esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Work
### Topology Reconstruction  
Network topology reconstruction refers to improving the characteristics of a network by redesigning its structure, adjusting the connections between nodes, or adding new nodes. When certain nodes in the network fail, they are replaced to restore the normal operation of the network. Its essence lies in finding the shortest path between disconnected subsets in the network. [PERSON] [13] proposed a connectivity determination algorithm for minimum link design in multilateral networks using algebraic graph theory, which has low complexity, and a communication topology reconstruction method was proposed for multiple multi-agent systems with different functions after networking. [PERSON] [14] proposed a partition dual connectivity recovery algorithm to solve the problem of poor fault tolerance in partition connectivity. The core idea is to construct backbone polygons in the central region of the network. [PERSON] [15] proposed an obstacle avoidance-based partition connectivity restoration method for wireless sensor networks. [PERSON] [16] proposed a multi-objective optimization genetic algorithm by introducing virtual fragments and a hierarchical chromosome structure to address the issue of optimal data collector location and movement path. The algorithm also includes custom encoding and decoding. Currently, the recovery methods for air-sea cross-domain communication networks are commonly based on traditional graph theory and shortest path algorithms with [PERSON]'s algorithm being a particularly well-known approach. [PERSON]'s algorithm is widely applied in network recovery due to its efficiency in identifying the shortest path. Numerous studies have proposed enhancements to [PERSON]'s algorithm, integrating dynamic changes in network topology to facilitate network recovery. However, a key limitation of these approaches is their typical focus on a singular objective--minimizing communication cost--while neglecting critical factors such as node load and link quality. A notable example of this is the dynamic network reconfiguration method proposed by [PERSON] [17], which utilizes [PERSON]'s algorithm to dynamically calculate optimal paths and reconfigure routers, thereby improving network reliability and transmission efficiency. While this method allows for real-time network reconstruction and path optimization during network failures, it is prone to challenges when the network topology becomes more intricate or when multiple failures occur simultaneously [18; 19]. In such scenarios, the method is likely to result in increased path computation delays and elevated resource utilization [20; 21].
## 3 Results and Discussion
### Inhibition Assay on Mycotoxic Models  
Of the 26 strains from pork products, only 11 presented a slight activity against _F. grantinearum_ and/or _P. crustosum_, as observed in Table 3.  
From these strains, nine were capable of inhibiting _F. grantinearum_, whereas only three inhibited _P. crustosum_. Additionally, only one of the strains presented an activity against both fungi (_D. hansenii_ PA45), while none of them had this capability against _A. parasiticus_.  
Regarding strains from game meat products, eight strains showed a biocontrol activity against at least one mold, although only three strains showed a percentage of radius inhibition higher than 40% (Table 3). Aureobasidium pullulans GE118 and Candida santamariae GE120 inhibited almost 50% of the _A. parasiticus_ mycelium. Some strains presented a slight biocontrol activity against the other two molds, but _Rh. mucilaginousa_ (GE109) inhibited the _P. crustosum_ growth at almost 60%.  
_D. hansenii_ and _W. anomalous_ have previously shown a biocontrol capability against molds due to their zymocines production [3; 14]. Another pathway for growth inhibition, used by _A. pullulans_, is the production of \(\beta\)-glucanases or quitinases [15]. [PERSON] et al. [16] have reported that _Rh. mucilaginousa_ strains were able to reduce the blue and grey mold decay on fruits.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Yeast Strains**} & \multicolumn{3}{c}{**\% Mycelium Radius Inhibition**} \\ \cline{3-5}  & & \multicolumn{2}{c}{_A. parasiticus_} & _F. grantinearum_ & _P. crustosum_ \\ \hline  & C. metapsilosis (PA4) & - & 6.7 & - \\  & C. metapsilosis (PA25) & - & 9.9 & - \\  & C. metapsilosis (PA41) & - & 6.3 & - \\  & D. hansenii (PA30) & - & 4.1 & - \\
**Fermented cured** & D. hansenii (PA45) & - & 4.1 & 11.1 \\
**sausages from** & D. hansenii (PA43) & - & 15.4 & - \\
**pork meat** & D. hansenii (PB86) & - & - & 17.3 \\  & [PERSON] servazzi (PB72) & - & - & 7.8 \\  & M. guillermondii (PA3) & - & 3.6 & - \\  & S. cerevisiae (PA9) & - & 5.3 & - \\  & W. anomalous (PA42) & - & 10.3 & - \\ \hline  & _A. pullulans_ (GE118) & 43.4 & - & - \\  & C. santamariae (GE120) & 48.9 & - & - \\
**Fermented cured** & D. hansenii (GD106) & - & 12.4 & - \\
**sausages from** & _H. valbyensis_ (GE108) & - & 5.1 & - \\
**game meat** & _H. valbyensis_ (GE115) & - & - & 28.7 \\  & _Rh. mucilaginousa_ (GD99) & - & 9.4 & - \\  & _Rh. mucilaginousa_ (GE109) & - & 3.8 & 59.0 \\  & W. anomalous (GE119) & 5.0 & - & - \\ \hline \hline \end{tabular}
\end{table}
Table 3: Yeast strains biocontrol activity: Inhibition % of the mycelium growth radius of mycotoxicing cells.  
