esa-sceva/satcom-chunk-collection huggingface dataset:
# A Survey on Satellite Communication System Security
## 3 Background
### Satellite Communication Environment  
SCS environments are fundamentally different from terrestrial-based communication networks. This is primarily because satellites operate continuously in the unique conditions of space. While this special environment brings many advantages to SCSs, it also introduces various disadvantages. Therefore, in this section, we will examine the environmental aspects that need to be considered in SCSs [19].  
**Vacuum:** The space environment is essentially a vacuum. The boundary between the Earth's atmosphere and outer space, commonly referred to as the Karman line, is defined as being approximately 70-90 km above the Earth's surface. Most SCSs orbit at a minimum altitude of 200 km in LEO or higher, meaning they are essentially in a vacuum. This vacuum state has a variety of impacts on satellites [20].  
**Gravity:** The zero gravity state is a condition with minimal resistance. This significantly affects satellites, whether they are subjected to external forces or generate internal torque. For example, Earth's gravity is a factor to consider. The gravitational field of the Earth primarily determines the motion of the satellite's center of mass. This gravitational field is not linear and uniform across all regions, leading to orbital perturbations. Additionally, the gravitational fields of the Sun and Moon can cause disturbances. Moreover, the strength of the Earth's gravitational field varies with altitude, and as a result of this gravitational gradient, the force does not pass through the satellite's center of mass. This means that this effect can generate torque within the satellite itself.  
**Solar Radiation Pressure:** Solar radiation pressure, although a very weak force, can significantly affect a satellite's orientation in a resistance-free environment due to its continuous influence. The solar radiation pressure can be considered as a force that arrives perpendicularly from the sun to the satellite. Typically, the resultant force of solar radiation does not align with the satellite's center of mass. Consequently, this misalignment generates torque that can disturb the satellite's orientation.  
**Torques of Internal Origin:** Satellites consist of various components, and the movement of parts like antennas, solar panels, and fuel can generate torque on the satellite. For instance, as the propellant is consumed, the center of mass of the satellite may shift, and adjusting the direction of the satellite's antenna can induce torque on the satellite's body. Therefore, to compensate for these effects, forces acting on the satellite's center of mass need to be periodically applied.
# Extension of DBSCAN in Online Clustering: An Approach Based on Three-Layer Granular Models
###### Abstract  
In big data analysis, conventional clustering algorithms have limitations to deal with nonlinear spatial datasets, e.g., low accuracy and high computation cost. Aiming at these problems, this paper proposed a new DBSCAN extension algorithm for online clustering, which consists of three layers, considering DBSCAN, granular computing (GrC), and fuzzy rule-based modeling. Firstly, making use of DBSCAN algorithms' advantages at extracting structural information, spatial data are clustered via DBSCAN into structural clusters, which are subsequently described by structural information granules (IG) via GrC. Secondly, based on the structural IGs, a series of granular models are constructed in the medium space, and utilized to form fuzzy rules to guide clustering on spatial data. Finally, with the help of structural IGs and granular rules, a rule-based modeling method is constructed in the output space for online clustering. Experiments on a synthetic toy dataset and a typical spatial dataset are implemented in this paper. Numerical results validate the feasibility to the proposed method in online spatial data clustering. Moreover, comparative studies with conventional methods and existing DBSCAN variants demonstrate the superiorities of the proposed method, as well as accuracy improvement and computation overhead reduction.  
online clustering; DBSCAN extension; granular computing; three-layer model Article
# Application of Acoustic Emission Technique in Landslide Monitoring and Early Warning: A Review
## 3 Fundamentals of Acoustic Emission Technique  
### Acoustic Emission Phenomenon  
AE denotes the emission of elastic stress waves resulting from sudden stress release due to irreversible internal structural changes within materials [80]. These stress waves propagate through the material as two main types: compressional (primary) waves, characterized by alternating compressions and rarefactions moving in the same direction as the wave propagation, and shear (secondary) waves, which entail particle movement perpendicular to the energy transport direction [81].  
