esa-sceva/satcom-chunk-collection huggingface dataset:
# A Fading Suppression Method for \(\Phi\)-OTDR Systems Based on Multi-Domain Multiplexing
## 1 Introduction
The orthogonal SOP pulse pair (OSPP) method was used to generate orthogonal polarization state pulses [16]. By summing the IF signals of the orthogonal polarization states, the polarization fading noise was effectively suppressed, enabling the disturbance events to be clearly resolved. However, the summing operation reduces the effective pulse repetition frequency by half, thus causing the frequency response of the system to be halved as well. However, these methods cannot suppress the interference fading noise. Therefore, fading suppression remains incomplete. FDM suppresses fading through the frequency-independent distribution of the RBS. When using probe lights with different frequencies, it is possible to avoid the fading phenomenon because the locations of the fading areas vary for different independent probe frequencies. Based on the differences in the frequency distribution of the RBS, the fading noise can be suppressed by multiplexing the different probe frequencies. Three different probe frequencies were used for an \(\Phi\)-OTDR system to select the optimal probe signal at any time [17]. It predicted the occurrence of fading before it actually happened. The fading probability was reduced to 1.15%. In this case, polarization fading was not taken into account. A method based on FDM and optimal tracking was proposed to predict the optimal phase signal and achieved continuous suppression of fading [18]. The distortion caused by fading was suppressed to 1.26%. The enhancement of the SNR in a multifrequency \(\Phi\)-OTDR system had an analytical relationship of \(\sqrt{n}\) between the enhancement of the SNR and the number of probe frequencies used [19]. The higher the suppression of fading noise, the greater the number of frequencies that need to be multiplexed. As the number of probe frequencies increases, more frequency signals need to be transmitted within a limited frequency bandwidth. Complex frequency modulation and data acquisition equipment is required, making the system costly. Based on the above description, SDM, PDM, and FDM are considered three relatively effective and practical methods for suppressing the fading noise. Although each of these methods has certain limitations, the optimal trade-off between the complexity of hardware and performance in various application scenarios can be achieved by exploring reasonable scheme designs and algorithm combinations. For different application requirements, optimal fading noise suppression can be obtained within the acceptable cost range. In this paper, a fading suppression method for \(\Phi\)-OTDR systems based on multi-domain multiplexing (MDM) is proposed. We discuss the fading noise suppression effects when these methods are used individually, as well as how to select the number of polarization states in PDM, the number of frequencies in FDM, and the spatial weighting intervals in SDM in multi-domain multiplexing, to achieve the lowest hardware cost and the best fading suppression effect that meet the application requirements.
