esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
It is worth noting that the hydrogen distribution is not uniform with the density gradually decreasing from AlO\({}_{\rm x}\) surface to the AlO\({}_{\rm x}\)/ITO interface. The results confirm that a large amount of hydrogen elements is introduced during treatment. The obvious overlap of Al and In signals is mainly due to the diffusion of Al and In elements at the ITO/AlO\({}_{\rm x}\) interface. Finally, TFTs with AlO\({}_{\rm x}\) gate insulator and InScO\({}_{\rm x}\) channel layer were fabricated to verify the formation of EDL. InScO\({}_{\rm x}\) semiconductor can effectively decrease the influence of water and oxygen in the environment on the stability of TFTs [27]. Figure 5a,b shows the transfer curves of the TFTs without and with water treatment, respectively. Interestingly, the TFT without water treatment exhibits clockwise hysteresis in the transfer curve between forward and reverse gate sweeps, while the one with water treatment exhibits anticlockwise hysteresis. The anticlockwise hysteresis of the water-treated TFT is ascribed to the low migration speed of the H\({}^{+}\) ions [28]. When the gate voltage increases, the H\({}^{+}\) ions migrate to the AlO\({}_{\rm x}\)/InScO\({}_{\rm x}\) interface slowly; after the gate voltage reaches the highest value  
Figure 4: Distribution of H, C, Al, and In elements for the sample of AlO\({}_{\rm x}\) film deposited on ITO surface with water treatment and 350 \({}^{\circ}\)C annealing analyzed by TOF-SIMS. and begin to decrease, some of the H\({}^{+}\) ions still move toward the AlOx/InScOx interface, causing further increase in the EDL capacitance. As a result, the current for the reverse sweep is higher than that for the forward sweep (due to the higher capacitance). The saturation mobility (\(\mu_{sat}\)) was extracted by fitting a straight line to the plot of the square root of the \(I_{D}\) versus \(V_{G}\) and using the following equation:  
\[I_{D}=\frac{W\mu_{sat}C}{2L}(V_{G}-V_{th})^{2}\ldots \tag{1}\]  
where \(C\) is the areal capacitance of the gate dielectric, and \(W\) and \(L\) are the channel width and length, respectively. The calculated mobilities for forward- and reversed-sweep curves of the TFTs without water treatment is 6.75 and 9.71 cm\({}^{2}\) V\({}^{-1}\)s\({}^{-1}\), respectively, while those for forward- and reversed-sweep curves of water-treated ones are 6.72 and 5.02 cm\({}^{2}\) V\({}^{-1}\)s\({}^{-1}\), respectively. Although the TFT with water treatment is high on-current, the mobility is lower than that of the TFT without water treatment. The results confirm that the mobility of the water-treated TFTs is overestimated if using the same capacitance (C\({}_{i}\)) of the untreated TFTs for calculating mobility. The key properties of the TFTs with/without water treatment are summarized in Table 1.
## 3 System Model
### Computational Model
#### 3.3.3 Migrating the Computation  
When the MEC server experiences an excessive workload or when neighboring servers have available resources, the \(UE_{i}\) can migrate its computational tasks to the adjacentservers. This migration incurs additional transmission overhead for the \(UE_{i}\), which involves transferring data between MEC servers. The magnitude of the transmission overhead is determined based on the allocated computational resources provided by the target server in response to the migration request from the \(UE_{i}\). The \(i\), \(b^{\prime}\) in \(t_{i,b^{\prime}}^{mig}\), \(t_{i,b^{\prime}}^{edge}\), and \(E_{i,b^{\prime}}^{mig}\) signifies the transmission from \(UE_{i}\) to the AP where another MEC server is located; therefore, the transmission delay is  
\[t_{i,b^{\prime}}^{mig}=\frac{m_{i}}{R_{i}^{mig}}+Z_{i}^{mig}. \tag{13}\]  
Assuming that the migration cost only depends on the task size, which is denoted as \(Z_{i}^{mig}=\delta m_{i}\), the transmission energy loss is  
\[E_{i,b^{\prime}}^{mig}=p_{i,b^{\prime}}t_{i,b^{\prime}}^{mig}. \tag{14}\]  
The computational delay and energy consumption of the \(UE_{i}\) is computed based on the resource allocation strategy of the migration server, with the computational formula remaining unchanged. Consequently, the total cost of the migration computation is expressed as  
\[C_{i}^{mig}=\alpha\left(t_{i,b^{\prime}}^{mig}+t_{i,b^{\prime}}^{edge^{ \prime}}\right)+\beta E_{i,b^{\prime}}^{mig}. \tag{15}\]
