esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### The Flavor Profile and Comparison of Meat Sausage Analogue
Furthermore, 4-Allylanisole, which was regarded as an anisic, sweet, spicy odor, and the only ether detected in the sausage samples, was the second most abundant volatile in DMMSA at 24.840 \(\mathrm{\SIUnitSymbolMicro g}^{-1}\). DMMSA was richer in esters and aldehydes which have the most important role in product flavor because of their low threshold [33]. Of the aldehydes, benzaldehyde is associated with almond flavor derived from the bacterial catabolism of amino acids and has been detected at high levels during the air-drying of fermented sausages [28; 34]. Linaly acetate, with a green herbal aroma, accumulated at high levels in DMMSA (4.215 \(\mathrm{\SIUnitSymbolMicro g}^{-1}\)). The only acid in the sausage samples was acetic acid, detected in DMMSA at 8.626 \(\mathrm{\SIUnitSymbolMicro g}^{-1}\), probably originating from the catabolism of carbohydrate by _P. pentosaecus_ carbohydrate catabolism [35]. The composition of meat is affected by the animal species, breed, feeding regime and other factors [36]. However, proteins and lipids are the main substrates for the chemical reactions and microbial metabolism responsible for forming flavors in animal-based sausages [37]. As the meat analogues were prepared from protein-rich ingredients, plant-based sausage analogues might be involved in a similar pathway for forming aromas in their animal meat counterpart [38; 39]. Additionally, 1-Hexanol, acetic acid, hexanal, nonanal, 3-hydroxy-2-butanone, hexanoic acid, 2,3-butanedione, 2-methyl butanoate, \(\beta\)-myrrene, \(\alpha\)-terpinene, terpinolene and linalool, have often been detected in fermented sausages inoculated with a starter culture [27]. In animal-based dry-fermented sausages which undergo spontaneous fermentation, hexanal, octanal, nonanal, 3-hydroxy-2-butanone, 1-octen-3-ol, ethyl acetate and ethyl hexanoate are essential for forming the overall flavor profile. In the present study, 1-hexanol, acetic acid, nonanal, linalool, \(\alpha\)-terpinene and terpinolene were detected, as well as flavor compounds uniquely found in traditional dry sausages from Northeast China, such as 4-allylanisole, cineole and linalool which were also found in DMMSA at high levels [40]. Meanwhile, protein has been proved to contribute to the retention of aroma compounds from the addition of substances such as flavorings and spices [18]. These results indicated that the profile of the sausage analogue could be close to that of real meat sausages, in both flavor and taste through adsorbing and maintaining the spices. Figure 5: (**A**) The number of different types of volatiles in MMSA and DMMSA; (**B**) The content of different types of in MMSA and DMMSA.
# Novel MIA-LSTM Deep Learning Hybrid Model with Data Preprocessing for Forecasting of PM2.5
## 3 Proposed MIA-LSTM Model
### Anomaly Detection and Removal
'x' was input into fixed-vector 'z', which resulted in the model learning about complex temporal correlations between the input variables. Multivariate LSTM for Forecasting of Particulate Matter  
LSTM has a forget gate based on a sigmoid function that helps to discard insignificant information from previous timestamps. The input gate further helps to keep useful information coming from previous timestamps as well as information from the current input of the neuron; it does so by using the sigmoid and tanh functions, respectively. Next is the memory cell, where the forget gate output and input from the input gate are added point-wise, which is responsible for handling long-term dependencies. This memory cell stores meaningful information. Finally, there is an output gate that provides the output to the other neuron by taking the information from the memory cell and the input gate by performing the point-wise operation. Pollutants usually show similar behavioral patterns when studied with respect to time. While applying LSTM, the important information which is used for prediction is stored in the memory cell, and irrelevant information is discarded by forget cell. For the input LSTM layer used for prediction, the inputs were the pollutant and meteorological data at the \(t\)th time instant. The state of the hidden layer at this instant was \(h_{t}\), which included short-term memory information for the pollutant and meteorological data. The present output was provided by \(o_{t}\), the internal memory of the cell, and represented by \(c_{t}\)  
