esa-sceva/satcom-chunk-collection huggingface dataset:
# Analysis and Reduction of Nonlinear Effects in Optical Fiber Frequency Transfer
## 3 Simulation and Results
### Stimulated Brillouin Scattering
The attenuation coefficient of the optical fiber \(\alpha\) is 0.2 dB/km. The linewidth \(\Delta v_{laser}\) of the laser source and the linewidth \(\Delta v_{B}\) of the Brillouin scattering are 1 kHz and 10 MHz, respectively. The gain coefficient \(\gamma_{B}\) of the Brillouin scattering is 5 \(\times\) 10\({}^{-11}\) m/W. powers for \(\gamma_{B}\) values of 1 \(\times\) 10\({}^{-11}\) m/W, 6 \(\times\) 10\({}^{-11}\) m/W, and 1 \(\times\) 10\({}^{-10}\) m/W are approximately 232 mW, 39 mW, and 23 mW, respectively. When \(\gamma_{B}\) ranges from 1 \(\times\) 10\({}^{-11}\) m/W to 1 \(\times\) 10\({}^{-10}\) m/W, the threshold power of Brillouin scattering remains essentially constant for \(L\) = 50 km and \(L\) = 10 km. For a fixed \(L\) = 10 km and \(\gamma_{B}\) = 7 \(\times\) 10\({}^{-11}\) m/W, the threshold power of Brillouin scattering is approximately 7 mW. This is, notably, 26 mW lower than the corresponding value for \(L\) = 1 km. Figure 5 illustrates the threshold powers required to generate Brillouin scattering as a function of the laser source linewidth \(\Delta v_{laser}\) for different fiber lengths: 1 km, 10 km, and 50 km. Generally, when the fiber length \(L\) is fixed and the laser source linewidth \(\Delta v_{laser}\) ranges from 1 Hz to 1 MHz, it does not impact the threshold powers for generating Brillouin scattering. The threshold powers remain approximately 46 mW for \(L\) = 1 km, 10 mW for \(L\) = 10 km, and 8 mW for \(L\) = 50 km, respectively. However, when \(\Delta v_{laser}\) is determined between 1 MHz and 10 MHz with a constant fiber length \(L\), the threshold powers of Brillouin scattering increase proportionally. For instance, at \(L\) = 10 km, the threshold powers are approximately 12 mW and 16 mW for \(\Delta v_{laser}\) = 2 MHz and \(\Delta v_{laser}\) = 6 MHz, respectively. Figure 4: The threshold power \(P_{crit-B}\) for generating the Brillouin scattering as a function of the attenuation coefficient \(\alpha\) of the optical fiber and the gain coefficient \(\gamma_{B}\) of the Brillouin scattering. The effective mode area \(A_{eff}\) of the fiber is 1 \(\times\) 10\({}^{-10}\) m\({}^{2}\). (**a**) The attenuation coefficient \(\alpha\) of the optical fiber. (**b**) The gain coefficient \(\gamma_{B}\) of the Brillouin scattering.
