esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
In EC, optimising parameters such as pH, current density, and electrolysis time for enhanced dye removal are emphasised [47; 49; 50]. Recent studies have demonstrated the effectiveness of aluminium electrodes in EC, further underscoring the importance of parameter optimisation for improved performance [51; 52; 53]. Several studies have previously explored the removal of TY using EC under various operating conditions and electrode configurations. [PERSON] et al. [54] examined TY removal using iron and aluminium electrodes, achieving nearly 100% colour and 90% COD removal at pH 5.78, within just 6 min of electrolysis, at a current density of 120 A m\({}^{-2}\) and an interelectrode distance of 1.5 cm. They also reported that the Fe/Al electrode pair (anode/cathode) was more effective than the Fe/Fe configuration. [PERSON] and [PERSON] [55] investigated TY removal using iron and steel electrodes, obtaining 50% removal efficiency after 70 min of treatment at 5 V, for initial dye concentrations ranging from 30 to 68 mg/L. More recently, [PERSON] and [PERSON] [56] found that EC cells equipped with iron and aluminium anodes and an aluminium cathode in a series connection achieved higher removal efficiency compared to simple EC cells using only an iron anode and aluminium cathode. [PERSON] et al. [57] focused on the influence of sodium chloride (NaCl) concentration on the EC process for simultaneous TY removal and hydrogen generation. Their study recorded the highest removal efficiency of 93% at NaCl concentrations of 0.8 and 1 g/L after 240 min, underscoring the method's promise for future environmental applications. Although several studies have explored the EC process for TY dye removal using various electrode combinations and operating conditions, the present work provides a more comprehensive and integrative investigation focused specifically on the performance of aluminium electrodes. This study stands out by simultaneously monitoring key solution parameters (pH, temperature, and removal efficiency), while also conducting a microscopic assessment of anode consumption, detailed sludge characterisation using SEM/EDS and FTIR analysis, and evaluation of the settling behaviour of post-EC suspensions. Furthermore, the economic aspect of the EC process is addressed through operational cost analysis. The optimisation of process variables is conducted using response surface methodology (RSM), offering a statistically robust and efficient experimental design. This integrated approach delivers novel insights into the mechanistic, practical, and economic dimensions of TY removal with EC and advances the development of cost-effective and environmentally sound water treatment strategies.
## 4 Conclusions  
In summary, an SLM Ho:SSO laser with two intra-cavity Fabry-Perot etalons was developed and operated at 2111.9 nm. To our knowledge, this is the first SLM Ho:SSO laser reported to date. In the SLM operation, the output power reached a maximum as 590 mW at 2111.91 nm when the incident power was 10.2 W. The slope efficiency and the optical conversion efficiency were 9.7% and 5.8%, respectively. The \(M^{2}\) factor was calculated to be 1.17, when the output power was 590 mW. The SLM Ho:SSO laser has promising prospects in optical remote sensing, differential absorption light detection and ranging (LiDAR), and eye-safe coherent LiDAR.  
This work is supported by the National Natural Science Foundation of China (61405046) and the Natural Science Foundation of Heilongjiang Province (51305089). All authors contributed extensively to the study presented in this manuscript. [PERSON]-[PERSON] and [PERSON] designed the Laser system, and tested the spectrum of the lasers. [PERSON] calculated the laser cavity. [PERSON] and [PERSON] did the experiments and analyzed the experimental data. All authors contributed with valuable discussions and scientific advices in order to improve the quality of the work, and also contributed to write the final manuscript. The authors declare no conflict of interest.
## 4 Discussion  
As discussed in the Section 3.2, color quality of OLED products is the critical factor for the general lighting applications. To investigate the mechanism of CCT shift, spectrum change with operating time for OLED panel and luminaire were analyzed as shown in Figure 4. Figure 4a shows the normalized spectrum of type A OLED panel and Figure 4b shows that of type E OLED luminaires. In case of OLED panel, as the operating time increases then the normalized blue spectrum goes down while other spectrum maintain its initial ratio. However, in case of OLED luminaires the degradation of blue spectrum dramatically changes even at two thousands of operating hours. Green spectrum undergoes additional variation which is not measured in the OLED panel.  