### Model Data  
Observational data, including remote sensing and in situ measurements, capture real-time or historical ocean conditions, whereas model data simulate or forecast ocean states under given physical, chemical, and biological processes. These numerical models use mathematical frameworks such as partial differential equations or machine learning algorithms, to represent ocean dynamics, including currents, temperature distribution, and other variables, across various spatial and temporal scales. Common modeling frameworks in ocean climate change fields include the Coupled Model Intercomparison Project (CMIP), WaveWatch III (WW3), and GraphCast [52; 53]. CMIP orchestrates multimodel comparisons and standardized simulations to advance the understanding of climate projections and inter-model variability. WW3 is a third-generation wave model developed by NOAA to simulate and forecast global ocean waves by solving the spectral action balance equation and accounting for key physical processes such as wave generation, propagation, and dissipation. It can also assimilate data from other sources, such as satellite observations or in situ buoy measurements. GraphCast, on the other hand, is a DL-based forecast framework that leverages large datasets and AI algorithms to predict ocean and atmospheric conditions, offering an alternative to traditional physics-based models [54].  
Model data fill gaps in observational coverage by providing consistent, high-resolution information on variables such as sea surface height (SSH), temperature, and pressure over a specific region and period. However, model outputs may deviate from reality due to uncertainties in parameterizations, boundary conditions, and initial states. To address these challenges, continuous advancements in modeling and the incorporation of more sophisticated data assimilation techniques help mitigate these issues by anchoring simulations in real-world measurements, as described in Section 2.3.
## 3 Materials and Methods
### Energy Management System
#### 3.1.3 Grid-Based EMS  
Grid-based EMS is activated when FESS speed lies within [\(\omega_{m,nom}\),\(\omega_{m,max}\)], meaning that FESS is ready to exchange the required energy with the grid. Consequently, referring to Figure 3, given both \(P_{DC}^{*}\) and \(P_{g}^{*}\), FESS power is determined simply through (2) as  
\[P_{m}^{*}=P_{g}^{*}+P_{DC}^{*}. \tag{5}\]  
However, \(P_{m}^{*}\) given by (5) has to be limited in accordance with not only M/G torque and power limitations, but also in order to ensure that FESS always operates within [\(\omega_{m,nom}\), \(\omega_{m,max}\)]. Consequently, maximum and minimum power values, both denoted by \(\hat{P}_{m}\) for the sake of simplicity, can be defined as in Figure 5; this reveals that delivered or absorbed power derating occurs when the rotor speed approaches its minimum and maximum allowable value, respectively. As a result, excessive FESS discharge and overcharge are prevented successfully. The power derating regions are identified by \(\omega_{m,low}\) and \(\omega_{m,light}\), whose choice should be done properly; particularly, on the one hand these thresholds should be fairly close to minimum and maximum rotor speed values in order to exploit FESS full power capability over a wide speed range. On the other hand, a smooth transition should be advisable in order to prevent sudden and frequent FESS power variations due to some speed ripple. Apart from setting \(\hat{P}_{m}\) properly, an additional power term should be considered in defining the overall FESS reference power (\(\delta P_{m}^{*}\)), as highlighted in Figure 3. Otherwise, when \(\omega_{m}\) equals \(\omega_{m,nom}\) and the reference grid power demand is greater than or equal to zero, rotor speed would drop below \(\omega_{m,nom}\) as no power can be drawn from the grid. Since this should not occur at _ready_ operating mode, \(\delta P_{m}^{*}\) is provided by a speed loop,  
Figure 4: Schematic representation of the (**a**) \(V_{DC}\) loop and (**b**) speed loop.  
whose reference speed is set at \(\omega_{m,nom}\) and whose reference torque can be only greater than zero; this means that this speed loop provides a positive reference torque signal only when the speed is below \(\omega_{m,nom}\), while providing zero otherwise. In conclusion, considering both \(\hat{P}_{m}\) and \(\delta P_{m}^{*}\), the FESS reference power can be finally achieved, as well as \(P_{g}^{*}\) in accordance with (5).