Each LSTM neuron is represented by the following equations:  
Input gate:  
\[f_{t}=\sigma(W_{f}[h_{t-1},x_{t}]+b_{f} \tag{3}\]  
Forget gate:  
\[i_{t}=\sigma(W_{i}[h_{t-1},x_{t}]+b_{i} \tag{4}\]  
State update:  
\[\widetilde{c_{t}}=tanh(W_{c}[h_{t-1},x_{t}]+b_{c} \tag{5}\]  
\[c_{t}=f_{t}*c_{t-1}+i_{t}*\widetilde{c_{t}} \tag{6}\]Output gate:  
\[o_{t}=\sigma(W_{o}[h_{t-1},x_{t}]+b_{o} \tag{7}\]  
\[h_{t}=o_{t}*tanh(c_{t}) \tag{8}\]  
\(W_{f},W_{i},W_{c},W_{o}\) represent the weight of the forget gate, input cell state, weight matrix, and output gate. \(b_{f}\), \(b_{i}\), \(b_{o}\), \(b_{c}\) = corresponding bias  
\(\tilde{c}\) represents input of cell state to memory cell. \(\sigma\) represents the sigmoid activation function used by the gates whereas the input and cell state use the \(\tanh\) function.
## 4 Performance Evaluation
### Non-Clustered Network Performance Evaluation
Figure 2: Network performance in terms of (**a**) number of inactive nodes and (**b**) number of disconnected nodes. The effect of power control on network end-to-end performance is depicted in Figure 3. The figure reveals that using power control, the network achieves around 20% higher PDR ratio compared to without the use of it, in both 100 and 200 nodes scenarios. The main reason is the number of disconnected nodes from the network in which they cannot reach the rest of the network as observed in Figure 2b. In terms of average hop count depicted in Figure 3b, due to higher transmission range when power control is ON, it shows slightly lower average hop count, which can result in a lower delay. By increasing the number of nodes to 200, the network expands and shows, on average, 2 hop longer routes, but the same behavior. The network performance in terms of power consumption is shown in Figure 4. It can be observed from Figure 4a that using adaptive power control, the network consumes more energy (as it is expected) and the average remaining energy per node is less than that when not using power control. This is due to the increase in transmission power of some nodes to keep the network connected. It can be seen from Figure 4b that using adjustable transmission power, the network performs the same with and without power control when having 100 nodes, but when having 200 nodes, the network consumes more energy for each packet while using the power control. The reason is again the extra power used in the power control case to keep the network connected. However, considering that it achieves 20% extra PDR (see Figure 3a), it is a reasonable tradeoff.
# Covert Anti-Jamming Communication Based on Gaussian Coded Modulation
## 2 Signal Design
### Gaussian Coding with Coding Gain
#### 2.2.2 Gaussian Codebook  
The Gaussian codebook method is another coding method with coding gain. If the length of the binary message vector is \(L\), then \(2^{L}\) codeword vectors of length \(2N\) are pregenerated. Then, the one-by-one mapping encoding function \(\operatorname{Enc}:\left\{-1,1\right\}^{L}\mapsto\mathbb{R}^{2N}\) can be constructed between \(2^{L}\) message vectors and codeword vectors. The codeword can be decoded using the ML method. \(O(N2^{L})\) computations are required to decode the original message from the proposed codeword generated by \(\operatorname{Enc}\). Here, we construct a codebook \(\mathbf{G}\) with a Toeplitz matrix of which the columns are cyclic shifts of a Gaussian random vector \(\mathbf{g}=\left[g_{1},g_{2},\ldots,g_{2N}\right]^{T}\), rather than generating every element of \(\mathbf{G}\) with an independent and identically distributed (i.i.d.) Gaussian. There are two benefits to constructing the codebook as a Toeplitz matrix. First, the Toeplitz structure accelerates the computation of the decoding method by using the fast Fourier transform (FFT). Second, such a codebook can be stored in a more memory-efficient manner than a random codebook because the Toeplitz codebook requires as much memory space as \(O(N)\), whereas the i.i.d. codebook requires as much memory space as \(O(N2^{L})\). If the computational costs of the FFT and inverse FFT (IFFT) are considered, the overall computational complexity becomes \(O(N\log N)\).  