To analyze the effect of panel size on the color quality, variation in CIE 1976 color coordinates of OLED panel type C (200 mm \(\times\) 50 mm) and OLED panel type D (320 mm \(\times\) 110 mm) were evaluated. Measured u' and v' variation of OLED panel type C and D have similar tendency with operating time up to six thousand hours shown in Figure 5a. Amount and direction of u' and v' shifts in each type of OLED panel are almost same even  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Type**} & **Initial Operating** & **Maintained Operating** & **Delta Operating** \\  & **Voltage [V]** & **Voltage [V]** & **Voltage [V]** \\ \hline A & 6.03 & 6.11 & 0.07 (1.2\%) \\ B & 6.20 & 6.54 & 0.34 (5.5\%) \\ C & 6.09 & 6.38 & 0.29 (4.8\%) \\ D & 6.06 & 6.28 & 0.22 (3.6\%) \\ E & 6.71 & 7.42 & 0.71 (10.6\%) \\ F & 6.41 & 7.42 & 1.01 (15.8\%) \\ \hline \hline \end{tabular}
\end{table}
Table 7: Operating voltage maintenance characteristics of OLED products after aging of 6000 h.  
Figure 4: Normalized spectrum variation with operating time. (**a**) OLED panel type A (**b**) OLED luminaire type E.  
with different initial u' and v' points. Such characteristics are more clearly identified by Au' and \(\Delta\)v' values as shown in Figure 5b. Because of the decrease in the blue spectrum, main direction of this shift heads for yellow-red direction [20,21]. Amount of maximum shift in OLED panel type D compared to that of OLED panel type C is over 2.4 times in \(\Delta\)u' and 2.0 times in \(\Delta\)v', respectively. Thus, in case of OLED panel which has large surface area needs more careful design in the applications to maintain color quality.  
In case of OLED luminaire, color shift has two different kind of mechanism as shown in Figure 6a,b. One is yellow-red shift as similar in OLED panel case up to two thousand hours of operation and the other is green shift due to the variation in green spectrum as shown in Figure 4a,b from two thousand hours to six thousand hours of operation. Amount of maximum shift in OLED luminaire comes to 0.0013 in \(\Delta\)u' and 0.0071 in \(\Delta\)v', respectively.  
Figure 5: Color shift of OLED panel dependence on panel size. (**a**) OLED panel type C. (**b**) OLED panel type D.  
Figure 6: Color shift of OLED luminaire type E. (**a**) u’ and v’ color shift of luminaire type E. (**b**) \(\Delta\)u’ and \(\Delta\)v’ variations of luminaire type E.
## 5 Conclusions  
In summary, we proposed the statistical misalignment model and the power-efficient system configuration for the MIMO based bi-directional vertical FSO systems. The detected misalignment increased with the number of transmitted beams and decreased with the number of positioning sensors. Considering the bi-directional nature of wireless back-haul networks, the effect of increased pointing error showed different results, and the transmission performance was restricted to the worse. Thus, the symmetric configuration of transceivers in the UAV and ground terminals can improve the achievable diversity gain by minimizing the increase in pointing error. The proposed technique can be used to design power-efficient FSO systems for non-terrestrial wireless back-haul networks for next-generation industries.  
Conceptualization, S.-J.K.; methodology, S.-J.K.; software, S.-J.K.; validation, S.-J.K. and S.-K.H.; formal analysis, S.-J.K.; investigation, S.-J.K.; resources, S.-J.K.; data curation, S.-J.K.; writing\(-\)original draft preparation, S.-J.K.; writing\(-\)review and editing, S.-J.K. and S.-K.H.; visualization, S.-J.K.; supervision, S.-K.H.; project administration, S.-K.H.; funding acquisition, S.-K.H. All authors have read and agreed to the published version of the manuscript.  
