esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Findings Results
### Descriptive Findings
#### 4.1.2 The Total Volume of Publications over Time  
In the broader context, it is important to monitor the progress in the overall number of publications over time. From the analysis of Figure 4, we can identify several phases in the development of research output. In the emerging phase (2014-2017), very few articles were published each year--specifically, not more than 1 to 3 publications per year. The year 2018 saw no publications at all, indicating an early stage of limited interest or exploration in this field. This phase suggests either a lack of interest or merely tentative engagement with the topic.  
Next, there was a transitional phase (2018-2020). In 2019, a noticeable increase occurred, with eight articles being published. During this period, the volume of publications remained small but consistent, with seven to eight articles published each year in 2019 and 2020. This phase marks the beginning of growing interest in the research area, as the community started to gain momentum.  
Figure 3: Network diagram illustrating keyword co-occurrence.  
A remarkable surge in publications is evident during the acceleration phase (2021-2024). The number of published papers increased sharply, rising from 17 in 2021 to 30 in 2024. In both 2023 and 2024, a total of 30 articles were published, indicating high research activity. This sudden rise suggests that the topic is becoming very popular within scientific circles, demonstrating its relevance and significance.  
Overall, the data show remarkably rapid progress in this area, especially between 2021 and 2024, when nearly 73% of all 119 publications were produced. This trend indicates the growing importance and interest in the field, which has now become a major focus of academic research. The exponential growth in the volume of publications suggests that the subject has moved beyond its niche status to become mainstream within the scientific community. The sharp increase in publications during the acceleration phase may be due to a combination of factors, such as technological advances, increased funding opportunities, and greater societal recognition of the subject's relevance.  
Based on the information presented in this article, it is evident that, despite being relatively young, this research area has gained significant momentum in recent years. There are still possibilities for further growth as new challenges and opportunities arise. The rapid increase in scientific output suggests that this field will likely continue to evolve, with ongoing contributions expected from an increasingly diverse group of academics and organizations.
## 2 Related Works
### Anomaly Detection and Exfiltration  
Exfiltration countermeasures can be placed into three categories: preventative, detective, and investigative [13]. Under the detective category, there are further subtypes, such as host-based, network-based, and content-based detection. Our proposed framework falls under the host and network-based anomaly detective countermeasure category, as we are using both host data (ports), as well as network data (total flow). For example, a study used a Multivariate Anomaly Detection with Generative Adversarial Networks (MAD-GAN), consisting of Long Short-Term Memory (LSTM) Generator and Discriminator components to learn multivariate time series data from cyber-physical Systems (CPSs) [14]. One application example for this is CPU usage. Test data were then evaluated on an anomaly score, based on discrimination and reconstruction. Another study used an Isolation Forest machine learning model to enhance existing DNS exfiltration detection approaches by incorporating both high-throughput tunneling and low-throughput malware detection into their solution [15]. The one-class classifier Isolation Forest model was trained on DNS logs, which periodically extract features such as character entropy and longest meaningful word. Similar to the study that used the MAD-GAN architecture, an anomaly score was given for each sample and if it exceeded a certain anomaly threshold, an alert was raised.
## 5 Discussions
### Summary of the Performances of the Multi-Rx and Multi-Tx Constellations  
The assessment of the performances of the multi-satellite constellations showed that: 1 the number of LEO-LEO occultation events increases rapidly with the increase in the number of satellites and orbits; 2 with the NPO constellation, it is easy to achieve global coverage, and the NEO constellation is beneficial for middle and low latitude zonal observation; 3 for the unconstrained LEO-LEO occultation constellation design, it is advisable to adopt a polar orbit or at least NPO constellations; 4 to co-utilize existing or planned satellites to achieve the demonstration of the LEO-LEO technique by such platforms, NEO constellations can also be used; 5 to obtain more LEO-LEO occultation events and rapid global coverage, the two-orbit constellation is preferred for less than 24 satellites; otherwise, the multi-orbit constellation is recommended; and 6 a well-designed LEO-LEO occultation constellation composed of 36-72 satellites can meet the basic requirements of global numerical weather prediction in terms of occultation event numbers and timeliness.