AE testing relies on the passive detection of spontaneously emitted transient elastic waves from the surface or subsurface of a structure (body) using transducers, which convert these waves into electrical signals through the piezoelectric effect. These signals are then amplified for recording and analysis [82]. Unlike ultrasonic testing, which transmits mechanical waves artificially, AE sources arise naturally from micro-failures induced by service or testing within the monitored system (structure, component, or ground mass). This constitutes an active non-destructive testing methodology that is sensitive to early damage development, as shown in Figure 3.  
Numerous micro- and macro-level processes involving localized stress redistributions are considered potential mechanisms for AE generation in soils and rocks [83]. Inter-particle friction, sliding, rolling, plowing, and collisions occur during shearing or complex loading of granular materials [84]. Inherent heterogeneities facilitate the formation of force chain  
Figure 3: Working principle diagram of AE sensor.  
networks, where load transfers concentrate on narrow contacts experiencing slip-stick fluctuations under changing boundary conditions. These fluctuations are closely associated with localized elastic wave releases. In both laboratory and field measurements, brittle cracking, crack-crack interactions, crack growth acceleration, and coalescence within monolithic rock materials are widely documented as sources of microseismic AE [85]. Furthermore, fracture processes at material interfaces such as joints, fault zones, cementation planes, and clay-rich laminae also emit AE signals [86].  
In landslide monitoring, body waves and surface waves both produce AE, depending on their orientation relative to sensor placement [87]. However, body waves typically predominate as the primary component of the failure process due to their higher velocities. Attenuation refers to the gradual reduction in amplitude and energy as waves propagate outward from the AE source. This attenuation is primarily caused by geometric damping resulting from wavefront expansion, material intrinsic absorption that converts mechanical energy into heat, and scattering at internal interfaces that alters wave particle movements. The significant variation in attenuation observed in geological materials has important implications for the detection range of AE, highlighting the need to consider propagation paths when designing AE monitoring systems.
## 3 Results
### Simulation Comparison of HO-ADRC and PID Control Performance for the Square Wave Speed Input Signal  
The input signal is set as the square wave speed signal with an amplitude of \(1\) rad/s and a frequency of \(4\) Hz to compare the control effect of the HO-ADRC and PID. The comparative result plots are shown in Figure 8.  
As can be seen from Figure 8, the output signal using the HO-ADRC has little overshoot and achieves the accuracy requirement of \(98\%\) in a relatively short time (\(10.9\) ms). However, the output signal with PID reaches the accuracy requirement of \(98\%\) after \(17.5\) ms.
# A New Compact Double-Negative Miniaturized Metamaterial for Wideband Operation
## 6 Conclusions  
In this paper, a new Modified-Z-shape negative refractive index (NRI) metamaterial unit cell and array structure are presented, exhibiting a wider bandwidth in the major portion of C- and X-bands with double-negative (DNG) characteristics. For calculation of the transmission (\(S_{21}\)) and reflection (\(S_{11}\)) coefficients of the unit cell and array configurations, CST Microwave Studio was used. The proposed metamaterial is able to exhibit a negative region with a bandwidth greater than 3.6 GHz, which is better than the reference prototype. Similarly, the 4 \(\times\) 4 array also displays a negative index region with a bandwidth greater than 4 GHz if the proposed structure is rotated 90 degrees. Moreover, C- and X-bands are widely used for satellite communications, especially in many GEO satellites, which are designed to operate in the C-band area. Moreover, the C-band is applicable to modern radio communication, satellite TV networks, and so on. In addition, the X-band is relevant in the military, government, and civil institutions for weather radar monitoring, air and maritime vessel traffic control, defense tracking, and vehicle speed detection for law enforcement. Finally, the proposed double-negative metamaterial unit cell has potential for wider bandwidths in the case of negative refractive index applications in addition to the other metamaterials in the microwave frequency range.
