esa-sceva/satcom-chunk-collection huggingface dataset:
# DARAL: A Dynamic and Adaptive Routing Algorithm for Wireless Sensor Networks
## 4 Simulation
### Number of Messages Sent during the Set-Up Phase  
The number of messages involved in the network setting-up phase can be used as a good estimator of the protocol control overhead. Figure 13 shows a global comparison between \(O\)-\(RPL\) (R), \(AODV\) (A) and \(DARAL\) (D). Therefore, Table 4 also shows the results. As we can see, DARAL seems to be more efficient in terms of number of messages. The relationships between these differences has been statistically analysed. Every sample was independently analysed using a one-sample Kolmogorov-Smirnov test, resulting in a rejection of the data normality. Then, we proceeded to run a Kruskal-Wallis test, and the results showed that the differences between \(O\)-\(RPL\), \(AODV\) and \(DARAL\) are statistically significative.  
Figure 12: Convergence time results for small-, medium- and large-size scenarios.  
\begin{table}
\begin{tabular}{c l c c c} \hline \hline
**ND** & **Algorithm** & **Avg. \(\pm\) dev Small-** & **Avg. \(\pm\) dev Medium-** & **Avg. \(\pm\) dev Large-Scenario** \\ \hline
5 & O-RPL & 916.05 \(\pm\) 982.93 & 1163.8 \(\pm\) 737.02 & 3022.7 \(\pm\) 2209.2 \\
5 & AODV & 122.59 \(\pm\) 55.53 & 377.08 \(\pm\) 45.73 & 640.17 \(\pm\) 227.99 \\
5 & DARAL & 49.05 \(\pm\) 17.61 & 60.51 \(\pm\) 23.39 & 90.27 \(\pm\) 11.18 \\
10 & O-RPL & 154.67 \(\pm\) 295.97 & 156.11 \(\pm\) 253.97 & 91.28 \(\pm\) 196.67 \\
10 & AODV & 121.7 \(\pm\) 33.9 & 317.95 \(\pm\) 36.15 & 688.87 \(\pm\) 35.46 \\
10 & DARAL & 38.91 \(\pm\) 15.81 & 66.68 \(\pm\) 20.86 & 84.95 \(\pm\) 9.41 \\
15 & O-RPL & 40.6 \(\pm\) 151.51 & 1.85 \(\pm\) 5.25 & 0.76 \(\pm\) 0.99 \\
15 & AODV & 105.22 \(\pm\) 33.28 & 292.41 \(\pm\) 38.45 & 655.26 \(\pm\) 49.85 \\
15 & DARAL & 40.74 \(\pm\) 18.59 & 61.02 \(\pm\) 13.02 & 98.43 \(\pm\) 11.98 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Average convergence time (seconds (secs)) in small-, medium- and large-size scenarios.
# Detection of Electromagnetic Seismic Precursors from Swarm Data by Enhanced Martingale Analytics
###### Abstract
They result from cumulative strain over time, culminating in lithospheric rupture and bedrock displacement [10]. Studies have shown seismic anomalies occurring before earthquakes, termed precursors or abnormal signals, suggesting an energy exchange between the lithosphere and the ionosphere--the ionized upper atmosphere. Ground-based and space-borne measurements offer insights into precursor signals. Space-borne measurements, like those from CHAMP, DEMETER, and Swarm satellites, provide valuable data for studying ionospheric activity and its relation to seismic events. Swarm data, in particular, have shown promise in detecting seismic event precursors through the analysis of EM variations [11; 12; 13]. This paper presents an enhanced Martingale algorithm for detecting abnormal changes in EMF data. We focus on probabilistic approaches for anomaly detection, as they do not require predefined thresholds, which is advantageous given the difficulty in defining anomalies within the EMF domain. Building on previous work, we propose a novel method utilizing Martingale probability theory, offering a robust probabilistic model without the need for predefined thresholds [14; 15].
