esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Optical Sensing with NDs
### Temperature Sensing  
ODMR is very useful for testing the temperature sensing properties of NV centers in diamond and ND. Both the spin resonance and coherence time of the NV\({}^{-}\) center are sensitive to the variations of temperature at the nanoscale [16]. In Reference [102], the ODMR spectra of single 100-nm NDs under a green laser pump (Figure 16a,b) were measured, and temperatures with a precision of 0.1 K were demonstrated (Figure 16c). The local nano-sources of heat were gold nanoparticles independently laser-heated and made to interact with the fluorescent NDs (Figure 16b). More complex magneto-optical detection schemes demonstrated that fluorescent NDs can achieve temperature sensitivities down to the 1 mK Hz\({}^{-1}\)/2 regime [102; 103; 104]. These results prove that the NDs are among the most sensitive temperature probes known.  
Figure 14: Panel (**a**) ND phantoms made of commercial NDs. (**b**) Chicken breast illuminated by an LED. The stripe on the chicken breast marks the edge of the phantom, which is placed within the chicken breast, 5 mm behind the front surface, facing the LED emitting at 621 nm. Fluorescence is collected out to the side. (**c**) ND phantoms imaged outside of the chicken breast. Reprinted with permission from [86]. Reproduced with permission of the American Chemical Society, 2013.  
Some other temperature sensing schemes have been demonstrated, and selection of them can be found in [105; 106; 107; 108; 109; 110].  
Figure 16: **(a)** ODMR spectrum around the NV center of a single ND. The four red points indicate the RF frequencies used to extract the temperature (maximum slope of the dip). **(b)** Confocal spatial map of fluorescent NDs (circles) and gold nanoparticles (cross). **(c)** Temperature of a single ND as a function of laser power with (red) or without (blue) laser illumination on a nearby gold nanoparticle. **(d)** Temperature changes of a single ND, as a function of the distance from the illuminated gold nanoparticle indicated by the cross. Reprinted with permission from [102], [PERSON], 2013.  
Figure 15: **(a)** Schematic drawing of the NV center; **(b)** simulated electric field 6 \(\upmu\)m below the microstructure (where the NV is located) for 1 V voltage applied; **(c)** observed shift of the ODMR resonance lines for different voltages applied; **(d)** schematic of the used confocal setup with Helmholtz coils for magnetic field alignment. Reprinted with permission from [10], [PERSON], 2011.
## 2 System Model of the Proposed Scheme  
The proposed data-hiding system is shown in Figure 1. First, a content owner encrypts the original uncompressed image using an encryption key and creates an encrypted image. Then, a data-hider produces codewords c from the data using an RS encoder and embeds the codewords into the encrypted image using a data-hiding key. The two different images, the encrypted image \(E\) and the codeword-embedded image \(E^{\prime}\), are created, and the codeword-embedded image is transmitted to the receiver. The content owner and the data-hider can be the same or different entities. When the data-hider is different from the content owner, the data-hider does not know the original image, but the data-hider can embed data in the encrypted image. In a receiver, the encrypted image containing additional data is decrypted using the encryption key. According to the data-hiding key, the estimated values for the embedded codeword are extracted, and the hidden data must be recovered after decoding the RS codes. From the help of the error-correction capability of the RS codewords, performance for recovering the image can be enhanced.
# Bi-Directional Point Flow Estimation with Multi-Scale Attention for Deformable Lung CT Registration
## 3 The Proposed Approach
### The Training Objective
#### 3.5.1 Bi-Directional Multi-Scale Flow Loss  
At each resolution level \(l\), we supervise the predicted displacement fields by comparing them against their corresponding ground-truth flows. Specifically, we define a bi-directional loss that penalizes deviations from the ground truth in both the forward (source-to-target) and backward (target-to-source) directions:  
\[\mathcal{L}_{\text{flow}}^{(l)}=\sum_{\mathbf{p}_{i}\in\mathcal{P}^{(l)}}\| \hat{f}_{i}^{(l)}(\mathbf{p}_{i})-f_{i}^{(l)}(\mathbf{p}_{i})\|_{2}+\sum_{ \mathbf{q}_{i}\in\mathcal{G}^{(l)}}\|\hat{g}_{i}^{(l)}(\mathbf{q}_{i})-g_{i}^ {(l)}(\mathbf{q}_{i})\|_{2}, \tag{15}\]  
where \(\hat{f}^{(l)}\) and \(\hat{g}^{(l)}\) denote the predicted forward and backward displacement functions, respectively, while \(f^{(l)}\) and \(g^{(l)}\) represent their ground-truth counterparts. This symmetric formulation provides balanced supervision, encouraging consistent flow estimation in both directions.