# Inter-Annual Variability of Peatland Vegetation Captured Using Phenocam- and UAV Imagery
## 2 Materials and Methods  
### Study Area  
Auchencorth Moss is a low-lying ombrothropic peatland, located in southeast Scotland (55\({}^{\circ}\)47\({}^{\prime}\)33 N, 3\({}^{\circ}\)14\({}^{\prime}\)37 W; 267 m a.s.l.). Shown in Figure 1, the site covers an area of around 1000 ha, with extensive homogeneous fetch over blanket bog to the south, west, and north. A series of regular, parallel ditches indicate the area was historically drained (>100 years ago), although these are now overgrown with vegetation. The peatland has naturally returned to a rewetted state and is currently used for low-density sheep grazing (<1 ha\({}^{-1}\)). The main soil types in the catchment area are histosols (85%), followed by gleysols (9%), humic gleysols (3%) and cambisols (3%), although the presence of these soil types is mainly limited towards the catchment edge [66].  
The site experiences a temperate maritime climate (Koppen-Geiger classification, Ref. [67]), consisting of mild winters with infrequent snowfall, and warm temperatures during the summer months. Mean annual air temperature and total annual precipitation at the site are 7.7 \(\pm\) 0.6 \({}^{\circ}\)C and 963 \(\pm\) 176 mm, respectively (all values for the period 2002-2017 unless otherwise stated). The site is generally wet, with average daily water table depth (WTD) close to the surface (WTD = 0.9 \(\pm\) 2.6 cm since recording started in 2007, where positive values indicate a water-table below the surface). WTD displays seasonal variability, and daily values over the long-term measurement record range from \(-\)8.5 to 50 cm, reaching their lowest during summer months. The site has mostly functioned as a net sink for CO\({}_{2}\) since 2002, with an average annual uptake of \(-\)1.16 \(\pm\) 0.8 mol CO\({}_{2}\) m\({}^{-2}\) y\({}^{-1}\) (for 2002-2023; range: \(-\)9.88 \(\pm\) 2.9 mol CO\({}_{2}\) m\({}^{-2}\) y\({}^{-1}\) to 6.14 \(\pm\) 2.5 mol CO\({}_{2}\) m\({}^{-2}\) y\({}^{-1}\)).  
A regular hummock-hollow micro-topography characterises Auchencorth Moss, typical of many peatlands. Raised hummocks (ca. 40 cm diameter, 30 cm tall) are dominated by a mixture of pleurocarpous mosses (e.g., _Pleurozium schreberi, Rhytidiadelplus squarrosus, Hylocomium splendens_) and vascular vegetation (e.g., _Eriophorum vaginatum_, _Deschampsia flexuosa_, _Juncus effusus_), whereas hollow microsites (due to their lower elevation) are generally wetter, dominated by mosses (e.g., _Sphagnum_ spp., _Polytrichum commune_) and some grasses.
# Decentralized Multi-Agent Control of a Manipulator in Continuous Task Learning
## 1 Introduction  
### Context  
Artificial intelligence and machine learning are quickly becoming core tools in the robotics domain, allowing incorporation of intelligence in robots, making them able to analyze, learn, and improve their behavior with minimum human intervention [1, 2]. In particular, reinforcement learning (RL) [3] offers great potential to benefit from the experience to learn control tasks autonomously, as it is successfully shown in complex environments and scenarios [4, 5]. However, reinforcement learning typically requires collecting a large number of samples to accumulate such an experience (i.e., training data), which still limits the application of RL in real physical systems such as robots,and pushes the current computer technology to the limit. Moreover, the control problems in robotics are best represented with high-dimensional matrices, continuous state, and action spaces, which actually make the application of this type of technique expensive even for highly efficient processors [6]. Therefore, it is important to consider new ways to simplify the learning problem, to reduce the computational efforts required by the RL. Recent works have considered ways to speed up the training process by learning contextual affordances [7], where the robot learns to perceive the effect of performing an action on an object using the current state. This is particularly important for learning vision-based manipulation skills since it greatly reduces the training time [8]. Some other works have instead focused on the explainability of RL agents, allowing a learning agent to explain the decision of selecting an action over other possible actions [9]. Exploration is another main challenge in RL agents that refers to the problem of choosing a policy that allows a learning agent to discover high-reward regions of the state space. In [10], the performance of different exploration strategies have been compared for on-policy and off-policy algorithms showing that the value-difference-based exploration strategy combined with softmax action selection improve the learning efficiency. Providing interactive feedback about the agent's actions is another approach to deal with the exploration issue [11].  
