esa-sceva/satcom-chunk-collection huggingface dataset:
# Social Internet of Vehicles for Smart Cities
## 2 SIoV Technologies and Components: Next Generation of Vehicles
### Mobile Applications  
The integration of network adapters, on-board units, different types of sensors and Global Positioning system (GPS) receivers enables vehicles to gather and analyse information about themselves and the surrounding environment and disseminate this information to nearby vehicles [31].  
Depending on their primary purpose, VANET applications are classified into comfort applications and safety applications. Comfort or entertainment applications provide ease, high levels of comfort and tranquillity to all passengers, including the driver. This can include information of current restaurant menus, prices and discounts on highway services, nearby gas stations, shop discounts and current prices for nearby hotels. For passengers, this can also include online gaming, Internet access and social instant messaging [51]. Safety applications use dedicated short-range communications (DSRC) to improve road safety and avoid accidents or reduce their severity.  
These five features will expose SloV to a plethora of sensor data that need to be analysed, to enable vehicles to become context-aware.  
In their seminal work, [PERSON] _et al._[20] envisioned the concept of a social network among cars as a cyber-physical layer on top of the physical vehicular network. Based on this concept, the authors proposed application scenarios that target both drivers, authorities and cars and discussed privacy issues that arise from such a system. Using this work as a basis, we elaborate in the following sections on how next generation vehicles can achieve higher levels of vehicle context-awareness. This is followed by a discussion as to how approaches to social network analysis can be applied within an SloVs and what new security and privacy challenges arise.
## 2 Background and State-Of-The-Art
The study resulted in the development of a hybrid model based on artificial neural networks and the grasshopper optimisation algorithm capable of predicting construction work efficiency with satisfactory precision. The following general conclusions can be drawn from the literature review:  
* Although there are some common factors affecting efficiency, productivity, or performance across different regions and types of construction works, these analyses are often dependent on regional specificity and environmental issues,
* Various models for predicting efficiency, productivity, or performance have been investigated and developed, but there is no universal tool that can be applied to all cases. * The selection of tools for efficiency, productivity, or performance modelling is individual and should be motivated and justified by the availability of data.
# A Novel Anti-Jamming Decision-Making Algorithm Based on Knowledge Graph Technology
## 2 System Model  
### Block Diagram  
An intelligent anti-jamming communication system model is shown in Figure 1. The main difference between this intelligent anti-jamming communication system and the traditional wireless communication system is that besides the transceiver signal processing module, a jamming cognition and anti-jamming decision-making model are introduced. Our work mainly studies the anti-jamming decision-making model.  
The anti-jamming decision-making module can also be further refined, as shown in Figure 2:  
The intelligent communication anti-jamming decision-making module knowledge graph makes its anti-jamming decision following two different procedures: (1) if the jamming pattern is known, then the decision-making module will decide anti-jamming strategies by directly searching its knowledge graph based on the known jamming pattern and related parameters; (2) if the jamming pattern is unknown, then firstly the time-frequency domain features of unknown jamming pattern and its parameters need to be extracted, then the decision-making module will search its knowledge graph based on the acquired jamming pattern and its parameters for suitable anti-jamming strategies.  
The decision-making module is implemented on the user equipment (UE) side by combining the anti-jamming knowledge graph and the proposed algorithm. One UE is considered in this paper; however, multiple UEs can also be realized due to the extensibility of the knowledge graph, which is also the advantage of the proposed approach.  
Figure 1: The block diagram of an intelligent anti-jamming communication system model.  
Figure 2: The detailed block diagram of the anti-jamming decision-making module in Figure 1.  
Therefore, in the case of prior knowledge of communication jamming, the anti-jamming decision is made according to the search results of the knowledge graph, which effectively improves the speed and accuracy of the anti-jamming decision. When there is no prior knowledge about jamming in the knowledge graph, some jamming features stored in the knowledge graph can also be used to assist decision-making and improve the efficiency of decision-making.
