esa-sceva/satcom-chunk-collection huggingface dataset:
# Optical Link Design for Quantum Key Distribution-Integrated Optical Access Networks
## 5 Conclusions  
In this paper, we reviewed the optical link design perspectives of QKD-integrated OAN systems, analyzing both technical feasibility and potential implementation challenges. First, we examined the evolution of PON technologies. In legacy PON systems operating at speeds up to 10G, US signals are transmitted in the O-band, while DS signals are transmitted in either the S- or C-band. The 25G and 50G PONs are expected to utilize only the O-band for both DS and US signals to mitigate CD penalties, and they require a high launched power. Secondly, we reviewed QKD implementations in P2 MP topology. Implementing DV-QKD in OANs requires a US structure to minimize the number of expensive SPDs. However, for links exceeding 10 km, this approach can barely support two users--and even then, only if the PON signal launch power is significantly reduced. Such a condition is impractical for beyond-10G PON systems. As a result, CV-QKD emerges as a more suitablealternative, offering simplified scheduling and reduced crosstalk in a multi-user secret key distribution. Among various CV-QKD architectures, the DS CV-QKD scheme is particularly advantageous for OAN integration due to its low crosstalk characteristics. Finally, we suggested the structural design of DS CV-QKD-integrated OAN systems and analyzed the key module configurations required for CV-QKD implementation. By reviewing the existing research, we assessed these configurations from a cost-effectiveness perspective, providing insights into practical deployment strategies for QKD-enabled OANs.  
Investigation, S.B.; supervision, S.-T.K. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by 2024 Research Grant from Kangwon National University and in part by the Institute for Information and Communications Technology Planning & Evaluation (IITP)-Information Technology Research Center (ITRC) (No. RS-2024-00437191).  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflicts of interest.
## 3 Results and Discussion  
### Dough Fermentation  
In Figure 1, pH results of the fermented doughs are presented. The main objective of this study was to use reduced fermentation times for easier scaling up to industrial applications. pH was measured every 2 h from 0 to 6 with a final measurement at 24 h to evaluate _Lactobacillus paracasei_ fermentation.  
Figure 1: pH values for dough fermentation; data is mean (\(\pm\)standard deviation) of three replicates. a, b, c, d, e different letters indicate significant differences (\(p<0.05\)).  
Statistically significant differences between control dough and fermentation dough can be seen parting from 4 h of fermentation, being 6.06 \(\pm\) 0.00 and 5.95 \(\pm\) 0.005. Reducing processing times, while providing health benefits, could be of interest in the food industry. Other authors have reported lower pH values obtained after a 24 h fermentation, these values being 2.90 and 3.5, respectively [16; 17]; while in this study, the pH values obtained were 4.59 \(\pm\) 0.005 and 4.92 \(\pm\) 0.00, respectively, for fermented and control dough. Direct comparisons are difficult, because the elements employed, such as flour source, probiotic microorganisms and its concentration, are different. Generally speaking, a lower pH value is desirable since it is used as an indirect indicator of microorganism proliferation and thus higher biotransformation of health beneficial compounds, as well as dough's rheology modification.  
Control dough pH results suggest that natural cassava's microbiota could be fermenting carbohydrates present in the flour, however, not achieving the same results as the probiotic dough. Several lactic acid bacteria have been reported to be commonly found in cassava, such as _L. plantarum_, _L. amylolyticus_ and _L. mammihotivornans_[18; 19; 20]. As far as we are concerned, there are no reports of an antagonistic effect between _L. paracase_ and the most common LAB in cassava. They have even been used together in other foodstuffs [21]. This suggests that pH differences can be attributed to _L. paracasei_ inoculation used during this study.  
_L. plantarum_ has shown to be one of the lactic acid bacteria to adapt easily to cassava sourdough fermentation [22]. However, to the best of our knowledge, there is no study making a comparison in cassava sourdough between _L. plantarum_ and _L. paracasei_.
# Bending Performance of a Prestressed Concrete Composite Girder Bridge with Steel Truss Webs
## 4 Test and Finite Element Result Analysis
### Failure Mode  
The crack distribution and failure mode photos of the concrete top and bottom plates for the test specimen after stopping the test are shown in Figures 12 and 13, respectively. It can be seen in Figures 12 and 13 that during the loading process, the crack distribution and vertical deformation of the test specimen were axisymmetric with respect to the mid-span section. As can be seen in Figure 12, the crack distribution area at the concrete bottom plate was 421 cm long, and the cracks in the mid-span position extended through the section side to the top plate, where the average distance between the cracks was about 9.5 cm. The cracks in the flexural shear zone were distributed within 90 cm from the middle diaphragm. Figure 13 shows that the steel truss web did not deform significantly throughout the entire loading process, and the joint connector remained undamaged. This indicates that the test specimen lost its bearing capacity due to excessive deformation while maintaining good overall mechanical performance.  
