esa-sceva/satcom-chunk-collection huggingface dataset:
# Optical Link Design for Quantum Key Distribution-Integrated Optical Access Networks
## 4 Integration of CV-QKD Protocol in OANs
### Architecture for DS CV-QKD-Integrated PONs  
Figure 7 illustrates the architecture of a CV-QKD-integrated PON. Since PON systems operating at line rates up to 50 Gb/s are expected to utilize the O-, S-, and C-bands, the CV-QKD signals can be allocated in the L-band, where fiber loss is low and sufficient wavelength separation from PON signals is ensured. At the OLT, a QKD transmitter, a PON transmitter, and a receiver can be multiplexed in the wavelength domain. By  
Figure 6: Tolerable excess noise in TDM-based CV-QKD as a function of the number of ONUs. The calculated data are obtained from [38].  
employing a DS transmission structure for QKD signals, the secret keys can be broadcast to multiple users through TDM. This approach not only facilitates efficient multi-user access but also minimizes crosstalk during key distribution to end users. After transmission through the feeder fiber, QKD signals are distributed to subscribers via a power splitter. A WDM filter then separates the PON US and DS signals from the QKD signal, ensuring minimal interference. In this architecture, CV-QKD signals can be implemented using a local LO (LLO) scheme [43], which allows for the extraction of the desired wavelength components with high frequency selectivity. Consequently, the LLO scheme is a promising approach for mitigating interference and enhancing the stability of CV-QKD integration in next-generation PONs.
## 3 Conversion Gain Results
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & \multicolumn{4}{c}{**Conversion Gain (dB)**} \\ \cline{2-7}  & \multicolumn{2}{c}{\(nf_{s}+f_{1}\)} & \multicolumn{4}{c}{\(nf_{s}+f_{2}\)} \\ \cline{2-7}  & \(f_{s}+f_{1}\) & \(5f_{s}+f_{1}\) & \(10f_{s}+f_{1}\) & \(f_{s}+f_{2}\) & \(5f_{s}+f_{2}\) & \(10f_{s}+f_{2}\) \\ \hline Co-direction, Standard Modulation & 43.3 & 35.8 & 21.8 & 43 & 35.5 & 21 \\ Co-direction, Differential Modulation & **43.4** & **39.3** & **31.8** & **43.1** & **38.5** & **31** \\ Counter-direction, Standard Modulation & 41.1 & 33.8 & 19.8 & 41 & 33.5 & 19 \\ Counter-direction, Differential Modulation & 41.3 & 37.3 & 29.9 & 41.1 & 36.5 & 29 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Simulated up-conversion gains of the simultaneous up-converted signals at \(nf_{s}+f_{m}\) in the co- and counter-direction schemas for the standard and differential modulation modes. It is worth noting that a low range of the IF frequency of the data signal at the SOA-MZI input to be sampled at the SOA-MZI output was requested. The range of the IF signal should be up to 2 GHz. I decided to split this range up to 10 channels in this work, where the IF frequency spanned from 0.2 GHz for channel one to 2 GHz for channel ten. However, I could also divide it up to twenty channels or more. In that case, the IF frequency ranged from 0.1 GHz for channel one to 2 GHz for channel twenty. As a result, ten channels were chosen in order to up-convert them at the output of the sampling SOA-MZI.
# Satellite Image Super-Resolution by 2D RRDB and Edge-Enhanced Generative Adversarial Network +
## 5 Conclusions  
In this work, we combine the RRDB and the CNN with 2D topology to build a model with dense and complex connections. In the generator sub-network, we are able to generate SR images more close to the ground truth. Also, better and clearer features from edge can be obtained in the EESN. Moreover, the original Laplacian edge-feature extraction method has been replaced by the Canny algorithm. We also incorporate different loss functions to enhance the visual quality of the resultant SR image. By doing extensive experiments on four open databases, the proposed SR approach can generate images with better visual quality [45, 46]. Based on the results of objective evaluation [47, 48], our approach is indeed superior than the current SR models on satellite images. In the future, we can improve and optimize some parts of this model. For example, using different pretrained networks to extract features and compute the perceptual loss to improve the SR images. We also can try to use different identification methods or add the salient region method, which have shown good output SR results in other works. In addition, the discriminator network can be modified to have better judgments to decide whether the output image is true or fake.  