This work was supported by an Institute for Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korean Government (MSIT) (No. 2019-0-00685, Free space optical communication based vertical mobile network).  
Not applicable.  
The authors declare no conflict of interest.
# Thermodynamics and Magnetism of SmFe\({}_{12}\) Compound Doped with Co and Ni: An Ab Initio Study
## 1 Introduction
According to [PERSON] et al. [91], the increases in \(\mu_{0}M_{s}\) and \(T_{c}\) can be assumed to be preferential site occupation by Ni atoms. According to [PERSON] et al. [92], Ni substitution (NdFe\({}_{11}\)Ni\({}_{1}\)) yields negative formation energy values relative to the NdFe\({}_{12}\) compound, bcc Fe, and fcc Ni, and the most stable site for Ni is \(8j\). Ni substitution does not diminish the magnetization significantly, as in the case of Ti, V, Cr, and Mn dopants. [PERSON] et al. [152] studied the structure and magnetic properties of the SmMn\({}_{12-x}\)Ni\({}_{x}\) alloys, which are stable in the ThMn\({}_{12}\)-type structure for the Ni concentration, \(4\leq x\leq 8\). [PERSON] et al. [152] asserted that the SmMn\({}_{12-x}\)Ni\({}_{x}\) alloys are magnetic at the low Neel temperature \(T_{N}\approx 60\) K, due to antiferromagnetic ordering in the Sm sublattice, whereas the Mn and Ni sublattices are nonmagnetic. [PERSON] et al. [153] studied the structure and magnetic properties of the NdMn\({}_{12-x}\)Ni\({}_{x}\) alloys, which are stable in the ThMn\({}_{12}\)-type structure for the Ni concentration of \(3.6\leq x\leq 6\). The absence of any maximum in the temperature reliance of the magnetic susceptibility indicates the investigated alloys are nonmagnetic. The main intent of the present research is to investigate the effects of nickel on the phase stability of the Sm(Fe\({}_{1-x}\)Co\({}_{x}\))\({}_{12}\) alloys and to evaluate their magnetic properties. We utilize ab initio calculations using the following techniques: (i) the fully relativistic exact muffin-tin orbital method (FREMTO) and (ii) the full-potential linear muffin-tin orbital method (FPLMTO). Both methods include all relativistic effects, such as spin-orbit coupling (SOC). The application of these two methods allows us to establish accurate results that are independent of technical implementation and relies on the individual strength and durability of each method. Related details of the ab initio computational methods are outlined in Section 2. The results of the density-functional theory (DFT) calculations of the ground-state properties of the Sm(Fe-Co-Ni)\({}_{12}\) alloys are outlined in Section 3. We demonstrate the results of the DFT calculations of the magnetic properties of the Sm(Fe-Co-Ni)\({}_{12}\) alloys in Section 4. Lastly, a discussion and concluding remarks are outlined in Section 5.