Figure 11: Comparison of the load–strain curves of the steel truss webs: (**a**) Strain distribution of the steel truss webs along the longitudinal direction; (**b**) Load–strain curve of the steel truss webs.  
Figure 14 shows the crack distribution region obtained by FE analysis when the load of 165 kN was applied. A comparative analysis of Figures 12 and 14 shows that cracks in the bottom plate concrete of the composite girder were mainly distributed in the pure bending section due to the large bending moment. The measured cracking area of the bottom plate concrete was similar to that of the FE analysis, and the cracking range obtained by the FE analysis was about 470 cm wide, which was slightly larger than the test result.
###### Abstract  
In recent years, there is a growing concern about the alarming spread of antimicrobial resistance (AMR) in different environments. Increasingly, many species of bacteria, fungi and viruses are becoming immune to the most commonly used pharmaceuticals. One of the causes of the development of the resistance is the persistence of these drugs, excreted by humans, in municipal and hospital wastewater (WW). Consequently, wastewater treatment plants (WWTPs) are a primary source of antimicrobial resistance genes as novel pollutants. This systematic review sought to examine the relevant literature on pharmaceutical residues (PRs) responsible for AMR in municipal and hospital WW in order to propose a classification of the PRs of greatest concern and provide an updated source for AMR management in WWTPs. Among 546 studies collected from four databases, 18 were included in the present review. The internal and external validity of each study was assessed, and the risk of bias was evaluated on a 20-parameter basis. Results were combined in a narrative synthesis discussing influent and effluent PR concentrations at 88 WWTPs, seasonal variations, differences between hospital and municipal WW, environmental risk assessment values of antimicrobial substances and treatment facilities removal efficiencies. Among the 45 PRs responsible for AMR evaluated in this study, the antibiotics ciprofloxacin, clarithromycin, erythromycinycin, metronidazoleazole, ofloxacin, sulfamethoxazole and trimethoprim constitute a considerable risk in terms of ubiquitous distribution, worrying concentrations, risk quotient values and resistance to removal treatments. Gaps in knowledge, data and information reported in this review will provide a valuable source for managing AMR in WWTPs.  
wastewater; pharmaceutical residues; antimicrobial resistance; wastewater treatment plants; occurrence; removal; environmental risk assessment; classification
## 3 Results and Discussion  
### Appearance Properties of FAA Tea at Four Harvest Times  
As illustrated in Figure 1a, FAA tea produced from March to June exhibited significant morphological and color changes. The color shifted from yellow to dark green, and the tea extracts became visibly darker. These changes can be attributed to the high content of chlorophyll, carotenoids, flavonoids, and other pigmented compounds in FAA tea, which inevitably affect the color of the product [29].  
Figure 1: The FAA tea appearance characteristics. (**a**) Image of dry FAA tea, FAA tea infusions, and FAA tea residues; (**b**) L* value; (**c**) a* value; (**d**) b* value; (**e**) the DPPH* scavenging ratio (%) and IC50/DPPH*** (mg/mL); (**f**) ABTS**+ scavenging ratio (%) and IC50/ABTS**+ (mg/mL); (**g**) FRAP (mmol/L). (The letters in the figures represent the significant differences among different treatments.)  
The color of the FAA tea soup plays a crucial role in consumer acceptance. Results from the color difference analysis, shown in Figure 1b-d, revealed variations in the L*, a*, and b* values across the four harvest periods. Here, L* represents brightness (0 = black, 100 = white), a* denotes the red-green axis (+a = red, -a = green), and b* indicates the yellow-blue axis (+b = yellow, -b=blue) [30]. Over the harvest periods, the brightness (L*) initially decreased and then increased, while both a* and b* values initially rose and subsequently declined. FAA-5 marked a turning point, with the lowest L* and highest a* and b* values, corresponding to the darkest color. In contrast, FAA-6 exhibited intermediate L*, a*, and b* values, resulting in a bright and visually appealing tea soup.
