esa-sceva/satcom-chunk-collection huggingface dataset:
# Incidence of Phage Capsid Organization on the Resistance to High Energy Proton Beams
## 3 Results and Discussion
In a previous work, we observed that the presence of a foreign amino acid sequence on the capsid protein pVIII of P9b and 12 III1 phage clones generated an increase in H-bonds and steric interactions in capsid proteins, resulting in the increased resistance to UV-C radiation damage [26]. The comparison of the susceptibility to the proton beam and UV-C of the three phages is reported in Figure 5.  
It is well known that ionizing radiations induce damage proportionally to the radiation LET and the plot of the damage vs. LET represents an important aspect for many experts, especially in the field of Physics, Chemistry, Radiology, Biology and Medicine. It is expected that phage survival decreases with the LET value of the used ionizing radiation, as indicated by the dashed lines in Figure 5. Therefore, a lower dose for protons and a higher dose for photons was needed for the inactivation of the virus. Although the UV-protons comparison (Figure 5) was performed at comparable doses, the data indicated a noticeable change in the susceptibility to UV-C and proton beam exposure of 12 III1, compared to wild-type pC89. The survival ratio P9b/pC89 was 37.6 for UV and 11.5 for protons, while the survival ratio 12 III1/pC89 was 6.13 for UV and 0.014 for protons. Thus, proton beams induced major damage compared with UV photons in all the investigated phages, as expected by the major radiation LET. Concordantly, the survival percentage was reduced from a factor 23 up to a factor 9800 using protons. A possible explanation could be derived from the different nature of the two radiations, in that, when phages are exposed to UV-C radiation, the damage could be predominantly ascribed to the direct effects of absorbed radiation by nucleic acids [46]. The formation of dimers (cyclobutane pyrimidine and pyrimidine) and photoproducts (pyrimidine) has been reported to increase in proportion to the UV-C dose, modifying the DNA structure. On the other hand, when the same phage clones are exposed to the proton beam, they could also be subjected to indirect damage due to OH- and H+ products resulting from the  
Figure 5: Susceptibility comparison of pC89, P9b and 12 III1 phages to proton beam and UV-C.  
water radiolysis [47]. Differently to P9b, 12 III1 foreign peptide, composed by aromatic and other amino acids favorable to carbonylation, could be subjected to a high degree of oxidative modifications and, consequently, a fragmentation of the protein backbone. These findings are in accordance with our previous results, obtained by treating the same phages with H\({}_{2}\)O\({}_{2}\)[26]. In fact, in these experimental conditions, only P9b showed an increased resistance to induced stress, while no significant difference was recorded between 12 III1 and pC89.
# Enhancing Air Quality Prediction with an Adaptive PSO-Optimized CNN-Bi-LSTM Model
## 3 Model Introduction
### Cnn-Bi-Lstm  
The CNN-Bi-LSTM amalgamates the strengths of both CNN and Bi-LSTM, yielding remarkable breakthroughs, particularly in natural language processing and classification tasks [35]. In this study, the CNN-Bi-LSTM model employed a streamlined process. It ingested the previous \(t\) hours\({}^{\prime}\)\(c\times m\) variables data from \(n\) stations. The input format mirrored that of a typical CNN, configured as \(t\times m\times c\times n\) (\(6\times 6\times 3\times 4\)). The input, convolution, and pooling layers were utilized for initial feature extraction from the input data. These extracted features were then vectorized into a 1-D array and fed into the Bi-LSTM layer, which examines their sequential patterns, effectively merging spatial and temporal feature analyses. Finally, the air quality index for the next \(t^{\prime}\) hours at \(n\) stations were obtained through the fully connected layer and output layer. Figure 7 illustrates the structure of the CNN-Bi-LSTM model.  