## Results
### The Removal of Chlorophyll-_a_, MCs and T&O Compounds via Treatment Processes in the DWTP
#### 2.3.4 Chlorination (CHL)  
Post-treatment chlorination provides the last defense against cyanobacterial cells, MCs, and T&O compounds reaching consumers in water treated by the DWTP. In this study, the chlorophyll-_a_ removal rates by chlorination were between 30.1% and 90.2% compared to the concentrations in the filtration effluent. After chlorination, the maximum concentration of each intracellular MC and T&O compound was 0.03 \(\upmu\)g/L (IMC-LR in the summer) and 1.9 ng/L (_p_-CYC in the spring), respectively. For extracellular MCs, the chlorination process further reduced the concentrations from 15.7 to 74.2%  
Figure 2: The concentrations of chlorophyll-_a_ (left, column) and cumulative removal rates (right, line + scatter) in different seasons of the water treatment processes including raw water (RW), coagulation-flocculation (C/F+, with potassium permanganate and powdered activated carbon in summer and autumn), sedimentation (SED), filtration (FIL) and chlorination (CHL) in the drinking water treatment plant associated with Intake-A. Error bars indicate the standard deviation of data in three months of each season.  
compared to those in the filtration effluent. However, the concentration of each T&O compound increased from 31.0 to 266.7% after chlorination except for _d_-GEO in the spring and autumn and _d_-MIB in the summer and autumn, respectively.  
In the finished water, the average concentrations of chlorophyll-_a_ for each season were between 0.1 \(\pm\) 0.0 \(\mathrm{\SIUnitSymbolMicro\text{g}}\)/L and 0.7 \(\pm\) 0.1 \(\mathrm{\SIUnitSymbolMicro\text{g}}\)/L. The maximum concentrations of MC-LR, MC-RR and MC-YR in the finished water were 0.4 \(\pm\) 0.3 \(\mathrm{\SIUnitSymbolMicro\text{g}}\)/L (in the summer), 0.7 \(\pm\) 0.3 \(\mathrm{\SIUnitSymbolMicro\text{g}}\)/L (in the autumn) and 0.9 \(\pm\) 0.3 \(\mathrm{\SIUnitSymbolMicro\text{g}}\)/L (in the autumn), respectively. The concentrations of T&O compounds varied within a wider range compared to those of MCs in the drinking water. The concentrations of GEO above its OTC of 4 ng/L were detected in the summer (8.4 \(\pm\) 1.6 ng/L) and winter (8.7 \(\pm\) 3.5 ng/L). The CYC exceeded its OTC throughout the whole year with concentrations ranging from 19.5 \(\pm\) 2.4 ng/L to 67.2 \(\pm\) 13.8 ng/L. In the summer, the concentrations of MIB and ION were 52.5 \(\pm\) 30.5 ng/L and 12.2 \(\pm\) 3.3 ng/L, respectively. Both readings exceeded their corresponding OTC standard.  
Figure 3: The concentrations of extra- (E) and intra-cellular (I) microcystitis (MC-LR, MC-RR and MC-YR) (column) and cumulative removal rates (line + scatter) in different seasons of the water treatment processes including raw water (RW), coagulation-flocculation (C/F+, with potassium permanganate and powdered activated carbon in summer and autumn), sedimentation (SED), filtration (FIL) and chlorination (CHL) in the drinking water treatment plant associated with Intake-A. Error bars indicate the standard deviation of data in three months of each season.
# Operation Optimization of the Sea Container Fleet Based on the Double-Level Planning Model
## 4 Conclusions  
This paper focuses on the optimization of the operation of the ocean container fleet, considering multiple constraints such as cargo capacity, speed, journey time, and the number of ships and speed. A nonlinear mixed integer model based on double-level programming is constructed. This model not only reveals the inherent connection between speed, fuel cost, and route allocation results but also highlights the significant impact of speed changes on route allocation results. To demonstrate the model's advantages, this paper takes three Asia-European routes as examples and obtains the optimal route  
Figure 4: The influence of fuel price on shipping factors; **(a)** Impact on cost, speed, and fuel costs; **(b)** Impact on cost, the number of vessels, and fuel cost; **(c)** Impact on carbon tax and speed; and **(d)** Impact on carbon tax and number of ships.  
allocation plan and optimal speed. For example, in speed and route allocation, scheme 6 saves USD 39,000 in total weekly operating costs.  