# A Service-Caching Strategy Assisted by Double DQN in LEO Satellite Networks
## 2 System Model
### Communication Model  
We assumed that all edge users within the satellite coverage area could establish service with the satellite. We calculated the intersection point of the line connecting each satellite to the center of the Earth with the Earth's surface, and then we computed the distance between the intersection point and the user as \(d\). Each user selects the satellite with the minimum \(d\) to establish a communication link. It is easy to understand that, when the satellite is directly above the user, its projection coincides with the user, resulting in the highest communication quality. To represent the access relationship between users and satellites, we use the variable \(\alpha\triangleq\left[\alpha_{m,k}\right]_{\forall(m,k)\in(\mathcal{M}\times \mathcal{K})}\) as  
\[\alpha_{m,k}=\left\{\begin{array}{ll}1,&\text{SUE}_{k}\text{ is served by LEO}_{m},\\ 0,&\text{otherwise}.\end{array}\right. \tag{3}\]  
Based on the real application scenario, we assumed that each edge user can only access one satellite. Therefore, the following constraints can be derived:  
(C1): \(\sum_{\forall m\in\mathcal{M}}\alpha_{m,k}\leq 1,\forall k\in\mathcal{K}\). (4)  
Once SUE\({}_{k}\) is served via LEO\({}_{m},letW_{k}^{\text{SUE}}\), which is the bandwidth allocated to SUE\({}_{k}\), \(p_{m,k}\) indicates the transmission power of this user. The orthogonal bandwidth assignment is assumed in this work based on which the signal-to-noise ratio (SNR) of SUE\({}_{k}\) can be written as  
\[\gamma_{m,k}^{\text{SUE}}=\frac{p_{m,k}h_{m,k}}{\sigma_{m}W_{k}^{\text{SUE}}}, \tag{5}\]  
where \(\sigma_{m}\) is the noise power density per Hz at LEO\({}_{m}\). The achievable rate of SUE\({}_{k}\) at LEO\({}_{m}\) can be expressed as  
\[R_{m,k}^{\text{SUE}}=W_{k}^{\text{SUE}}\log_{2}\!\left(1+\gamma_{m,k}^{\text{ SUE}}\right)=W_{k}^{\text{SUE}}\log_{2}\!\left(1+\frac{p_{m,k}h_{m,k}}{\sigma_{m}W_{ k}^{\text{SUE}}}\right). \tag{6}\]  
Taking into account the LEO association decision, the achievable transmission rate of SUE\({}_{k}\) can be described as  
\[R_{k}^{\text{SUE}}\!\left(\mathbf{p},\mathbf{W}^{\text{SUE}},\boldsymbol{ \alpha}\right)=\sum_{\forall m\in\mathcal{M}}\alpha_{m,k}R_{m,k}^{\text{SUE}}, \tag{7}\]  
where \(\mathbf{p}\triangleq\left[p_{m,k}\right]_{\forall m,k}\) and \(\mathbf{W}^{\text{SUE}}\triangleq\left[W_{k}^{\text{SUE}}\right]_{\forall k}\). Regarding the communication rate demand at each SUE, the following constraint is introduced,\[(\text{C2}):\quad R_{k}^{\text{SUE}}\left(\mathbf{p},\mathbf{W}^{\text{SUE}}\,, \boldsymbol{\alpha}\right)\geq\tilde{R}_{k}^{\text{SUE}}\,,\quad\forall k\in \mathcal{K}, \tag{8}\]  
in which \(\tilde{R}_{k}^{\text{SUE}}\) indicates the required transmission rate of \(\text{SUE}_{k}\).
# Artificial Intelligence of Things as New Paradigm in Aviation Health Monitoring Systems
## 4 Discussion
### Comparison and Implications of Traditional and AIoT-Based Health Monitoring Models  
The contrast between these two models is stark. While the traditional model operates on a limited set of variables and relies heavily on predetermined rules, the AIoT model is dynamic, adaptive, and capable of processing vast amounts of complex, interrelated data.  
The traditional model's simplicity makes it easier to implement and understand, but it lacks the nuance and predictive power of the AIoT approach. The AIoT model, with its ability to consider historical data, real-time sensor inputs, and contextual information, can provide far more accurate failure predictions and optimized maintenance schedules.  
The decision-making process in the AIoT model is particularly noteworthy. Instead of relying on fixed rules, it can adapt to changing conditions and learn from past outcomes. This leads to more efficient resource allocation and can significantly reduce unnecessary maintenance while also minimizing the risk of unexpected failures.  
Moreover, the AIoT model's incorporation of performance metrics and predictive capabilities allows for a more strategic approach to aircraft health management. It enables operators to balance short-term costs against long-term performance benefits, potentially extending aircraft lifespan and improving overall operational efficiency.  
While the traditional model has served the aviation industry well for many years, the AIoT model represents the future of aircraft health management. Its ability to process and analyze vast amounts of data, adapt to changing conditions, and make predictive, context-aware decisions promises to revolutionize how we approach aircraft maintenance and operations. As this technology continues to evolve and mature, we can expect to see significant improvements in aviation safety, efficiency, and cost-effectiveness.  
The main differences of both models are shown in Table 3.  
The AIoT model represents a significant advancement over the traditional model. It incorporates more data sources, uses advanced AI techniques for analysis and decision-making, and provides a more holistic and adaptive approach to health management. This results in more accurate failure predictions, optimized maintenance scheduling, and overall improved system performance.