# High-Speed, High-Performance DQPSK Optical Links with Reduced Complexity VDEE Equalizers
## 5 Conclusions  
Optical transmission systems with phase modulation signaling combined with direct detection reception perform exceptionally well with respect to amplitude modulation formats. However their performance is dependent upon the exact configuration of the channel and the receiver choice. Especially, DQPSK formats exhibit high spectral efficiency and impairment robustness, hence, cost efficient systems could benefit from DQPSK deployment. In specific network segments, however, the cost of deployment and operation is an important issue. For that reason, constraint-limited performance evaluation is performed for all DQPSK configurations.  
Typically, DQPSK receivers are either single-ended or balanced, and each case may have benefits depending on the optical channel. When electronic equalization is applied to compliment optical dispersion compensation, usually signals stemming from receivers are utilized to equalize channel dispersion. Depending on the receiver, these are either four port configurations in the case of four input single-ended receivers or a two port configuration from the balanced receiver. In this paper, starting from the requirement for low-complexity/high-performance equalizers and inspired by the need to alleviate linear dependency of the four input ports of joint single-ended equalizers and enhance numerical accuracy, the effect of reducing the number of single-ended ports in the equalization process is investigated with exceptional results. Due to the nature of DQPSK, when one destructive port is not engaged in the equalization, VDFE and low complexity SVDFE equalizers manage to compensate for residual dispersion. Especially when deployed in multi-span systems, three-port partially-joint single-ended SVDFE equalizers can exhibit exceptional performance while offering a 25% reduction in the complexity of the system with respect to the four port counterparts.  
This research was supported by the Operational Program \"Education and Lifelong Learning\" of the Greek National Strategic Reference Framework (NSRF) Research Funding Program: THALES PROTOMI, grant number MIS 377322.  
[PERSON] designed and performed the experiments; [PERSON] and [PERSON] developed and implemented the equalizers; all authors analyzed the data; all authors contributed into the writing of parts of the paper; and [PERSON] and [PERSON] coordinated the overall work.  
The authors declare no conflict of interest.
## 2 Materials and Methods
### Instrumental Colour Measurement  
Colour measurements were conducted on storage days 7, 14, and 21 in three samples from each production batch. Colour measurements were carried out with the Minolta CR-410 colorimeter (Konica Minolta Company, Osaka, Japan) with a 50-mm measuring area (aperture size) using illuminant source C and 2\({}^{\circ}\) standard observer angle. The light projection tube (CR-A33a, Konica Minolta, Japan) was applied over the aperture port while measuring. Initially, before the measurement, calibration was performed using a white calibration plate (Y = 93.9, x = 0.3136, y = 0.3201). The measurements were conducted in triplicate on the surface colour of intact yoghurt samples. The measurements were recorded using the SpectraMagic(tm) NX software (Lite 3.4 version) (Konica Minolta Company, Osaka, Japan). Changes in colour and texture during storage were evaluated using the same yoghurt samples prepared in custom-made white containers (Figure 2).  
The colour measurement was conducted using the CIE Lab* system. In addition to the L*, a*, and b* parameters, chroma (colour saturation), hue, and the whiteness index values were determined according to the following equations, as reported by [PERSON] et al. [46], for chroma and the whiteness index, and [PERSON] et al. [47], for the hue:  
\[\text{Chroma}=\sqrt{\left(\text{a*}^{2}+\text{b*}^{2}\right)}\]  
\[\text{Hue angle}=\tan^{-1}\left(\frac{\text{b*}}{\text{a*}}\right)\times\left( \frac{180}{\pi}\right)\]  
\[\text{Whiteness index}=100-((100-\text{L*})^{2}+\text{a*}^{2}+\text{b*}^{2}) ^{0.5}\]  
Total colour difference (\(\Delta\text{E}_{\text{Lab}}\)) between the Control (C) and samples from the UOP and POP treatments within the same storage day was also calculated according to the following equation [46]:  
\[\Delta\text{E}_{\text{Lab}}=\sqrt{\left(\text{L*}_{\text{C}}-\text{L*}_{ \text{T}}\right)^{2}+\left(\text{a*}_{\text{C}}-\text{a*}_{\text{T}}\right)^{2 }+\left(\text{b*}_{\text{C}}-\text{b*}_{\text{T}}\right)^{2}}\]  
where C = Control and T = treated samples i.e., UOP and POP.
