_{i}\)) from the baseline schedule. The precedence networks were created using the Precedence Diagram Method (PDM). In the PDM, there are four precedence relationship constraints: Finish to Start (FS), Start to Start (SS), Finish to Finish (FF), and Start to Finish (SF), with overlapping time (L\({}_{i}\)). The PDM calcuation procedure used sequential forward and backward calculations via the network to calculate the early start times (\(ST_{j}\)) to early finish times (\(FT_{i}\)), and late start times (\(LS_{i}\)) to late finish times (\(LF_{i}\)) for each activity. From these values, actual construction time (\(Ta\)) and total float time (\(TF_{i}\)) were determined using Equations (1) and (2):  
\[Ta=Max\ FT_{i} \tag{1}\]  
\[TF_{i}=LF_{i}-FT_{i}-D_{i}-e_{i}+1 \tag{2}\]
## 4 Discussion
A detailed examination of the model's class-wise accuracy (Figure 10b) points to a significant challenge stemming from the dataset itself; the model struggles with under-represented classes, particularly CLQ-6. This issue stems directly from the inherent data imbalance and the scarcity of training samples for certain grades. To overcome this limitation and enhance the model's generalization capability, our future work will focus on implementing advanced data augmentation techniques. Due to the very small number of samples for CLQ-1 and CLQ-6, using traditional augmentation methods like SMOTEwould lead to severe overfitting. Therefore, some current research introduces potential data augmentation strategies, particularly for addressing spectral imbalances. For instance, [PERSON] & [PERSON] [50] in their work on Comparative Evaluation of Modified Wasserstein GAN-GP and State-of-the-Art GAN Models for Synthesizing Agricultural Weed Images in RGB and Infrared Domain introduced the concept of using spectrally consistent, structurally coherent synthetic imagery, which is directly applicable to reflectance-based land classification. Furthermore, Cultivated Land Quality (CLQ) grades are inherently ordinal variables, where the penalty for misclassifying an adjacent class should be less than for a distant one. Although previous CLQ assessments have often employed multi-class classifiers, the experimental results in Table 5 indicate that ordinal classification models generally outperform traditional multi-class classifiers, which demonstrates the potential of these models in the field of CLQ assessment. In the future, we will investigate methods such as CORAL neural networks or ordinal regression with deep ensembles to more accurately model how land quality degrades over time.
## 5 Results  
### Prior Knowledge Analysis  
In this analysis, a prior knowledge analysis was performed to investigate whether the students of the two groups had an equivalent prior knowledge level before participating in the experiment. To conduct this analysis, an independent sample \(t\)-test was used to evaluate the prior knowledge tests of the students from the two groups. As tabulated in Table 1, the mean value and standard deviation of the students in the control group were 57.72 and 30.11. Those values of the students in the experimental group were 66.36 and 30.82. Then, the homogeneity of variance was assessed by [PERSON]'s test, whose result showed that the within-group variances were considered equal (\(F=0.017\), \(p=0.514>0.05\)). The result of the independent sample \(t\)-test showed no significant difference between the two groups with regard to the prior knowledge of prime factorization (\(t(20)=0.665\), \(p=0.514>0.05\)).  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Group**} & \multirow{2}{*}{**N**} & \multirow{2}{*}{**M**} & \multirow{2}{*}{**SD**} & \multicolumn{2}{c}{_[PERSON]’s Test_} & \multicolumn{2}{c}{\(t\)**-Test**} \\ \cline{5-8}  & & & & \(F\) & \(p\) & \(t\) & \(p\) \\ \hline Experimental Group & 11 & 66.36 & 30.82 & & & & \\ Control Group & 11 & 57.72 & 30.11 & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: The independent sample \(t\)-test result of the prior knowledge for the two groups.  
Figure 4: Experimental procedure.
# Telecommunication Systems for Small Satellites Operating at High Frequencies: A Review
## 2 Asteria
### Hardware Characteristics  
The hardware components used for the ASTERIA telecommunication systems have been procured through Vulcan Wireless Inc.  
The patch antennas (Figure 2, Table 1) are functional in full duplex mode over the entire Near Earth Frequency range (2.0-2.11 GHz in uplink and 2.2-2.3 GHz in downlink). They have a peak gain of 7 dBi in downlink and 6 dBi in uplink and a half power beamwidth of approximately 70 degrees. They are right-hand circularly polarized, and present an impedance of 50 Ohm. The antennas are compatible with the CubeSat Form factor in size (82 mm \(\times\) 82 mm \(\times\) 12 mm) and mass (76 g). Pattern and additional characteristics are shown in [1].  
Figure 1: ASTERIA block diagram.  
The transceiver (Figure 3) is a full duplex radio that receives a direct carrier BPSK-modulated signal at either 4 kbps or 32 kbps while transmitting a direct carrier OQPSK-modulated signal at either 10 kbps or 1 Mbps. The transceiver assembly has a single interface with the command and data handling (C&DH) for radio telemetry and an interface for a 28 Volt line connected directly to the ASTERIA power system. In terms of frequencies, it is designed as a Software Defined Radio to operate in the entire Near Earth Frequency band. Hence, it can be reprogrammed to transmit at any frequency in the 2.2-2.3 GHz and to receive at any frequency in the 2.0-2.11 GHz range. This feature has been critical for the design of the ASTERIA telecommunication system. Due to the tight schedule, the engineers had to design the system while simultaneously filing for a telecommunication license. As a result, it was important to select a radio that was robust with respect to changes in frequency within the band. The transceiver features different coding options from Reed Solomon to convolutional, has a sensitivity threshold of \(-\)126 dBm, and it is compatible with the CubeSat form factor in size is (\(82\times 82\times 38\) mm), mass (400 g), and power consumption (3.5 W in receive mode and 8.5 W in full duplex for 1 W of radiated power). Additional information and performance metrics for the transceiver can be found in [1].