## 2 Literature Review and Methodology  
### Relevant Theories  
State of charge (SOC) is a performance parameter for a battery which indicates the amount of remaining charge left as well as the power level. When the SOC level is 100%, the battery is fully charged; when its 0%, it is fully discharged. In practical applications, however, the SOC level will range between 90% and 30% [6]. Equation (1) provides a basic estimate of the SOC [7].  
\[\text{SOC }(\%)=\frac{\text{Q}_{\text{stored}}}{\text{Q}_{\text{max}}}\times 100 \tag{1}\]  
State of health (SOH) is another performance parameter for a battery which indicates the remaining capacity usable for the charging and discharging process. It also shows the level of degradation of the battery from the usage; this is shown in Equation (2). The initial SOH of battery is 100% but after 300 to 600 cycles, the SOH typically drops to between 60% and 70% due to battery degradation [6]. The SOH also indicates how long the battery's useful lifetime is by comparing the battery's present conditions to its initial manufacturing condition [8].  
\[\text{SOH }(\%)=\frac{\text{Q}_{\text{max}}}{\text{C}_{\text{rated}}}\times 100 \tag{2}\]  
An equivalent circuit modelling (ECM) technique [9] is used to model and represent the dynamics of a Li-ion battery, to comprehend and simulate its complex behaviour. Figure 1 demonstrates the ECM of a battery. The ECM is essential for simulating batteries, providing real-time monitoring of key parameters and insights into internal behaviours like SOC and SOH. This makes it crucial for optimising battery performance and developing effective control protocols in a BMS [10].
## 2 Results
### In Vivo Influence of 8-Nonenoic Acid on Tilapia Eggs Challenged with _S. parasitic_  
The survival of tilapia eggs/larvae challenged with _S. parasitic_ in the presence of 8-nonenoic acid was examined in in vivo bioassays. We performed a preliminary assay to find the lowest effective concentration of 8-nonenoic acid on _S. parasitic_ that we could use in the tilapia egg bioassays. We did not find any additive effect when adding 7.5, 10 or 15 mg/L to the flasks compared to 5 mg/L. We did find a reduction in larvae's survival when it was treated with 1 mg/L 8-nonenoic acid, a survival rate of 27%. The assays were initiated on eggs that were 4 days postfertilization. At this time point, the eggs have already started to develop but are still a few days before hatching. We evaluated the number of hatched larvae 5 days post-inoculation with the pathogen. The control treatment (eggs with ethanol only) showed 100% survival and proper development of the larvae (Figures 5 and 6A). Within 2 days of adding _S. parasitic_ to the flasks, we could see mortality characterized by round filamentous growth around the eggs. Survival rate 5 days post-inoculation was 22.5% (Figures 5 and 6D). In this treatment, all larvae died after 14 days. When 5 mg/L 8-nonenoic acid was added to the eggs challenged with _S. parasitic_, the survival rate increased to 77% (Figure 5). In this case, mortality was not characterized by round filamentous units, but rather by a non-vital egg/larva (Figure 6C). In this treatment, challenged larvae were  
Figure 4: Bioactivity of 8-nonenoic acid against _S. parasitica_. Different concentrations of 8-nonenoic acid were added to solid (PDA) and liquid (DDW and PDB) media. Results present the percentage of maximal (control) growth of _S. parasitica_ at the different concentrations. The results were subjected to ANOVA followed by Tukey–Kramer multiple comparison test; different letters above points on curve indicate a significant difference between solid and liquid media at \(p\leq 0.05\).  
reared for more than 2 weeks to follow their development, which was normal. Ninety-eight percent of the eggs in flasks that were supplemented with 5 mg/L 8-nonenoic acid only hatched and developed properly, suggesting that the compound could be used in future applications on the eggs at this concentration. No toxicity of 30 mg/L 8-nonenoic acid was observed, and hatching and development were normal. Interactions between treatments were found to be significant: control vs. _S. parasitic_ (_p_ < 0.001), 5 mg/L 8-nonenoic acid only vs. _S. parasitic_ (_p_ < 0.001) and 5 mg/L 8-nonenoic acid + _S. parasitic_ vs. _S. parasitic_ (_p_ = 0.0015).