In this paper, a decentralized learning approach is proposed for the robot to learn to perform a target task in a simulation environment through the use of a multi-agent framework (being implemented as On or Off policy RL algorithms), and transfer such knowledge to the real robot for real-task execution. Results and achieved performance are then analyzed in terms of how this approach can improve the computational effort and learning curve speed, while processing the learning algorithms, by comparing the proposed multi-agent framework with a centralized learning schema (used as a baseline).
# LEACH-MTC: A Network Energy Optimization Algorithm Constraint as Moving Target Prediction
###### Abstract  
When some nodes cooperatively track moving targets in a wireless sensor network, some things including network working node selection and network energy consumption are influenced. Thus, this paper proposes an improved algorithm LEACH-MTC (LEACH with Moving Target Constraint) based on low energy adaptive clustering hierarchy protocol (LEACH). First, based on the two-step linearization of the nonlinear dynamic model, the state of the nonlinear moving target is predicted by the extended Kalman filter (EKF). Second, combining the state prediction of the moving target and the performance of collaborative monitoring, this paper constructs an ellipse monitoring area of some working nodes to consist with the direction of the target movement. Subsequently, the node sleep strategy corresponding to the state prediction of moving target is designed. Finally, the cluster head selection strategy is proposed based on energy balance utilizing the state prediction of the moving target. Simulation results show that the proposed LEACH-MTC algorithm can not only ensure the real-time consistency between the changing direction of area and the direction of target movement, but also increase the number of working nodes' survival and reduce the network energy consumption.  
wireless sensor network; state prediction; node sleep strategy; cluster head selection; energy optimization
## 4 Conclusions  
VOCs present in gaseous emissions of biological treatment facilities of organic wastes have been investigated and characterized by different researchers. The main VOCs related to organic waste handling and treatment are terpen, although ketones, alcohols and organic acids have also an important contribution to the total VOC content measured. The presence of sulphur and nitrogen compounds has to be also highlighted. Different relationships have been established between VOC concentration and odor level on specific situations and for a specific range of VOCs and odor concentrations. There is no universal correlation covering the whole concentration range and the entire group of VOC families.  
The analysis of VOCs is mainly performed by GC-MS although other methods such as gas detection tubes or electronic noses have been used in some cases. GC-MS appears as the most reliable method covering a wide range of VOC concentrations and being capable to identify and quantify a large number of compounds from different families. Gas detection tubes permit the measurement of many compounds present at relatively high concentrations. As an economical and quick method, it can be useful for the self management of gaseous emissions in waste treatment plants. Electronic noses permit the characterization of the families of VOC present in a gaseous sample and the comparison between VOC emissions from different points of a treatment plant of different installations, although they must be considered a first approach.  
Different methods for air emissions sampling in waste treatment plants have been proposed. The lack of homogeneity among these methods is one of the drawbacks in VOC emission measurement from this type of facilities.  
Finally, it must be highlighted that biofiltration is the gaseous pollutant abatement technology that is most widely used in organic waste treatment plants for VOC removal.
## 3 The Gain Parameter Adaption and Scheduling Control Method  
The gain scheduling control method can be used to control the nonlinear system only if there are some control parameters under steady-state and a linear model that can reflect the system performance. Based on the online estimation method of the influence coefficient, we propose the gain parameter adaption and scheduling control method for spindle online dynamic balance. There are three main components of the method application, the gain table should be built first by many trial weight experiments. Then the gain parameters are indexed during the online dynamic balance period and the gain parameters are renewed when the system dynamic characteristics change.