## 2 Methods  
In a SAXS experiment, the sample is exposed to an X-ray beam with energy ranging from 8 keV (typical of Cu K\({}_{\alpha}\) from in-house laboratory sources) to 20 keV (used in synchrotron sources to minimize radiation damage). Data can be obtained in fractions of a second by recording the scattering pattern on electronic detectors, making it possible to routinely follow the effect of temperatures, solvent conditions, and deformation [39].  
The scattered intensity is measured as a function of the scattering angle, 2\(\theta\), but is usually expressed as a function of the scattering vector, \(q\), also referred to as the momentum transfer vector. The relations between 2\(\theta\) (degrees or radians), \(q\) (A\({}^{-1}\)), and the widely used Bragg spacing between diffraction planes, \(d\) (A) and \(\lambda\) (A), the X-ray wavelength, are given in Equations (1) and (2).  
\[q=\frac{4\pi}{\lambda}\sin\theta \tag{1}\]  
\[d=\frac{2\pi}{q} \tag{2}\]  
Peaks or reflections in the scattering pattern at certain \(q\)-values correspond to specific characteristic distances in the material. For SAXS, \(q\) is typically less than \(\sim\)0.25 A\({}^{-1}\) (\(d\) > 25 A), whilst for WAXS (wide-angle X-ray scattering), \(q\) > \(\sim\)0.25 A\({}^{-1}\) (\(d\) < 25 A).  
The coherence size of the scattering domains, \(L\), also known as the crystallite size (A), is calculated from the width of the peak using the Scherrer equation (Equation (3)),  
\[L=\frac{2\pi K}{\Delta q} \tag{3}\]  
where \(\Delta q\) is the peak's full width at half maximum, FWHM, and \(K\) is a constant, typically 0.9 [40; 41]. \(L\) represents the length over which the structure is ordered. The three distinct classes of materials, fibers, lamellae, and solutions, are discussed in the following sections along with examples.
# Decision-Making Model for Dynamic Scenario Vehicles in Autonomous Driving Simulations
## 4 Decision-Making Model for Dynamic Scenario Vehicles
### Achievement  
As depicted in Figure 1d, \(veh_{sv}\) estimates whether the remaining distance \(d_{left}\) in the current road section is sufficient to change from the current lane to the nearest target lane for a forced lane change. The achievement motivation does not emerge if the remaining distance is sufficient. If the remaining distance is insufficient, \(veh_{sv}\) further checks whether the current lane is the target lane for forced lane change. If this criterion is satisfied, \(veh_{sv}\) maintains the current driving state via the action \(b^{cn}_{normal}\); otherwise, \(veh_{sv}\) switches to the target lane (\(b^{cl}_{destination}\)).  
Remaining Distance Estimation  
Multiple target lanes exist for the forced lane change, which can be considered as \(lane_{s}\) on road section \(R\).  
\[Lane_{s}=\sum\limits_{i=1}^{N}lane_{s}^{i},\ N\geq 1 \tag{6}\]  
The target lane \(lane_{t}\) closest to the current lane \(lane_{sv}\) is selected from \(Lane_{s}\). The function \(ID(x)\) is the identification (ID) of the acquired lane.  
\[LaneDistance(lane)=abs(ID(lane)-ID(lane_{sv})) \tag{7}\]  
\[lane_{t}=argmin(LaneDistance(lane_{s})),\ lane_{s}\in Lane_{s} \tag{8}\]  
For the sake of computational convenience, the following condition was assumed:  
\[0<ID(lane_{sv})\leq ID(lane_{t}) \tag{9}\]  
Considering the average vehicle speed \(\overline{v}^{i}_{R}\) on each lane of the road section \(R\), we estimated the longitudinal distance \(d_{sv\to t}\) from \(lane_{sv}\) to \(lane_{t}\) as follows:  
\[d_{sv\to t}=\sum_{i=ID(lane_{t})-1}^{ID(lane_{t})-1}\left(v^{i}_{R} \overline{v}_{w}+\frac{\overline{v}^{i}_{R}+\overline{v}^{i+1}_{R}}{2} \tau_{c}\right) \tag{10}\]  
where \(\tau_{w}\) denotes the time that \(veh_{sv}\) waits to change lanes, and \(\tau_{c}\) indicates the time required by \(veh_{sv}\) to change lanes. Furthermore,  
\[d^{\prime}_{left}=d_{left}-d_{0} \tag{11}\]  
where \(d_{0}\) denotes the distance reserved in the road section, and \(d^{\prime}_{left}\) represents the actual remaining distance of the road section.  
The goal motivation emerges only if \(d_{sv\to t}>d^{\prime}_{left}\).