## 2 Materials and Methods
### Data Validation for the Sensorial System
#### 2.2.1 General Testing Methodology  
To obtain information as near as possible to the operation conditions, we conducted individual measurements for each of the eight sensorial element assemblies, (as shown in Figure 7, these are: the assembly formed by the cylindrical housing, eccentric support mounting, sensor support base, pressure sensor, and ground contact element). The methodology to record one set of data is:  
* The team checks that the evaluated sensorial element's unique identifier is visible.
* The evaluated sensorial element is placed on a planar surface, with the cylindrical housing downwards and the ground contact element upwards.
* The evaluated sensorial element is electrically connected to the data acquisition system.
* All the electrical connections are verified visually for compliance with the wiring diagram.
* The calibrated test weights are placed for an easy and safe access.
* Preliminary readings are conducted to validate that the sensorial element, the data acquisition system and the data logging are working properly.
* The measurement protocol is conducted.
* The recorded data is verified for integrity.
* The recorded data is stored accordingly.  
Figure 8: The hardware diagram.
## 6 Discussion and Implications
### Threats to Validity  
As with all scoping reviews, several limitations apply:  
* Selection Bias: Although the PRISMA-ScR process was followed (Section 3), some relevant but non-indexed studies may have been missed.
* Publication Bias: The review relies on peer-reviewed and indexed literature, which may underrepresent studies with inconclusive, negative, or incomplete results. Additionally, the exclusion of non-English publications may limit insights from other linguistic and regional contexts. While this is common in scoping reviews, it remains a relevant limitation.
* Subjectivity in Categorization: The thematic classification of technical debt types was grounded in the literature and expert interpretation, but alternative taxonomies may be proposed in future work.
* Limited Evaluation Data: The pilot study in Section 4.6 involved a relatively small number of participants in an educational setting, and the broader deployment (currently at 60+ responses) has not yet been statistically analyzed.
* Context-Specific Observations: Some examples and interpretations are tailored to academic or gamified platforms, and may require adaptation for industrial or large-scale AI competitions.
* Limited Access to Commercial Platforms: The lack of access to proprietary codebases and internal infrastructures from commercial competition providers (e.g., Kaggle, DrivenData) restricts our ability to assess organizer-side technical debt in those environments. Nonetheless, the questionnaire remains applicable from the participant perspective, paving the way for future investigations into how such competitions influence development workflows and debt accumulation on the submission side.  
These limitations notwithstanding, the study provides a grounded, reproducible, and practically oriented framework for understanding technical debt in AI competitions.
# Physics-Informed Graph Neural Networks for Attack Path Prediction
## 2 Materials and Methods
### Dataset
#### 2.1.1 Realistic Attacks and Environments  
The dataset consists of 1033 samples, each containing an environment and an attack path. As discussed in Section 2.2, this dataset is sufficient for a physics-informed architecture which can generalize with limited training samples. We combined simulation and expert validation methods: environment graphs were simulated using BloodHound [76], a tool for auditing Active Directory (AD) environments, and attack paths for each environment were retrieved using Caldera [77], an APA framework for automated red teaming. This approach enables the rapid generation of environments and the selection of relevant Techniques, Tactics, and Procedures (TTPs), emulating an adversary (see [78]). Similar approaches combining automation with expert oversight for path generation have been proposed by other authors [79; 80], in which generated paths are manually reviewed by experts. In our case, the initial dataset was reduced from 1150 graphs to 1033 (\(\simeq\)10.17%) after manual inspection. There are also limits to this approach, which we discuss in Section 4.  
The environments represent AD domains as directed graphs \(G=(V,E)\), with nodes \(v\in V\) representing Users, Computers, Domains, Group Policy Objects (GPOs), and Organizational Units (OUs) (see [81] for details). The edges \(e\in E\) encode non-traversable relationships (e.g., OU\({}_{j}\) inherits properties from GPO\({}_{i}\)[76]) and traversable ones (e.g., User\({}_{i}\) can open a session on Computer,\({}_{j}\)). Node features provide additional information regarding the entities (e.g., vulnerabilities, privileges). Each network consists of 100 users with dedicated computers, connected across four subnets, with an average of 15% vulnerable hosts per environment. For further details, we kindly invite the reader to refer to the dataset documentation.  