Conceptualization, Y.-Z.C. and T.-J.L.; methodology, Y.-Z.C.; software, Y.-Z.C.; validation, Y.-Z.C. and T.-J.L.; formal analysis, Y.-Z.C.; investigation, Y.-Z.C. and T.-J.L.; resources, Y.-Z.C. and T.-J.L.; data curation, Y.-Z.C.; writing--original draft preparation, Y.-Z.C. and T.-J.L.; writing--review and editing, T.-J.L.; visualization, Y.-Z.C.; supervision, T.-J.L. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
## 3 Results and Discussion
### Sand Pack Test Model Validation Result  
The SRT dataset for the sand pack test has only one set, as shown in Table 2, where the dataset was fitted only to predict the amount of sand produced per unit area. The best model among all six hyperparameter configurations was selected according to the performance metrics. The validation result for the regression problem of the sand pack test is shown in Table 10.  
The model validation result for the sand pack test presented above showed that the hyperparameter configuration in model 1 gave the lowest MSE and RMSE but the second lowest for MAE. Furthermore, it provided the highest R\({}^{2}\) in predicting sand production. Hence, model 1 was selected as the best model to predict sand production for the sand pack test. The actual versus predicted plot of model 1 for the sand pack test is shown in Table 10.  
In Figure 7, the actual amount of sand produced per unit area is portrayed by the red line, while predicted values are shown by the blue line with 79 observations and 84% accuracy. The prediction points of sand production below 0.2 lb/ft\({}^{2}\) do not approximately follow the actual points, but the points above 0.2 lb/ft\({}^{2}\) are almost identical.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **MSE** & **RMSE** & **MAE** & **R\({}^{2}\)** \\ \hline
1 & 0.007 & 0.083 & 0.060 & 0.837 \\ \hline
2 & 0.009 & 0.096 & 0.052 & 0.821 \\ \hline
3 & 0.011 & 0.104 & 0.063 & 0.724 \\ \hline
4
## 5 Experiment Results and Analysis
### Measurement of CH\({}_{4}\) in the Atmosphere  
Atmospheric methane was measured using the developed PAS system. Figure 11a shows the measurement of methane in the air, which shows two peaks: one peak on the right corresponding to the methane absorption line at 1653.72 nm, and another peak on the left related to the water absorption line at 1653.50 nm. The peak corresponding to the absorption peak of the measured methane is 0.083 mV, and the methane concentration is calculated to be 2.07 ppm. The result shows that the system is capable of detecting methane in the air. Figure 11b presents a continuous measurement of 0 ppm methane (nitrogen), atmospheric methane, and 10 ppm methane in the PA cell within a measurement duration of 5 h, which validated the developed MPC-PA (multi-pass cell photoacoustic spectroscopy) sensor. The signal-to-noise ratio of photoacoustic signal in the measurement of 10 ppm methane was determined to be 32.0, which results in a limit-of-detection of 0.313 ppm in 10 s.  
Figure 11: Measurement result of air and continuous measurement for different gases. (a) 2f PA signal of air. (b) Continuous measurement results of nitrogen, air, and 10 ppm methane.  
Figure 10: The results of 6000 s of continuous measurement of methane concentration and the corresponding Allan–Werle analysis of variance.
## 2 Results and Discussion
### Comparison to Thermal Conditions
Similar to results obtained with the propargyl alcohol resin, soluble terminal alkynes with a degree of polarity afforded higher yields under microwave conditions than observed under thermal conditions. Also, the ethynyl benzyl alcohol resin demonstrated the need to optimize reaction conditions for alkynes of differing reactivity and microwave transparency. Interestingly, the thermal yields of the reaction with the catalyst coordinating alkyne **5** were higher, unlike reactions observed on the propargyl alcohol resin. This observation may be reflective of the decreased reactivity of the ethynyl benzyl alcohol resin no longer aiding the previously observed microwave enhancement in the presence of this type of soluble alkyne.