## 7 Details of Matrices of the Investigated Multibody Structure  
If one applies the abovementioned algorithm of multibody dynamics to the investigated multibody subpart (Figure 3a), one can obtain the following equations for elements of the matrices:  
\[M_{14}=-l_{z}^{4}.m^{4}.c_{3p4}; \tag{34a}\] \[M_{24}=-l_{z}^{4}.m^{4}.s_{3p4};\] (34b) \[M_{34} =I^{4}+l_{z}^{4}.m^{4}.\Big{(}l_{z}^{4}+d_{z}^{4}.c_{4}\Big{)};\] (34c) \[M_{44}=I^{4}+l_{z}^{4}.l_{z}^{4}.m^{4}; \tag{34d}\]\[M_{13} =-l_{z}^{4}\cdot m^{4}\cdot c_{3\mathrm{p}4}-c_{3}\cdot\left(d_{z}^{4} \cdot m^{4}+l_{z}^{3}\cdot m^{3}\right); \tag{34e}\] \[M_{23} =-l_{z}^{4}\cdot m^{4}\cdot s_{3\mathrm{p}4}-s_{3}\cdot\left(d_{z}^{4} \cdot m^{4}+l_{z}^{3}\cdot m^{3}\right);\] (34f) \[M_{33} =I^{4}+I^{3}+l_{z}^{4}\cdot m^{4}\cdot\left(l_{z}^{4}+d_{z}^{4} \cdot c_{4}\right)+d_{z}^{4}\cdot m^{4}\cdot\left(d_{z}^{4}+l_{z}^{4}\cdot c_{4 }\right)+l_{z}^{3}\cdot l_{z}^{3}\cdot m^{3};\] (34g) \[M_{11}=M_{22}=m^{3}+m^{4}\] (34h) \[F_{4} =\hat{q}_{3}\cdot\hat{q}_{3}\cdot d_{z}^{4}\cdot l_{z}^{4}\cdot m^{4} \cdot s_{4}+l_{z}^{4}\cdot g\cdot m^{4}\cdot s_{3\mathrm{p}4};\] (34i) \[F_{3} =l_{z}^{4}\cdot g\cdot m^{4}\cdot s_{3\mathrm{p}4}+g\cdot s_{3} \cdot\left(d_{z}^{4}\cdot m^{4}+l_{z}^{3}\cdot m^{3}\right)-\hat{q}_{4}\cdot \hat{q}_{4}\cdot d_{z}^{4}\cdot l_{z}^{4}\cdot m^{4}\cdot s_{4}-2\cdot\hat{q}_ {3}\cdot\hat{q}_{4}\cdot d_{z}^{4}\cdot l_{z}^{4}\cdot m^{4}\cdot s_{4};\] (34j) \[F_{2} =-\hat{q}_{3}\cdot\hat{q}_{3}\cdot\left(l_{z}^{4}\cdot m^{4} \cdot c_{3\mathrm{p}4}+c_{3}\cdot\left(d_{z}^{4}\cdot m^{4}+l_{z}^{3}\cdot m ^{3}\right)\right)-\hat{q}_{4}\cdot l_{z}^{4}\cdot m^{4}\cdot c_{3\mathrm{p}4 }\cdot(2\cdot\hat{q}_{3}+\hat{q}_{4})-g\cdot\left(m^{3}+m^{4}\right);\] (34k) \[F_{1} =\hat{q}_{3}\cdot\hat{q}_{3}\cdot\left(l_{z}^{4}\cdot m^{4} \cdot s_{3\mathrm{p}4}+s_{3}\cdot\left(d_{z}^{4}\cdot m^{4}+l_{z}^{3}\cdot m ^{3}\right)\right)+\hat{q}_{4}\cdot l_{z}^{4}\cdot m^{4}\cdot s_{3\mathrm{p}4 }\cdot\left(2\cdot\hat{q}_{3}+\hat{q}_{4}\right); \tag{34l}\]  
where \(c_{i}=\cos(q_{i});s_{i}=\sin(q_{i});c_{3\mathrm{p}4}=\cos(q_{3}+q_{4});s_{3 \mathrm{p}4}=\sin(q_{3}+q_{4});d_{z}^{4}\) is the length of the upper body of the pendulum; \(l_{z}^{i}\) is the vertical component of position of the mass centre of body #\(i\) with respect to the fixing point of joint #\(i\); \(m^{i}\) is the mass of body #\(i\); \(l^{i}\) is the moment of inertia of body #\(i\) with respect to its mass centre; and \(g\) is the gravity acceleration.