Each attack path (see Figure 1) represents an \"identity snowball\" attack scenario [11; 79; 80], in which an adversary gains initial access to a low-privileged account (e.g., via phishing) and escalates privileges by compromising additional accounts and systems. The goal is ultimately to reach the Domain Administrator (DA) [11; 31]. While this attack model is widely popular among security auditors, it is also leveraged by known threat groups [82], who use Bloodhound to find the shortest paths to high-value targets [31].  
Given its relevance in both offensive and defensive security and motivated by existing research, we focused on this scenario for our experiments.
# A System-Level Methodology for the Design of Reliable Low-Power Wireless Sensor Networks
## 5 Energy-Latency-Reliability Trade-Off
### Packet Success Probability  
In order to fairly compare the three different data transfer schemes from an energy, latency, and reliability point of view simultaneously, as will be discussed in Section 6.2, the packet success probability must be estimated first. In the probabilistic binary symmetric channel (BSC) model, a bit is independently flipped with a bit error probability \(P_{e}\) during transmission. If node A is sending packets to node B through a BSC, where all packets have the same size of N bits, the probability that a packet from A arrives error-free at B is given by  
\[P=(1-P_{e})^{N}. \tag{17}\]  
This probability can be enhanced by using error correction techniques. Namely, cases of FEC, BR, and SAW-ARQ retransmissions are investigated in this work.
# Machine Learning-Powered Rainfall-Based Landslide Predictions in Hong Kong--An Exploratory Study
## 2 Landslide-Related Data Inventory in Hong Kong
### Rainfall  
The occurrence of landslides in Hong Kong is closely correlated with the rainfall intensity and duration. In this regard, the GEO has been operating an extensive network of automatic rain gauge stations to collect real-time rainfall data, which have also been used for operating the Landslip Warning System [4]. Currently, there are 121 automatic rain gauge stations in the network, which covers the whole territory of Hong Kong with an average density of one rain gauge per 10 km\({}^{2}\) (Figure 2b). The rainfall data of each GEO rain gauge are transmitted to a cloud platform at 1 min intervals through dual-active mobile networks. The system has been continuously enhanced by the state-of-the-art of the Internet-of-Things technology, to provide more accurate and reliable rainfall monitoring data over the territory of Hong Kong. With the data, spatial and temporal distributions  
Figure 1: Data inventories considered in the exploratory study.  
of rainfall with different durations for a given rainstorm can be readily determined for subsequent analyses.
## 3 Results and Discussion
### DOM and AEOM Optical Indicators  
Figure 3a-c show the optical indicators of fractioned DOM and AEOM. SUVA\({}_{254}\) is positively correlated with aromaticity, molecular weight, and hydrophobicity of DOM/AEOM [24; 25; 27; 34]. SUVA\({}_{254}\) values of AEOM was significantly lower than values of DOM (Figure 3a). SUVA\({}_{254}\) values of HMW were significantly higher than the LMW DOM/AEOM, respectively. However, all SUVA\({}_{254}\) values of DOM and AEOM were <3.0, which suggested that the chemical properties of DOM/AEOM attributed to the hydrophilic compounds and poor aromaticity [27].  
FI is an indicator that describes the relative contribution of terrestrial and microbial sources to the DOM/AEOM pools [24; 28]. Figure 3b shows the FI values of DOM/AEOM. The FI values of DOM/AEOM increased following the decreased molecular weight of fractioned DOM/AEOM. DOM FI values of MW-A (>10 kDa) were significantly lower than the LMW fractioned DOM (\(p\) = 0.02). The FI value of HMW AEOM (1.48 \(\pm\) 0.06, >1 kDa) was significantly lower than the LMW AEOM (1.80 \(\pm\) 0.12, \(p\) < 0.001). The low value of FI in the HMW AEOM may be generated by the lignin phenolic compounds when labile POM was hydrolyzed/oxidized [2; 5].  