The concatenation method presented in Section 2.2.1 is attractive in practice because well-studied finite-field coding methods can be directly exploited as the outer code. A proper outer code can be selected according to the purpose and requirements of the system. We used the codebook method in the following simulation section because the Toeplitz codebook method provides a good restricted isometric property (RIP) condition and is independent of the outer code design.
## 3 Results and Discussion
### Data Analysis
#### 3.3.5 Comparison with Previous Studies
Differences in our results from those calculated using the method of [31] may be attributed to the presence of environmental noise in our data. In addition, imperfect geophone couplings within our array deviate it from the perfect equally weighted array used in [31]. Finally, our hammer source generates a slightly variable minimum-phase wavelet in contrast to the exactly repeatable zero-phase Ricker wavelet used by [31].
# High-Precision Chip Detection Using YOLO-Based Methods
###### Abstract  
Machining chips are directly related to both the machining quality and tool condition. However, detecting chips from images in industrial settings poses challenges in terms of model accuracy and computational speed. We firstly present a novel framework called GM-YOLOv11-DNMS to track the chips, followed by a video-level post-processing algorithm for chip counting in videos. GM-YOLOv11-DNMS has two main improvements: (1) it replaces the CNN layers with a ghost module in YOLOv11n, significantly reducing the computational cost while maintaining the detection performance, and (2) it uses a new dynamic non-maximum suppression (DNMS) method, which dynamically adjusts the thresholds to improve the detection accuracy. The post-processing method uses a trigger signal from rising edges to improve chip counting in video streams. Experimental results show that the ghost module reduces the FLOPs from 6.48 G to 5.72 G compared to YOLOv11n, with a negligible accuracy loss, while the DNMS algorithm improves the debris detection precision across different YOLO versions. The proposed framework achieves precision, recall, and [EMAIL_ADDRESS] values of 97.04%, 96.38%, and 95.56%, respectively, in image-based detection tasks. In video-based experiments, the proposed video-level post-processing algorithm combined with GM-YOLOv11-DNMS achieves crack-debris counting accuracy of 90.14%. This lightweight and efficient approach is particularly effective in detecting small-scale objects within images and accurately analyzing dynamic debris in video sequences, providing a robust solution for automated debris monitoring in machine tool processing applications.  
g +
Footnote †: journal: _Algorithms_
## Materials and Methods
### Landsat Satellite Imagery Utilization for Vegetation Index Analysis  
Utilizing the Earth Engine API provided by USGS and NASA, Landsat satellite imagery was imported spanning the years 1984 to 2020. A critical component of this analysis involved the computation of the MSAVI, to assess the health and productivity of vegetation. The MSAVI, a refinement of the Soil-Adjusted Vegetation Index (SAVI), was formulated to mitigate the influence of bare soil reflectance on vegetation assessment, rendering it useful  
Figure 3: The Sen slop estimate of the Modified Soil-Adjusted Vegetation Index (MSAVI) to determine the crop health and yield (1984–2020) is classified as a negative and positive trend.  
