esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Environmental Microbiology  
Recent advances in understanding of the role of microbial communities in subterranean ecosystems, namely their contribution to biogeochemical processes affecting the composition of the subterranean atmosphere and the availability of nutrients is reviewed by [PERSON] et al. [6]. These bacterial communities have been found to actively contribute to the uptake-fixation-production of CO2, CH4, and NxOx gases. In addition, the bacteria from these still poorly explored niches display potential to serve as producers of bioactive compounds with antibacterial, antifungal, or anticancer activities, as well as enzymes with biotechnological applications. Moreover, methanotrophs and heterotrophs in subterranean environments can interact and stimulate each other's growth, and their growth is associated with the production of bioactive compounds. The advent of massive sequencing technologies and the omics sciences have brought to bear important tools for continued exploration of the potential of biological processes in subterranean ecosystems.  
Taking into consideration the origin and proportion of wastewater effluents (urban, hospital, livestock farms, or industrial), [PERSON] et al. [7] do an analysis of the efficiency of wastewater treatment plants (WWTPs) in reducing the prevalence of antibiotic resistance genes (ARGs). They also examine the effect of the treated wastewater on the receiving water bodies (rivers). Their first conclusion is that the prevalence of ARGs in raw wastewater is affected by the origin of the sewage. For example, agricultural sewage containing animal waste is associated with a higher abundance of the _sul2_ gene, while hospital wastewater appears to be the main source of the _sul1_ gene. The authors also conclude that WWTPs are important sources of dissemination of ARGs to receiving water bodies. Metagenomic and metatranscriptomic analyses will be applied to these samples to increase knowledge about system dynamics, to develop effective strategies for managing risks, and to evaluate effects on human health.  
In some natural ecosystems, invasion by exotic plants poses a threat to the equilibrium of the environment. For this reason, [PERSON] et al. [8] studied the influence of Alternanthephiloxeroide (alligator weed) invasion on wetland organic matter accumulation and bacterial community changes. Samples were collected from two sediment depths (0-15 and 15-25 cm) in invaded and normal habitats in two natural wetlands and two constructed wetlands. They observed that the contents of the light fractions of organic carbon and nitrogen were significantly higher in the constructed wetlands than in the natural wetlands, and the contents of the C and N fractions were also higher in the invaded areas than in the normal wetland habitats. The presence of the invasive weed also correlated with changes in the wetlands' microbiota. Although _Proteobacteria_, _Bacteroidetes_, _Acidobacteria_,and _Chloroflexi_ were the dominant bacterial phyla in the wetlands, the abundance of _Acidobacteria_, _Actinobacteria_, and _Gemmatimonadetes_ was significantly higher in invaded areas.
# Opportunistic and On-Demand Network Coding-Based Solutions for LPWAN Forwarding
## 3 Challenges and Motivation
### Reliability in Uncertain Wireless Conditions  
As the wireless link is unstable, Figure 3 shows the possible transmission and reception between an end-device, several relays, and a gateway in LPWAN cases. Let us suppose that R1 and R2 receive the message, while R3 fails owing to the propagation problem. If fixed-path routing assumes that R3 is the best forwarder, it is not delivered to the destination. Even if the gateway can acknowledge the loss packet and request retransmission, commonly, the request message has to be forwarded via R3 during the end-device's receiving window period. This kind of process is inefficient.  
We employ opportunistic forwarding that allows any relay to forward an overheard packet via priority mechanisms to prevent packet duplication between the relays. According to Figure 3, R2 can forward the packet after noticing that R3 of higher priority, fails to deliver the packet. Therefore, we can achieve better reliability and throughput than that of fixed-path forwarding.