Figure 7 illustrates the meticulous pre-processing steps undertaken for the input dataset, underscoring the importance of data normalization before model ingestion. This vital pre-processing phase ensures that all features contribute equally to the learning process without bias induced by differing scales. The Bi-LSTM model, depicted in its intricate cyclic architecture, emerges as a sophisticated evolution of the LSTM, ingeniously marrying the strengths of its predecessor with bidirectional processing capabilities. It retains LSTM's prowess in tackling the vanishing gradient problem and effectively modeling long-term dependencies while augmenting these qualities by concurrently examining historical and forecasted data. This dual-directional inspection mechanism enables the extraction of a broader spectrum of temporal features, thereby enriching the model's interpretive depth and enhancing resilience against anomalous data points or outliers. Incorporating a Dropout layer within this architectural blueprint serves a strategic purpose. By randomly \"dropping out\" or temporarily turning off a fraction of neurons during training, the model is encouraged to avoid reliance on any specific subset of inputs, fostering a more distributed and collaborative learning dynamic. Consequently, this regularization technique enhances the model's generalization capabilities, mitigating the risk of overfitting--where the model becomes overly tailored to the training data, compromising its ability to generalize to unseen instances. The judicious use of Dropout thus fortifies the Bi-LSTM's adaptability and predictive accuracy across diverse datasets and real-world scenarios.
## 7 Conclusions  
The AMCTD protocol was evaluated under various conditions, including the presence and absence of attacker nodes. The performance of the protocol was evaluated in different scenarios involving attacker nodes, and it was found that active attacks significantly decreased the protocol's performance across multiple parameters. The study found that active attacks resulted in  
* a reduction of up to 10% in the number of functioning nodes in the UWSNs environment;
* a decrease of up to 6% in throughput;
* an increase of up to 7% in transmission loss;
* a rise of up to 25% in energy cost; and
* an increase of up to 20% in end-to-end latency.
# Unsupervised Low-Light Image Enhancement in the Fourier Transform Domain
## 5 Conclusions  
In this paper, an unsupervised low-light image enhancement method in the Fourier transform domain is proposed. This method harnesses the potential of the CycleGAN model to effectively enhance luminance in the amplitude domain via Fourier transform, which not only enables us to separate the noise better, but also enhance the luminance in the amplitude domain without amplifying the noise in low-light images. By considering the degradation process from normal-light to low-light conditions using the CycleGAN model, this approach better simulates real-world environments and adapts to the specific requirements of image processing in low-light scenarios. In addition, we also incorporate the cycle consistency loss functions both in the space domain and the Fourier transform domain. The spatial domain cycle consistency loss enables an assessment of the quality of the generated images at the pixel level, and the cycle consistency loss function in the Fourier transform domain enables the similarity of brightness information. The experiments of adding noise also confirm the effectiveness of our proposed model well. The experimental demonstration in this paper has yielded significant improvements in both objective evaluation metrics and subjective visual quality, showcasing a novel research idea for low-light image enhancement. In the future, we will focus on some enhancement tasks for large images, which still require a lot of training time for current models.  
Methodology, Z.W.; Software, Z.W.; Formal analysis, Z.W.; Writing--original draft, F.M.; Writing--review & editing, F.M.; Supervision, J.Z.; Project administration, J.Z. All authors have read and agreed to the published version of the manuscript. This research received no external funding.  
**Data Availability Statement:** Publicly available datasets were analyzed in this study. This data can be found [[https://github.com/VITA-Group/EnlightGAN](https://github.com/VITA-Group/EnlightGAN)]([https://github.com/VITA-Group/EnlightGAN](https://github.com/VITA-Group/EnlightGAN)), accessed on 26 December 2023.  
**Conflicts of Interest:** There is no conflict of interest in the research and writing of this paper.
# A Combinational Buffer Management Scheme in Mobile Opportunistic Network
## 3 Buffer Management
### Queuing Strategy
We update the relevant information package of the message in the node when the nodes make contact and timely remove the message that was sent successfully so as to quickly clear the redundant messages in the network. The node carries more messages through exchanging information in order for the message to reach the destination node quickly. Our proposed strategy establishes a reasonable queuing strategy and a novel message migration method based on the local information in the mobile opportunistic network. The combinational buffer management scheme evaluates the message and node utility value more comprehensively through multiple attributes of the message.