during the initial vegetation growth stages characterized by significant soil exposure. The calculation of the MSAVI is based on the following formula:  
\[\text{MSAVI}=(\text{NIR}-\text{Red})/(\text{NIR}+\text{Red}+\text{L})\times(1+ \text{L}) \tag{1}\]  
\[\text{L}=(\text{NIR}-\text{Red})\times(\text{NIR}-\text{s}\times\text{Red})/( \text{NIR}+\text{Red}) \tag{2}\]  
In Equations (1) and (2), \"NIR\" denotes the reflectance in the near-infrared band, and \"Red\" signifies the reflectance in the red band of the electromagnetic spectrum, capturing the essential components of plant health. The \"L\" factor serves as a soil brightness compensation factor, adjusting the index to diminish the background soil effect, while \"s\" represents the slope of the soil line derived from plotting red against near-infrared reflectance data. This approach to vegetation monitoring through the MSAVI provided us with a means to assess the vegetation dynamics by highlighting areas of plant growth and zones potentially impacted by environmental stressors. By using the temporal depth and spatial resolution of Landsat data, this approach offered insights into vegetation trends over the period, contributing significantly to the goal of understanding the effects of climate variability on ecosystem health and agricultural productivity.
# Towards Optimizing Multi-Level Selective Maintenance via Machine Learning Predictive Models
## 4 Proposed Methodology
### Selective Maintenance with Multi-Strategies
#### 4.3.2 Resolution Method  
To solve the problem by determining an optimal maintenance plan, a metaheuristic algorithm based on the extended deluge algorithm was implemented. The extended great deluge is a local search metaheuristic that iteratively repeats the replacement of a current solution \(S\) by a new one \(S^{*}\), until some stopping condition has been satisfied. The new solution is selected from a neighborhood \(N(S)\)[44]. The concept of accepting or rejecting the candidate solution from the neighborhood is different from other methods. In the extended great deluge approach, the algorithm accepts every solution whose objective function is less or equal to the upper limit \(C\), which is monotonically increased during the search by \(dC\).
## 2 Impedance Spectroscopy
### Impedance Spectroscopy Modelling  
The equivalent and representative model of the electrical response of the materials/systems must be simple, and the choice of its components must be consistent and in accordance with the physical/chemical mechanisms that take place [5,100,101,107,109]. Consequently, constructing an equivalent model based on a set of elements involves their association according to the classical rules of the theory of electrical circuit analysis, i.e., in series or parallel.  
In modelling impedance spectra, the scientific community generalizes and widely accepts using a series association of elements. However, according to this theory, if an element constituting a specific material/system is not supported, i.e., it is not considered to be present (for example, if in a defined measuring range, for specific values, one contribution is not identified), then the approach based on parallel association will be more  
Figure 4: Z’ and Z′′ parts of the impedance as a function of the angular velocity form their respective Bode diagrams.  
Figure 5: Simplified Randles cell equivalent circuit model and Nyquist representation.  
coherent. An open circuit represents a nonexistent element, according to the theory of electrical circuit analysis. Thus, in a serial association, this would represent a discontinuity in the circuit and, consequently, an interruption of all conduction mechanisms (Figure 6a). In contrast, in a parallel association (Figure 6b), the nonexistence of an element would not have such a consequence since all the remaining conduction mechanisms would maintain their contribution to the total conductance/impedance of the material/system. However, the parallel association rules make this approach much more complicated.
# Recycling 3D Printed Concrete Waste for Normal Strength Concrete Production
## 4 Conclusions  
In conclusion, empirical testing of the mean compressive strength confirms the robustness of the concrete mixes, aligning with BV-MI 01: 2005(H) standards for respective strength classes. In C40/50 concrete, both the 50% and 67% replacement levels consistently exhibit higher compressive strength than the 0% replacement level. Transitioning to C30/37, a notable trend emerges: the 50% replacement level shows decreased strength compared to both the 0% and 67% replacement levels, while the 67% replacement level consistently displays superior compressive strength. In this range of replacement, the use of crushed concrete waste from 3D-printed concrete (3D RCA) is more similar to that of the RCAs from normal concrete. Additionally, adjusting the water content impacts the compressive strength in concrete mixes. At a 67% replacement, slight variations occur, while at 50%, additional water leads to a significant strength decrease.  