## 3 Methodology
### Feature Extractor  
In this paper, a CAE is utilized to extract the representative histopathological features of the patches. The CAE aims to solve the back-propagation problem in an unsupervised manner by only relying on the input image as a teacher by itself [38]. It has the in-built ability to compress the data efficiently by extracting the important features and removing noise in the data [39]. These features bring benefits to CBHIR, such as ignoring the noise in WSIs that might be noticeable due to the scanners. Also, they can reduce the demand for annotated images for training an FE, which is expensive and time-consuming [40; 41].  
Figures 2 and 3 illustrate the structure of the proposed custom-built CAE. In this CAE, a skip connection between a layer in the encoder and its corresponding layer in the decoder can improve the gradient propagation and increase the performance in capturing complex patterns of WSIs. The proposed CAE comprises an encoder with three convolutional layers [16; 32; 64]. Moving to the bottleneck, an attention block with a filter size of [32; 16; 1; 64] is introduced to enhance the feature vectors by robustness to noise or occlusions. The decoder is made up of three Conv2 DTranspose with [32; 16; 3]. The kernel size in this structure was fixed to 3 in all the layers.  
The primary objective of the CAE is to reconstruct input images in the output by minimizing the mean squared error (MSE). The CAE workflow involves feeding an input image (\(X\)) to the encoder, which compresses the input. Subsequently, the bottleneck compresses the output of the encoder to obtain a feature vector with 200 meaningful features (\(Y\)). Finally, the decoder reconstructs the output by receiving the feature vector (\(Y=Decoder(F)\)). In an ideal CAE, the output is identical to the input (_ideal_, \(X=Y\)). To achieve this ideal goal, the model endeavors to minimize the MSE by comparing the input (\(X\)) and the output (\(Y\)) [42].  
In this paper, the model was trained on a GPU with the NVIDIA GeForce RTX 3090 over 10 epochs, utilizing a batch size of 32 and a learning rate of 0.000001. By discarding the decoder from the CAE, the well-trained FE can be utilized for further experiments.
## 3 Extraction Techniques
### Emerging Extraction Technologies
#### 3.2.4 Supercritical Fluid Extraction (SFE)  
Another emerging technology that has been advantageously positioned for the green extraction of heat-sensitive biocompounds is supercritical fluid extraction (SFE). CO\({}_{2}\) is widely used for SFE due to it being low toxic, non-flammable, inexpensive (compared to organic solvents), and recyclable. According to [PERSON] et al. [5], new opportunities using unconventional supercritical solvents have been appeared such as SF6 or noble gases (or their mixtures), although supercritical CO\({}_{2}\) remains as the more used solvent for these operations. However, CO\({}_{2}\) is intrinsically non-polar; thus, to improve the extraction of polar compounds such as phenolic compounds, it requires the addition of polar cosolvents (modifiers) such as ethanol or methanol [28]. [PERSON] et al. [53] evaluated the extractive efficiency of SFE-CO\({}_{2}\), with or without a cosolvent, to extract the acidic compounds contained in hops flowers. The results revealed that the use of supercritical CO\({}_{2}\) with cosolvent improved the performance of \(\alpha\) acids (28.3%) compared to pure CO\({}_{2}\) (21.5%). In contrast, the highest yield in \(\beta\) acids (46.2%) was found in the extracts obtained with supercritical CO\({}_{2}\) versus 37.5% for SFE-CO\({}_{2}\) with ethanol.  
SFE has been applied successfully for the selective isolation of bitter acids from two hop cultivars (Hallertau Magnum and Herkules) [54]. The authors identified, by HPLC analysis, the presence of two main groups of molecules: \(\alpha\)-acids (55.2 _w/w_ and 46.9 _w/w_ in the Herkules and Hallertau Magnum hops, respectively) and \(\beta\)-acids (18.3 _w/w_ and 22.9 _w/w_, respectively) [54]. Columbulone accounted 38.7% of \(\alpha\)-acids in Herkules hops and 24.9% in Hallertau Magnum hops, and the colupulone was the major component of \(\beta\)-acids, reaching 57.2% and 44.2% in Herkules and Hallertau Magnum, respectively [54].  
To valorize the spent hops, [PERSON] et al. [55] proposed their use as a raw material for the recovery of xanthohumol. Under 80 \({}^{\circ}\)C and 850 bar, a yield of 1.23% of xanthohumol was obtained.  