## 3 Related Work
### Closed-Form Formulas for GDOP and WDGOP Calculation
#### 3.2.2 Closed-Form Formula for WGDOP Calculation  
To improve the WGDOP accuracy effectively and efficiently, the author of this paper proposed the closed-form solutions for two WGDOP formations for the case of processing four measurements with different variances and one of the measurements with better accuracy than the others [30]. When exactly four measurements are used, the formula provides the best computational efficiency. The computation load of the closed-form formula is greatly less than that of the matrix inversion method. The proposed efficient formula can provide the exact solution of WGDOP calculation. The WGDOP calculations formula can eliminate the poor geometry influence and reduce the computational overhead.
# DCO-MAC: A Hybrid MAC Protocol for Data Collection in Underwater Acoustic Sensor Networks
###### Abstract  
In underwater acoustic sensor networks (UASNs), medium access control (MAC) is an important issue because of its potentially significant effect on the network performance. However, designing a suitable MAC protocol for the UASN is challenging because of the specific characteristics of the underwater acoustic channel and network, such as limited available bandwidth, long propagation delay, high bit-error-rate, and sparse network topology. In addition, as the traffic load is non-uniformly distributed in a UASN for data collection, it is essential to consider the application feature for the MAC protocol. In this paper, we propose a MAC protocol in a data-collection-oriented UASN, abbreviated as the DCO-MAC protocol. In the proposed protocol, the network is partitioned into two kinds of sub-networks according to the traffic load. A contention-based MAC protocol is used in the sub-network with a light traffic load, while a reservation-based MAC protocol is used in the sub-network with a heavy traffic load. Meanwhile, the DCO-MAC protocol supports the access of mobile nodes. The theoretical analysis and simulation results demonstrate that, in a UASN for data collection, the proposed MAC protocol outperforms the other existing MAC protocols, in terms of the network throughput, end-to-end packet delay, energy overhead, and fairness.  
Keywords:underwater acoustic sensor networks (UASNs); medium access control (MAC); data-collection-oriented MAC (DCO-MAC) protocol; hybrid MAC protocol +
Footnote †: journal: Sensors
## 1 Introduction
A pitch angle control strategy is used to regulate the power through the pitch actuator, the only control system available in the wind turbine, as there is no power electronic interface between the generator and the load to allow controlling the wind turbine outputs. A modified PI pitch angle control is proposed to reduce fluctuation in the power, reduce activity of the pitch actuator and maintain a consistent brilliance of the banks' LEDs. In case of excess wind power compared to the power reference, as requested by the supervisor, the pitch actuator of the wind turbine will change the pitch angle of the blades to reduce the generated power to the reference one. For the other case, where the wind turbine cannot produce enough power to meet the demand of the supervisor, less priority loads will be automatically turned OFF by the automatic power management system. The Boolean-based method presented in this work can be applied for a small-scale network with a limited number of isolated loads and can be used in remote areas where less demand of energy is required. The rest of the paper is organized as follows: the experimental setup (wind turbine and electronic load) is described in Section 2; the proposed power management system for automatic switching of the banks is detailed in Section 3; the modified pitch angle controller, to deal with power fluctuation, is analyzed in Section 4; And, the experimental results are presented in Section 5.
# Design and Performance Analysis of Navigation Signal Based on OFDM
## 5 Performance Evaluation of OFDM Navigation Signals
### Anti Interference Ability
#### 5.5.3 Multipath Interference  
Multipath interference is a major interference signal for GNSS receivers. In this section, we consider a simple multipath interference scenario. There are two paths in the transmission channel: a direct path and a reflection path. The power of the reflection path is 3 dB lower than that of the direct path. The lead-lag interval is 10 ns, and the receiver bandwidth is selected to be 36 MHz. Figure 14 shows the relationship between the multipath envelope error of the NELP code tracking loop and the average error of the multipath envelope. When the multipath delay is within 15 m, the multipath error of the OFDM modulated navigation signal is relatively large, but when the multipath delay is greater than 15 m, the multipath error of the OFDM navigation signal is small. It can be seen from the figure that the multipath error of OFDM(8) is better than that of OFDM(128). After analysis, it can be found that the main reason is that the subcarrier spacing of OFDM(8) is 2.4 MHz, while the subcarrier spacing of OFDM(128) is 0.15 MHz. A wider subcarrier spacing can effectively improve the multipath error. By analyzing the communication signal, the guard interval of the OFDM has a strong anti-multipath ability. Therefore, in follow-up research, the anti-multipath ability of the navigation signal can be enhanced through the modification and design of the receiver and through the use of the cyclic prefix.  