# Remote Sensing of Ecosystem Water Use Efficiency in Different Ecozones of the North China Plain
###### Abstract  
Water use efficiency (WUE), as an environmental factor of metabolism in different ecosystem functional areas, is a key indicator of the ecosystem carbon-water cycle. WUE is defined as the ratio of carbon absorbed by ecosystems to water evaporated. Exploring the spatiotemporal variation in carbon and water cycles in different ecological zones of the North China Plain and their driving factors is important for the ecological management and sustainable development of the different ecological zones in the North China Plain. Based on remote sensing data products, this paper studies the spatiotemporal variations of WUE and their driving factors in different ecological functional areas of the North China Plain from 2001 to 2017. This study found that: (1) The spatial distribution of WUE and gross primary production (GPP) in the North China Plain is similar, with the multiyear average of WUE at 0.74 g C m\({}^{-2}\) y\({}^{-1}\). The variation trend of WUE is mainly affected by the variation trend of GPP (44.38% of the area of the North China Plain). (2) The change trend of WUE mainly showed a mild decrease and a mild increase, accounting for 73.22% of the area of the North China Plain; the area with medium-low fluctuation of WUE accounted for the largest proportion, accounting for 59.90% of the area of the North China Plain. In addition, the multiyear average values of WUE in the ecological functional area are Qin Ling Mountains deciduous forests > Central China loess plateau mixed forests > Mongolian-Manchurian grassland > Ordos Plateau stepe > Changjiang Plain evergreen forests > Huang He Plain mixed forests > Bohai Sea saline meadow, in the order from high to low. (3) The influence of precipitation on WUE was higher than that of temperature. The area of WUE that increased with the increase of precipitation accounted for 23.74% of the area of the North China Plain and was mainly distributed in the Qin Ling Mountains deciduous forests, Changjiang Plain evergreen forests, and Huang He Plain mixed forests' ecological functional areas. The results of the study can provide a reference and theoretical basis for the conservation and management of carbon and water cycles in the functional areas of North China's ecosystems.  
WUE; spatiotemporal change; driving factors; ecosystem functional area; climate change +
Footnote †: journal: _Sustainability_  
1 Beijing Key Laboratory for Architectural Heritage Fine Reconstruction & Health Monitoring, School of Geomatics and Urban Spatial Information, Beijing University of Civil Engineering and Architecture, Beijing 100044, China; [EMAIL_ADDRESS]  
2 College of Geoscience and Surveying Engineering, China University of Mining & Technology, Beijing 100083, China; [EMAIL_ADDRESS]
## 4 The Quadrature Chaos Shift Keying Communication System's Performance Analysis
### Simulation Study  
The QCSK data transmission system simulation comprises five essential blocks implemented in MATLAB. The MATLAB code scheme for studying the data transmission system is depicted in Figure 10.  
The information signal is generated in the first block. Input parameters for the MATLAB code include bit length, number of bits, and sampling frequency. The simulation duration for the next step is calculated once the randomly generated bit sequence is saved to a text file. The subsequent step involves simulating the drive chaos oscillator, preserving three chaotic signals: one for chaotic synchronization and two for CSK. The simulation is carried out using the MATLAB function _LTSpice2 Matlab_(), which takes the required simulation netlist or code specifying the simulated scheme and then calls the LTSpice software (Version 17.1.15) for the necessary simulation. The third stage involves signal processing prior to quadrature modulation, channel emulation, quadrature demodulation, and signal processing after demodulation. The response chaos oscillator is then simulated, with the synchronization signal from the previous signal processing block serving as input. The simulation is carried out with the help of the MATLAB function _LTSpice2 Matlab_(). The next step is to detect the information signal, which is accomplished by calculating the correlation coefficient within a sliding window. Consequently, the code outputs a BER (Bit Error Rate) curve.
# Unique Triboelectric Nanogenerator Using Carbon Nanotube Composite Papers
## 4 Discussion
### Effect of Changing Amount of CNTs  
It was found that changing the amount of CNTs did not cause a reduction in the triboelectricity generation performance but did cause a difference in the output intensity. In this experiment, the amount of CNTs contained in the CNT composite paper was varied among three different values (5 mg, 15 mg, and 30 mg), and the maximum output was observed when the CNT composite paper contained 15 mg of CNTs. There was no significant difference in the output when the amount of CNTs was 5 mg or 30 mg, and an approximately 3.7 times higher output was acquired when the amount of CNTs was 15 mg than when the amount of CNTs was 5 mg or 30 mg. This suggests that there is an optimal amount of CNTs. This is because the CNTs are not fully dispersed in the CNT composite paper when the amount of CNTs is small. However, when the amount of CNTs is large, the area of direct contact between the CNTs and the aluminum electrode becomes larger, which is thought to reduce the output. Based on this, as well as the discussion in Section 4.1, CNTs are believed to play a role in triboelectricity generation by efficiently transferring the electric charge generated by the pulp and electrode to the counter electrode.