Some studies about the extraction technologies of different phytochemicals from hops are presented in Table 1.
# DSVN: A Flexible and Secure Data-Sharing Model for VANET Based on Blockchain
## 6 Experiment and Analysis
### Experimental Simulation
#### 6.2.3 Transaction Cost and Transmission Rate  
In blockchain simulation phase, we built the FISCO BCOS [42] consortium blockchain using the build_chain.sh scripts and deployed the IPFS command line version on numerous servers with Ubuntu 20.04 system environment. The consortium blockchain uses PBFT consensus. Therefore, we set the initial number of consensus nodes to 4 to satisfy the 3f+1 requirement [43]. We evaluated the performance of processing a single transaction with different numbers of nodes. Furthermore, we evaluated the model's transactional concurrency capabilities. The file size in the test transaction is 1 MB. The experimental results are shown in Figure 9.  
Figure 9: (a) Cost of transaction (b) Throughput.  
As shown in Figure 9a, there are two types of test transactions: data upload confirmation and data download confirmation. The results show that the latency of the two types of transactions increases linearly with the increase of the number of nodes. The increase in delay is mainly due to the increased communication overhead caused by adding nodes in the blockchain to reach a consensus. In addition, the delay of data upload is about 360 ms higher than that of data download. The data upload delay is mainly the overhead of data encryption and metadata storage, and the data download delay is mainly the overhead of data download and data decryption. The difference in latency is because the metadata storage overhead in IPFS is much greater than the data download overhead. As shown in Figure 9b, the transaction throughput of the model is positively linearly related to nodes. The number of nodes is initially 4, and the throughput of the model is 47 transaction/s. When the number of nodes increases to 34, the throughput is 54 transaction/s. The throughput of the model grows slowly. After the number of nodes reaches 34, the throughput of the model roughly stabilizes between 54-56 transaction/s.  
In addition, the experiments tested the performance of data file upload and download. Figure 10 shows the time spent on file transfers of different sizes. The experimental file sizes range from 1 MB to 1024 MB. The experimental results show that the time overhead of transfer increases exponentially with the file size. When the file size is less than 16 MB, the transfer time is less than 5 s for uploading and less than 2 s for downloading. The main factors affecting the transfer time are not the network bandwidth but the data encryption and decryption and the metadata storage. When the file size exceeds 16 MB, the average upload transfer rate is about 12.8 MB/s and the average download transfer rate is about 27.6 MB/s. Therefore, the network bandwidth is the main factor affecting the transfer time.
## Materials and Methods  
### Study Areas  
We chose the cities of Daye and Honghu as the study areas, which are two typical areas of Jianghan Plain in Hubei province, China. The city of Daye (114 deg31'-115 deg20'E, 29 deg40'-30 deg15'N) is located in the southeast of Hubei province, on the south bank of the middle reaches of the Yangtze River. The area features a typical subtropical continental monsoon climate, with four distinct seasons, and sufficient water and heat for crop production. The annual average temperature is 16.9 degC, and the average annual precipitation is 1385.8 mm. The city area is mainly hilly, with an altitude of 120-200 m. The zonal soils in this area are mainly red soil and yellow-brown soil. The area is a production base of grain, cotton, and oil-bearing crops. The Daye area is rich in mineral resources, and features a number of coppers, iron, coal, and limestone mines [13]. At the same time, the mining has greatly damaged the ecological environment, and the farmland soil near the mining area has been seriously polluted. Long-term mining and metallurgy activities have caused the soil to accumulate different levels of heavy metals.  
The city of Honghu (113 deg07'-114 deg05'E, 29 deg39'-30 deg12'N) is a county-level city in the municipal region of Jingzhou, Hubei province, in the middle and lower reaches of the Yangtze River, in the southeast of Jianghan Plain. The landform type in this area is mainly alluvial plain. The city has good soil quality, and the soil acidity-alkalinity is moderate, making the area suitable for planting crops. However, excessive application of pesticides and fertilizers has caused serious pollution in the area. The water quality of Honghu Lake has been severely degraded, and much of the cultivated land has been exposed to heavy metals such as Cr, Pb, and Cd. The two study areas are shown in Figure 1.  
Figure 1: Study area locations. Study area I is Daye. Study area II is Honghu.