## 2 Results
### Expression Abundance Analysis of the Candidate Reference Genes  
To assess the expression abundance of the ten candidate reference genes, qRT-PCR was performed using ten-fold diluted cDNA extracted from the roots, stems, leaves, flower buds, and fruits at the different developmental stages as templates. The cycle threshold (Ct) values obtained directly reflected the expression levels of each candidate reference gene, and the expression abundance was inversely correlated with Ct values, while lower Ct values indicated higher gene expression levels. As shown in Figure 3, the average Ct value of _UBQ10_ was 19.65, which was the lowest among all genes. All of its Ct values ranged from 18.18 to 22.91, highlighting _UBQ10_ as the candidate gene with the highest expression abundance. Conversely, the average Ct value of _Act100_ was 27.06, which was the highest among all the genes. All of its Ct values ranged from 22.37 to 30.93, indicating that _Act100_ had the lowest expression abundance. The lengths of the box plots in Figure 3 represent the data distribution. A shorter box plot suggests that the data were concentrated within a narrow range while a longer box plot indicates more dispersed data with significant differences. Although _UBQ10_ exhibited the lowest expression abundance, _Act1_, _His3_, and _UBQ_ showed better stability.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Gene** & **Full Name** & **Primer Sequence (5\({}^{\prime}\) to 3\({}^{\prime}\))** & **Slope (\(k\))** & **Amplification Efficiency (\%)** & **Correlation Coefficients (\(R^{2}\))** \\ \hline \(\alpha\)-_TUB_ & \(\alpha\)-tubulin & F: TCCCTTCCAACCATTCCC & \(-\)3.157 & 107.4 & 0.996 \\ \(\beta\)-_TUB2_ & \(\beta\)-tubulin2 & F: CCTATCCACAGCCACAG & \(-\)3.113 & 109.5 & 0.990 \\ \(\beta\)-_TUB6_ & \(\beta\)-tubulin6 & F: ACTTTGAGGCCATACAG & \(-\)3.140 & 108.2 & 0.996 \\  & & R: AACGAATCAGAGATATACAGC & & & \\ _His 3_ & Histone H3 & F: TAAAATCAGAGAGAG & & & \\ _CHS 1_ & Chalcone synthase 1 & F: GCAAAGAAGCAGCCAC & & & \\ _UBQ10_ & Ubiquitin10 & F: TACAGAAGAAGCCAGCCAG & & & \\  & & R: CACGAAGAGACAGAA & & & \\  & & F: AAACCCTAACTGGAAAGAC & & & \\  & & R: TACACACGTAGAGAGCC & & & \\ _Act1_ & Actin1 & F: TACCGATAGAGAGAG & & & \\  & & R: AGAATGCCTGGAGAAGAG & & & \\ _Act100_ & Actin100 & F: TTICCCTTATGCCCAGCTG & & & \\  & & R: TTCCCCCTCAGCAGTAG & & & \\  & Glyceraldehyde-3- & & F: AACTGAATTGGATATGCTTTC & & \\ _GAPDH_ & phosphate & R: CTCGGACTCCTTGAT & & & \\  & dehydrogenase & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Primer sequence, correlation coefficients, and amplification efficiency of qRT-PCR for the ten candidate reference genes of _E. ruttacarpa_ var. _officinalis_.  
Figure 3: Ct value distribution box-plots of the ten candidate reference genes in the fruits for the four periods (**A**) and four tissue types (**B**).
## 4 Discussion  
Detecting leaf pests and diseases in agriculture is critical to ensuring crop health and yield. In recent years, various machine learning and deep learning models have been developed to automate this process, leading to more efficient and accurate detection. In this case, we evaluated the performance of our proposed model compared to existing state-of-the-art methods. We evaluated their model's accuracy, parameter size, detection speed, and dataset to understand its strengths and limitations.  
