esa-sceva/satcom-chunk-collection huggingface dataset:
# Reusing Monolingual Pre-Trained Models by Cross-Connecting Seq2 seq Models for Machine Translation
## 4 Experiment
### Ablation Study  
#### 4.5.1 Intermediate Layer  
We conduct several experiments to see the effects of different architecture and size of the intermediate layer. We consider the feed-forward network with various filter sizes, a self-attention layer, a single sublayer of the encoder that consists of a self-attention layer  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Model** & **En-De** & **De-En** & **En-Ro** & **Ro-En** & **En-It** & **It-En** \\ \hline Random (Baseline) & 26.11 & 33.83 & 27.49 & 37.47 & 29.10 & 31.44 \\ ENC2 ENC & 28.36 & 37.16 & 29.31 & 39.06 & 31.18 & 34.22 \\ Cross-connected & 29.06 & 37.75 & 30.04 & 39.73 & 32.17 & 35.05 \\ Cross-connected + IL & **29.63** & **38.10** & **30.64** & **40.39** & **32.29** & **35.47** \\ \hline
**Model** & **It-Ro** & **Ro-It** & **It-De** & **De-It** & **De-Ro** & **Ro-De** \\ \hline Random & 21.73 & 22.27 & 18.91 & 19.70 & 18.37 & 19.50 \\ ENC2 ENC & 22.58 & 23.64 & 20.74 & 22.17 & 20.94 & 20.53 \\ Cross-connected & 22.36 & 23.91 & 21.53 & 22.65 & 20.98 & 21.20 \\ Cross-connected + IL & **23.03** & **24.33** & **22.04** & **23.32** & **21.26** & **21.71** \\ \hline \hline \end{tabular}
\end{table}
Table 2: BLEU scores for the IWSLT14 En-De, IWSLT14 En-Ro, IWSLT14 En-It, and IWSLT17 It-Ro datasets. IL is the intermediate layer and its configuration is different for each model. We indicate the best scores with **bold** font.  
and a feed-forward network with filter size 2048, and a simple dense layer of size 512 with layer normalization and dropout.  
The results are shown in Table 3. Overall a feed-forward network reports the best score though the size varies. The single dense layer consistently results in low scores as expected, except for Ro-It. However, more numbers and bigger size of the intermediate layer do not necessarily lead to higher BLEU scores, which indicates that a larger number of parameters does not exactly mean better performance. Further, we assumed that the self-attention mechanism would benefit the mapping of the encoder and decoder, but it does not result in the best score except for the En-Ro experiment which uses a sublayer of the encoder as the intermediate layer. Overall, We recommend a feed-forward network with filter size 512 or 768 because this network usually performs well with relatively fewer parameters than the encoder sublayer or the self-attention layer.
# Candidate Waveforms for ARoF in Beyond 5G
## 3 Candidate Modulation Formats for MmWave Over UDWDM-PONs  
Waveform formats are mainly divided into two categories: multi-carrier and single carrier waveforms. Through this paper, we study different proposals belonging to any of both aforementioned groups. The transmitter structures for all the cases analyzed in this section are summarized in Table 1.
## 4 Study Area and Data Selection
### Input Data
#### 4.2.2 \(t\)-test and \(p\)-value  
The \(t\)-test analysis was performed by classifying all the input data according to whether flood damage had occurred at the riverside or not and the \(p\)-value was derived to determine whether the collected data are the main factors for forecasting the water surface elevation. The \(t\)-test analysis is a statistical technique needed to determine whether the difference in mean value between two groups is statistically significant. The following assumptions must be satisfied in the \(t\)-test analysis.  
Firstly, the data should be continuous numbers with equal intervals (identical interval and continuity). Secondly, the two groups must be independent of each other (independence). Thirdly, the numerical value of the data should be normal (normality).  
The \(p\)-value was derived if the above assumptions were met. The process of derivation is shown in Figure 3. The factor could be considered as a significant factor if the \(p\)-value is less than the value which the researcher set as the threshold and the value of the threshold was 0.05 in general [37]. The results of the \(t\)-test analysis and the \(p\)-value for the various input data, such as precipitation, outlet discharge of Paldang dam, water surface elevation of Hangang River, tidal level of Incheon, wind speed, and concentration of dissolved oxygen are summarized in Table 3. Moreover, precipitation, outlet discharge Paldang dam, water surface elevation of Hangang River, and tidal level of Incheon used as input data in this study. Because these factors were considered to be important factors in forecasting the water surface elevation through the results of the \(t\)-test analysis and \(p\)-value. On the other hands, the wind speed (\(p\)-value = 0.06) and concentration of dissolved oxygen (\(p\)-value = 0.29) were not important factors for forecasting the water surface elevation.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Method** & **Contents** \\ \hline Use of relevant station data & Correct using a normal value of station \\  & Correct with linear interpolation \\  & The thresholder decides and correct the value \\ \hline Use of data from nearby stations & Correct using a relationship with upstream and downstream station values \\ \hline \hline \end{tabular}
\end{table}
Table 2: Correction method for outlier and missing value of hydrological data [36].