## 1 Introduction
Among them, non-polar polydimethylsiloxane (PDMS) fibers are preferred for the extraction of non-polar analytes, including many volatile flavor compounds. Carboxen-polydimethylsiloxane (CAR-PDMS) fibers exhibit better extraction efficiency than 50/30 \(\upmu\)m DVB/CAR/PDMS coated SPME fiber attached to a manual PDMS fiber, and similar fibers, but show inferior repeatability and their equilibration is more time-consuming [8]. Monoterpenes, sesquiterpenes, and aliphatics have been identified as the major volatile compounds in _Cymbidium_, with their total exceeding 90%. While there have been previous reports on the analysis of volatile components [(_E_)-4-hexadecen-6-yne, 6-oxoheptanoic acid methyl ester, dodecane hexadecanoic acid, isopropyl myristate, and tetradecanoic acid) of _Cymbidium_ varieties [9; 10], there are no reports on the characteristics of the volatiles contributing to the floral scents of the new \"Sunny Bell\" hybrid. Thus, in the present study, HS-SPME coupled with GC-MS was used to characterize the volatile components of \"Sunny Bell\" flowers. Specifically, this study aimed to evaluate the volatile polymorphisms of different floral organs from \"Sunny Bell\" to determine the floral organs of significant scent. \begin{table}
\begin{tabular}{c c c c c c} \hline \multirow{2}{*}{**Cultivars**} & \multicolumn{2}{c}{**Flower**} & \multirow{2}{*}{**Plant Size**} & **Peduncle** & **No. of** & **No. of** \\  & **Color**1  & \multirow{2}{*}{**Preference**1  \\ \cline{2-2} \cline{4-4}  & \(\mathbf{Color}\)1  & **Shape** & **Attitude** & **Fragrance** & **Bloom** \\ \hline
**Sunny Bell** & & & & & \\
**(Wongyuo** & RP59B & incurred & small & erect & mild & (From Feb.) \\
**F1-47** & & & & & \\
**Silk Road** & & & & & \\
**(control)** & R56A & some & medium & semi-upright & strong & winter \\  & & & & & (From Jan.) \\ \hline \end{tabular}  
* Based on the Royal Horticultural Society (RHS, 2001) color chart.  
\end{table} 
Table 1: Morphological characteristics of a new hybrid, _Cymbidium “_Sunny Bell”_. \begin{table}
\begin{tabular}{c c c c c c} \hline \multirow{2}{*}{**Cultivars**} & \multicolumn{2}{c}{**Flower**} & \multirow{2}{*}{**Peduncle**} & **No. of** & **No. of** \\  & & **Length (cm)** & & **Flowers/Peduncl** & **Peduncle** & **Preference**1  \\ \hline
**Sunny Bell** & & & & & \\
**(Wongyuo F1-47)** & & & & & \\
**Silk Road** & & & & & \\
**(control)** & & & & & \\ \hline \end{tabular}  
* Preference evaluation was undertaken at the _Cymbidium_ exhibition held at the National Institute of Horticultural and Herbal Science (NIHHS) in 2013. Poor (1)–Excellent (5). 2 All data are presented as mean \(\pm\) standard deviation (n = 15).  
\end{table} 
Table 2: Comparative characteristics of flowers of a new hybrid, _Cymbidium “_Sunny Bell”_.
## 2 Related Work
### Encrypted Traffic Datasets and Construction Methods  
#### 2.1.1 Classic Open Datasets in Encrypted Traffic Analysis  
The datasets of encrypted traffic analysis can be traced back to malicious traffic analysis. As one of the most classic datasets, DARPA99 [8] is an intrusion detection dataset which contains Secure Shell (SSH) traffic [9]. The datasets of detection currently include the University of MASSachusetts (UMASS) dataset [10], the University of California Irvine Security (UCIS) dataset [11], the MAWILab (MAWI) dataset [12], and the National Incident Management System (NIMS) dataset [13]. In encrypted malicious traffic detection, there are the Honeynet Project ISOT(ISOT) dataset [14], the MCFP Project dataset [15], and the University of Science and Technology of China TFC (USTC-TFC) dataset [16]. They all have some encrypted traffic samples.  
The most popular dataset in encrypted traffic classification is the ISCXFlowMeter based (ISCX) VPN-nonVPN dataset [3] published in 2016. It provided essential support for various classification researches. However, it is worth noting that not all encrypted traffic datasets contain malicious traffic, which mainly depends on the purpose of the dataset. Itcontains malicious data if the dataset is used for malicious traffic classification or intrusion detection. If it is for application classification, especially for network management-oriented research, the dataset contains only the benign traffic of multiple applications or services.  