Based on Table 7, several observations can be made. When it comes to accuracy, our model, with an mAP of 85.13%, represents a significant improvement over the Pest R-CNN model by [PERSON] et al., 2022 [20], which achieved an mAP of 60.2%. However, when it comes to maize disease detection, the EfficientNet model from [PERSON] et al., 2022 [21] slightly outperforms ours with an accuracy of 98.52%. [PERSON] et al., 2020 [22] effentnet-b4 model accuracy reaches 97%, but the model size is larger, 268.62 M, while our model not only has a smaller parameter size, 24.296 M, but also realizes a competitive mAP, highlighting its efficiency. In terms of detection speed, our model can process each image in a mere 0.025 s, outpacing the ResNet50 model from [PERSON] et al., 2021 [23], which takes 0.076 s per image. This rapid detection speed is crucial for real-time applications in agriculture. The diversity and quality of datasets used for training play a crucial role in model performance, with many studies like those by [PERSON] et al., 2022 [24], and [PERSON] et al., 2021 [25], utilizing the PlantVillage dataset. However, our model is trained on a dataset collected from the network, which may contain a wider range of scenarios and conditions than the PlantVillage dataset. In essence, while each model has its own strengths and weaknesses, our proposed model strikes a commendable balance between accuracy, efficiency, and speed. The ability to detect both disease and pest tasks further increases its potential for practical agricultural applications.  
Figure 12: Web application interface diagram.  
Future research can be approached from several perspectives. Firstly, the model's training can be deepened further, and various data enhancement techniques can be employed to enhance the model's generalization ability. Secondly, research can be combined with other fields, such as remote sensing technology, to achieve rapid detection of pests and diseases on a larger scale. Finally, lightweight optimization of the model needs to be considered to enable it to run on low-performance equipment, thereby extending its practical application in the field.
# Delay-Sensitive NOMA-HARQ for Short Packet Communications
## 4 Numerical Results  
In the simulations, we consider an uplink NOMA with two users and allow a maximum of 1 retransmission using HARQ. Using the MM, the PER, delay, and throughput performance are analyzed for each user with various transmission rates \(R_{i}\), SNRs, power-splitting ratios \(w_{i}\) for NOMA, and \(\alpha_{i}\) for non-orthogonal HARQ and length of packet \(n\). For simplicity of presentation, we set \(k_{1}=k_{2}=k\), unless specified otherwise. For simulation, we primarily focused on the CC-HARQ scheme for the detailed analysis and compared it with IR-HARQ for some special cases. When IR-HARQ is employed, the retransmission parity length can be adjusted at the cost of a slight signaling overhead. However, this can increase packet-level latency. In contrast, when CC-HARQ is employed, the whole packet is repeated to increase reliability. Therefore, CC-HARQ is more suitable for URLLC applications due to a simpler design with less signaling overhead.
# Synchronization of Chemical Synaptic Coupling of the Chay Neuron System under Time Delay
## 1 Introduction
In terms of stability, a time lag often leads to system instability, resulting in various forms of bifurcation, inducing periodic oscillations, multiple steady-state, almost period, and even chaotic motion [22; 23; 24; 25; 26; 27; 28]. [PERSON] [29] studied the Chay neuron model under constant current stimulation based on the stability theory. The results show that with the change of the control parameter I (external constant current), Hopf bifurcation will occur. In terms of chaotic motion, a time lag causes chaos in first-order nonlinear dynamical systems. For systems without a time delay, neither first-order systems, nor second-order autonomous systems, can produce chaos [30]. [PERSON] [31] discussed the influence of time delay on the synchronous dynamics of four ring-coupled chaotic HR neurons. By calculating the synchronization difference, it was found that a proper time lag will induce or improve the complete synchronization between chaotic neurons. The time lag can also cause the system to generate high-dimensional odd, strange attractors with multiple positive Lyapunov exponents, without being limited by the system dimension [32; 33]. In terms of synchronization and its transfer dynamics, the time delay can increase or suppress the synchronization of the coupled system. A time delay can induce not only the in-phase and anti-phase synchronization of the coupled system, but also the phase-synchronized transfer of different coupled systems and the transfer of different types of synchronization [34; 35; 36]. This paper introduces a mathematical model consisting of a pair of chemical synaptic-coupled Chay neurons and analyzes the basic dynamic behavior of the coupled neuron system. The paper is organized as follows. Section 1 introduces the synchronization and time delay and reviews the research progress in nonlinear dynamics. Section 2 introduces the Chay neuron model and the modification of a neuron's electrical activity when some parameters change. Section 3 presents the coupling Chay neuron system, analyzes the electrical activity of the coupled neurons and the synchronization, and evaluates the effect of the time-delay stimulation on the synchronization of the coupled neuron system under a certain coupling strength. Finally, Section 4 summarizes the main results.