# Degree-Constrained Steiner Problem in Graphs with Capacity Constraints
## 3 Notations and Definitions
### Problem Formulation
**Property 2**: _The minimum cost solution of the capacity-type degree-constrained Steiner problem is a hierarchy._  
**Proof. ** Suppose that the solution exists (the conditions for the existence are presented in Section 4). Any solution is a spanning structure and it can be given by a homomorphism-based triplet \((S,h,G)\). Suppose that there is an optimal solution respecting the degree constraints, which is not a hierarchy. In this case, \(S\) is not a tree but another connected graph. Therefore, it contains cycles. Some edges can be removed from the cycles in \(S\) without removing nodes, thus preserving the node coverage in \(G\) and satisfying the constraints with lesser cost. Consequently, in the minimum cost solution, \(S\) is a tree, and the triplet defines a hierarchy. A degree-constrained minimum Steiner hierarchy (DCMSth) is illustrated in Figure 1. The cost of any edge in \(G\) is unitary, \(M=\{a,f,h\}\), and the capacity constraints are indicated. Trivially, a DCMStT does not exist, but a Steiner hierarchy satisfying the constraints exists. It contains the node \(e\) twice, and each occurrence of this node respects the degree constraint. The capacity-constrained minimum Steiner problem (the computation of the \"Steiner hierarchy\") is NP-hard. A particular case of the problem is where the degree constraint \(D(v)=d(v)\) for each node \(v\in V\). This case is the classic NP-hard Steiner problem. Consequently, our more general problem is NP-hard. The following section discusses the conditions for the existence of a feasible solution for the Steiner hierarchy problem.
## 3 Results  
### Landscape Indices Analysis  
The landscape indices were analyzed at the class level. The results of _CA_ and _PLAND_ show that within the 200 m radius buffer zones, urban area and open water occupy a large proportion of the area, above 70%. Within the 600 m radius buffer zones, the proportions of urban area and farmland are larger, at 39% and 28%, respectively. Within the 1000 m, 1500 m, and 2000 m radius buffer zones, urban area and forest land are the dominant landscape composition types, which together occupy nearly 70% of the total area of buffer zones. Generally, grassland area was the lowest and its proportion in the 200 m radius buffer zones was less than 0.1%. In contrast, the largest proportion of grassland of 1.7% was found within the 600 m radius buffer zones (Figure 4).  
The statistics of the other landscape indices show that the variation in _LPI_ indicates that the urban area is the dominant landscape composition type in the study area. _LPI_ had the largest value within the 600 m radius buffer zones and its largest patch occupies over 80% of the total area of the buffer zones. Apart from grassland, the _NP_ values of all other landscape composition types increased when the radius enlarged. The variations in _CONTIG_, _COHESION_, and _AI_ of farmland, forest land, open water, and urban area were similar, which all decreased when the radius increased. The _DIVISION_ values of  
Figure 4: Proportion of the area of landscape composition types in the total buffer zone area with each radius.  
different landscape composition types varied considerably. The _DIVISION_ values of farmland, open water, and urban area increased when the radius enlarged, whereas that of forest land followed an opposite trend. _DIVISION_ of grassland did not obviously change as the area of grassland was quite small and its patches were too decentralized (Figure 5).
## 4 Discussion  
### Performance Distribution  
The GA process generated a modified rotor case 1 with a much higher efficiency and slightly lower pressure ratio, and the agent modified the GA result to further improve the pressure ratio. Different design variables changed and influenced the flow simultaneously, making it relatively complex to interpret. The flow mechanisms were analyzed, showing that geometric modifications were effective and considerably interpretable.  
The deviation of the local pressure ratio \(\pi\), the local efficiency \(\eta\), and the absolute flow angle \(\Delta\alpha\) were plotted in Figure 17. The local \(\eta\) at the near tip region increased and \(\Delta\alpha\) decreased in the GA case, which was noted as 0 steps, so the total \(\eta\) improved. The local \(\pi\) diminished at all spanwise locations, especially those higher than 50%, resulting in a decline in the total \(\pi\).  
