esa-sceva/satcom-chunk-collection huggingface dataset:
# Fog-Based CDN Framework for Minimizing Latency of Web Services Using Fog-Based HTTP Browser
## 4 Implementation and Experimental Results
### Experiment Setup and Results
#### 4.2.3 Retrieving Service Resources from Different Fog Nodes Experiment  
Our third experiment was conducted on a local network with four (4) fog nodes. The specifications of these four fog nodes were as follows: one Raspberry Pi 4 Model B, three Raspberry Pi 3 Model B. All four nodes were connected to the local network and have the resources of the six websites offloaded to all of them. A Linux based machine with a 1.8 GHz Intel Core i7 processor was attached to the network as an edge device with the Fog Browser ready for sending consumer requests. The proposed framework had the same deployment as the first experiment.  
We ran this experiment four times. In each run, we instructed the Fog Browser to retrieve the resources of each website either from one fog node, two fog nodes, three fog nodes, or four fog nodes, concurrently. We calculated and compared the latency of each experiment run. We used the same one hop latency measurement from the second experiment as the latency measurement of using one fog node, since it had the same experimental configuration. As shown in the bar chart in Figure 10, our experiments show that the latency metric was independent of the number fog nodes, providing the same resources. The experiments achieved almost the same latency enhancement as the first experiment. However, having a number of replicas for the resources in the same local network can improve the reliability of the fog-based CDN.  
Figure 9: Fog-based CDN latency (different network distances) vs. cloud-based CDN latency.
# Fault Tolerant Spectral/Spatial Optical Code Division Multiple Access Passive Optical Network
## 3 Performance Analysis
Furthermore, it can be observed that the signal quality deteriorates rapidly beyond the acceptable range, and a slight decrease in the received power results in relatively large values of BER. Consequently, it can be concluded that the proposed system is able to provide an acceptable BER at the received powers of approximately \(-19.4\) dBm. Beyond the said limit, the proposed system is not able to maintain signal integrity for an acceptable BER value of \(10^{-9}\). Figure 6: BER at different RNs for varying optical fiber (OF) lengths. Figure 8: BER versus received power at different lengths of the OF media. Figure 7: OF at different RNs for varying optical fiber (OF) lengths. Table 3 shows the performance of the proposed system in terms of eye diagrams. The analysis presented in Figure 6 through Figure 8 is utilized to extract the eye diagrams at different operation points for ONT1 of each RN. It can be observed in Table 3 that, as we move from RN1 towards RN3, the height of the eye tends to decrease, indicating a deterioration in signal quality and overall performance of the system. This can be attributed to the fact that the encoded signal has to pass across more splitters and OF media along the proposed ring-based feeder networks, which increases the overall dispersion and attenuation of the signal across the network. This increases dispersion and attenuation and deteriorates the signal-to-noise ratio that, in turn, affects the overall BER values. Consequently, an increase in the BER is observed as the signal moves deep into the network. The desirable performance delivery of the proposed architecture can be attributed to several key factors of the quasi-three-dimensional DW-ZCC OCDMA code. The code renders ideal correlation properties because the receiving node is able to recover the intended spectrum with maximum power units of \(w_{1}\times w_{2}\). Similarly, all interfering signals are canceled out completely, owing to the zero cross-correlation property of the DW-ZCC code and utilization of the SDD technique. These features enable the complete recovery of the intended signal with a maximum signal-to-noise ratio. Consequently, performance degradation factors like interference from multiple subscribers are nullified along with a reduction in the inherent noise sources at the receiving photodiode [4; 5]. Another factor that is contributing towards the desirable BER values is the overall length of the OCDMA code. A large cardinality matrix of the DW-ZCC code is developed by employing a quasi-three-dimensional approach that is able to support a significantly greater number of users as compared to the previous 1D and 2D versions. Furthermore, PINN and short noise that are proportional to the length of the code are also mitigated at the receiving end, which enables the attainment of desirable BER values for subscribers communicating simultaneously at 10 Gbps over the shared medium.