Arona Formation by Yeasts Isolated from Pork Fermented Cured Sausages and Fermented Cured Sausages from Game Meat
# Predictive Energy-Aware Routing Solution for Industrial IoT Evaluated on a WSN Hardware Platform
## 4 PEAR Solution: Model Design and System Implementation
### PEAR Impact on Network Topology Changes  
As indicated in the previous section, PEAR triggers network topology changes in order to provide intercluster balancing. The network topology and its changes are dynamic and cannot be known beforehand since it is influenced by factors such as number of devices, interferences, physical position and distance between field devices.  
To provide a representation of how PEAR influences the topology, Figures 4-6 illustrate the network topology evolution in time. Phase 1 represents the inception of network formation while phase 2 and phase 3 are snapshots of its development at larger timestamps. Two examples are given: firstly, the R33 device belonging to a low energy profile (green square), and secondly, the R11 device belonging to a high energy profile (blue square). During the network formation, one can observe that R33 device had an increasing number  
Figure 3: The PEAR post-network-formation pseudocode algorithm.  
of child devices in phase 1 and phase 2, but it was freed from the child device load. Therefore, in phase 3 the R33 device has only one child device, namely the I22 device. This emphasizes the PEAR capability to reduce the energy consumption via a topology change. On the other hand, R11 device has a direct link to the backbone router (BBR) and no child devices in phase 1. However, because R11 device can accept a higher energy load while respecting its energy profile, PEAR increases its number of child devices, thus providing intercluster balancing.
## 4 Spatiotemporal Feature Extraction Techniques  
### Traditional Feature Extraction Methods  
In the Internet of Things (IoT) context, researchers have employed various methods to extract network features. For example, three commonly used feature extraction algorithms have been compared: Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), and Autoencoders (AE). Among them, the core of PCA and LDA is singular value decomposition (SVD), while AE comprises an encoder and decoder, which can extract features more effectively [53]. Although these methods can reduce data dimensionality and extract important features to some extent, they often struggle to handle complex spatiotemporal dependencies within the data, leading to inefficiencies in addressing dynamic attack patterns [34].
# Hybrid LSTM Model to Predict the Level of Air Pollution in Montenegro
## 1 Introduction
There is a wide range of metaheuristic algorithms [8] and one of the most commonly used is the Genetic Algorithm (GA), which is inspired by the process of natural selection [9]. It can effectively search for optimal or suboptimal solutions in a large solution space. Bagging is an ensemble learning technique that enhances predictions by consolidating multiple models trained on diverse subsets of data. By aggregating the predictions of individual models, bagging reduces overfitting and increases the stability and robustness of the algorithms. It helps to capture different patterns and relationships present in the data, increasing the model's overall performance by enhancing accuracy, handling data noise, and increasing robustness [10]. This paper focuses on the development of an advanced hourly CAQI prediction model through the hybridization of metaheuristics, ML algorithms, and ensemble learning techniques. More precisely, three different techniques are combined: GA, the LSTM algorithm, and a bagging approach. To our knowledge, this combination of techniques has not been applied yet to air pollution analysis in Montenegro. Regarding the numerical results, a comparison with standard ML prediction algorithms (SVM) is made. Our results show that the proposed hybrid model significantly outperforms the SVM model in terms of accuracy and convergence.