# Economic Feasibility of Underground Pumped Storage Hydropower Plants Providing Ancillary Services
## 3 Results and Discussion
### Feasibility Analysis
Therefore, the construction of new underground infrastructure as the lower reservoir of a UPSH plant is not economically feasible. Figure 6: Feasibility analysis of HPS 3 with surface upper reservoir, considering DTCs between 4 and 10 h and investment costs between 1000 and 3000 \(\upxi\) kW\({}^{-1}\). (**a**) Earnings before interest and taxes (EBIT), where the red line represents zero EBIT; (**b**) payback period; (**c**) net present value (NPV), where the red line represents zero NPV; (**d**) internal rate of return. The profitability analysis of HPS 1, HPS 2 and HPS 3 is shown in Table 4, considering an investment cost of 1675 \(\upepsilon\) kW\({}^{-1}\) (estimated in Section 3.1) and a DTC of 8 h. The results obtained when the existing underground infrastructure is used are much better than those of the previous scenario. The maximum NPV is 185.70 M\(\upepsilon\) and has been obtained in HPS 3, while the IRR increases to 7.10%. The minimum IRR is reached in HPS 1 (6.6%). Finally, the minimum PBs decrease to 15 years in all HPSs. This demonstrates that a UPSH plant could become economically feasible using the existing infrastructure and participating in the ancillary services markets.
## 3 Landing Trajectory Data Correction  
### Data Preprocessing  
The dataset used in this paper consists of QAR data for aircraft of the same model (A320) landing at one airport in China. The QAR data have been decoded and each flight corresponds to a CSV file, with a total of 180 flights. Each CSV file contains multiple rows, with each row corresponding to one second, covering multiple QAR parameters. In this paper, we first preprocess the data through the following steps:  
_Data cleaning_: In the original QAR data, there are some decoding or sensor errors, which may cause some incorrect parameters or information loss. Therefore, it is necessary to filter out these fields or complete missing information. In addition, some CSV files are incomplete and do not cover the entire flight process from takeoff to landing, while others are flight training data with the same departure and arrival airports. These flights are filtered out through the data cleaning step.  
_Conversion of discrete state parameters_: Some QAR parameters are discrete state variables. For example, the landing gear state is either 'AIR' or 'GROUND', corresponding to the air-ground switching of landing gears. AIR represents that the landing gear is in the air, while GROUND represents that the landing gear is on the ground. To show the landing gear state of the aircraft, these two states need to be converted into binary values. In this paper, to obtain more intuitive results, AIR is represented as 1, and GROUND is represented as 0. Similar operations are performed for other discrete state parameters, such as the activation of the autopilot and the flap position.  
_Parameter transformation_: In addition to the aforementioned data processing, some parameters require advanced transformations, such as the wind speed and wind direction. The crosswind and tailwind during the landing phase are important factors related to flight safety incidents. However, the crosswind and tailwind information were not included in the original QAR parameters. Hence, it is necessary to combine the aircraft magnetic heading (flying direction) with wind speed and wind direction parameters to calculate the effect of wind on the aircraft. Given the wind direction \(\beta\) and aircraft magnetic heading \(\alpha\), we apply trigonometric functions to calculate the components of wind speed that are parallel and perpendicular to the magnetic heading of the aircraft, as shown in the following equation:  
\[\theta=\beta-\alpha \tag{1}\]  
\[WIN_{CRS}=WIN_{SPD}\times\sin\theta \tag{2}\]  
\[WIN_{ALG}=WIN_{SPD}\times\cos\theta \tag{3}\]  
where \(\alpha\) is the magnetic heading of the aircraft, \(\beta\) is the wind direction, \(WIN_{SPD}\) represents the wind speed, \(WIN_{CRS}\) represents the crosswind component, and \(WIN_{ALG}\) represents the headwind component (\(WIN_{ALG}<0\) corresponds to the tailwind).
# Effect of Carotid Stenosis Severity on Patterns of Brain Activity in Patients after Cardiac Surgery
## 3 Results
#### 3.2.1 Neurophysiological Data  
This analysis included 111 patients who have undergone combined coronary and carotid artery revascularization or isolated CABG. According to the results of the preoperative examination, they were divided into three groups: the group of left-sided CEA+CABG (n = 30), the group of right-sided CEA+CABG (n = 24), and the group of isolated CABG (n = 57). The patients with combined coronary and carotid surgery had significant CA stenoses as assessed by digital angiography (NASCET criteria).  