It is easy for subsequent researchers to validate the effect of their research because the open datasets have been widely used in many pieces of research. However, through in-depth analysis of the above datasets, it can be found that there is a severe problem of unbalanced sample distribution, and the encryption protocol and application protocol versions are also out of date to some extent. The above defects make it difficult for the open datasets to represent the traffic of the current network environment.
## 2 Model Experiments
### Instruments and Equipments  
The experiment equipment consists of four parts: dynamic compaction device, model box, sensor and data acquisition system. Among them, the front surface of the model box was divided into small grids with dimensions of 6 cm \(\times\) 5 cm using markers to be able to monitor the soil deformation (Figure 2). To monitor the horizontal displacement, additional marks are added at 1, 2, 3, and 4 times the rammer radius. The dynamic compaction device played the most important role in the model experiment. In order to simulate the dynamic response of the fill foundation, a semicircular steel hammer with a diameter of 120 mm, a mass of 2.5 kg freely falls from a certain altitude and applies dynamic load to the foundation. Meanwhile, to prevent hammering from rotation and subsidence, two through-holes with a diameter of 10 mm are set, which can freely fall from the guide rod.  
The analyzed data in this paper include soil deformations, earth pressure, crater depth and reinforced depth, deflection, and strain distribution of first reinforced layer. Earth pressure cells were used to study the transfer characteristics of tamping energy and its specifications are shown in Table 2. Three earth pressure cells (P1, P2, and P3) were installed in the soil beneath the tamping central point at an interval of 75 cm, and three earth pressure cells (P4, P5, and P6) were embedded in the lateral part of the model box at an interval of 75 cm (Figure 2). Strain gauges with resistance value of \(120\pm 0.2\)\(\Omega\) and sensitivity ratio of \(2.11\pm 1\%\) were bonded to longitudinal bars of reinforced geogrid and were installed along the tensile direction at an interval of 3 cm. The specifications of the strain gauges are shown in Table 3. Data were collected and maintained by dynamic strain indicator.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Measuring Range (MPa)** & **Resolution Ratio \%:F-S** & **Insulation Resistance (M\(\Omega\))** \\ \hline
0.1 & \(\leq\)0.05 & \(\geq\)200 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Physical specifications of strain gauge.  
Figure 2: Model box and installation of earth pressure cell.
## 2 Materials and Methods
### Review of VTS Operation
\[D_{r}=\sqrt{\left(x_{t}-x_{o}\right)^{2}+\left(y_{t}-y_{o}\right)^{2}} \tag{1}\]  
\[V_{r}=V_{o}\times\sqrt{1+\left(\frac{v_{t}}{v_{o}}\right)^{2}-2\times\frac{v_{ t}}{v_{o}}\times cos(\theta_{o}-\theta_{t})} \tag{2}\]  
\[\theta_{r}=cos^{-1}\bigg{(}\frac{V_{o}-V_{t}\times cos(\theta_{o}-\theta_{t})} {V_{r}}\bigg{)} \tag{3}\]  
\[\text{CPA}=D_{r}\times sin(\theta_{r}-\alpha_{t}-\pi) \tag{4}\]  
\[\text{TCPA}=D_{r}\times cos(\theta_{r}-\alpha_{t}-\pi)/V_{r} \tag{5}\]  
where \(D_{r}\) denotes the relative distance between the own ship and the target ship, \(V_{r}\) is the relative speed, \(\theta_{r}\) is the relative course, \(\alpha_{t}\) is the true bearing of the target ship, and \(\alpha_{r}\) denotes the relative bearing. The relationship between CPA and TCPA, which is used as a major factor in determining vessel traffic management priorities, can be interpreted as follows: the closer CPA is to zero, the closer the two vessels meet. A CPA of 0 indicates that the two ships will collide after a certain time. However, prioritization and decision making cannot be solely based on CPA; rather, it should be determined by referring to the TCPA, which is the time required to reach the CPA. A negative TCPA indicates that the CPA of both vessels has already been surpassed, whereas a TCPA close to zero indicates that the two vessels have insufficient time to take collision avoidance measures. Therefore, CPAs and TCPAs are regarded as major factors that can predict the risk of sailing. However, the allowable CPA and TCPA are formed in various ways depending on the area where the vessel sails. Therefore, in this study, CPA and TCPA were used as input data for major decision making, considering the clustering areas that can reflect navigation characteristics according to the navigation area.