## 5 Conclusions
### Key Findings and Practical Implications  
Model Performance: The YOLOv8n model achieved high precision (0.828) and recall (0.957), with a mean average precision (mAP) of 0.945, making it effective for defect detection. In contrast, larger models like YOLOv8l, YOLOv8x, YOLOv9m, and YOLOv9c, while offering nuanced detection capabilities, showed slower speeds and varied detection accuracies. This indicates a trade-off between model complexity and performance, highlighting the need for balancing accuracy with computational efficiency.  
Real-World Application: The system's practical application was illustrated through defect classification, demonstrating its effectiveness in identifying strawberries with defects. Data collected from the SME showcased a higher detection rate using the software compared to human inspection, particularly for Class C defects, which are more challenging to detect visually.  
The software solution enhances defect reporting and quality control accuracy through digitalization--streamlining defect reporting with a user-friendly interface--and accessibility--offering an affordable, user-friendly solution suitable for the strawberry processing industry.  
Implementing the IFS FOOD standard in the participating company further enhances quality control by ensuring compliance with food safety regulations, promoting rigorous quality measures, and fostering continuous process improvement.
## 1 Introduction
tend to have lower thermal inertia for more porous material, while higher thermal inertia for the denser material. Typical examples show that for a low thermal inertia below 50 J m\({}^{-2}\) s\({}^{-0.5}\) K\({}^{-1}\) (tiu, hereafter), the surface is composed of fine-grained materials such as lunar regolith, while for a high thermal inertia beyond 2000 tiu, the surface consists of non-porous dense rocks [1]. On the other hand, carbonaceous chondrite meteorites that are primitive and enriched in volatiles are likely analogues to C-type asteroids [12; 13], which show an intermediate thermal inertia between 600 to 1000 tiu [14]. Thermal inertia can be derived from thermal measurements, since diurnal temperature profiles show a large peak-to-peak temperature difference for the case of lower thermal inertia, while a temperature change becomes much smaller and the peak temperature is delayed from the local noon for the case of higher thermal inertia. For the Moon, Mars, large asteroids, and satellites, the surface is covered with fine-grained or graveled regolith that is ejected by hypervelocity meteoritic impacts and sedimented on the surface. However, an in-depth study is required for small bodies since their surface condition has been poorly understood [15] since most of the ejecta could be escaped and the basement might be exposed due to low gravity. Hayakusa2 is the first mission to investigate the surface physical state of a small body through thermography [4]. In past planetary missions, thermal radiometry has been mainly conducted along the track of the orbiting spacecraft from a low circular orbit. However, thermal imaging is requested for Hayabusa2 because the spacecraft was not orbiting the asteroid but staying at a distant stational position, so that the whole surface was imaged by asteroid rotation to determine the temperature distribution and its diurnal temperature profiles of each geologic site. In Section 2, the Hayabusa2 and the asteroid Ryugu are introduced in more detail. In Section 3, the Hayabusa2 results during the approaching phase are displayed. In Section 4, the Hayabusa2 results during the proximity phase are shown, and in Section 5, the technical applications in the future missions is introduced and discussed, and in Section 6, the concluding remarks of this paper are described.