# The Inactivation of Microscopic Fungi in Bakery Products Using Hurdle Technology--A Case Study
## 3 Results
### Microbiological Testing of the Environment in a Bakery  
At this stage of the work, analyses were carried out to assess the occurrence of microscopic fungi in the production space in two zones--the drilling room and the MAP packaging hall--in the bread production plant. The study included quantitative analysis of microscopic fungi in the air and on production surfaces.  
In the drilling room, the number of fungi in the air was at level \(10^{2}\)-\(10^{3}\) cfu/m\({}^{3}\) (Table 4). The average fungi content in this room compared to outside air is not statistically significant. On the other hand, in the hall where MAP packaging takes place, compared to the drilling room, the number of tested microorganisms decreased to an average level of \(7.6\times 10^{1}\) cfu/m\({}^{3}\). In this case, there are statistically significant differences in the number of microscopic fungi between the halls.  
\begin{table}
\begin{tabular}{c c c} \hline
**Raw Material** & **Amount [kg]** & **Parameters** \\
**Composition** & **Amount [kg]** & **Parameters** \\ \hline Wheat flour & 19.60 & \
\begin{tabular}{c} Flour type 550. Parameters: gluten 30–32, falling number: \\ 280–320 p/L, and alveograph: 0.77–0.90 \\ It is permitted to reduce or add water up to 1 L, \\ depending on the water absorption capacity of the flour \\ Salt & 0.50 \\ Sourdough & 10 \\ Gluten & 0.56 \\ \end{tabular} &
\begin{tabular}{c} Flour type 550. Parameters: gluten 30–32, falling number: \\ 280–320 p/L, and alveograph: 0.77–0.90 \\ It is permitted to reduce or add water up to 1 L, \\ depending on the water absorption capacity of the flour \\ Wheat flour (5 kg), water (5 kg), and starter culture 1 or \\ 2 (8 g) fermentation at 28 \({}^{\circ}\)C, 16 h \\ \end{tabular} \\ \hline \end{tabular}
\end{table}
Table 3: Production specifications for sourdough-containing baked goods.  
On the surface of the hot dog drilling machines (samples 1-3), the number of microscopic fungi was between 7.4 \(\times\) 10\({}^{3}\) and 6.8 \(\times\) 10\({}^{4}\) cfu/25 cm\({}^{2}\). In contrast, in the MAP packaging plant, on the surface of the machines and on the conveyor belt, the number is more than 1 log lower. The differences between the average number of fungi on surfaces in the two halls studied are statistically significant. It can therefore be concluded that both the air and surfaces in the packaging hall are significantly (\(p<0.05\)) less contaminated.  
In most manufacturing plants, internal limits are being developed to allow a certain number of microorganisms both in the air and on surfaces, taking into account the specifics of production. Nevertheless, the level of surface contamination, especially in the driller, with microscopic fungi of the order of 10\({}^{3}\)-10\({}^{4}\) cfu/25 cm\({}^{2}\) is high.
###### Abstract  
Employing league championship optimization (LCA) technique for adjusting the membership function parameters of the adaptive neuro-fuzzy inference system (ANFIS) is the focal objective of the present study. The mentioned optimization is carried out for better estimation of the soil compression coefficient (SCC) using twelve key factors of soil, namely depth of sample, percentage of sand, percentage of loam, percentage of clay, percentage of moisture content, wet density, dry density, void ratio, liquid limit, plastic limit, plastic Index, and liquidity index. This information is widely useable in designing high-rise buildings located in smart cities. Notably, the used data is collocated from a real-world construction project in Vietnam. The hybrid ensemble of LCA-ANFIS is developed, and the best structure is determined by a three-step sensitivity analysis process. The prediction accuracy of the proposed hybrid model is compared with typical ANFIS to examine the efficiency of the combined LCA. Based on the results, applying the LCA algorithm lead to a 4.88% and 6.19% decrease in prediction error, in terms of root mean square error and mean absolute error, respectively. Moreover, the correlation index rose from 0.7351 to 0.7539, which indicates the higher consistency of the hybrid model results. Due to the acceptable accuracy of the proposed LCA-ANFIS model, it can be a promising alternative to common empirical and laboratory methods.  