# Matched Filtering for MIMO Coherent Optical Communications with Mode-Dependent Loss Channels
## 5 Numerical Simulation of Linear MIMO FSE Receiver for MDL-Impaired Optical Channel
### Signal-to-Noise at the Input of the Receiver  
The signal-to-noise ratio at the input of each mode \(\left(\frac{S}{N}\right)_{in}(i)\) for \(i\in\{0,\ldots,D\}\) is defined as:  
\[\left(\frac{S}{N}\right)_{in}(i)=\frac{P_{in}(i)}{N_{0}/2}. \tag{40}\]  
The signal-to-noise at the input of the receiver \(\overline{SNR}_{in}\) in dB can be written as:  
\[\overline{SNR}_{in}=10\cdot\log_{10}\left(\frac{\frac{1}{D}\cdot\sum_{i=1}^{D }P_{in}(i)}{N_{0}/2}\right)=10\cdot\log_{10}\left(\frac{1}{D}\cdot\sum_{i=1}^ {D}\left(\frac{S}{N}\right)_{in}(i)\right), \tag{41}\]  
and it is taken from the set of values in Table 1. \(P_{in}(i)\) is the receiver input power in the mode \(i\) for the current channel realization.
# An Active Path-Associated Cache Scheme for Mobile Scenes
## 5 Simulation Results and Analysis
### Latency  
Latency is the average time it takes for us to record the requested content to reach the user. This indicator is one of the key indicators for measuring the performance of the cache solution, and the delay is related to the user experience.  
Figure 8: Performance of Latency.  
Figure 7: Performance of Cache Hit Ratio.  
We adjust the range of Alpha from 0.6 to 1.2 according to the settings in Table 2. From Figure (d)d-f, it can be seen that the delay of all schemes is significantly reduced. For larger Alpha, the content that users need is usually more concentrated, and our strategy adapts the content at the edge of the network to these behaviors and caches, thereby greatly reducing the transmission delay. Experiments show that our strategy is significantly better than other programs.  
It can be seen from Figure (a)a-c that as the cache space increases, the request delay decreases more and more slowly. The slight optimization delay has a huge impact on the user experience improvement. Our strategy maintains the best performance in three different network topologies. Compared with EDGE, a typical scheme of active caching, MINE shows considerable superiority. Furthermore, from the trend of EDGE delay performance, we have reason to believe that even if the cache space continues to increase, the performance of EDGE is difficult to maintain at a high level. Compared with some of the best-performing caching solutions, MINE has also achieved significant improvements.
# Non-Autoregressive Sparse Transformer Networks for Pedestrian Trajectory Prediction
## 4 Experiments
### Visualization
#### 4.5.2 Sparse Directed Interaction Visualization  
NaST can successfully extract the sparse social interaction of a crowd. We visualized the sparse directed interactions in different scenes, as shown in Figure 7. The images in the top row are different scenes in the ETH [29] and UCY datasets [30]. The sparse directed interaction graphs of each scene are shown in the bottom row. In the scene images, the solid lines represent the observed trajectories, the colored dots indicate the current location, and the dash lines represent the trajectories in the future. Two graphs under each scene are the corresponding sparse directed interaction graphs, which illustrate that one pedestrian is only influenced by a part of the surrounding neighbors. In Figure 7a, the red pedestrian is only influenced by the blue and green ones (the interaction is represented by the directed edges in the left sparse directed interaction graph in Figure 7a). However, the degree of influence from the two neighbors to the red node is different: the thickness of the edges is proportional to the interaction weights. From the left sparse directed interaction graph in Figure 7a, we can see that the influence from the blue node to the red is much larger than from the green one. The right sparse directed interaction graph in Figure 7a shows the influence from the yellow and red nodes to the blue one. In addition, the two sparse directed interaction graphs in Figure 7a illustrate that the interaction between the red and blue nodes is different to each other. It is also demonstrated in Figure 7c that the red and blue pedestrians head from the opposite direction, the red one is influenced by the green and blue pedestrians, and the blue one is also influenced by the red pedestrian. However, the influence between the red and blue pedestrians is not the same degree to each other. When they meet, the red pedestrian is walking straight and keeps their direction without change, and the blue one detours to avoid collision. Therefore, the interaction weight from the red pedestrian to the blue one is larger than blue to red. It is confirmed in the directed graph that the edge from red node to the blue node is much thicker than that from blue to red.  
Figure 7: Sparse direct interaction visualization in different scenes. Solid lines represent observed trajectories, colored dots indicate the current location, and the dash lines represent the trajectories in the future. **(a)** The scene in ZARA1. **(b)** The scene in ETH. **(c)** The scene in HOTEL.