## 3 Proposed MFFCNN-Based Fault-Diagnosis Method
### Multiscale Transformation
For the corresponding local maximum \(p_{j}\), there will be \((L\)-\(m)/ws+1\) local maximum at the end of the traversal and will finally constitute the feature map \(\mathbf{p}_{t}\) output by the maximum pooling layer (P1(\({}^{(k)}\)). The specific mathematical formulas are as follows:  
\[p_{j}=\max_{(j-1)w+1\leq i\leq jw}\{a_{i}\}, \tag{5}\]  
\[\mathbf{p}_{t}=\Big{[}p_{1},p_{2},\cdots,p_{j},\cdots,p_{(L-m)/ws+1}\Big{]}. \tag{6}\]  
For each granular signal \(\{y^{(k)}\}\) after C1(\({}^{(k)}\)) and P1(\({}^{(k)}\), a certain number of new feature maps are generated. Then, these feature maps are used as the input of C2(\({}^{(k)}\)), and the same operations in Equations (3)-(6) are repeated, outputting a new feature map. Similarly, assuming that \(K\) convolution kernels are used in C3(\({}^{(k)}\)), the output of the max-pooling layer (P3(\({}^{(k)}\)) is \(K\) new feature maps, and take \(\mathbf{q}^{(k)}\) as the concatenation result of the corresponding feature maps obtained after each granularity signal \(\{y^{(k)}\}\) through the above process, formalized as follows:  
\[\mathbf{q}^{(k)}=[\mathbf{p}_{1},\mathbf{p}_{2},\cdots,\mathbf{p}_{K}]. \tag{7}\]  
Finally, the feature representation \(\mathbf{q}^{(k)}\) output after each granularity signal \(\{y^{(k)}\}\) undergoes continuous feature learning is flattened into a one-dimensional feature vector \(\mathbf{q}\). Since the scale factor \(k=1\), \(2\), \(4\) is selected in this paper, vector \(\mathbf{q}\) can be represented by the following:  
\[\mathbf{q}=[\mathbf{q}^{(1)},\mathbf{q}^{(2)},\mathbf{q}^{(4)}]. \tag{8}\]  
It can be seen from (8) that the final feature representation \(\mathbf{q}\) has three different scales. Therefore, compared with the traditional single-scale representation, multi-scale feature learning has a larger feature-capture range, which is conducive to extracting rich and complementary features. The characteristics of the system provide a better distinguishing effect for the next step of fault classification.
## 2 Background
### DGA Malware  
Malware programs such as Conficker and Kraken, which have caused considerable worldwide damage, implement DGAs as one aspect of their capabilities. Moreover, researchers have found improper codes embedded in websites and web-based advertisements that are designed to promote the spread of such malware programs over broad target areas [15; 16].  
Figure 1 schematically depicts callbacks implemented by DGA malware. In this figure, communications labeled \(\mathbf{Q}\) are requests for name resolution from DGA malware to a recursive DNS server (RDNS), while communications labeled \(\mathbf{R}\) are responses to these requests. We begin by assuming that DGA-generated domain names, being the capability of a C&C, are preregistered in an authoritative DNS server (ADNS). The malware first dynamically generates multiple domain names, such as d8 wgr9p8p7.com, fpeqbiwxfx.edu, and gwx123 nrs.net, based on its DGA and then directs DNS queries regarding these domain names to the RDNS in the network to which the malware itself belongs. Upon receiving a query, the RDNS returns the address assigned to the domain name if it is registered in the ADNSs or a nonexisting domain (NXDOMAIN) response if it is not. Finally, any domain, e.g., gwx123 nrs.net in this figure, that corresponds to an affirmative DNS response is assumed to be the C&C, and the malware attempts callbacks for the address assigned to that domain.  
The character strings generated by typical DGA malware for use as malicious domain names are easily distinguished from benign domain names. In GameOver Zeus [17], domain names are random sequences of letters and numerals ranging in length from 15 to 30 characters, such as 1 ygx14u1 wnf8 hb1 twhv8619h8 ygr.net and cipu0 wdgsnq9u8s t8 milymOhq.com. These domain names are designed to avoid namespace collisions with previously registered domains. In Rovnix [6], a type of dict-DGA malware, domain names are generated by concatenating words from dictionaries, such as accelerateaccountant.in.net and accelerateactress.in.net. These domain names are difficult to distinguish from human-generated domain names due to their string similarities.  
The objective of a DGA is to establish highly available communication channels between the malware and the C&C. One result of this process is that communication barriers based on blacklists are easily avoided by changing C&C domains. Furthermore, communications from within the network to the outside span a wide range of destination addresses and are thus difficult to detect since they are not restricted by firewalls. Notably,  
Figure 1: Callback communication between DGA malware and C&C.  
with identical DGAs, the malware and the C&C avoid the need to exchange any information related to changing the domain name.