Then, as the geometry was incrementally modified by the agent, the local pressure ratio \(\pi\) increased. The local \(\pi\) at spanwise locations higher than 50% surpassed the reference after 17 steps, which contributed most of the \(\pi\) improvement. The local \(\pi\) near the hub was also decreased in GA case 1 and improved by the RL agent, with a smaller variation range than the tip section. Together with \(\pi\), \(\Delta\alpha\) increased spanwise by the RL agent, mainly because of the variation in the design variables \(\beta_{y}\), \(\chi_{in}\), and \(\chi_{out}\). The agent maintained most of the \(\eta\) improvement gained by the GA while slightly decreasing the local \(\eta\) at 80% spanwise of the rotor after modification.
## 2 Provision of Energy and Macronutrients in Pediatric Patients on PN
### Effects on Fatty Acid Patterns  
Several studies evaluated the effects of HPN with SMOFlipid on fatty acid patterns in pediatric patients (Table 1). [PERSON] et al. conducted the first double-blind RCT including infants and children on HPN receiving either SMOFlipid or an SO-based ILE [48]. Both ILEs were administered at a mean dose of 1.4 g lipids/kg BW/day. After 4 weeks, the EPA and DHA contents of RBC and plasma phospholipids (PL) were significantly increased in the SMOFlipid group compared to the controls. Moreover, the ratio of n-3 to n-6 FAs in plasma and RBC-PL was significantly elevated with SMOFlipid compared to SO-ILE [48]. Similar results have been published by [PERSON] et al. [92] (Table 1) from a study involving 38 pediatric patients with a median age of 5.56 (0.9-21.9) years on long-term PN receiving composite FO-containing ILE (SMOFlipid) (n = 23) or OO-based ILE (Clinoleic) (n = 15) at a dose of 1.3 g lipids/kg BW/day.  
It has been claimed that pure FO-based ILEs (e.g., Omegaven) are safe for achieving a normal EFA status [113]. Yet, ESPGHAN does not recommend the long-term use of Omegaven as the sole source of IV lipid [23]. As a corollary, the effects of the long-term use of SMOFlipid for children on long-term HPN receiving a composite FO-based ILE on RBC fatty acid profile and EFA status also remain to be clarified. With that aim, 46 children highly dependent on PN (median PNDI 120% and daily ILE dose of 1.5-2 g/kg BW) for >1 year were included in a prospective study when they had received the composite FO ILE for >6 month. Out of this baseline group, only 25 children remained highly PN-dependent (SMOF1, n = 25) and could be assessed a second time, 2.4 years later (SMOF2, n = 25). An independent control group (\"weaned off PN\" group; n = 24) included children with short bowel syndrome who received SMOFlipid and were weaned off PN for >2 years (median: 4 years). No differences for growth parameters, citruline, or bilirubin were observed between the SMOF groups (0.2 < \(p\) < 0.8). The weaned-off group did not differ from the SMOF groups for growth parameters (0.2 < \(p\) < 0.4), but citruline was higher (_p_ < 0.0001), reflecting intestinal sufficiency. SMOFlipid induced higher percentages of EPA (8.4% \(\pm\) 2.9%) and DHA (11.7% \(\pm\) 2.2%) in RBC profiles in the SMOF2 group compared to weaned-off children (0.8% \(\pm\) 0.4% and 6.6% \(\pm\) 2.3%, respectively), but lower proportions of ARA. However, the Holman index, a marker of EFA deficiency, did not vary between groups (_p_ = 0.9). Authors concluded that the long-term (>2.4 years) use of SMOFlipid was well tolerated in HPN-dependent children. The RBC fatty acid profile alterations were consistent with the omega-3 PUFA-enriched composition of this emulsion without evidence of EFA deficiency (see Table 1) [15].  
