esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract
Environmental reconnaissance has thereafter gained importance in the last few decades [23; 24; 25]. Over the years, it has been realized that the diversity and complexity of an environmental habitat is unmatched and therefore following the traditional practices might limit our understanding of the ecological resistance. A universal approach is desirable to concurrently identify both the infectious and non-infectious carriers present within a system. Several molecular assays were developed and a high-throughput quantitative polymerase chain reaction (HT-qPCR) has been emphasized as one of the most efficient genome-centric approaches to map the entire resistance of a territory [26; 27; 28]. The popularity of HT-qPCR is due to its ability to capture environmental concentrations of ARGs in real time. The multiplex primers add more benefit by mapping hundreds of genes, mobile genetic elements (plasmids and class 1, 2, and 3 integrins), 165 rRNA genes (bacterial copies), and the WHO-recommended taxonomic groups simultaneously in a single run [26; 29]. Although widely used, the HT-qPCR is limited in picking low abundance genes (>10 copies) and unknown genes. The environmental samples are very challenging and getting reasonable quantifiable material from these settings is difficult. This is recently being compensated for with the use of next-generation sequencing. The shotgun metagenomic (SMS) method is nowadays considered a gold standard in terms of mapping the entire resistance [30; 31; 32; 33; 34; 35; 36; 37; 38; 39; 40]. Not only does it provide information on all the genes present within the milieu but it also captures cryptic genes. Once the sequence data are available, taxonomic, functional, and resistance profiling can be conducted simultaneously. Higher sequencing coverage allows the filtering of low-abundance genes. Researchers have started applying SMS [37; 38; 41], but it is relatively expensive and requires high bioinformatics skills to derive meaningful conclusions. However, owing to its popularity, regular technical upgrades are being conducted, and several new platforms are being made available. The cost is also getting reduced with time. To compare the efficacy of the two methods, in the present investigation, we employed the high throughput quantitative PCR (HT-qPCR) method to identify selected antibiotic-resistant genes and the associated mobile genetic elements from marine sediments receiving effluent discharges along the urban coast of Kuwait. We also compared the output of the HT-qPCR with the results of the shotgun metagenomic sequencing (SMS).
## 10 Research Challenges and Future Directions
### Scalability and Cost Trade-Offs  
While AI annotation agents often prove more economical than human annotators across numerous use cases, the computational requirements of state-of-the-art LLMs introduce non-trivial cost considerations, particularly when processing millions of instances. API usage expenses increase rapidly, and deploying large models on-premises requires substantial infrastructure investment. Organizations face the challenge of optimizing the balance between cost and quality. On the other hand, smaller or open-source models fine-tuned for specific annotation tasks provide cost benefits, but these often come with reduced accuracy compared to more capable models. One effective strategy involves using advanced models to annotate a subset of training data, followed by fine-tuning specialized, more efficient annotation models for broader deployment. Active research investigates how to distill the annotation capabilities of frontier LLMs into compact, task-specific models. Latency also becomes a key factor in real-time or interactive annotation settings, where multi-second processing per item creates operational bottlenecks.  
Quantitative analyses highlight stark differences in deployment strategies. For instance, cloud-based deployments (e.g., via APIs like OpenAI's GPT-4) can incur high ongoing costs, with estimates showing daily operational expenses exceeding CNY 700,000 for large-scale LLM services due to reliance on expensive GPUs [172]. In contrast, on-premise or local deployments using open-source models (e.g., Llama variants on dedicated hardware) offer upfront infrastructure costs but can reduce long-term expenses by up to 46% through better control over resources and avoidance of per-query fees [173]. Hybrid approaches, such as local-cloud offloading, further optimize this by running lightweight models on-device for simple tasks and offloading complex ones to the cloud, achieving latency reductions of 50-80% and cost savings of up to 89% in serverless setups [174]. Future research should focus on implementing cascade architectures that apply lightweight heuristics or efficient models for simple decisions, while reserving high-compute LLMs for more complex cases. Model inference (e.g., quantization and model pruning) is a notable direction to reduce memory footprint by accelerating inference speeds and enabling deployment on resource-constrained devices without significant loss in accuracy [175; 176].