# A Survey on Energy-Efficient Design for Federated Learning over Wireless Networks
## 5 Improving Energy Efficiency in FL Using Emerging Technologies
### Nonorthogonal Multiple Access  
NOMA is a technique that enhances bandwidth efficiency and interoperability in future networks, especially for 6G IoT systems [135; 136]. Unlike orthogonal multiple access, NOMA enables multiple users to share the same time-frequency resources simultaneously, significantly boosting wireless system performance. The integration of NOMA into FL has demonstrated significant potential for improving FL frameworks in wireless networks, particularly when paired with emerging approaches such as resource allocation strategies [137], dynamic user selection [138], IRS [139], and CSI techniques [140]. Moreover, with a focus on energy-efficient FL, studies in [141; 142; 143; 102] have provided comprehensive insights into implementing energy-efficient FL within NOMA networks, highlighting their ability to significantly enhance FL performance.  
In [142], the authors proposed a joint resource allocation scheme using a two-dimensional search algorithm and an efficient bisection algorithm to minimize the total energy consumption in wireless-powered FL networks utilizing NOMA. The study in [141] presented energy minimization in IoT-based CFL by combining WPT, NOMA, and IRS. The proposed method utilizes semidefinite programming and a bisection algorithm, outperforming benchmark schemes in terms of energy efficiency. Integrating NOMA into the UAV systems demonstrated substantial improvements in energy-efficient FL. For instance, the authors in [143] investigated energy reduction strategies in MEC systems leveraging NOMA with UAVs, overcoming challenges posed by dynamic 5G and beyond networks, such as frequent changes in task requests and device positions that would typically increase energy consumption with stationary edge deployments.  
NOMA techniques also proved effective in satellite communications, facilitating rapid, bandwidth-efficient model transmissions among satellites, and thus accelerating the FL training process. In [102], the authors introduced NomaFedHAP, a novel approach for integrating NOMA into satellite communications NOMA to speed up CFL in LEO satellites. By utilizing HAPSs as parameter servers, the approach mitigates mitigates Doppler shifts and optimizes model aggregation. Simulations showed that NomaFedHAP achieves faster and more accurate CFL convergence compared with existing methods.
# Optical Lensless-Camera Communications Aided by Neural Network
## 2 Proposed Scheme
### Image Decoding Algorithm
#### 2.1.1 Image Preprocessing  
In actual applications, the LED transmitter may use colored or white LED bulbs to transmit different types of signals (e.g., warning or danger signals). Any image can be decomposed into three component images _(Y, Cb, and Cr)_[10]. Different component images from different light sources can result in a different decoding effect in different environments. Accordingly, we first selected the appropriate component to train the neural network, then performed decoding. Figure 3a is the analysis of the \"off\" image of the illumination LED without ambient light interference. Figure 3b is the analysis of the \"on\" image of the illumination LED without ambient light interference. Figure 3c is the analysis of the \"on\" image of the blue LED without ambient light interference. Figure 3d is the analysis of the \"on\" image of the illumination LED with blue ambient light interference.  
By comparing the \(Y\), \(Cb\), \(Cr\) components of the captured images in different environments, we determined that we should choose the right component for a training neural network in different environments. After comparing the \(Y\), \(Cb\) and \(Cr\) components of Figure 3a,b as well as Figure 3a,d, we found that, in these two cases, we should choose \(Y\) component for training. After comparing Figure 3a,c, we determined that the \(Cb\) component should be selected for training in this case.  
Figure 2: Comparisons of images captured by different cameras: (a) “on” image captured by a camera with lens; (b) “off” image captured by a camera with lens; (c) “on” image captured by a lensless camera; (d) “off” image captured by a lensless camera.
# Crack Defect Detection Method for Plunge Pool Corridor Based on the Improved U-Net Network
## 4 Experiment
### Comparative Experiment  
This paper verifies the superiority of the improved model by comparing three segmentation models: U-Net, DeepLabV3+ [36], U2 Net [37], SegFormer [38], and CrackSegNet [39]. The figure below shows the detection results of U-Net, DeepLabV3+, U2 Net, and the improved model on the plunge pool corridor crack dataset. The visual comparison is shown in Figure 10, The red box represents a relatively large difference, the comparison of evaluation metrics are shown in Table 1.  