Likewise, [PERSON] et al. found no significant differences in the Holman index between pediatric patients of all age groups receiving HPN with SMOFlipid or OO/SO-ILE (Clinoleic) compared to the healthy controls [92].
## 5 Experiments
### Ablation Study  
Here, to verify the effectiveness of each key module, we present the following variants of MultiDS.  
* \(-Att\): MultiDS without hierarchical attention;
* \(-Att_{da}\): MultiDS without domain-agnostic attention in hierarchical attention generator;
* \(-Att_{ds}\): MultiDS without domain-specific attention in hierarchical attention generator;
* \(-Att_{cs}\): MultiDS without class-specific attention in hierarchical attention generator.
* \(-Cal\): MultiDS without domain calibration vector;
* \(-Cal_{df}\): MultiDS without domain features in domain calibrator;
* \(-Cal_{ds}\): MultiDS without domain-specific attention in domain calibrator.  
The experimental results are shown in Table 6. Based on the above results, we come to the following conclusions. (1) The effect of hierarchical attention is significant. When it is removed, model performance decreases by an average of 12.27%. This proves the effectiveness of hierarchical attention. (2) By removing domain-agnostic attention, model performance decreases by an average of 3.01%. We believe the reason is that the distributional signatures calculated from 20 Newsgroups contain high-quality knowledge of general semantics. This knowledge covers multiple domains with small domain biases and is therefore beneficial for processing information in different domains. (3) Domain-specific attention and class-specific attention also improve model performance. Besides, due to the relatively small number of domain information and class information in few-shot scenarios, the power of distributional signatures is limited. (4) Domain calibration vector is beneficial to model performance, and the average improvements is 0.35%. It proves that domain calibrator can extract informative features from domain information and domain-specific attention to calibrate domain distributions.
# Deep Learning Model with Sequential Features for Malware Classification
## 4 Experiments and Analysis of Results
### Feature Selection Experiments  
After obtaining the opcode sequence, the n-gram method was used for feature extraction of the instruction file from the.asm file. The frequency f of the instruction n-gram in the.asm file was calculated as the feature and then used as input.  
Normalization pre-processes the values of the frequency of the feature extracted from the n-gram of the malicious code, and the one-hot encoding method pre-processes the values of the malicious family categories. For example, the Ramnit family can be represented as 00000001.  
In the experiment, the feature extraction was first performed by selecting \(N=3\), and then the instruction frequency threshold was selected as 300. Then, the test was conducted by increasing the value at 200 intervals, and the highest value selected was 1100. The experimental results showed that when instruction frequency increased, classification accuracy showed a trend of first rising and then decreasing, and the classification effect was best when the frequency was selected as 700, as shown in the lower panel of Figure 12.  
In the experiments on change in the \(N\) value, the comparison experiment of N value was conducted using the frequency with the best effect in the instruction frequency experiment, i.e., a frequency of 700. As the N value increased, classification accuracy showed a trend similar to that of frequency, which also showed a trend of increasing and then decreasing. Through analysis of the experimental results, the classification effect was best at \(N=3\), and the experimental results are shown in the upper panel of Figure 13. Therefore, in subsequent experiments with the classification model, \(N=3\) was selected as the feature for input, and a frequency of 700 was selected as the input to the model.  
For the byte code feature, the byte sequence with a length of fewer than 1500 bytes frequency was selected, then the experiment was conducted by increasing the frequency at an interval of 1000, and sequences with no more than 4500 in length were selected for the experiment. The experiment found that accuracy gradually decreased, so sequences within a length of 1000 were selected for training. The experiment then found that accuracy decreased compared to sequences within a length of 1500; thus, in subsequent features, we selected byte code with lengths below a 1500 bytes frequency for the fusion experiment.  
Figure 12: Experiment on the selection of \(N\) value and frequency.