## 2 Materials and Methods  
### Enrichment of Meat Products with Extracts  
Two natural extracts, recovered from rosemary leaf and rose petal post-distillation side streams, were used to enrich cured pork meat (bacon). Preparation of the bacon samples was performed in the production facilities of a collaborating meat producer in Greece, following the conventional industrial preparation procedure. Rosemary leaf extract (RSME) was recovered via acetone extraction of post-stean distillation rosemary biomass, in which carnosol and carnosic acid (phenolic diterpenes) were identified through high-performance liquid chromatography analysis with a diode-array detector (HPLC-DAD) as the key bioactive compounds. Rose petal extract (RSE) was recovered via ultrasound assisted extraction of post-water distillation rose biomass, using a 71% \(v/v\) ethanol-water solution as an extraction solvent, for 25 min, with 40 mL/g dry sample, and at 53% ultrasound power, as described in detail by [PERSON] et al. (2023) [26]. The total phenolic content in the two extracts was 225 mg/g and 400 mg/g, for RSE and RSME, respectively. In preliminary (unpublished) tests, RSE was found to have antimicrobial activity; whereas, on the other hand, RSME did not show such an activity. Regarding the antioxidant activity, RSME showed higher levels of antioxidant activity against lipid oxidation when compared to RSE. Thus, the groups of bacon samples prepared in this study were selected to study the antimicrobial effect of RSE with or without the use of RSME that could synergistically provide oxidation stability, as well.  
Four groups of bacon samples were prepared as described in Table 1. The extracts were used to enrich the infusion brine (sodium nitrite solution) injected in the raw meat, followed by the rest of the wet curing process steps. The use of nitrites was reduced by 50%, compared to the traditionally produced commercial bacon samples, in the samples enriched either with the RSE or with a mixture of RSE and RSME. In the case of the RSE-enriched bacon samples, the samples contained 90 ppm rose phenolic compounds (90 mg phenolic compounds/kg raw meat), while in the case of the combined RSE and RSME-enriched samples, the samples contained 45 ppm rose phenolic compounds (45 mg phenolic compounds/kg raw meat) and 45 ppm rosemary phenolic diterpenes (45 mg of carnosol and carnosic acid/kg raw meat), corresponding to a 150 mg sum of carnosol and carnosic acid on the basis of the fat content, according to EU regulation (EC Regulations no. 1333/2008 and no. 1129/2011, and the published amendment of 23 December 2020).
# RecPOID: POI Recommendation with Friendship Aware and Deep CNN
## 3 Result and Discussion
### Discussion on Performance  
According to Figures 5 and 6, RecPOID consistently outperforms UFC, LFBCA, and LORE expressively on both popular datasets in terms of two metrics, Recall@K, Precision@K. For instance, in Yelp, ours obtains 0.037 and 0.032 in terms of Precision@5 and Precision@10, respectively. It means our RecPOID method exhibiting 0.01, 0.015, and 0.02 performance improvement compared with UFC, LFBCA, and LORE, respectively. This superiority is perhaps outlined by the fact that the suggested RecPOID completely utilized the combination of the clustering method and friendship relations to recommend point-of-interests. As shown in Figures 5 and 6, it is interesting that even if we have small K, our strategy can attain great recommendation outcomes yet. Particularly, Recall@5 is smaller than Recall@10, verifying the effectiveness of the suggested technique. However, the LFBCA method's performance does not perform better than our proposed model but outperforms LORE consistently in all assessment metrics. LORE method does not perform well in every evaluation metrics in both datasets. This finding approves that top-relation based on the overlapping in obtained clusters is the POI recommendation's principal item.  
Figure 5: Comparison between RecPOID and the other three methods.  
Figure 6: Comparison between RecPOID and the other three methods.  
The two evaluated data sets have different data sparsity. The results of the evaluation illustrate that the proposed algorithm works better on the Gowalla dataset than Yelp, and the significance of friends at Gowalla is greater than Yelp. It means there are richer social relations in Gowalla and more under the influence of their friends' check-ins.  
Analyzing the other measures also confirms that the RecPOID model is well fitted for the subject problem. That means the recommender method is very sensitive, i.e., 94.1%, indicating that the technique is highly complete. Thus, the model can effectively and (almost) completely learn users' preferences according to behavior of similar friendships. Moreover, the specificity of the RecPOID is also significantly high, exceeding 91.8%. Therefore, the module can effectively identify irrelevant POIs. According to the specificity and sensitivity metrics, it is clear that the model is highly capable of correctly identify desirable and undesirable POIs.  
To further analyze the outcomes, we conducted a paired t-test to compare the differences between RecPOID and the other techniques. We found that our method's improvement is statistically significant on all datasets (\(p\)-value \(<\) 0.01).  