BIX is an indicator used to assess the relative contribution of autochthonous DOM in water and soil samples [28; 29]. Figure 3c shows BIX values with size-fractionated AEOM/DOM. The BIX values of total and bulk AEOM were significantly lower than the BIX values of DOM (\(p\) < 0.001). The AEOM autochthonous sources were lower than the DOM sources. BIX values of HMW AEOM/DOM were significantly lower than LMW AEOM/DOM, respectively (\(p\) = 0.001). Bulk AEOM contained median autochthonous sources but bulk DOM contained strong autochthonous sources. The high BIX value (>1.0) corresponded to recently produced DOM of autochthonous origin [29]. In our study, the BIX values of fractioned DOM ranged from 1.04 to 1.10, which indicated the DOM contained a strong autochthonous source [25; 29].  
In present study, the selected indicators suggested that AEOM had higher terrestrial and allochthonous sources than DOM. However, the aromaticity of DOM was higher than AEOM. Other work has shown that generally, the high terrestrial and low autochthonous sources have high aromaticity of DOM [10; 44]. The low SUVA\({}_{254}\) values in this study attributed to hydrophilic substances and poor aromatics. The hydrophilic substances may not fully develop the aromaticity in DOM and AEOM. In addition, the DOM had a complex composition where the three selected indicators had an insignificantly correlation with each other. Nevertheless, the POM underwent a hydrolysis/bioxidation process where the three selected indicators had a significant correlation with each other in AEOM (Table 2). The correlation suggested the extracted AEOM had more stable and uniform chemical properties.
# Millimeter-Wave Smart Antenna Solutions for URLLC in Industry 4.0 and Beyond
## 5 Research Opportunities and Future Directions
### mmWave MIMO  
Since 60 GHz IEEE 802.11 ay standard supports MIMO configuration [58]; thus, more advanced MIMO techniques should be designed and experimentally analyzed for different antenna directivities and single-polarized or dual-polarized transmissions. Moreover, suitable research opportunities exist for the design and implementation of dynamic adaptive dual-polarized antenna arrays for millimeter-wave, as well as low-power and low-complexity MIMO systems [159; 160] that allow for flexible analog and digital beamforming and advanced signal processing techniques. MIMO, massive MIMO, and multiple antenna techniques at mmWave bands are hot cake topics for researchers [60].  
Furthermore, for the wideband properties of 60 GHz channels, severe hurdles for MIMO communications may be created, particularly in multi-user scenarios. A detailed survey of MIMO channel models in wireless communication is presented in [161]. It is worthwhile to investigate adaptable systems that adjust to changing channel circumstances to attain the required performance. Additional beamforming gain might be obtained by using a denser array. Directivity and spatial multiplexing gain are provided by a mmWave MIMO architecture with a series of sub-arrays [162]. As a result, the diversity in the spatial domain is fully used, enhancing communication resilience and resulting in exceptionally high SNR channel links that are practically fading-proof in an industrial environment. Furthermore, MIMO with beamforming has been reported to enhance PHY security at 60 GHz [163], which is an active area of research for Industry 4.0 and beyond.
# An Ultra-Wideband Frequency Selective Rasorber with Low Infrared Emissivity
###### Abstract  
The paper proposes an ultra-wideband frequency selective rasorber (FSR) with low infrared emissivity for the composite detection threat of both radars and infrared sensors. Firstly, the equivalent circuit (EC) method based on transmission line (TL) theory is utilized to analyze the absorption/transmission conditions. Then, based on the analysis above, sinusoidal microstrip lines with non-frequency-varying characteristics are adopted in the design, which significantly enhances the transmission bandwidth of FSR. The FSR demonstrates an absorption band ranging from 2.65 GHz to 8.80 GHz and a transmission band ranging from 9.15 GHz to 17.71 GHz. Furthermore, an infrared shielding layer (IRSL) exhibiting low emissivity in the infrared band and high transmittance in the microwave band is applied to the FSR. The simulation and experiment results verify that the IRSL-FSR demonstrates an ultra-wide transmission band ranging from 9.16 GHz to 17.94 GHz and an ultra-wide absorption band ranging from 2.66 GHz to 8.01 GHz. Additionally, it exhibits a low emissivity value (0.23) in 8-14 \(\mu\)m, providing a viable solution to the formidable challenge of radar-infrared bistelath for satellites and other communication-enabled flying platforms.  