Through fuel sensitivity analysis, it can be seen that as fuel prices rise, the optimal speed of liner ships gradually slows down, and the number of ships gradually increases. Carbon tax costs should be adjusted accordingly. This provides an important reference for decision makers to adjust route allocation and speed strategies under different market conditions.  
In view of the importance of green, low-carbon shipping in future development, this paper only considers carbon tax as a variable. Therefore, in the process of building an optimized model of container ship operating costs, it should be combined with environmental sustainability (low carbon), development, and other factors for further research.  
Conceptualization, P.X. and H.W.; Methodology, P.X. and H.W.; Software, P.X. and H.W.; Validation, P.X. and H.W.; Formal analysis, P.X. and H.W.; Investigation, P.X. and H.W.; Resources, P.X. and H.W.; Data curation, P.X. and H.W.; Writing--original draft, P.X.; Writing--Review and editing, H.W.; Visualization, P.X. and H.W.; Supervision, P.X. and H.W.; Project administration, H.W. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data Availability Statement: Data are contained within the article.  
The authors declare no conflicts of interest.
# Anti-Multipath Performance Improvement of an M-ary Position Phase Shift Keying Modulation System
## 3 The Scheme of Multipath Delay Estimation and Normalized Symbol Joint Decision  
To reduce the interference of multipath to the signal, it is necessary to estimate the delay value of the multipath, and then design the modulation parameters of the signal to achieve the purpose of separating the multipath. At the demodulator end, not only the multipath information is used for the decision, but also the noise introduced by the multipath information is reduced, so that the demodulation performance can be improved.
## 4 Simulation Validation
### Validation 3: Linear Quadratic Regulator Based on Fuzzy Control Algorithm  
The dynamic simulation was performed using the NREL 5 MW model as the target wind turbine. The simulation conditions involved above-rated wind speeds (18 m/s) with a Class A standard turbulence model, and winds from different seeds were applied for wind estimation in the validation of the feed-forward control algorithm. Each condition was executed for 600 s in a dynamic simulation environment.  
The results of the validation on the LQRF control algorithm using estimated wind speeds according to different wind speed estimation methods are shown in Figure 10. The simulation results depict rotor speed, pitch angle, generator torque, and output. The black line represents the simulation results of the LQRF control algorithm based on the drive train model method, while the red line denotes the simulation results based on the extended Kalman Filter method. Although operational performance appears similar, differences in the standard deviations of rotor speed and output can be observed. Table 6 presents the comparison results of mean and standard deviation for rotor speed and generator output. Slight differences in operational performance can be observed depending on the applied wind speed estimation method. For the LQRF control algorithm using the drive train model method, the standard deviations of rotor speed and generator output are found to be 22.83% and 20.75% lower, respectively, compared to when the Kalman Filter method is applied. According to the simulation results, the performance of the LQRF control algorithm using the drive train model method appears relatively superior. According to the simulation results, although the same control algorithm was applied, the drive train model method showed greater reductions in the standard deviations of rotor speed and generator output, leading to the conclusion that the accuracy of the estimated wind speed is crucial, even when modern control algorithms are applied.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Condition**} & \multicolumn{4}{c}{**Rotor Speed [rpm, \%]**} & \multicolumn{4}{c}{**Electrical Power [MW, \%]**} \\ \cline{2-9}  & **Mean** & **Diff.** & **Std.** & **Diff.** & **Mean** & **Diff.** & **Std.** & **Diff.** \\ \hline DTM & \(12.11\) & - & \(0.1104\) & - & \(5.00\) & - & \(0.0482\) & - \\ EKF & \(11.96\) & \(-1.24\) & \(0.1356\) & \(22.83\) & \(4.94\) & \(-1.20\) & \(0.0582\) & \(20.75\) \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of simulation results with the application of the LQRF control algorithm.  
Figure 9: Power output and estimated available power results (re-plotted).