fuzzy systems; hybrid optimization; geotechnical design; coefficient of compression Article
# Gait Planning Research for an Electrically Driven Large-Load-Ratio Six-Legged Robot
## 4 Gait Planning of the Large-Load-Ratio Six-Legged Robot
### Motion Planning of the Quadrangular Gait  
Based on Table 2, the motion planning of the quadrangular gait is implemented for the large-load-ratio six-legged robot. Cosine programming is employed for the foot trajectory of the robot. The leg which has the minimum distance from the PEP is stipulated to first enter the transfer phase. The duty ratio \(\beta_{\rm R}\) is \(2/3\). Each leg needs to fulfill a swing task and two support tasks in a period of the quadrangular gait.  
The foot trajectory of the leg \(i\) can be divided into three parts in the transfer phase. In the first part, the foot of the leg \(i\) chooses the velocity \(v_{\rm BT}\) to uplift from 0 to \(h_{\rm TB}\) along the \(Z_{\rm B}\) direction during the time \(0-t_{1}\). In the second part, the foot of the leg \(i\) moves to the AEP along the cosine curve during the time \(t_{1}-t_{2}\). In the third part, the foot of the leg \(i\) selects the velocity \(v_{\rm BT}\) to fall from \(h_{\rm TB}\) to 0 along the \(Z_{\rm B}\) direction during the time \(t_{2}-T/3\). Then, the leg \(i\) is completely turned into the support leg. The motion planning of the leg \(i\) under the quadruped gait is the same as that of the tripod gait in the transfer phase. Although the mathematical models of the foot trajectories under the tripod gait can be directly applied to the quadruped gait, the total swing time \(T/2\) of leg \(i\) under the tripod gait needs to be changed to \(T/3\) under the quadrangular gait.  
When the motion vector \(a\) is constant, the support distance of leg \(i\) is changed from \(2a\) under the tripod gait to \(a\) under the quadrangular gait. The total support distance of the support phase is \(2a\) along the opposite direction of the motion vector \(a\). The mathematical model of the foot trajectory in the support phase can then be written for the leg \(i\) under the quadrangular gait as follows,  
\[\left\{\begin{array}{l}{}^{\rm B}X_{\rm S}={}^{\rm B}a_{x}\big{(}\cos\big{(} \frac{3\pi}{T}t-\frac{\pi}{2}\big{)}-1\big{)}+{}^{\rm B}P_{\rm FJ1}^{(i)}\\ {}^{\rm B}Y_{\rm S}={}^{\rm B}a_{y}\big{(}\cos\big{(}\frac{3\pi}{T}t-\frac{\pi }{2}\big{)}-1\big{)}+{}^{\rm B}P_{\rm FJ1}^{(i)}\\ {}^{\rm B}Z_{\rm S}={}^{\rm B}P_{\rm FJ1}^{(i)}\\ T/3\leq t\leq T\end{array}\right. \tag{24}\]
# Tree Seedings Detection and Counting Using a Deep Learning Algorithm
## 2 Materials and Methods
### Dataset
#### 2.1.3 Data Augmentation  
Data augmentation is a technique of increasing the limited data to be significant, meaningful, and more diverse. Data augmentation prevents overfitting and improves the accuracy of the detection model [22]. To further enhance the model's performance, it is necessary to use data augmentation methods to expand the sapling dataset. Referring to related literature work, the data in this study were augmented via 13 strategies of the major data augmentation methods, such as flipping, rotating, flipping and rotating at the same time, adjusting brightness, and flipping and adjusting brightness at the same time. Examples of the applied data augmentation are shown in Figure 4. The data augmentation process was applied after the data labeling process. Thus, images were augmented simultaneously with the label files. Therefore, the dataset was divided into two datasets in the ratio of (8:2): the training dataset and the validation dataset. The augmented training dataset contained 7000 images, and the augmented validation dataset contained 2000 images.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Plant** & **Captured Images** & **Captured Images** & **Number of Captured Images** \\  & **Area** & **Way** & **Images** \\ \hline Dragon spruce & Tianheng mountain & DJI Inspire1 UVA & 60 \\ Black & Tianheng mountain & Mobile phone & 300 \\ Chokeberries & Acheng district & Mobile phone & 300 \\ Scots pine & Acheng district & Mobile phone & 300 \\ Total & & & 660 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Information about the collected dataset.  