SEM analysis of normal and recycled concrete specimens post-compressive test revealed distinctive cracking patterns. Normal concrete showed cracks in the interfacial transition zone (ITZ) between the natural coarse aggregate and the cement mortar, resulting in easy separation. Recycled concrete exhibited cracks in the ITZ between the recycled coarse aggregate and the cement mortar, along with aggregate failure. These findings  
Figure 10: SEM images at ITZ. (a) Normal concrete; (b) Recycled concrete.  
highlight divergent failure mechanisms and underscore the importance of crack analysis for concrete performance enhancement.  
An overarching discovery is that the efficacy of the 67% replacement level holds regardless of the concrete strength class. Remarkably, it not only outperforms the no-replacement scenario but even exceeds the compressive strength of reference specimens. This underscores the strategic advantage of a 67% replacement level in optimizing compressive strength across diverse concrete classes.  
Conceptualization, R.N.; methodology, G.M.M. and R.N.; software, G.M.M.; validation, R.N.; formal analysis, G.M.M.; investigation, G.M.M. and R.N.; resources, G.M.M. and R.N.; data curation, R.N.; writing--original draft preparation, G.M.M.; writing--review and editing, R.N.; visualization, G.M.M.; supervision, R.N. All authors have read and agreed to the published version of the manuscript.  
The authors of the present manuscript are grateful to the Hungarian Scientific Research Fund (OTKA 146724 project) for support.  
Not applicable.  
Data Availability Statement: Data are contained within the article.  
The authors of this manuscript would like to thank Duna-Drava Cement Ltd., (Vac, Hungary) for the cement provided for the experiments.  
The authors declare no conflict of interest.
# Toward Integrated Satellite Operations and Network Management: A Review and Novel Framework
## 2 State of the Art
### Satellite Operations and Management Systems  
This section reviews the state of the art in satellite operations and management systems, considering sporadic connectivity constellations and persistent connectivity constellations separately. The first subsection examines traditional satellite operations, where mission management and communication systems are designed with intermittent connectivity periods in mind. Given that EO satellite operations share similar scenario constraints, they are also considered, providing valuable insights into managing sporadic connectivity constellations for telecommunications services. The second subsection explores how persistent connectivity constellations leverage advanced network management approaches to optimize operations, such as SDN and MANO. The continuous connectivity in these constellations enables management frameworks that closely resemble terrestrial network operations, facilitating automation, dynamic resource allocation, and service orchestration. This review highlights the evolution of satellite management paradigms and their convergence with modern networking technologies.
# Target Tracking Based on a Nonsingular Fast Terminal Sliding Mode Guidance Law by Fixed-Wing UAV
## 2 Problem Formulation
### Relative Motion Model
 \tag{6}\]  
The purpose of the above conditions is to make the status of the tracking system converge in finite time: (i) the distance convergence requires the position of the UAV will merge into the desired distance to the target; (ii) the direction of velocity convergence asks for the vehicle follows an appropriate heading angle (or course angle); and (iii) the convergence of the state (\(r(t)\)) and \(\dot{r}(t)\) will be reached.
###### Abstract  
With the rapid development of wireless communication technologies, free-space optical (FSO) communication has gained much attention in recent years. The FSO technology, which is usually used for communication in terrestrial environments, is expanding its regime to communication in maritime environments. In this study, we analyze the ergodic capacity of FSO communication between mobile platforms in maritime environments under foggy conditions, pathloss, and pointing error. More specifically, based on the moment expressions, we derive the closed-form expression of asymptotic ergodic capacity using conventional detection techniques (i.e., heterodyne and intensity modulation/direct detection). The derived analytical results are cross-verified with simulation results via Monte Carlo simulations. The results show that the combined effects of fog with pathloss and pointing error degrade the FSO performance and that it is important to apply the appropriate detection technique according to the communication environment.  
ergodic capacity; fog; free-space optical communication; maritime; mobile platform; pathloss; pointing error +
Footnote †: journal: Applied  
11 May 2023  
Revised: 4 June 2023  
Accepted: 7 June 2023  
Published: 9 June 2023  
**Correction Statement:** This article has been republished with a minor change. The charge does not affect the scientific content of the article and further details are available within the backmatter of the website version of this article.  
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