## 3 Adaptive Real-Time Routing Protocol
### Adaptive QoS Routing Protocol
Upon establishing a path to node \(j\), packets are delivered along \(P^{n}_{j}\). ```
1:\(n\leftarrow\) Number of available real-time paths
2:\(P^{1\ldots n}_{i}\leftarrow\) Path between node \(i\) and the sink where its end-to-end delay is bounded within \(D_{i}\ \triangleright P^{1}_{i}\) has the shortest delay whereas \(P^{n}_{i}\) has the longest delay, \(P^{1}_{i}\leq P^{2}_{i}\leq\ldots\leq P^{n}_{i}\)
3:\(n\leftarrow\) Path_Search_Probing(i,sink) \(\triangleright\) Obtain number of possible multiple paths from node i and sink
4: Transmit packets along \(P^{n}_{i}\ \triangleright\) Packets are transmitted along the longest end-to-end delay in \(P^{1\ldots n}_{i}\)
5:for every \(DBP_{ObjID}\)do\(\triangleright\) After obtaining \(DBP_{ObjID}\) value of \(T_{ObjID}\)
6:if\(DBP_{ObjID}\geq 0\)then\(\triangleright\) If application stream meets the requirement
7:if\(DBP_{i}<0\)then\(\triangleright\) If the session stream is not meeting the requirment
8:if\(n-1>0\)then
9: Transmit packets along \(P^{n-1}_{i}\)\(\triangleright\) Change the path along alternative with the next longest delay if possible
10:else
11: Maintain \(P^{n}_{i}\)\(\triangleright\) Current path is maintain if no alternative is available
12:endif
13:endif
14:else\(\triangleright\) If application stream doesn't meet the requirement, more complex path selection algorithm is performed
15:\(t\leftarrow\ 1\,DBP_{ObjID}+DBP_{i}\,|\quad\)\(\triangleright\) Compute how many distance from positive status with two DBP values
16:if\(DBP_{i}\geq 0\)then\(\triangleright\) if current session meets the requirement
17:if\(n-1>0\)then\(\triangleright\) if an available path exists
18: Transmit packets along \(P^{n-1}_{i}\)\(\triangleright\) Change alternative path among multiple paths. Adaptive approach in the aspects of session
19:else
20: Transmit packets along \(P^{1}_{i}\)\(\triangleright\) Change path with the shortest end-to-end delay in case of two requirements missing
21:endif
22:else\(\triangleright\) if current session and application don't meet the requirement at the same time. This is the worst case
23:if\(t<\) Threshold then\(\triangleright\) Distance is not bounded within the threshold
24: Transmit packets along \(P^{1}_{i}\)\(\triangleright\) Change the path with the shortest end-to-end delay
25:else
26: Transmit packets along \(P^{n-t}_{i}\)\(\triangleright\) Change the \((n-t)^{th}\) path according to distance to meeting status
27:endif
28:endif
29:endif
30:endfor
```  
**Algorithm 1** Adaptive QoS routing protocol for packet transmission from node \(i\) to the sink.
## 3 Comparative Study  
Based on the methodologies mentioned in the previous section, comparative experiments are carried out to study the propagation of UGWs under the influence of rail damage. To excite stable ultrasound stress waves, a PZT actuator has been pre-installed on the rail web. Both FBG sensors and high-resolution AE sensors (PICO Acoustic Emission Transducers, produced by Physical Acoustics, Princeton, NJ, USA) have been utilized to receive the UGWs, and comparisons have been made to analyze the detailed performance of both sensors.