## 5 Data Processing and Definition of the EAR Index
### Definition of the Normalization and Weight Values
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Parameter** & **Weighting Factor \(\mathbf{w_{\rm f}}\)** & **Normalization Factor \(\mathbf{V_{\rm n}}\)** & **Normalization Factor \(\mathbf{m.u.}\)** \\ \hline \multicolumn{4}{c}{**Emissions—Category [A]**} \\ \hline GWP & 3.00 & \(8.86\cdot 10^{1}\) & (kg of CO\({}_{2}\) eq.)\({}^{-1}\) \\ ODP & 1.00 & \(5.12\cdot 10^{-1}\) & (kg of CFC\({}_{11}\) eq.)\({}^{-1}\) \\ AP & 1.00 & \(4.35\cdot 10^{-1}\) & (kg of SO\({}_{2}\) eq.)\({}^{-1}\) \\ POCP & 1.00 & \(7.39\cdot 10^{-2}\) & (kg of C\({}_{2}\)H\({}_{4}\) eq.)\({}^{-1}\) \\ EP & 1.00 & \(1.07\cdot 10^{-1}\) & (kg of (PO\({}_{4}\))\({}^{3}\) – eq.)\({}^{-1}\) \\ ADPE & 1.00 & \(2.36\cdot 10^{-4}\) & (kg of Sb eq.)\({}^{-1}\) \\ ADPF & 2.00 & \(3.39\cdot 10^{3}\) & (MJ)\({}^{-1}\) \\ \hline \multicolumn{4}{c}{**Use of resources—Category [B]**} \\ \hline PERE & 0.00 & - & - \\ PERM & 1.43 & \(6.40\cdot 10^{1}\) & (MJ)\({}^{-1}\) \\ PERT & 1.43 & \(6.03\cdot 10^{2}\) & (MJ)\({}^{-1}\) \\ PENRE & 0.00 & - & - \\ PENRM & 1.43 & \(2.78\cdot 10^{3}\) & (MJ)\({}^{-1}\) \\ PENRT & 1.43 & \(5.75\cdot 10^{3}\) & ([PERSON])\({}^{-1}\) \\ SM & 1.43 & \(4.34\cdot 10^{2}\) & (kg)\({}^{-1}\) \\ RSF & 1.43 & \(1.91\cdot 10^{0}\) & ([PERSON])\({}^{-1}\) \\ NRSF & 1.43 & \(1.13\cdot 10^{2}\) & ([PERSON])\({}^{-1}\) \\ FW & 0.00 & - & - \\ \hline \multicolumn{4}{c}{**Output flows and waste—Category [C]**} \\ \hline HWD & 1.00 & \(6.43\cdot 10^{-1}\) & (kg)\({}^{-1}\) \\ NHWD & 1.00 & \(1.98\cdot 10^{9}\) & (kg)\({}^{-1}\) \\ RWD & 1.00 & \(1.91\cdot 10^{-2}\) & (kg)\({}^{-1}\) \\ CRU & 0.00 & - & - \\ MFR & 2.50 & \(6.70\cdot 10^{-1}\) & (kg)\({}^{-1}\) \\ MER & 2.50 & \(3.40\cdot 10^{-1}\) & (kg)\({}^{-1}\) \\ EEE & 2.00 & \(3.40\cdot 10^{0}\) & (MJ)\({}^{-1}\) \\ EET & 0.00 & - & - \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Selected values for weighting and normalization factors. Finally, the value of the EAR index is evaluated as the weighted sum of the three contributions \(A\), \(B\) and \(C\) according to the following equation:  
\[EAR=w_{\text{A}}\cdot A+w_{\text{B}}\cdot B+w_{\text{C}}\cdot C \tag{6}\]  
The application of the three different weights \(w_{A}\), \(w_{\text{B}}\) and \(w_{\text{C}}\) for each environmental impact category, respectively, equal to 0.65, 0.25, and 0.10, was decided on the basis of the following considerations:  
* Emissions are quantified through established methodologies and therefore it was decided to emphasize the impact by assigning a higher coefficient;
* Energy consumption and emissions are mutually affected;
* Since the from cradle to the gate approach has been adopted, a lower coefficient was chosen to be applied to the \"output flows and waste category. Table 8 shows an example of EAR index calculation for the wearing course of the Norwegian market (Norge wearing). The source data are the ones reported in Table 6.
## 2 Energy-Aware Multi-UAV Dispatch Algorithm (EAMUD)
### The Event Queue and UAV Pool of GCS
Figure 2: Data structure of \(Ev_{q}\) (**A**) the event time, (**B**) the event \(j\) at time \(t\), (**C**) the sub-event \(i\) of event \(j\) at time \(t\), (**D**) information of each sub-event. Further, the remaining energy \(E_{u}^{Rem}\) for the original UAV to consume on communication and hovering at the position of \(Ev_{j,i}\) is the difference of its energy \(E_{u}^{Bat}\) before dispatching and that, i.e., \(2\times E_{j,i}^{Fly}\), for its round-trip flight. Hence, \(com_{j,i}\) and \(\Delta t\) are the quotient of \(E_{u}^{Rem}\) divided by the unit energy, \(E_{u}^{Com}+E_{u}^{Ho}\), consumed on communication and hovering. Please note that the unit energy consumed on communication, i.e., \(E_{u}^{Com}\), is equal to the sum of energy consumed for the U2U and U2V communication, i.e., \(E_{tU2U}\) and \(E_{tU2V}\). Finally, GCS inserts a handover sub-event into its event queue at time \(t_{next}\), i.e., \(t+\Delta t\), for dispatching the handover UAV to replace the original one by EAMUD.