In Figure 10, the U-Net and DeepLabV3+ models show little overall difference in performance within this dataset. Nevertheless, DeepLabV3+ uses spatial pyramid pooling, allowing it to achieve a larger receptive field. As a result, it performs better in crack-feature extraction, with fewer occurrences of crack breakpoints, undetected cracks, and misiden  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **MIOU (\%)** & **MPA (\%)** & **F1-Score (\%)** & **Accuracy (\%)** \\ \hline U-Net & 79.1 & 87.15 & 84.36 & 99.1 \\ DeepLabV3+ & 80.5 & 88.04 & 84.79 & 99.2 \\ U2 Net & 82.11 & 89.45 & 85.52 & 99.11 \\ SegFormer & 82.64 & 89.69. & 85.78 & 99.32 \\ CrackSegNet & 81.37 & 88.53 & 84.94 & 99.23 \\ Ours & 84.44 & 92.05 & 87.75 & 99.46 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparative experiments of different detection models.  
tifications. By introducing the residual module, U2 Net can acquire more diverse crack features; SegFormer integrates the transformer with a lightweight multi-layer perceptron (MLP) decoder, allowing the encoder to produce high-resolution fine-grained features and low-resolution coarse-grained features; and the CrackSegNet decoder incorporates a spatial attention module to improve the feature representation of cracks. Yet there are still instances of crack breakpoints, undetected cracks, and misidentifications. In comparison to the previous models, the improved U-Net network model in this paper can capture crack features more effectively, and there is a significant reduction in the number of crack breakpoints, misidentifications, and undetected cracks.  
By calculating the four metrics of MIOU, MPA, F1-score, and accuracy for the test dataset, the statistical results are presented in the table below. The U-Net and DeepLabV3+ models exhibit similar performances, and U2 Net shows an improvement compared to the previous two. Compared with the pre-improvement U-Net model, the improved model in this paper has an increase of 5.34% in MIOU, 4.90% in MPA, 3.39% in F1-score, and 0.36% in accuracy. This shows that the improved model has improved in terms of both the extraction of crack features in different channel dimensions and the handling of the features of irregular shapes.
# A Label-Free Aptamer-Based Fluorescent Assay for Cadmium Detection
## Results and Discussion
### Detection Selectivity  
The selectivity of the method was evaluated by monitoring the fluorescence response when the system was challenged with other metal ions (Na\({}^{+}\), Cu\({}^{2+}\), Mg\({}^{2+}\), K\({}^{+}\), As\({}^{3+}\), Ca\({}^{2+}\), Hg\({}^{2+}\), Zn\({}^{2+}\), and Pb\({}^{2+}\)) at 0 ng/mL, 1 ng/mL, 10 ng/mL, 1 ug/mL, and 1000 ug/mL. Compared with background (0 ng/mL), there was very little change in \(F/F_{0}\) on exposure to these metal ions. However, a large decrease in \(F/F_{0}\) was induced by Cd\({}^{2+}\) (Figure 3). These results show that the method has good selectivity for Cd\({}^{2+}\) over other metal ions.
# Survivable Deployments of Optical Sensor Networks against Multiple Failures and Disasters: A Survey
## 3 Sensing Connectivity and K-Node (Edge) Sensing Connectivity
### Disaster-Resilient Survivability Requirement  
Due to wide distribution and high severity, natural disasters are big threats to normal operation of OSNs. Natural disasters typically and frequently occur in countries with large territories, such as Russia, Canada, China, and the USA. Using China as an example, the types of natural disasters mainly include earthquake, landslide, mud-rock flow, flood, volcano, rainstorm, land freezing and thawing, snowstorm, altitude cold, and high temperature. Nowadays, the quality of the global environment continues to deteriorate and natural disasters occur frequently. For example, in 2008, the 7.8-magnitude Wenchuan earthquake in China led to around 30,000 km of cut fiber optic cables and 4000 telecom offices becoming ineffective. Rescuers could only use satellite to contact the outside world, which brought great difficulty to rescue work. Consequently, the disaster-resilient sensing and survivability of OSNs has attracted more and more attention. Table 5 presents the characteristics of different kinds of natural disasters. A disaster having wide distribution, high probability, and high severity can seriously influence the normal operation of OSNs. Therefore, all disasters can be divided into several groups according to their degree of influence. To reduce the influence of natural disasters, we defined the following regulations of light-path protection and data center backup. These regulations can help design disaster-resilient OSNs.  