# Prediction of Beck Depression Inventory Score in EEG: Application of Deep-Asymmetry Method
## 5 Conclusions  
Owing to the development of deep learning and machine learning technology, brain-wave-based depression prediction technology is being developed. In particular, the method of imaging EEGs and using them as the CNN model input has been shown to be more effective than providing RAW EEGs directly to the model. In this study, we constructed a regression model that predicts the BDI score, which is the depression severity score of depressed patients, using the deep-asymmetry method, which has shown good performance in the existing patient depression classification problem. Depression severity is very important as it substantially affects the subsequent treatment options and follow-up measures. As a result, we were able to observe the highest level of performance even in the BDI regression problem. In addition, this study will substantially contribute to building a CNN-based model whose operation can be explained in the future study by providing images extracted from important features of EEGs.
# Solution of Orifice Hollow Cathode Plasma Model Equations by Means of Particle Swarm Optimization
## 2 Plasma Model of Hollow Cathode  
### Cathode Architecture  
In the typical OHC configuration (Figure 1), a conductive tube, made of refractory metal (e.g., tantalum or molybdenum) or graphite, drives the flow of a neutral gas (e.g., xenon, argon, and other non-noble gases). At the end of the main tube, an orifice plate is usually placed. By changing the diameter of the orifice, the internal pressure can be adjusted ([PERSON], 2017). A hollow cylinder, namely an insert made of aow-work functional material, is placed inside the tube. The insert is the core part of the cathode because it emits electrons by means of a thermionic effect. Barium-based and rare-earth emitters (e.g., LaB\({}_{6}\), CeB\({}_{6}\)), which have, respectively, a work function of about 2 eV and 2.7 eV ([PERSON], 2017) allow us to provide current densities as high as \(10^{5}\) A/m\({}^{2}\) at a surface temperature of about 1300 K and 1900 K, respectively. The main drawback for Barium-based emitters is that they are highly susceptible to oxygen contamination, whereas rare-earth emitters require very high temperatures to work, and care must be taken when they are in contact with many refractory metals at high temperatures because they react. For this kind of coupling, a thin graphite sleeve is usually placed between the emitter and the main tube.  
A heater is wrapped around the tube to trigger thermionic emission. If it is designed well, a cathode can sustain the emitting temperature by means of plasma heating; thus, the heater can be turned off. For this reason, thermal management is a key factor in OHCs (e.g., thermal shields around the heater are often used to reduce radiationosses).  
Another important element is the keeper electrode that encloses the heater-main tube assembly. It has the double function of helping the extraction of the electrons to the discharge plasma and protecting the cathode from the ion bombardment.  
Figure 1: (**a**) Schematic of typical Orifice Hollow Cathode and (**b**) sketch of plasma model regions.
# From Google Earth Studio to Hologram: A Pipeline for Architectural Visualization
## 3 Results
For each of the two selected sites, a total of 13 keyframe positions--defined by their latitude and longitude--were computed using Equations (2) and (3), as shown in Table 1 and illustrated in Figure 7. The altitude was set to 115 m for Sagrada Familia and 100 m for Arc de Triomphe, reflecting the relative elevation required for optimal image capture. Using the defined camera trajectory, 712 high-resolution frames were rendered per site, satisfying the required number of perspective images for the CHIMERA hologram fabrication. Videos S1 and S2 show the resulting image sequences for the Sagrada Familia and Arc de Triomphe, respectively. Figure 6: Selection of central viewpoint and corresponding 120\({}^{\circ}\) camera arc used for holographic capture of the two study sites. (**a**) Nativity Facade of Sagrada Familia viewed from Carrer de la Marina; (**b**) Eastern facade of Arc de Triomphe viewed from Place Charles de Gaulle, facing the Avenue des Champs-Elysees. Hogels were generated from perspective images and sequentially recorded on two 15 \(\times\) 20 cm U04 holographic plates using a CHIMERA holoprinter. Upon illumination with an RGB LED light source positioned 50 cm away at a 45\({}^{\circ}\) angle, the holograms produced a vivid, full-color, and realistic 3D reconstruction of the Sagrada Familia and Arc de Triomphe, as shown in Figure 8 and Video S3. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Sagrada Familia**} & \multicolumn{2}{c}{**Arc de Triomphe**} \\ \hline