## 3 Proposed System
The spoofing node can be detected only by cross checking its declared ID with the classification result since each class is labeled with a specific node ID. The probability that a unauthorized node is classified as authorized, named the _probability of miss spoofing detection_, \(P_{msd}\) is the probability that an unauthorized node claiming the \(i\)-th ID is classified as belonging to the \(i\)-th class. The basic idea is that a spoofing node cannot know how the sink node will classify its signal; hence, even if it is able to steal a valid ID, likely this ID will not correspond to the classification output. This probability increases as the number of authorized nodes in the network increases. We underline that \(P_{ban}\) directly derives from the ML algorithm. Indeed, denoting with \(P(i,j)\) the probability that the predicted class is \(j\) when the true class is \(i\) (see the confusion matrix in Figure 3), we have that \(P_{ban}=\sum_{i=1}^{N}\sum_{j=1\
eq i}^{N}\frac{1}{N(N-1)}P(i,j)=1-\sum_{i=1}^ {N}\frac{1}{N}P(i,i)=1-Accuracy\). Hence, the \(accuracy\) is the probability that a node is correctly classified within the class labeled with its ID. Figure 2: Flow diagram of the proposed approach. Conversely, \(P_{msd}\) does not directly derive from the ML algorithm; indeed, it depends on the probability of selecting a given ID that decreases as \(N\) increases. We want to stress that the proposed method represents an additional level of security (in addition to the first authentication step) especially for low-complexity nodes, where complex encryption algorithms cannot be executed. In particular, (i) high-level authentication can be only used to assign a unique ID to the node, then during communication the reliability of the received data is related to the outcome of the proposed method since encryption is not used, and (ii) high-level authentication provides both an unique ID and a secret key that can be used in successive encrypted communications. In this case, the proposed approach is an additional security level that avoids spoofing, even if the secret key is detected by the attacker, especially in the presence of low-robust encryption algorithms.
# Optical Design and Stray Light Analysis of Underwater Spectral Radiometer
## 4 Stray Light Suppression and Simulation Analysis
### Stray Light Suppression Methods  
According to the grating diffraction equation, the second-order diffraction spectrum in the 400-475 nm range overlaps with the first-order diffraction spectrum in the 800-950 nm range, sharing the same diffraction angle, and directly falls onto the image plane of the detector, Figure 10a shows the spectral overlap on the detector. To mitigate the interference from this portion of stray light, based on the optical system simulation results, the distribution area where the second-order diffraction spectrum overlaps on the detector surface was determined, and the specific mapping positions of wavelengths on the detector are illustrated in Figure 10b. A long-wave bandpass film was applied to the optical window surface corresponding to this area to eliminate the impact of the short-wave second-order spectra.  
Figure 9: Stray light-to-signal electron ratio and its relationship with signal-to-noise ratio.  
Although the light of other diffraction orders and the remaining spectrum of the second-order diffraction spectrum cannot directly reach the detector surface, a considerable part of the light will still reach the detector surface after repeated reflections from the optical and mechanical components of the spectrometer. Through optical tracing, the key surface of the source of stray light in the optical system can be found, and the extinction thread is designed to roughen the mechanical surface, so as to reflect or scatter on the mechanical surface to prevent stray light from falling on the detector surface [20]. The physical picture of the extinction thread and the principle diagram of the extinction screw thread to suppress stray light are shown in Figure 11.  
In order to further prevent stray light from entering the optical system, several light baffles (light stops) are installed within the shading hood. The function of the light baffle is to completely eliminate primary stray light. The stray light entering the light baffle is blocked and absorbed by the inner walls, while the remaining stray light is reflected into adjacent light baffles to block it. In this way, primary stray light is completely eliminated before entering the optical lens. The light baffles and the walls of the shading hood together form a semi-enclosed light trap, which is used to block the transmission path of indirect stray light (reflected light, scattered light) and reduce the transmission energy multiple times. This is particularly effective for stray light at large angles outside the axis [21]. Figure 12b is a schematic diagram of light ray tracing after adding the light-shielding ring.  
Figure 11: (a) Design of the structure for the extinction screw; (b) Schematic diagram of the extinction screw.  
Figure 10: (a) The phenomenon of overlapping second-order spectra in a grating; (b) Mapping of wavelengths onto the detector.
# Fertility Detection of Hitching Eggs Based on a Convolutional Neural Network
## 3 Experiment and Results Analysis
### Comparison with Other Methods  
To illustrate the feasibility of our method, we compared our method with state-of-the-art methods on the classification performance of 9-days-after hatching eggs. Restricted by hardware conditions and the differences in datasets used by various methods, we cannot reproduce these methods on our datasets. Since the dataset used in our method is the heartbeat signal of hatching eggs, which is different from the dataset used in other methods, and we lack the public dataset, thus we only compared the performance on their own datasets. Each approach has its own feature extraction method, so we also make the comparison on the methods of feature extraction. The comparison of experimental results is listed in Table 8.  