Figure 13: The equivalent carrier-to-noise ratio.
# Design and Application Space Exploration of a Domain-Specific Accelerator System
## 6 Results Analysis
### Precision Analysis  
Large-scale matrix operation and multiple iterations may bring about very large numbers, so we choose floating-point for calculations. However, the floating-point numbers can have decidedly worse accuracy than fixed-point numbers in the same word length, so we need to go through an accuracy analysis to ensure precision requirements, especially in the tasks that involve multiple different types of algorithms. For two STAP cases with different dimensions, which are 96 \(\times\) 4096 and \(48\times 2048\) respectively, we compare the output data with MATLAB results. The maximum and average square root relative errors are listed below in Table 5. The data under test are randomly generated single-precision floating-point numbers. According to the results, we observe that the average error is about \(5\times 10^{-7}\) and it incurs negligible accuracy loss.
## 3 Results and Discussion
### Evaluation Metrics  
This study uses precision (P), recall (R), F1-score (F1), and accuracy [24] as evaluation metrics. The definitions of all the metrics are as follows:  
\[\mathrm{P}=\frac{\mathrm{TP}}{\mathrm{TP}+\mathrm{FP}} \tag{10}\]  
\[\mathrm{R}=\frac{\mathrm{TP}}{\mathrm{TP}+\mathrm{FN}} \tag{11}\]  
\[\mathrm{F1}=\frac{2\times\mathrm{P}\times\mathrm{R}}{\mathrm{P}+\mathrm{R}} \tag{12}\]  
\[\mathrm{Accuracy}=\frac{\mathrm{TP}+\mathrm{TN}}{\mathrm{TP}+\mathrm{TN}+ \mathrm{FP}+\mathrm{FN}} \tag{13}\]  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Epoch & 200 \\ Batch size & 32 \\ Learning rate & \(1\times 10^{-5}\) \\ Regularization parameter & 0.1 \\ Optimizer & Adam \\ Activation function & ReLU \\ Loss function & Cross-entropy \\ \hline \hline \end{tabular}
\end{table}
Table 2: Training hyperparameters.  
Kappa [25]: measures model prediction-actual result consistency (range: [\(-\)1, 1]). Closer to 1 = better consistency; 0 = random guessing; \(-\)1 = complete opposition. Complements accuracy limitations in imbalanced datasets.  
\[\text{Kappa}=\frac{\text{P}_{\text{o}}-\text{P}_{\text{e}}}{1-\text{P}_{\text{e}}} \tag{14}\]  
P\({}_{\text{o}}\) is the proportion of correctly predicted samples (accuracy), and p\({}_{\text{e}}\) is the proportion of correct predictions by random chance.  
MCC [26]: Evaluates models using TP, TN, FP, and FN, assessing the performance in imbalanced datasets. Ranges from [\(-\)1, 1]: 1 = optimal, 0 = random guessing, \(-\)1 = complete misclassification.  
\[\text{MCC}=\frac{\text{TP}\times\text{TN}-\text{FP}\times\text{FN}}{\sqrt{( \text{TP}+\text{FP})(\text{TP}+\text{FN})(\text{TN}+\text{FP})(\text{TN}+ \text{FN})}} \tag{15}\]  
TP, FP, TN, and FN denote the numbers of true positives, false positives, true negatives, and false negatives, respectively.  
Macro Average [27]: Independently calculates metrics (e.g., precision, recall, F1) for each class, averages them without weighting by sample size, reflecting the model's cross-class average performance. Example: macro-averaged precision formula.  
\[\text{macro avg}_{\text{P}}=\frac{1}{\text{n}}\sum\limits_{\text{i=1}}^{ \text{n}}\text{P}_{\text{i}} \tag{16}\]  
where n is the number of classes, and P\({}_{\text{i}}\) is the precision of the i-th class.  
Weighted Average [27]: calculates weighted metrics per class using sample size proportions (larger classes have higher influence), addressing imbalance. Example: weighted-averaged precision formula.  
\[\text{weighted avg}_{\text{P}}=\frac{\sum\limits_{\text{i=1}}^{\text{n}}( \text{P}_{\text{i}}\times\text{support}_{\text{i}})}{\sum\limits_{\text{i=1}}^ {\text{n}}\text{support}_{\text{i}}} \tag{17}\]  
where support\({}_{\text{i}}\) is the number of samples in the i-th class.