In summary, this research's contributions provide a novel deep learning method to recommend an accurate sequence of top-k POIs to users considering a similar pattern's friendship. The proposed RecPOID has improved the accuracy of POI recommendations compared to other state-of-the-art methods.
# Semantic Segmentation of Aerial Imagery Using U-Net with Self-Attention and Separable Convolutions
## 2 Literature Review
### Orchard Tree Identification: U-Net-Based Segmentation  
In 2021, ([PERSON] et al., 2021) proposed a semantic segmentation method to identify orchard trees in aerial images. They employed U-Net to enhance the efficiency of the approach, particularly in terms of accuracy. The designed model prioritized the automatic localization and detection of orchard tree canopies under various constraints [22].
## 3 Results and Discussion  
### Adsorption Tests  
The adsorption tests performed in the present study had the objective to pre-evaluate how the addition of MWCNTs (0.01% _w/w_) or montmorillonite (0.01% _w/w_) to the soil can contribute to improve the adsorption capacity of the soil. Tests using only aqueous suspensions of MWCNTs or montmorillonite were also conducted to assess the adsorption capacity of these materials on their own. Knowing the initial concentration of heavy metals added to the soil, the quantities adsorbed by the solid particles (soil alone or soil and MWCNTs/montmorillonite) was calculated based on the difference between the initial concentration and the measured concentration in collected water samples at different times (5 min, 20 min, 1 h, 4 h, and 24 h).
# Study on Image Classification Algorithm Based on Multi-Scale Feature Fusion and Domain Adaptation
## 2 Related Background Knowledge
### Attention Mechanism  
Convolutional Neural Networks (CNNs) possess strong data fitting capabilities; however, due to limitations imposed by optimization algorithms and computational resources, enhancing the CNN performance on large-scale datasets can be challenging. Computational power often becomes a bottleneck that restricts model improvements. Inspired by the human nervous system, the brain cannot handle overloaded visual tasks, but through the visual attention mechanism, humans focus on key objects, significantly improving their processing efficiency [32]. Attention mechanisms have a long history of research, and in 2014, Google and the DeepMind team successfully integrated attention mechanisms with Recurrent Neural Networks (RNNs), giving rise to Attention-based RNNs (AttRNNs).  
Figure 1: Structure of a Generative Adversarial Network.  
These networks are widely used in sequence-to-sequence tasks such as machine translation and speech recognition. Common types of attention mechanisms include channel attention, spatial attention, and hybrid attention.  
Channel attention (CA), also referred to as item-wise attention, focuses on assigning different weights to each feature map obtained from various convolutional kernels. The idea is that each kernel extracts a different feature map, capturing varying amounts of important information. By applying a weight coefficient to each feature map, the network can emphasize the feature maps that contain more relevant information.  
Spatial attention (SA), also known as location-wise attention, takes the data features as input and learns a weight mask through a neural network. This mask predicts the importance of different parts of the feature map, enhancing or suppressing specific features depending on their relevance.  
Hybrid attention (HA) combines both spatial and channel attention mechanisms to enhance the model's ability to focus on the most important parts of the input. A classic example of a hybrid attention module is the Convolutional Block Attention Module (CBAM) [33], which incorporates both the Channel Attention Module (CAM) and Spatial Attention Module (SAM). The CAM computes the attention weights for each channel, allowing the network to prioritize specific channel features, while the SAM calculates attention weights for each spatial location, focusing the network on important regions in the image. The outputs of the CAM and SAM are combined through element-wise multiplication to generate the final attention weights.  
The CBAM enhances the network's sensitivity to both channel and spatial features, improving the classification accuracy. Furthermore, the CBAM can be seamlessly integrated into existing CNN architectures as a submodule, improving the model's expressive power and robustness. This module has been widely applied in tasks such as object detection, image classification, and semantic segmentation, where it has consistently demonstrated an excellent performance.