(1) For natural disasters with a wide affected region, low probability, and high severity, all working and backup light paths should avoid passing through a common disaster area.  
(2) For natural disasters with a narrow affected region, high probability, and low severity, the number of passing disaster areas of a working light path or its backup light paths should not exceed a given upper bound.  
(3) For natural disasters with a wide affected region, low probability, and low severity, the number of passing disaster areas of a working light path or backup light paths should not exceed a given upper bound.  
(4) For natural disasters with a wide affected region, high probability, and low severity, all light paths and data centers should avoid passing through a common disaster zone.
## 3 Results and Discussion
### Mechanical Properties
The mentioned values of the dissociation energy are related to the standard heat of formation (enthalpy) for each constituting compound, which is described by Equation (2). The theoretical results of elastic moduli (Young's, bulk, shear, and longitudinal) are also listed in Table 2. As mentioned in Equations (3)-(6), the elastic moduli are directly proportional to \(V_{t}\) and \(\rm G_{t}\). In the present study, this is true for dissociation energy, where the elastic moduli are directly proportional to the \(\rm G_{t}\), while being inversely proportional to the packing factor \(V_{i}\) for all the studied glasses. Figure 3 depicts the variation of elastic moduli against the Sb\({}_{2}\)O\({}_{3}\) contents. The estimated values of elastic moduli demonstrated a strong dependence on the oxides' composition in the SCNSb glass network. Young's, bulk, shear, and longitudinal moduli increase proportionally with the Sb\({}_{2}\)O\({}_{3}\) content. The straight red lines in Figure 3 represent the linear fitting of the calculated results, where the values of R-squared are 0.962, 0.812, 0.971, and 0.920 for \(Y\), \(B\), \(S\), and \(L\) moduli, respectively. As mentioned in Equation (8), [PERSON]'s ratio is directly proportional to [PERSON]'s modulus. Thus, Figure 4 depicts that [PERSON]'s ratio (\(\mu\)) for the chosen samples decreases with an increase in the Sb\({}_{2}\)O\({}_{3}\) content. The calculated results showed that [PERSON]'s ratio reduced from 0.2450 to 0.2421 as the content of Sb\({}_{2}\)O\({}_{3}\) changed from 0 to 10 mol%. In comparison, the micro-hardness increases gradually with an increase in the Sb\({}_{2}\)O\({}_{3}\) content. It is increased from 4.758 to 5.091 GPa with an increase in the Sb\({}_{2}\)O\({}_{3}\) content between 0 and 10 mol%. The reason for this increase is the inverse proportionality between the micro-hardness and Poisson's ratio (see Equation (9)). The softening temperature (\(T_{g}\)) is a critical temperature in which the glass transition takes place. The \(T_{g}\) for the studied SCNSb samples is shown in Figure 5. The calculated values of \(T_{g}\) decreased gradually with the insertion of Sb\({}_{2}\)O\({}_{3}\) content. The \(T_{g}\) values decreased from 524.39 to 506.031 \({}^{\circ}\)C. This was due to the replacement of SiO\({}_{2}\), which has a high melting temperature (T = 1710 \({}^{\circ}\)C), with Sb\({}_{2}\)O\({}_{3}\), which has a lower melting temperature (T = 656 \({}^{\circ}\)C). The obtained results agreed with reference [28] reported by [PERSON]. [PERSON] concluded that for alkaline-based silicate glasses, the oxygen and metal atoms in the alkaline structure are held together by ionic bonds rather than covalent bonds. The metal atoms lose electrons while the oxygen atom takes these electrons. Figure 2: Variation of the dissociation energy per unit volume and the packing density of the studied glasses as a function of Sb\({}_{2}\)O\({}_{3}\) contents.