## 5 Discussion
#### Potential Implications  
This study makes several key contributions to the field of emotion classification within virtual reality (VR) systems, with significant practical implications across various domains. Using biosignals such as electrocardiography (ECG) and galvanic skin response (GSR), we developed a machine-learning-based model capable of accurately classifying emotions in real time. This technology has the potential to enhance the emotional responsiveness of VR environments, making them more adaptive and personalized for individual users. Emotion recognition has the potential to make virtual landscapes more responsive, adaptive, and emotionally engaging. Therefore, our findings have significant potential applications in a wide range of virtual environments, particularly in the educational, therapeutic, and entertainment domains. Applications in Education: In the field of education, our model has the potential to assist educators in detecting and responding to students' emotions in real time, thereby positively influencing the learning experience. Emotion recognition could be integrated into virtual learning environments to monitor students' emotional states, providing real-time feedback to both lecturers and students. For instance, signs of frustration or confusion during a lecture could be identified, allowing the system to offer additional guidance or adjust the pace of instruction accordingly. Conversely, when a student exhibits signs of engagement and excitement, the system could introduce more challenging content to maintain momentum and encourage further interest. This adaptive learning process could enhance student motivation, reduce disengagement, and ultimately improve learning outcomes. Applications in Therapy: From a psychotherapy perspective, the application of emotion recognition in virtual reality (VR) environments presents unique opportunities for personalized treatment. Virtual environments can be designed to simulate stressful or
# Additively Manufactured Polymers for Electronic Components
## 4 Applications in the Electronic Field of Additively Manufactured Polymers
### Thin-Film Transistors  
Concerning conducting polymers, recent advancements have initiated a new era of entirely polymer-based electronic components and circuits [137]. What sets polymers apart from conventional silicon-based technology is the simplicity of their processing steps, which translates into cost efficiency. While polymers may currently lag behind silicon in terms of switching speed, their true value shines when cost takes precedence over speed. In the fabrication of a typical organic thin-film transistor (OTFT), four essential layers are required: precisely two conductive electrodes, a dielectric layer, and a semi-conductive layer. A critical aspect of OTFT fabrication is achieving precise alignment of these layers at the micro scale, which poses significant challenges for certain printing techniques due to difficulties in maintaining micro-scale tolerance in printed patterns [161]. For this reason, there have been many reports on printed high-performance transistors utilising various printing techniques. Numerous studies have demonstrated the use of metallic nanoparticle inks, including silver, copper, gold, and aluminium, as well as conductive PEDOT:PSS, for printing conductive electrodes in OFETs. In an effort to enhance electrical conductivity, CNT-based inks have been employed as alternatives to metallic nanoparticle inks. For instance, thin lines of composites based on CNTs and poly(9,9-dioctylfluorene-co-dipenthoxybithiophene) were precisely aerosol jet-printed to fabricate OTFT with the aid of solvents like chloroform and xylene [92]. Furthermore, the synergistic combination of inkjet printing and spin-coating has paved the way for the creation of all-polymer thin-film transistors [162; 163]. One illustrative example involved the use of Baytron-P, an aqueous solution comprising poly(styrene sulfonic acid) (PSS) and conducting oligomeric PEDOT counter-ions, for printing electrodes [164]. This was achieved by dissolving the upper polymer layer and subsequently re-depositing it within the crater-like intrusion formed due to the pinning of the contact line, enabling the creation of transistor-based inverter circuits. Moreover, researchers have achieved successful inkjet printing of all-polymer transistors, incorporating materials such as poly(4-vinyl phenol) and polythiophene [164].
# A Dynamic Assessment Methodology for Accident Occurrence Probabilities of Gas Distribution Station
## 4 Conclusions
### Practical Contributions  
The proposed methodology delivers actionable solutions for GDS safety management:  
* Real-time updates of RE probabilities (e.g., X\({}_{6}\)'s probability increasing from 9.57 \(\times\) 10\({}^{-2}\) to 1.69 \(\times\) 10\({}^{-1}\) in Table 4) enable operators to prioritize inspections on high-risk components.
* Updated UE probabilities (e.g., CE: 6.18 \(\times\) 10\({}^{-2}\)) provide quantifiable metrics to align with safety thresholds, avoiding regulatory penalties and enhancing public trust in GDS operations.
* Integration with GDS integrity management platforms automates risk alerts (e.g., triggering controller replacements when X\({}_{60}\) exceeds 3.0 \(\times\) 10\({}^{-1}\)), transforming reactive protocols into adaptive strategies.
* Diagnostic analysis (Sections 3.3-3.5) identifies non-critical REs (e.g., X\({}_{29}\): internal coating peeling), allowing operators to reallocate maintenance budgets from low-impact areas to high-priority interventions, such as procedural training (X\({}_{4}\)) and controller redundancy (X\({}_{60}\)).