## 5 Conclusions  
In this study, based on the Sentinel-2 derived variables, three modeling algorithms (RF, SVR, and MLR) were compared to explore the possibilities of using the Sentinel-2 imagery variables for FSV estimation; the RF model was found to be the best method. The red edge B5 was found to be the most important variable in both the machine learning methods and the linear regression method for FSV estimations. Finally, the best RF model was used to predict the FSV and assess the FSV prediction accuracy. The FSV mapping filled the gaps in FSV study in Hunan Province, China, and provided a reference for the distribution of the FSV in the region. Furthermore, the study also indirectly proved the potential to use Sentinel-2 data combined with the RF model to estimate FSVs, laying the foundation for plotting the FSV in southern China. Future research will estimate FSVs using Sentinel-2 BOA data and deep learning algorithms.  
The following are available online at [[http://www.mdpi.com/2072-4292/12/1/186/s1](http://www.mdpi.com/2072-4292/12/1/186/s1)]([http://www.mdpi.com/2072-4292/12/1/186/s1](http://www.mdpi.com/2072-4292/12/1/186/s1)).  
Study design: [PERSON] and [PERSON]. ([PERSON]); Data curation: H.Z. and W.L.; Investigation, Y.H. and F.W.; Methodology: [PERSON], [PERSON] and X.X. ([PERSON]); Resources, J.G.; Software, Y.H., [PERSON] ([PERSON]), Z.S. and W.H.; Supervision, D.P.; Writing: the first draft, Y.H.; Writing: review & editing: [PERSON] and [PERSON]. ([PERSON]). [PERSON] and [PERSON]. ([PERSON]) are co-first authors of this article. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Key Research and Development Program of China, grant number 2016 YFD0600205, and Short-term International Student Program for Postgraduates of Forestry First-Class Discipline, grant number 2019 XKJS0501, and National Science Foundation of China, grant number, grant number 41901351, and the Terrestrial Ecosystem Carbon Inventory Satellite (TECIS), grant number 2017-21-4* and the Key Project of Natural Science Research of Anhui Education Department, grant number KJ2017A413.  
The authors are grateful to the Chinese Academy of Inventory and Planning, National Forestry and Grassland Administration for providing the in situ data used in this study.  
The authors declare no conflict of interest.
## 6 Conclusions  
This study investigates how various image augmentation techniques influence the detection of non-PPE using YOLOv8. Eight distinct augmentations--brightness, contrast, perspective, rotation, scale, shearing, translation, and their combined application--were examined. Three deep learning architectures (ViT, Swin Transformer, and PVT) were employed for validation.  
The main resulting findings indicate that brightness augmentation is the single most effective method for enhancing AP50 in non-PPE detection across all three architectures. While combining all techniques does improve accuracy relative to no augmentation, brightness alone outperforms the sum of augmentations in every architecture. Scale augmentation yields notable gains, whereas translation and perspective exert comparatively modest effects. Non-helmet detection consistently shows the lowest accuracy, underscoring the challenge of identifying helmets in these datasets. Notably, PVT achieves the highest scores, particularly under brightness, demonstrating marked per-class improvements--for instance, raising non-shoes detection from 71.33 to 77.14 and non-helmet from 66.35 to 68.66. Although PVT with brightness achieves the best AP50, applying all techniques results in superior AP50:95 for PVT. Moreover, test outcomes closely align with validation results, suggesting minimal overfitting. These findings highlight the nuanced role of targeted augmentations in improving non-PPE detection.  
The study concludes that no single augmentation method universally boosts both average and class-specific accuracy; each backbone architecture and PPE class benefits from its own tailored strategy. Although these approaches effectively detect non-PPE in the current context, their applicability to other datasets--even those with similar tasks--may be limited by variations in data attributes and distributions. Nevertheless, the proposed techniques could be adapted to related domains such as heavy-equipment detection or identifying hazardous worker behaviors, and the augmented models may serve as valuable pretrained baselines to streamline annotation processes. Further investigation into domain shifts, data distribution differences, and varying object morphologies will be essential for clarifying how well these methods transfer to broader contexts and tasks.  