## 4 Experimental Results
### Optical-Energetic Analysis of a Concentrating PV System with the Developed Solar Tracker
On the other hand, with the mean STE of partially cloudy days (STE = 0.536\({}^{\circ}\)), the NRP is to 0.91. In both cases, the mean NRP is greater than 0.9. Figure 15: Schematic diagram of a typical CPV module: (**a**) under ideal alignment and (**b**) under misalignment. To estimate the DC electrical energy generated by two high concentration photovoltaic (HCPV) modules under different STEs, SAM software was used. The main parameters considered in the simulation were as follows: two 375.55 W HCPV modules of 20 cells with an efficiency of 37%, a concentration ratio of 700 suns, an optical error factor of 0.9, which takes into account the losses in the plane of array (POA) irradiance because of lens optical errors and a wind flutter factor of 0.01 m/s. There is a reduction in cell power value due to module motion caused by the wind; therefore, SAM reduces the cell output power each time step in the simulation according to 1-flutter loops factor \(\times\) wind speed. The relative maximum powers of the CPV cells for different levels of STEs were obtained from the Tonatiuh simulation: 1, 0.98, 0.93, 0.67 and 0.31 for STE = 0\({}^{\circ}\), STE = 0.3\({}^{\circ}\), STE = 0.5\({}^{\circ}\), STE = 1\({}^{\circ}\) and STE = 1.5\({}^{\circ}\), respectively, (see Figure 17). It is important to mention that the STE or pointing error is the sum of different types of errors, some which are slowly time-varying, such as the misalignment error, and others of highly dynamic behavior,  
Figure 17: Effect of the misalignment error on the radiative flux on the cell surface. such as those due to wind loads and the control system [12]. In the simulation, it was assumed that the errors due to the control system and misalignment errors were constant over time and its effect was considered in the SAM's misalignment coefficient, which was determined, as shown in Figure 17. The only transient error simulated was that caused by wind loads at the location. The simulation was performed for Aguascalientes, Mexico (latitude N 21\({}^{\circ}\)50'39'' and longitude W 102\({}^{\circ}\)20'38''). Figure 18 represents the monthly DC electrical energy generated by the modules. It is important to note that when the absolute value of the STE is less than 0.6\({}^{\circ}\), the energy generated by the CPV modules is greater than 90% of its capacity. On the contrary, when the absolute value of the STE is greater than 0.6\({}^{\circ}\), the generation drops drastically. Therefore, the STS developed in this work will allow the CPV modules to reach at least 90% of their energy conversion efficiency both on sunny and partly cloudy days.
## 6 Conclusions  
In this paper, we research the multiedge-terminal MEC system of an intelligent factory in an Industrial Internet scenario to reduce the total energy consumption of the MEC system through multiedge-terminal computing offloading and resource allocation. The system consists of multiple small base stations connecting to edge servers and multiple mobile smart industrial terminal devices. The terminal devices choose to compute tasks locally or wirelessly offload them to the edge servers. For this system, we designed the IF-DDPG algorithm, which combines FL and DRL. This algorithm achieves a better strategy by jointly optimizing task offloading ratio, subcarrier number, transmission power, and computation frequency, thereby reducing the overall energy consumption of the multiedge-terminal MEC system. Furthermore, the paper details the principle of the IF-DDPG algorithm utilized in this work to tackle the optimization issue. Then, the paper analyzes the effect of variable parameters on IF-DDPG and compares the performance of IF-DDPG with the comparison algorithms under different conditions through simulation analysis. IF-DDPG's convergence was additionally theoretically examined. The simulation results demonstrate that the IF-DDPG algorithm outperforms in optimizing the total energy consumption of the system. In the future, we will explore the simultaneous optimization of system energy consumption and delay in the Industrial Internet multiedge-terminal MEC system to minimize the total system cost.  
Conceptualization, X.L. and J.Z.; methodology, X.L., J.Z. and C.P.; software, X.L. and J.Z.; validation, X.L., J.Z. and C.P.; formal analysis, X.L. and J.Z.; investigation, X.L. and J.Z., and C.P.; resources, X.L.; writing--original draft preparation, X.L. and J.Z.; writing--review and editing, X.L., J.Z. and C.P.; supervision, X.L.; project administration, X.L.; funding acquisition, X.L. and C.P. All authors have read and agreed to the published version of the manuscript.  
Figure 10: Effect of the number of available CPU cores on performance.  
**Funding:** This research was partially supported by Beijing Natural Science Foundation L222004, Beijing Natural Science Foundation Haidian Original Innovation Joint Fund (No. L212026), R&D Program of Beijing Municipal Education Commission (KM202211232011).  