## 5 Conclusions  
In our study, we investigated the temporal characteristics of the output laser in a fiber system modulated by a PRBS using the transient SBS three-wave coupling equation. During our analysis, we identified periodic high-intensity pulses occurring between the output laser and the backward Stokes light. These pulses were found to originate from the extended dwell times present in the PRBS signal, with particular emphasis on the longest and second longest dwell times. To address this issue, we optimized the PRBS signal while taking into account the SBS establishment time constant of 5.6 ns. The output laser peaks of the MLS1 and MLS2 signals were reduced from \(\pm 55\%\) to \(\pm 25\%\) and \(\pm 10\%\) relative to the original PRBSs, respectively, and the peaks of Stokes light were reduced from 39% to 19% and 11%, respectively. This was essential to maintain the stability of the lasers' output and protect the components in the upstream amplifier and main oscillator stage. The MLS2 signal modulation was particularly effective as a compromise solution, balancing the need to maintain the randomness of the sequence while also addressing the issue of long dwell times. Additionally, we experimentally verified that the rational optimization of the sequence does not reduce the SBS threshold, which also proved that our optimization method does not affect the output of lasers.  
The theoretical and experimental results obtained from this study provide crucial guidance for the development of high-power fiber lasers with narrow linewidths, offering valuable insights for optimizing their performance.  
Software, L.D.; Validation, Y.Z.; Resources, X.L.; Data curation, Y.Z. and Y.L. ([PERSON]); Writing--original draft, Y.Z.; Supervision, D.L., X.L., A.Y., Y.L. ([PERSON]) and Z.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Open Fund for Key Laboratory of Solid-State Laser Technology; Beijing Municipal Natural Science Foundation (1212009); Basic Scientific Research (JCKY2021110B175); Rapid Support Programme (80914020105).  
Not applicable.  
The original contributions presented in the study are included in this article; further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.  
Figure 8: Beam quality at an output power of 2.844 W.
## 1 Introduction
Accurate diagnosis is pivotal for the adoption of an appropriate therapy. Anti-epileptic drugs (AEDs) represent first-line treatment for epilepsy and despite the availability of more than 20 such drugs, approximately 30% of patients do not respond to this type of therapy [12]. Among all epilepsy types, temporal lobe epilepsy (TLE) is the most common drug-resistant form of epilepsy in adults. Current AEDs mainly act by directing transmembrane ion channel function or by promoting \(\gamma\)-aminobutyric acid (GABA)-mediated inhibition to decrease the electrical activity of the brain [3; 14]. For instance, phenytoin and lacosamide inhibit sodium channel activation; retigabine opens potassium channels; ethosuximide and lamotrigine block calcium channels; tiagabine inhibits GABA reuptake and phenobarbital and benzodiazepines enhance GABA receptors [3]. The AEDs generally focus on stabilizing and elevating the threshold of the CNS against hyperexcitability. On the other hand, the role of GABA neurotransmitters and GABA receptors in inhibiting activity in the central nervous system [15], and the role of glutamate neurotransmitters in aberrant hyperexcitability [16] have been shown to represent possible mechanisms which can be explored in order to develop new treatments. With extensive research focused on each of the above approaches, great progress has been made in deciphering epilepsy's pathophysiology at a molecular level. The better the understanding of the complex molecular mechanisms involved in the disease initiation and progression, the more unrealistic it appears that a drug affecting a single neurotransmitter receptor or an ion channel will be found to effectively treat epilepsy. Instead, combinations of pharmacological agents designed for an individual patient with a known expression profile should lead to better personalized clinical outcomes. In order to optimize such drug combinations, sophisticated quantitative analyses of protein-protein interaction networks should be implemented involving the key biomarker proteins of interest and the results of these analysis validated experimentally. In this paper we develop such a computational modelling effort for epilepsy that is based on thermodynamic measures of protein-protein network characterization, having previously provided a general approach [17] and also applied it specifically to other diseases such as various types and stages of cancer [18; 19; 20; 21; 22; 23; 24; 25; 26].