From the comparison with state-of-the-art methods, we can find some advantages of our method. Among these methods, the method of TB-CNN achieved multi-classification and made a great breakthrough on classification accuracy by using the CNN structure which is powerful and effective to extract blood vessel features. However, this method requires complex vascular image labeling before training; besides this, it also needs to further validate the effectiveness of hatching eggs detection in other periods. The near infrared hyperspectral imaging system is based on the connection of the InGaAs camera and line scanning spectrometer, which has a high cost. The method proposed in reference [7] not only is highly robust on image noise, eggshell color, and dirty spots on the eggshell, but also meets the needs of mass production both in detection accuracy rate and executive speed. However, the two methods mentioned above have difficulty in extracting some thin blood-vessel features of hatching eggs' vascular image, which infunences the accuracy of the method. By contrast, the feature we used in this paper, the heartbeat signal of hatching eggs, is simple and we have a lower cost of data acquisition devices. The method of visible transmission spectroscopy combined with multivariate analysis is developed and has the potential to be used for the detection of infertile eggs in commercial hatchery operations with its high detection accuracy. However, the transmission spectroscopy changes with the kinds of hatching eggs, therefore, the effectiveness of multivariate analysis remains to be verified on large-scale datasets of hatching eggs. Our method used the dataset of various periods of hatching eggs, and the heartbeat feature of hatching eggs is stable, which is beneficial for our CNN to extract effective features for classification. Our method is superior to the other methods mentioned above in detection accuracy and algorithm robustness. which benefits from the combination of the CNN model that we designed and the heartbeat signal of hatching eggs.
# A Comparative Analysis for Optimizing Machine Learning Model Deployment in IoT Devices
## 4 Result Analysis
### Validation of the Green Carbon Footprint Algorithm
#### 4.3.1 Determining Conversion Factors  
The conversion factors, namely, the Power to Carbon Conversion Factor (PGCF), Memory to Carbon Conversion Factor (MGCF), and a factor for the Temperature Cost (based on CPU temperature), are derived from a combination of industry standards, published research, and empirical data specific to the computational resources being assessed. This multi-source approach ensures a robust basis for the conversions, reflecting variations in energy sources, the efficiency of memory use, and the thermal characteristics of computing equipment.  
* Power to Carbon Conversion Factor (PGCF): This factor converts the energy consumed (in kWh) by the system into grams of a CO2 equivalent. It is influenced by the regional energy mix and the efficiency of the power supply. To establish this factor, data from local utility companies and renewable energy contributions are considered.
* Memory to Carbon Conversion Factor (MGCF): Memory utilization is converted to carbon emissions based on the energy cost of memory operations and the production impact of the memory hardware. This factor accounts for the embodied carbon of producing and maintaining memory hardware, amortized over its operational lifespan.
* Temperature Cost Conversion: The CPU temperature directly affects the system's energy efficiency and cooling requirements. A higher temperature indicates more energy-intensive cooling, translating to higher emissions. A mathematical model is developed to relate CPU temperature to carbon output, using empirical data from thermal performance studies.
# Modeling Acoustic Emission Due to an Internal Point Source in Circular Cylindrical Structures
###### Abstract  
In one of our previous publications, we developed the first mathematical model for acoustic emission from an internal point source in a transversely isotropic cylinder. The point source, as an internal defect, is the most fundamental source generating AE in homogeneous media; it is represented by a spatiotemporal concentrated force and generates three scalar potentials for compressional, and horizontally and vertically polarized shear waves. The mathematical formulas for the displacements were derived by introducing the concentrated force-incorporated potentials into the Navier-Lame equation. Since the publication of that paper, we detected some errors. In this paper, we correct the errors and extend the analytical modeling to a cylindrical shell structure. For acoustic emission in general circular cylindrical structures, we derived solutions by applying the boundary conditions at inner and outer surfaces of the structures. Under these conditions, we solve the radial, tangential, and axial displacement fields. Analytical simulations of the acoustic emission were carried out at several point source locations for circular cylindrical geometries. We show that the maximum amplitude of the axial displacement is dependent on the point source position and \(2\pi\)-aperiodicity of the cylindrical geometry. Our mathematical formulas are very useful for characterizing AE features generated from an internal defect source in cylindrical geometries.  
acoustic emission; concentrated force-incorporated potentials; cylindrical structures; mathematical formula +
Footnote †: journal: Applied Sciences
## 5 Conclusions  
A new approach that combines the CBR and DRL algorithms to aid in marine oil emergency response decision making is presented in this paper. The proposed method provides a useful task decomposition process that allows agents to learn tactical policies that can assist decision makers in making decisions across different marine spill instances. Compared with traditional CBR, the proposed method only requires knowledge of a marine oil spill scenario or the construction of scenario instances. Because the proposed method combines the reward function in reinforced learning with the decision intention and applies this approach to train multiple models with different decision intents, the suggested emergency response actions are easy to explain and more informative than those produced by the similarity matching-based CBR system. However, the article only gives two reward functions, which is not enough for a real complex marine oil spill accident, and this limitation will be the focus of future studies.  