**Institutional Review Board Statement:**  
**Informed Consent Statement:**  
**Data Availability Statement:** The data presented in this study are available on request from the corresponding author.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Results
### NDVI Computation  
Figure 5 presents a comparison between NDVI computed from MSI and OLI sensors on the test set (10,570 pairwise samples). Specifically, it reports the comparison between the sensor value pairs before applying adjustment coefficients. The orthogonal distance regression (ODR) slope is measured at 0.92, and the mean difference with a value of \(-\)0.0243 shows statistical significance (\(t\)-test, \(p\)-value 1.63 \(\times\) 10\({}^{-14}\)). Additionally, the Root-Mean-Square Deviation (RMSD) is computed to be 0.050 (Figure 5a).  
The same comparison is made after applying the adjustment factors from the OLS model in Table 6 (Figure 5b). The ODR slope has increased to 0.95, and the mean difference, with a value of \(-\)0.0211, is no longer statistically significant (\(t\)-test, \(p\)-value 0.7204). The RMSD remains unchanged. The comparison with Landsat-7 is omitted for brevity; however, it exhibits very similar behaviour to the previous comparisons.
## 3 Results and Discussion
### Effect of Plasma Treatment  
The impact of plasma treatment on callus induction in meristem and stipe is shown in Figure 13. The selected calluses are shown in Figure 14. Indirect plasma treatment showed callus induction in both meristem and stipe, while direct plasma treatment on meristem explants did not yield callus induction. Indirect or direct plasma treatment failed to enhance callus induction rate, as the highest development occurred at 0 s plasma treatment in both meristem (57.53 \(\pm\) 5.19%) and stipe (65.59 \(\pm\) 6.24%). Therefore, based on the findings of this study, plasma treatment is not recommended for callus development in _E. cava_. However, further research exploring alternative plasma techniques may reveal different outcomes.  
Figure 14: Callus induction in direct (**a**) for 5 s and indirect for 60 s, (**b,c**) plasma treated stipe explants cultured on standard PESI solid medium.  
Figure 12: Callus induction in meristem (**a**) on PESI solid medium supplemented 1 \(\upmu\)M SPM, and in stipe (**b,c**) on standard PESI solid medium without containing polyamines.
## 3 Results and Discussion
### Excess Water Analysis
#### 3.3.2 Change in Groundwater Storage  
Using the same cumulative water amount and an average specific yield of 0.15 provided by [31], if the excess water was directed to dS/dt(gw) in its entirety, the water table would have been forced to rise 21.2 m. Considering our previous analysis, using wells data, showed that there has not been a significant increase in water levels over the domain during our period of analysis, we also conclude that groundwater storage must only account for a fraction of the excess water.  
To further illustrate that change in groundwater storage alone cannot account for the total excess water generated by (P-Ru), we consider that the amount of water withdrawn in 1980, a record high single-year withdrawal amount [43], was withdrawn each individual year from 1901 to 2008. Using the value of total US groundwater withdrawal in 1980 and the percent of total withdrawal contribution from each hydrological unit [44], we estimated an amount for the study area of 46,543 million gallons per day. We applied this value to each year of our study period and divided by the study domain area of \(3.57\times 10^{12}\) m\({}^{2}\) to estimate the depth of the water withdrawal. We determined this maximum, overestimated groundwater withdrawal to be about 1.93 m in depth. Even if this entire withdrawn amount were restored as water storage, it would only represent about 61% of the 3.18 m of the excess water.  
This analysis leads to the reasonable conclusion that storage changes alone cannot account for of the totality of the observed (P-Ru) difference, even given vast overestimations of these storage changes. Therefore, other components of the water budget must have also responded to the observed (P-Ru) trends.
## 3 Theoretical Background
### Proposed DCGOSM-Based Speech Enhancement Technique
#### 3.9.1 Process of Obtaining the Required Sensing Matrix
After completion of this process, we obtain the sensing matrix (\(\phi_{i,USE}^{j}\)), final noise variance (\(v_{n}^{final}\)), dominating columns for speech signal components (\(V_{s}^{i}\)), and noisy-speech frames database (\(DB_{speech}\)).