Conceptualization, S.W. and J.K. ([PERSON]); methodology, S.W.; software, S.P.; validation, S.P. and J.K. ([PERSON]); formal analysis, J.K. ([PERSON]); investigation, J.K. ([PERSON]); resources, S.P.; data curation, S.W. and S.P.; writing--original draft preparation, S.W. and J.K. ([PERSON]); writing--review and editing, J.K. ([PERSON]) and J.K. ([PERSON]); visualization, J.K. ([PERSON]); supervision, S.W.; project administration, S.W.; funding acquisition, J.K. ([PERSON]). All authors have read and agreed to the published version of the manuscript. This research was funded by the National Research Foundation of Korea (NRF) grant funded by the Korean government (Ministry of Science and ICT) (grant number RS202400356697).  
Not applicable.
# A Survey of Electromagnetic Techniques Applied to Cultural Heritage Conservation
## 4 Deterioration of CH Artifacts: Causes and Process
### Organic Materials  
As in the inorganic case, when dealing with organic matter degradation, processes are due to a synergy of multiple factors. Even in this case, environmental conditions prepare the soil for the deterioration process. Particularly, moisture plays a leading role, since fungal attacks require humidity to thrive, and proteins degrade through hydrolysis. It is important to note that, although all of the different factors interact together, the presence of pests (inscets/biopathogens) is the most detrimental, being the factor that accelerates the degradation process the most.  
Biopatogens may be present in a dormant condition in the artifact, waiting for the right environmental conditions to emerge, and the manufacturing procedure used to create the artifact highly affects the development of the deterioration process.  
The organic materials of interest, due to their abundance in indoor artifacts of artistic value, are as follows:  
* Wood;
* Paper;
* Parchment;
* Leather;
* Textiles.  
Their worst biodeterioration factors are fungi, bacteria, and insects.
# Assessing the Performance of the Multi-Beam Echo-Sounder Bathymetric Uncertainty Prediction Model
###### Abstract  
Realistic predictions of the contribution of the various sources affecting the quality of the bathymetric measurements prior to a survey are of importance to ensure sufficient accuracy of the soundings. To this end, models predicting these contributions have been developed. The objective of the present paper is to assess the performance of the bathymetric uncertainty prediction model for modern Multi-Beam Echo-Sounder (MBES) systems. Two datasets were acquired at water depths of 10 m and 30 m with three pulse lengths equaling 27 us, 54 us, and 134 us in the Oosterschelde estuary (The Netherlands). The comparison between the bathymetric uncertainties derived from the measurements and those predicted using the current model indicated a relatively good agreement except for the most outer beams. The performance of the uncertainty prediction model improved by accounting for the most recent insights into the contributors to the MBES depth uncertainties, i.e., the Doppler effect, baseline decorrelation (accounting for the pulse shape), and the signal-to-noise ratio.  
predicted bathymetric uncertainty; baseline decorrelation; Doppler effect; additive noise contribution; measured bathymetric uncertainty
# Energy-Balanced Routing Algorithm Based on Ant Colony Optimization for Mobile Ad Hoc Networks
## 1 Introduction
In summary, the literature [17; 24; 25] just improved the convergence speed of the algorithm, while the literatures [20; 22; 23] only improved the energy aspect of the node. Aiming at these problems, in this paper, from the perspective of topology-based routing protocols, considering the mobility and energy-constrained features of nodes, the ant colony optimization algorithm is used to design routing protocols. This paper proposes an improved algorithm IEMACO which comprehensively considers the convergence of the ant colony algorithm and node energy balance. Firstly, the algorithm judges the offset of the transition probability based on random numbers. This improvement accelerates the convergence speed of the algorithm on the basis of ensuring global optimization. Then, this paper uses the remaining lifetime based method to define the robustness of the link, thereby improving the Key Performance Indicators (KPIs): the end-to-end delay and the Packet Delivery Ratio [27] and other network performance.