No adverse cardiovascular events (myocardial infarction, stroke, death, and repeated unplanned revascularization) were observed in the patients in the early postoperative period for simultaneous CABG+CEA or isolated CABG. In this cohort, POCD occurred in 34 (63.0%) patients with CABG+CEA, and in 32 (61%) patients with isolated CABG (OR = 1.33, 95% CI = 0.62-2.84, \(p\) = 0.59). Significant between-group differences were detected for the psychomotor and executive function indicators. At 7-10 days after surgery, the psychomotor speed in two neurodynamic tests was higher in the CABG group than in the group with CABG+CEA (\(p\) = 0.0002 and \(p\) = 0.005, respectively). In addition, the CABG patients had better indicators of executive control in the same tests at 7-10 days after surgery compared to the patients with CABG+CEA (\(p\) = 0.0004 and \(p\) = 0.02, respectively).  
Figure 3: Lateral differences in the alpha2-rhythm power changes of EEG resting state with eyes open in patients after on-pump CABG, depending on the presence of CA stenoses less than 50%: (a) before cardiac surgery; (b) after surgery; solid lines—the patients without stenoses, dashed lines—the patients with CA stenoses, error bars denote SE, *—\(p\) < 0.05 Newman–Keuls multiple comparison test.
## 3 Solution Approach
### Load Flow
#### 3.2.2 Forward Sweep  
The forward sweep in the backward/forward sweep technique entails updating nodal voltages from first-layer branches to last-layer branches. Computing the voltages at each node, starting with the feeder source node, is the aim of forward propagation. At the feeder substation, the voltage is adjusted to reflect its true value. The effective power in each branch is kept constant during the forward propagation phase at the value determined during the backward propagation step. The main steps of the suggested method, together with the relevant equations, are listed below.  
Step 1: Initialization of voltages \(V_{node}^{0}\)(assume an initial voltage at all nodes of \(n=2,3\ldots N\), where \(N\) is the number of nodes).  
\[V_{node}^{0}=V_{line}<0 \tag{14}\]  
Step 2: Iteration count initialization, K = 1. convergence_threshold = tolerance \(e=10^{-5}\).  
Step 3: Load Current \(I_{node}^{k}\) computation.  
\[I_{node}^{k}=\frac{S_{node}^{(k-1)}}{(v_{node}^{(k-1)}} \tag{15}\]  
For node = 2, 3, \(\ldots\), N. \(s\) represents the complex power at the load and \(v\) is the voltage. \(s=p+jQ\) (in the simulation, the parking lot is modeled as a bus, \(Q=0\)).  
Step 4: Backward sweep: update the current starting from the end nodes.  
\[I_{branch}=I_{node}^{k}+\sum I_{branch,downstream} \tag{16}\]  
Step 5: Forward sweep (start from the substation and move towards the end nodes).  
\[V_{node}^{k}=V_{previousnode}^{k}-(Z_{branch}*I_{branch}^{k}) \tag{17}\]  
For all node = 2, 3, \(\ldots\), N. \(Z_{branch}=R_{branch}+jX_{branch}=\sqrt{(R^{2}+X^{2})}\), where \(R\) is the resistance of each branch and \(X\) is the reactance of each branch.  
Step 6: Error.  
\[E_{node}^{k}=|V_{node}^{k}-V_{node}^{(k-1)}|/V_{line}\leq tolerance \tag{18}\]  
for node = 2, 3\(\ldots\), N.  
Step 7: Maximum error.  
\[e_{max}=max(E_{2}^{k},E_{3}^{k},E_{4}^{k}.\ldots...,E_{node}^{k}) \tag{19}\]  
Step 8: The load flow is converging if \(e_{max}\) is less than or equal to tolerance (\(e\)). If not, proceed to Step 3 and repeat the process after updating the iteration count to K = K + 1. The operation of the load flow computation utilizing the backward and forward sweep approach is shown in Figure 3.