## 3 Results
### SEM Analysis of the Coatings  
It was shown through an SEM analysis that the PP film that had been used in the tests had a slightly rough surface of the uncovered film (Figure 3a. SEM images under magnification of 200\(\times\) and 500\(\times\)). As was emphasized in Figure 3b-d, active coatings (Zn, Ut, and Fb) had no effect on PP surface morphology. In addition, it was noted that PP film was homogenously and thoroughly coated with the active layers (covered foil samples micrographs reflected uncovered foil samples). Additionally, the Zn covering was not visible when compared to the Ut and Fb coatings. The covering of homogenous PP film with Zn, Ut, and Fb coatings can have an effect on the release of active agents on the whole layer surface of the food product. As was shown in the Figure 3d, convex, spherical particles were noted on the Fb layer. Additionally, as noted in Figure 3c, small pores were seen on the Ut coating.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**The Sample**} & \multicolumn{2}{c}{**The Number of Bacterial Cells [CFU/g]**} \\  & _S. aureus_ & _E. coli_ & _B. atrophaeus_ \\ \hline PP & \(4.05\times 10^{6}\pm 4.87\times 10^{5}\) * & \(9.05\times 10^{7}\pm 1.6\times 10^{6}\) * & \(7.73\times 10^{5}\pm 1.14\times 10^{5}\) * \\ Zn & 0 **** & 0 **** & 0 **** \\ Ut & 0 **** & 0 **** & 0 **** \\ Pb & 0 **** & 0 **** & 0 **** \\ \hline \hline \end{tabular}  
*–Standard Deviation; One-way ANOVA test: ****-\(p<0.0001\).  
\end{table}
Table 1: The influence of coatings on _S. aureus_, _E. coli_ and _B. syringae_ growth.  
Figure 2: The influence of coatings on _P. syringae_ growth. PP—PP film; Zn—PP film covered with the coating containing 0.06 g ZnO nanoparticles in 100 mL carrier; Ut—PP film covered with the coating containing 0.03 g of ZnO nanoparticles and _U. tomentosa_ extract in 100 mL carrier; Fb—PP film covered with the coating containing 0.03 g of ZnO nanoparticles and _F. betulina_ extract in 100 mL carrier; One-way ANOVA; ****-\(p<0.0001\).  
Figure 3: Cont.
# An Efficient Opportunistic Routing Protocol with Low Latency for Farm Wireless Sensor Networks
## 5 Experiment and Analysis
### Retransmission Rate Analysis  
As the number of dead nodes increases, the retransmission rates of the five opportunistic routing algorithms all increase. Figure 9 shows the difference between the four comparison algorithms minus the retransmission rate of the algorithm in this paper. This algorithm considers the influence of nodes on network connectivity. The opportunistic routing not only focuses on the quality of the current forwarding path but also considers the long-term transmission quality of the network.  
It can be seen from Figure 10 that the median retransmission rate of our algorithm is low, which means that the retransmission times are relatively stable in the whole simulation cycle. At the same time, the median is low, so in the early stage network with sufficient energy, efficient data transmission can be realized. As shown in Figure 11, this algorithm has a low retransmission rate under the different number of nodes.  
Figure 8: End-to-end average delay with a different number of nodes.  
Figure 7: Box graph of end-to-end delay.
# Energy Optimisation of Industrial Limestone Grinding Using ANN
## 5 Evaluation Indicators and Process Parameters  
To improve the efficiency of the limestone grinding and classification circuit, it is essential to develop robust mathematical models supported by quantitative evaluation indicators. Considering both economic and technological criteria, two key energy indicators were formulated to assess the performance of the system.  
The first of these indicators is the specific electric energy consumption (\(S\)), which quantifies the ratio between the total power consumed by the main drive units--namely, the mill and the main fan--and the mass flow rate of the limestone (1). It is defined as follows:  
\[S=\frac{P_{\text{mill}}+P_{\text{fan}}}{\dot{m}_{\text{feed}}}\quad\left[ \frac{\text{kWh}}{\text{Mg}}\right] \tag{1}\]  
Figure 5: Electricity consumption by process.  