# Survey for Soil Sensing with IOT and Traditional Systems
## 6 Conclusions  
In this research, we present an overview of methods for sensing soil properties, covering both conventional systems based on reflectance spectroscopy and emerging IoT systems utilizing RF technologies. The study elaborates on foundational principles and preprocessing requirements in soil reflectance spectroscopy, focusing on key areas such as soil carbon content, organic carbon, moisture, and macronutrients. The paper also highlights cutting-edge IoT-based RF systems for soil analysis. To facilitate comparison, we include two summary tables that contrast results from both traditional and IoT-based approaches. Additionally, we delve into various methods for data interpretation and the metrics used for evaluating the outcomes of soil spectral assessments. In the concluding section, we examine potential future directions in the field of soil property sensing. Overall, this paper serves as an all-inclusive guide to soil property sensing, encompassing methods of sensing, preprocessing, property identification, data interpretation, and evaluation.  
Conceptualization, T.L. and J.W.; Methodology, J.W.; Software, J.W.; Validation, X.Z. and J.W.; Formal analysis, [PERSON] and T.L.; Investigation, X.Z. and L.X.; Resources, T.L. and J.W.; Data curation, J.W., T.L., X.Z. and L.X.; Writing--original draft preparation, J.W. and X.Z.; Writing--review and editing, T.L. and L.X.; Visualization, J.W. and X.Z.; Supervision, T.L. and L.X.; Project administration, T.L. and L.X. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
No new data were created or analyzed in this study. Data sharing is not applicable to this article.  
The authors declare no conflict of interest.
# Influence of Loess-Mudstone Strata Structure on Slope Seismic Stability of Loess Plateau in China
## 3 Results  
The calculation results of the safety factors for two types of slopes are shown in Figure 10. It can be seen that under the same seismic load, the loess-mudstone slope stability was generally higher than that of the pure loess slope, indicating that with the existence of an underlying mudstone layer, the seismic stability of the loess slope was higher. With the increase in the slope height and angle, the seismic stability of both slopes decreased. The thicker the loess cover, the lower the loess-mudstone slope seismic stability. With the increase in the mudstone dip angle, the loess-mudstone slope seismic stability increased. In addition, as can be seen from Figure 10c, as the thickness of loess increased, the safety factor variation curves of the two slope models had an intersection trend. After the calculation, when the loess cover thickness exceeded 33 m, the safety factors of the two slope models were consistent, indicating that there was a critical value for the loess cover thickness. When the loess cover thickness exceeded the critical value, it did not have a significant impact on the seismic stability of the loess-mudstone slope.  
The error of safety factor between the two kinds of slopes was defined as:  
\[Error=\frac{F_{SLM}-F_{SPL}}{F_{SPL}}\times 100\% \tag{3}\]  
where \(F_{SLM}\) is the safety factor of the loess-mudstone slope and \(F_{SPL}\) is the corresponding safety factor of the pure loess slope. The results are shown in Figure 11. The error distribution ranges of the two slope model safety factors were 4.6-12.5%, 6.0-12.1%, 2.1-28%, and 9.4-11.5%, respectively, with the variation in slope height, slope angle, loess cover thickness, and mudstone dip angle. It can also be seen that the existence of underlying strata had a significant impact on the seismic stability of the loess slope.  
The error of the two kinds of slope safety factors was proportional to the slope height. The error was largest when the slope height was 125 m, reaching 12.5%. The error was inversely proportional to the slope angle; when the slope angle was 20 deg, the error was largest, reaching 12.1%.  
Figure 11: _Cont._  
Figure 10: Comparison of safety factors between pure loess slope and loess-mudstone slope: (**a**) relationship between slope height and safety factor; (**b**) relationship between slope angle and safety factor; (**c**) relationship between loess cover thickness and safety factor; (**d**) relationship between mudstone dip angle and safety factor.  
Compared with the pure loess slope with the same slope height and slope angle, the error of the two kinds of slope safety factors gradually decreased with the increase in the loess cover thickness. When the loess cover thickness was 10 m, the error was largest, that is, 28%. When the mudstone dip angle increased, the error tended to increase. When the mudstone dip angle was 30\({}^{\circ}\), the error was largest (11.5%).