f  
requency selective rasorber (FSR); sinusoidal microstrip lines; ultra-wideband; infrared shielding layer (IRSL); low infrared emissivity
## 4 Matching Relationship Between Texture Friction Reduction Effect and Lubricant
### Influence of Lubricant on Friction Characteristics Under Different Texture Depths  
The simulation program was used to simulate the friction-reducing effect of circular textures with different depths (with a texture radius of rp = 75 \(\upmu\)m and a texture spacing of 180 \(\upmu\)m). Post-processing of the data revealed the impact of the lubricant on the friction-reducing effect, as shown in Figure 7.  
Analysis of Figure 4 reveals the following:  
Figure 7: FMEP under different texture depths.  
For a 2 \(\upmu\)m texture, the lubrication effect decreases as the lubricant viscosity increases, and the higher the rotational speed, the friction.  
For a 4 \(\upmu\)m texture, the wear reduction effect is different for different rotational speeds. In low speed (1000 r/min), with the increase in lubricant viscosity, its friction decreases; in the middle speed (1400 r/min), its wear reduction effect is not necessarily related to the lubricant viscosity; in high speed (1900 r/min), with the increase in lubricant viscosity, its friction increases. In general, the best wear reduction effect is achieved at medium speed (1400 r/min).  
For a 6 \(\upmu\)m texture, the friction decreases with increasing lubricant viscosity, and the higher the speed, the better the friction reduction effect.  
As the depth of the texture deepens from 2 \(\upmu\)m to 6 \(\upmu\)m, the wear reduction pattern changes considerably.
## 3 Results
### WUE Analysis for Eight Major Soil Types
#### 3.3.2 Multiple Fractal Analysis of Eight Major Soil Types  
The results of the fractal analyses for each of the eight major soil types in Anhui Province can be seen in Figure 16, in which the log(r)-log(Nr) plots for moist soil, brown soil, chrysolite, yellow-brown soil, yellow-brown soil, southern paddy soil, blood paddy soil, and brown soil are demonstrated. The fractal dimension, D, obtained for the eight soil types ranged between 1.9095 and 2.1668, and the slopes of the straight lines were fitted by the least squares fitted curves. The goodness-of-fit values were generally high, with fit rates R2 ranging from 0.9925 to 0.9975. The obtained fractal dimensions for the various soil types lay in the order of chrysolite > blood paddy soil > yellow-brown soil > moisture soil > southern paddy soil > brown soil > cinnamon soil > krasnozem from highest to lowest.  
Figure 14: Multiple fractal spectra describing the different natural terrain subdivisions: (**a**) the Huaibei Plain, (**b**) Jianghuai hilly region, (**c**) hilly and mountainous area in southern Anhui, (**d**) Dabie Mountains, and (**e**) plain alongside the river.  
Figure 16: Fractal dimensions of soil types: (**a**) moist soil, (**b**) cinnamon soil, (**c**) red soils, (**d**) chrysolite, (**e**) yellow-brown soils, (**f**) southern paddy soils, (**g**) blood paddy soils, (**h**) brown soils.  
Figure 15: WUE by soil type in Anhui Province.  
Figure 17 shows the multifractal spectra of eight soil types ranked from high to low: moist soil, brown soil, chrysolite, krasnozem, yellow-brown soil, southern paddy soil, blood paddy soil, and cinnamon soil. The spectra characteristics differ among soil types. Blood paddy soil's spectrum has a broad bow at mid-high \(\alpha\) values, indicating high spatial heterogeneity. Chrysolite and brown soil show high heterogeneity and complexity, with chrysolite having a higher initial f(\(\alpha\)) and wider \(\alpha\) range. Moist, krasnozem, and southern paddy soils exhibit more consistent spatial variation and better water retention. Cinnamon soil's spectrum declines rapidly after a flat peak, influenced by regional distribution, while yellow-brown soil shows greater heterogeneity in moisture distribution with a steady decline.