Figure 5: Results of the retrained model with the new reward function. (**a**) Q value of the emergency response action for oil spill scenario instance form the case “BRAER” (**b**) Q value of the emergency response action for oil spill scenario instance from the case “TANIO”.  
**Author Contributions:** Conceptualization, K.H.; methodology, K.H.; software, K.H.; validation, W.N., K.H. and N.L.; funding acquisition, N.L. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the National Key R&D Program of China under Grant 2017 YFC1405300.  
**Acknowledgments:** The authors would like to thank the reviewers and editors who provided valuable comments and suggestions on this article.  
**Conflicts of Interest:** The authors declare no conflicts of interest.  
**Appendix A**  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Order** & **Ship/Accident Name** & **Year** & **Location** \\ \hline
1 & AEGAN SEA & 1992 & A Corüra, Spain \\
2 & AGIOS DIMITRIOS 1 & 2009 & Zhuhai, China \\
3 & AGIP ARBUZZO & 1991 & Livrono Port, Italy \\
4 & ALFA 1 & 2012 & Elefis Bay, Greece \\
5 & ARAGON & 1989 & Morocco \\
6 & ARGO MERCHANT & 1976 & Nantucket Shoals, Massachusetts, USA \\
7 & BALTIC CARRIER & 2001 & Baltic Sea, between Germany and Denmark \\
8 & BRAER & 1993 & Garth’s Ness, Shetland \\
9 & BUNGA KELANA 3 & 2010 & Singapore Strait, 13 km south east of Singapore \\ \hline \hline \end{tabular}
\end{table}
Table 6: 55 selected significant oil spill cases since 1967.
# A Nighttime and Daytime Single-Image Dehazing Method
## 2 Background Theory and Related Work  
### Atmospheric Scattering Model  
[PERSON]'s model [8] (the atmospheric scattering model) describes a hazy image as one in which the light intensity \(I(x)\) at each image coordinate \(x\) is added by direct transmission \(D(x)\) and atmospheric light \(A(x)\):  
The light intensity \(I\) received by the imaging system is mainly composed of object-reflected light and atmospheric light. The image can be expressed as follows:  
\[I(x)=J(x)t(x)+A_{0}(1-t(x)) \tag{1}\]  
where \(x\) is the pixel, \(J(x)\) is the actual reflected light intensity of the object, \(I(x)\) is the light intensity received by the imaging system and \(A_{0}\) is the intensity of the atmospheric light at the time of emission.  
In an ideal case, we assume that the atmospheric transmission medium is uniform, and at this time \(t(x)\), can be expressed as follows:  
\[t(x)=e^{-\beta d(x)} \tag{2}\]  
where \(\beta\) is the attenuation coefficient caused by scattering in the medium and \(d(x)\) is the transmission distance of the atmospheric light.  
Compared to the traditional model, ambient illumination at night is affected by other light sources, such as street lamps and car lights. The estimation of ambient illumination should be a local variable, so the original fixed atmospheric light value \(A_{0}\) is overwritten as local ambient light \(A\). Hence, the transformation of Equation (1) results in the new defogging model, which is as follows:  
\[J(x)=\frac{I(x)-A(x)}{t(x)}+A(x) \tag{3}\]
# Realistic Multi-Agent Formation Using Discretionary Group Behavior (DGB)
## 3 Algorithm: Discretionary Group Behavior
### Formation Model  
Depending on what kind of formation is required, this part can be easily changed while we considered a uniform formation for our approach. For example, in Figure 2, \(V_{L}\) and \(V_{L}^{\prime}\) are the unit velocity and the reverse unit velocity of the leader, respectively. For having a three-column uniform shape, two steps are required. First, the reverse direction of the leader's unit velocity produces the first line positions in the way that \(V_{L}^{\prime}\) will be rotated in \(45^{\circ}\) degrees clockwise and vice versa. Second, the other locations in the formation will be calculated by the weighted sum of the reverse unit velocity of other members and the leader (Equation (1)). The final unit vector Upos that specifies the new position is defined by \(V_{agent}\), the reverse unit velocity of the agent at the position in upper line of the formation, and \(V_{leader}\), the reverse unit velocity of the leader. We considered more weight for the agents' unit velocity (W1) and it makes a more realistic motion for the whole group in orientations. This process can easily be adapted to have any other formations.  
\[U_{pos}=W_{1}\times V_{agent}+(1-W_{1})\times V_{leader}. \tag{1}\]  
Figure 1: The flowchart of the Discretionary Group Behavior (DGB) algorithm. The preprocessing step happens once and the other parts repeat in each time step.