## 4 Experimental Results
### Discussion
#### 4.4.1 Sensitivity-Constrained Optimization  
To evaluate the effect of the sensitivity-constrained optimization, we implemented some comparative experiments. Figure 3 demonstrates an illustrative example, in which only CP decomposes a single convolutional layer 1 of block 4 in ResNet-18. Our results demonstrate that we obtain superior accuracy compared to standard CP (without sensitivity-constrained optimization) with the same epoch of fine-tuning. Figure 4 demonstrates another illustrative example of the effectiveness of our method. It is crucial to note that the compressed network, which utilizes standard CP (without sensitivity-constrained optimization), fails to obtain the same Top-1 accuracy of the original network, even when the rank is set at 400 and fine-tuning is applied. Nevertheless, by using sensitivity-constrained optimization, the performance significantly improves and achieves the level of original accuracy. Even the model with a rank of 100 produces comparatively excellent results, with less than 2% accuracy loss.
## 3 Dynamic CH Selection and Energy-Efficient Routing Design
### Proposed Methodology
#### 3.4.2 Clusters Formulation Using the Potential Function  
Using the possible function indicated in Equation (18), the CHs are provided to sensor nodes once the CHs have been selected by ICOA. Sensor nodes with shorter transmission distances and more remaining energy are assigned to the CH. As a result, the quantity of energy consumed during the data transmission phase will be lower.  
\[\mathrm{sn}_{\mathrm{P}}=\frac{\mathrm{z}\,\times\,\mathrm{E}\big{(}\mathrm{ CH}_{\mathrm{j}}\big{)}}{\mathrm{D}\big{(}\mathrm{s}_{\mathrm{i}},\mathrm{CH}_{ \mathrm{j}}\big{)}} \tag{18}\]  
where \(\mathrm{z}\) is the proportionality constant, \(\mathrm{sn}_{\mathrm{P}}\) is the potential of the sensor node and \(\mathrm{D}\big{(}\mathrm{s}_{\mathrm{i}},\,\mathrm{CH}_{\mathrm{j}}\big{)}\) is the distance between the \(\mathrm{CH}_{\mathrm{j}}\) and sensor \(\mathrm{s}_{\mathrm{i}}\); The sensor is assigned to a specific CH with greater potential and \(\mathrm{E}\big{(}\mathrm{CH}_{\mathrm{j}}\big{)}\) denotes the residual energy of that CH. When the distance between the two different CHs and sensor nodes is equal, the sensor node is connected to the CH with higher energy.
## 4 Discussion
The results confirmed that proliferation occurred without cell apoptosis. However, as the cells proliferated, the number of SPIONs in each cell reduced, and fluorescence could not be measured in some cells. In vitro, fluorescence could be detected over seven days. While the cells containing SPIONs were cultured in a plate in the laboratory, the cells actually multiplied in all three dimensions in vitro. The fluorescence signal of SPIONs was retained without dispersion even during cell proliferation. In this study, cytotoxicity was confirmed before magnetic induction experiments,and cytotoxicity test is required after magnetic field in-vivo experiments. This could be studied and applied to the animal experiments in the future. To analyze the migration efficiency and to monitor the recovering effects in vitro, commercial SPIONs and dopamine-coated SPIONs were used as fluorescent materials. However, the commercial SPION could only manage 50% of the magnetic induction, even though it was not introduced into the cell. The optimal fluorescence signal that could be analyzed in vitro was \(2\times 10^{6}\) cells with 50 \(\mathrm{\SIUnitSymbolMicro m}\)/mL internalized SPIONs, and the migration efficiency was 95% or more within 30 min under a neodymium magnet at a 1.5 T magnetic force. While measuring the migration rate, the tube was laid horizontally, in order to avoid the effect of gravity as much as possible. Also, the viscosity of the medium does not get affected because there are no additives in the medium. Analysis using a Fluorescence-labeled Organism Bioimaging Instrument (FOBI) showed that the fluorescence value increased steadily as the number of cells increased, indicating that SPIONs were uniformly present in stem cells. Additionally, the comparison of commercial 50-nm SPIONs and our 100-nm SPIONs confirmed that our SPIONs had a higher migration efficiency and were not cytotoxic, even when using a larger amount than the recommended concentration (up to 50 \(\mathrm{\SIUnitSymbolMicro g}\)/mL). In contrast to existing commercialized SPIONs, our developed SPIONs could be used at high concentrations to increase the efficiency of migration using magnetism which was not cytotoxic.