To further enhance the precision of energy performance evaluation, a second indicator (\(W\)) was introduced (2). This modified index additionally incorporates the gas consumption of the mill, represented by the relative load of the gas burner:  
\[W=\frac{P_{\text{mill}}+P_{\text{fan}}}{\dot{m}_{\text{feed}}}\cdot\eta_{\text{ burner}}\quad\left[\frac{\text{kWh}}{\text{Mg}}\right] \tag{2}\]  
where:  
* \(S\): Specific electric energy consumption [kWh/Mg].
* \(W\): Modified energy consumption indicator accounting for burner load [kWh/Mg].
* \(P_{\text{mill}}\): Mill motor power consumption [kW].
* \(P_{\text{fan}}\): Main fan motor power consumption [kW].
* \(\dot{m}_{\text{feed}}\): Limestone feed mass flow rate [Mg/h].
* \(\eta_{\text{burner}}\): Relative load of the gas burner [%].  
The objective of process optimisation is to minimize both indicators, thereby reducing energy intensity while maintaining or improving technological performance.  
The modelling of these indicators was based on real-time process data collected by the SCADA (Supervisory Control and Data Acquisition) system implemented in the KW Czatkowice grinding plant.
# Analysis of the Most Relevant Factors for Routing in Internet of Space Things Networks
## 3 Methodology
### Base Experiment  
The space network mission for the base experiment considers the potential offered by ad hoc IoST networking to provide connectivity for underserved areas during natural or artificial disasters [62]. The base experiment assumes that a remotely located node sends information to an urban center through an ad hoc IoST network. The remote node is located in the Pacific (e.g., buoy) at coordinates (9\({}^{\circ}\)35\({}^{\prime}\)42.0\({}^{\prime\prime}\) N, 95\({}^{\circ}\)08\({}^{\prime}\)37.2\({}^{\prime\prime}\) W) for the implementation. The sink is located (31\({}^{\circ}\)52\({}^{\prime}\)07.7\({}^{\prime\prime}\) N, 116\({}^{\circ}\)39\({}^{\prime}\)59.1\({}^{\prime\prime}\) W) in an urban center. This scenario arises in early warning systems where an open ocean buoy transmits relevant weather and tide information to a coastal urban center. Depending on the deployment location, the ad hoc IoST network could serve as a primary or backup communication mechanism.  
The \"IoST Configuration files\" generated as the output of this submodule contain the parameters that define the mission scenario to be executed in the IoST Simulator. The most relevant configuration parameters for this module are the number of groups (G), the number of members of each group (N), and the number of simulation days (M).
# High Resolution Imaging Using Micro-Objectives Fabricated by 2-Photon-Polymerization
###### Abstract  
We experimentally demonstrate high-resolution imaging using micro-objectives fabricated by two-photon polymerization (2 PP) lithography, highlighting its potential as a flexible and precise fabrication method. The 2 PP manufacturing process offers the ability to develop micro-optics with customized geometries and material properties, including tailored refractive indices. This flexibility introduces new degrees of freedom in optical system design and expands the applicability of micro-optics to advanced imaging tasks where other materials and fabrication methods are insufficient. For our study, bi-convex micro-optics with different geometries with radii of curvature of \(<\)15 \(\upmu\)m and minimized contact areas (\(<\)1 \(\upmu\)m\({}^{2}\)) to ensure easy release from the substrate were fabricated with 2 PP and investigated for their optical performance. With these micro-optics, the tracks with a pitch of 320 nm and the pits and lands as small as 130 nm were successfully resolved on a BluRay disc surface, demonstrating for the first time the high-resolution imaging capabilities of bi-convex spherical micro lenses.  
high resolution imaging; micro-spheres; 2-photon-polymerization 2025