Figure 3: Images of the three types of seedlings after preprocessing, (**a**) dragon spruce, (**b**) black chokeberries, and (**c**) Scots pine.  
Figure 2: The manual annotation of the seedlings using the ‘LabellImg’ tool.
To address the limitations in recognizing abnormalities in ECG signals, this research makes the following contributions:  
* Develop iCOR, an end-to-end DL model with a comprehensive ECG signal processing system incorporating noise removal, feature extraction, learning process, and morphology classification;
* Combine noise removal and feature extraction of ECG signals using multi-layer filters with DL-based signal delineation of normal and abnormal pathology to enhance generalization capabilities; and
* Evaluate with unseen data to accurately assess its performance and accuracy. These steps aim to build a robust and accurate system for ECG signal interpretation, leveraging advanced DL techniques for improved delineation and reliability.
# Low Power Wide Area Network, Cognitive Radio and the Internet of Things: Potentials for Integration
## 7 Challenges/Future Directions of CR-LPWAN  
We categorize challenges in CR-LPWAN into challenges at the PHY and upper layer levels. Often, challenges at the PHY layer are known CR problems, which determine to a large extent the success of CR-LPWAN systems, whereas challenges at the upper layers are typical problems capable of constraining the efficacy of CR-LPWAN systems. These are discussed as follows:
# Nonlinear Model Predictive Control for Doubly Fed Induction Generator with Uncertainties
## 2 Problem Formulation
### MPC Control Structure
#### 2.2.5 System Stability Assessment Using Lyapunov Function Analysis  
In order to ensure the stability of the proposed control method, the Lyapunov function obtained from the variables of state \(\hat{\mathbf{x}}(\mathbf{t})\) and \(\widetilde{\mathbf{x}}(\mathbf{t})\) is placed in the following inequality [17]:  
\[V\Big{(}\widetilde{x}\Big{)}\leq V(\hat{x}) \tag{33}\]  
Equation (21) asserts that to ensure the stability of the system using the Lyapunov-based MPC proposed in this paper, the Lyapunov function derived from the predicted state variables of the system must always be less than or equal to the Lyapunov function derived from the obtained variables of the system model.
# Power Load Demand Forecasting Model and Method Based on Multi-Energy Coupling
## 2 Power Load Demand Analysis  
### Calculation of Power Load Demand  
In China, power grid companies are mainly responsible for the dispatch of stable and reliable electricity supply. Power load forecasting with power grid as calculation caliber is the most suitable method for load forecasting demand under the scenario of multi-energy coupling. Thus the load forecasting in this paper is aimed at the demand of power dispatching under the condition of multi-energy coupling, which uses the power grid as the calculation aperture. In the multi-energy coupling mode, partial energy self-sufficiency is realized, which makes the load demand of the region to the power grid reduce compared with the load demand without multi-energy coupling mode. Then the regional load demand should be the difference between the total power demand and the multi-energy coupling self-sufficient energy. The calculation method of power demand based on this scenario is shown in Equation (1).  
\[L_{t}=L_{n}-L_{c}, \tag{1}\]  
where, \(L_{t}\) is the load demand required by power grid dispatching, i.e., the expected result of the forecasting model in this paper; \(L_{t}\) is the total demand of all loads; and \(L_{c}\) is the energy self-sufficiency that is not a part of power grid dispatching in the case of multi-energy coupling.  
Under the background of the continuous development of Chinese society, the total load demand will increase with the continuous growth of economy, and the self-sufficiency of multi-energy coupling energy will increase with the technological progress and social investment. Due to its advantages in new energy consumption, power expense payment, policy support and other aspects compared with the electricity provided by the grid, the society and users will give priority to the multi-energy coupling self-sufficiency to meet the load demand. The results shown in Figure 1 can be obtained by qualitative analysis of load changes.  
When the growth rate of total load demand is faster than that of multi-energy coupled self-sufficient power, the relative change trend of load is shown in Figure 1a. When the growth rate of multi-energy coupled self-sufficient power is faster than the growth rate of total load demand, the relative change trend is shown in Figure 1c. With the same growth rate, its relative change trend is shown in Figure 1b.