## 3 Results and Discussion
### Effect of the Enzyme Treatment on the Sensory Quality of the Rock Tea Extract
#### 3.1.2 Sensory Effects of the Enzyme Concentration on the Tea Extracts  
Different concentration gradients were established for tannase, tyrosinase, and laccase to investigate the sensory effects of enzyme concentration on the tea extracts using sensory scores. For tannase, the best improvement effect in the sensory indexes of tea extract was  
Figure 1: Sensory scores of the tea extracts for the different enzyme reaction temperatures (**a**) and times (**b**). ck represents the sample that has not been treated with the enzyme. The error bar represents the standard error of the mean, and by the LSD test, the average of different lowercase letters indicates a significant difference (\(p<0.05\)).  
obtained at a concentration of 2.4 U/mL. At this concentration, the bittermess, astringency, and aftertaste increased by 20.5%, 19.7%, and 26.5%, respectively, compared to when the concentration was 1.2 U/mL (Figure 2a). Some researchers have noted that the content of gallated catechins is closely related to the bittermess and astringency of tea, while non-gallated catechins and gallic acid are the main components responsible for the aftertaste of tea. Under the action of tannase, the composition of catechins and the content of gallic acid change significantly under the action of tannase [12]. These findings might be related to changes in the taste of the tea extracts. When the concentration was 4 U/mL, tyrosinase and lactose showed similar trends in reducing the bittermess and astringency; however, tyrosinase had a slightly better effect on the overall flavor of tea extract, with the bitterness and astringency scores being 8.6% and 10.9% higher, respectively. The effects of the two polyphenol oxidases on the aftertaste differed; however, 4 U/mL was still determined to be the appropriate concentration for both enzymes (Figure 2b,c). Flavonol glycosides in tea are closely related to its bittermess and astringency, and polyphenol oxidase can enhance the oxidation of flavonoid glycosides, thereby improving these qualities [16]. The 2.4, 4, and 4 U/mL concentrations of tannase, tyrosinase, and lactose, respectively, were used in subsequent experiments to facilitate in-depth analysis. From the perspective of the influence of a single enzyme in the tea extracts, tannase had a better effect on the overall sensory enhancement than tyrosinase and lactose (_p_ < 0.05), with the overall flavor scores for the three enzymes being 7.02, 6.43, and 6.33, respectively (Figure 2).
## 3 Security Aspects Regarding Wireless Body Area Framework
### Authentication and Key Agreement
#### 3.2.2 Use of Implicit Certificates  
Conventional public key certificates consist of three elements: identification data, a public key, and a digital signature that binds the public key to the ID data and confirms that this binding has been verified by a trusted authority, typically a Certificate Authority (CA). Implicit certificates (ICs) deviate from this structure by eliminating the explicit public key and digital signature [17]. An IC comprises only identification data and cryptographic data, which serve as the foundation for deriving the IC's public key. The private key linked to this public key is exclusively held by the entity identified in the IC. The process of deriving the public key also encompasses validating the IC, since a public key derived from an invalid IC would imply the existence of an undiscovered private key. This leads to a crucial conclusion: an IC cannot be forged unless the CA's private key is exposed. The absence of a digital signature contributes to the IC's smaller size. Elliptic Curve Qu-Vanstone (ECQV) cryptography is an implicit certificate scheme that is based on elliptic curve cryptography. It optimizes cryptographic operations and could suit the resource-constrained nature of WBAN devices [18]. Leveraging the inherent efficiencies of elliptic curve cryptography, ECQV can be embedded in resource-constrained WBAN devices to provide more efficient authentication schemes.
## 3 Results  
### Literature Search  
A thorough, systematic literature search was carried out, and 1178 citations were found in three different databases, including PubMed, Scopus, and Web of Science ISI. Through a meticulous screening process, a total of 432 duplicate citations and 717 articles deemed irrelevant based on their titles and abstracts were excluded from further consideration. Subsequently, 29 publications were recognized as potentially relevant records. A comprehensive full-text screening resulted in the exclusion of 16 articles due to their failure to meet the eligible inclusion criteria, lack of sufficient data, or duplication of study content [16; 17; 49; 50; 51; 52; 53; 54; 55; 56; 57; 58; 59; 60; 61; 62] (Supplementary Table S2). As a result, the current systematic review and meta-analysis included a total of 13 cross-sectional studies. (Figure 1).