## 3 Materials and Methods
### Model Evaluation  
The effectiveness of the proposed segmentation strategy was evaluated using standard metrics commonly adopted in medical image segmentation, including the Dice Similarity Coefficient (DSC) or Dice score (Equation (1)), Recall (Equation (2)), and Precision (Equation (3)), as recommended in the recent literature and domain-specific evaluation guidelines [46].  
The Dice score, also known as (F1-score), is a widely adopted metric for evaluating overlap between predicted and ground truth segmentations [47]. It is defined as follows:  
\[DSC=\frac{2\cdot|X\cap Y|}{|X|+|Y|} \tag{1}\]  
where \(X\) denotes the model's predicted masks and \(Y\) denotes the ground-truth labels. The DSC ranges from 0 to 1, with a score of 1 indicating perfect agreement between prediction and ground truth.  
Recall quantifies the model's ability to correctly identify true positives predictions, and is computed as follows:  
\[Recall=\frac{TP}{TP+FN} \tag{2}\]  
where \(TP\) denotes true positives and \(FN\) false negatives.  
Precision evaluates the proportion of correctly predicted positive voxels compared to all voxels predicted as positive, and is defined as follows:  
\[Precision=\frac{TP}{TP+FP} \tag{3}\]where \(FP\) represents false positives. This metric reflects the model's ability to avoid incorrect labeling of non-relevant regions. High precision implies a low false positive rate, indicating reliable specificity in segmentation predictions.  
Furthermore, to provide a robust comparison between our model and Segformer3D as well as to objectively evaluate its performance, we conducted statistical analysis on the subject-level metrics. Specifically, the non-parametric Wilcoxon signed-rank test was applied, which is suitable for assessing the statistical significance of the differences between two models. Statistical significance was considered at a threshold of \(p\) < 0.05.
# In-Orbit Results and Attitude Analysis of the SNUGLITE CubeSatellite
## 2 Materials and Methods
### Beacon and Telemetry Data  
The SNUGLITE CubeSat has two kinds of beacon formats to efficiently broadcast the satellite survival and status information, namely the simple beacon (which transmits every 10 s) and the full beacon (which transmits every 30 s). The simple beacon broadcasts basic essential data comprising 'time,' GPS receiver,' battery status flag,' and 'battery voltage,' while the full beacon broadcasts detailed information such as satellite temperature, estimated attitude, power consumption and generation, and other satellite status flags. Table 2 describes the information relayed by both beacons.
# Super-Resolving Ocean Dynamics from Space with Computer Vision Algorithms
## 2 Materials and Methods
### Automatic Eddy Detection  
The angular momentum for eddy detection and tracking algorithm (AMEDA) is freely available software for the detection and tracking of oceanic eddies from 2D gridded fields of surface currents and/or sea surface height [53]. It is based on the computation of eddy local normalized angular momentum (LNAM) and on the observations of closed streamlines around the LNAM extrema. The algorithm was successfully applied to remotely sensed 2D fields and model outputs, e.g., [35; 53; 54], and enables the determination of eddies' contour and trajectories as well as eddy merger/splitting events. In this study, AMEDA was used to identify eddy shapes seen by standard altimetry products (described in Section 2.2) and the fields obtained from the combination of satellite altimetry ADT and high-resolution satellite SSTs. We relied on the AMEDA default configuration, accounting for the expected perturbation lengths in the Mediterranean area (i.e., considering the typical Mediterranean Rossby deformation radii).