**Key** & \multirow{2}{*}{**Latitude (DD)**} & \multirow{2}{*}{
\begin{tabular}{c} **Longitude** \\ **(DD)** \\ \end{tabular} } & \multirow{2}{*}{**Latitude (DD)**} & \multirow{2}{*}{**Latitude (DD)**} & **Longitude** \\
**Viewpoints** & & & & **(DD)** \\ \hline
1 & 41.40403 & 2.17112 & 48.87206 & 2.29538 \\ \hline
2 & 41.40361 & 2.17107 & 48.87214 & 2.29584 \\ \hline
3 & 41.40318 & 2.17112 & 48.87228 & 2.29627 \\ \hline
4 & 41.40276 & 2.17127 & 48.87246 & 2.29666 \\ \hline
5 & 41.40237 & 2.17151 & 48.87268 & 2.29700 \\ \hline
6 & 41.40201 & 2.17184 & 48.87293 & 2.29727 \\ \hline
7 & 41.40171 & 2.17225 & 48.87322 & 2.29747 \\ \hline
8 & 41.40146 & 2.17272 & 48.87352 & 2.29760 \\ \hline
9 & 41.40128 & 2.17324 & 48.87383 & 2.29764 \\ \hline
10 & 41.40117 & 2.17380 & 48.87415 & 2.29760 \\ \hline
11 & 41.40113 & 2.17437 & 48.87445 & 2.29747 \\ \hline
12 & 41.40117 & 2.17494 & 48.87473 & 2.29727 \\ \hline
13 & 41.40128 & 2.17549 & 48.87499 & 2.29700 \\ \hline \end{tabular}
\end{table} 
Table 1: Geographic coordinates (latitude and longitude in decimal degrees) of 13 key viewpoints computed in Earth Studio for Sagrada Familia and Arc de Triomphe. Figure 7: Thirteen perspective images were captured along a 120\({}^{\circ}\) arc to generate multi-angle views for holographic reconstruction of the selected landmarks: (**a**) Basílica de la Sagrada Familia; (**b**) Arc de Triomphe.
## 4 Bigram and Traditional Results
### Assessing Water Quality and Potability  
Evaluating water quality and potability is crucial for providing safe drinking water to the public. A comprehensive approach, encompassing the identification of potential contaminants, setting guidelines, gathering and analyzing samples, interpreting findings, implementing treatment measures, and continuously monitoring water quality, can effectively safeguard public health and preserve a dependable source of potable water.
# Prediction of Horizontal in Situ Stress in Shale Reservoirs Based on Machine Learning Models
## 3 Machine Learning Model
### Model Evaluation Metrics  
In the task of predicting in situ stress, metrics R\({}^{2}\), MAE, MSE, and RMSE were introduced to measure how well the models perform. R\({}^{2}\), which tells us how good the model fits the data, indicates the alignment between predicted and actual values. A model with R\({}^{2}\) closer to 1 has better predictive power. MAE, the average of absolute differences between predicted and true values, reflects the overall error size. It is less affected by outliers and directly shows how far off the predictions are in absolute terms. A smaller MAE means higher prediction accuracy. MSE, the average of squared differences between predicted and true values, assesses both the overall error and its dispersion. RMSE, being the square root of MSE, shares the same unit as the target variable, making it more intuitive. These metrics together allow for a comprehensive evaluation of the SVM, RF, and XGBost models. When comparing models, R\({}^{2}\) is the primary measure for assessing how well the model fits the data, while MAE, MSE, and RMSE provide additional perspectives on the prediction errors. The best-performing model, as determined by these metrics, is chosen for practical applications.  
\[\mathrm{R}^{2}=1-\frac{\mathrm{\sum}_{i=1}^{n}\left(y_{i}-\mathrm{y}_{i}^{ \prime}\right)^{2}}{\mathrm{\sum}_{i=1}^{n}\left(y_{i}-\mathrm{\bar{y}}\right) ^{2}} \tag{9}\]\[\text{MAE}=\frac{1}{n}\underset{i=1}{\overset{n}{\sum}}\left|y_{i}-y_{i}^{{}^{ \prime}}\right| \tag{10}\]  
\[\text{MSE}=\frac{1}{n}\underset{i=1}{\overset{n}{\sum}}\left(y_{i}-y_{i}^{{}^{ \prime}}\right)^{2} \tag{11}\]  
\[\text{RMSE}=\sqrt{\frac{1}{n}\underset{i=1}{\overset{n}{\sum}}\left(y_{i}-y_{i }^{{}^{\prime}}\right)^{2}} \tag{12}\]