# Miniaturized Near-Infrared Analyzer for Quantitative Detection of Trace Water in Ethylene Glycol
## 3 Results and Discussion
### Establishment and Optimization of Water Content Prediction Models  
#### 3.3.1 Comparison of Different Models  
Three near-infrared spectroscopy models were developed to analyze the water content of ethylene glycol using Partial Least Squares Regression (PLSR), Support Vector Machine Regression (SVMR), and Principal Component Regression (PCR) across the entire spectral range. The performance of these models was assessed and compared based on key metrics, including the training set correlation coefficient (Rc), root mean square error of calibration (RMSEC), validation set correlation coefficient (Rp), root mean square error of prediction (RMSEP), and Prediction Residual Error Sum of Squares (PRESS). A correlation coefficient closer to 1 indicates stronger predictive capability, while a smaller root mean square error signifies higher model accuracy. The comparative results are summarized in Table 3.  
As shown in Table 2, the partial least squares regression (PLSR)-based model exhibited a lower root mean square error of prediction (RMSEP) compared to principal component regression (PCR) and support vector machine regression (SVMR). The superiority of PLSR stems from its unique mathematical framework--by maximizing the covariance between spectral variables and water content, it effectively addresses the multicollinearity prevalent in near-infrared (NIR) spectral data. Unlike PCR (which relies solely on dimensionality reduction in spectral variables) and SVMR (which depends on kernel function mapping), PLSR accurately extracts key information associated with water content  
Figure 2: Raw near-infrared (NIR) spectra of ethylene glycol samples.  
even under highly correlated variable conditions, thereby significantly enhancing predictive robustness. Although deep learning approaches such as artificial neural networks (ANN) may offer higher accuracy in large-sample scenarios, their computational complexity (requiring \(>\)10\({}^{4}\) iterations) and overfitting risks with small sample sizes (\(n=36\)) render them impractical for real-world applications in this study.
## 3 Control Strategies
### DC-DC Interleaved Converter Control  
DC-DC power stage is not the aim of this paper, but, in the context of this paper, it has been considered relevant to briefly mention its high level control details. In the operation context described in this document, the DC-AC inverter operation requires a proper DC-link voltage level to hold an adequate behaviour under unbalances or nonlinear needs. In addition, it has been considered that the DC energy storage system presents a large voltage range. Due to this voltage wide voltage range, an uncontrolled constant DC-link voltage level can affect inverter's operation. The purpose of the DC-DC converter is to step-up the voltage of the storage system and provide an autonomous way to regulate the DC-link voltage level against different AC requirements.  
The DC-DC converter is controlled by means of two nested control loops, as shown in Figure 2(a). The inner control loop manages the battery inductor, \(L_{bnt}\), and limits the maximum desired battery current, \(i_{bnt}\). As detailed in Section 2.2, an interleaved topology is chosen, so a \(2\pi/3\) rad/s shifted phase PWM strategy is used with the same duty cycle for all the converter's legs. The outer control loop controls the DC-link voltage, \(u_{bus}\). Because of the unbalanced nature of AC connected loads, low frequency voltage ripples (at \(\simeq\)100 Hz) in the DC-link can be severe. This voltage control loop needs a high bandwidth to overcome this ripple and keep constant \(u_{bus}\). Thus, an adaptive 100 Hz notch filter [50] is used in the feedback control chain for this purpose to quickly compensate for any variation around the fundamental rated grid frequency (50 Hz).  
To avoid over-modulation situations in the DC-AC stage, a preventive controller is suggested at the higher level of the DC-DC control scheme in order to provide an increment of \(u_{bus}\), \(\Delta u_{bus}\), to the rated DC-link voltage. Thus, the reference \(u_{bus}{}^{*}{}^{\prime}=u_{bus}^{*}+\Delta u_{bus}\) is generated where \(u_{bus}^{*}\) is the desired standard DC-link voltage, as can be deduced from Figure 2(b). The maximum available duty cycle of the inverter, \(d_{DC-AC_{\text{min}}^{*}}\), is compared with the maximum of the duties of the inverter's active  
Figure 3: Interleaved DC–DC converter control schemes. Proportional Integral (PI).  
phases (\(d_{u}\), \(d_{v}\) and \(d_{w}\)). If there is not enough DC bus for the inverter, the DC reference is stepped up to its maximum threshold, taking into account the limitations of the DC-link. In another case, \(\Delta u_{bus}\) is equal to zero in the steady-state as a consequence of the lower saturation limit of the PI controller, as shown in Figure 3b. Thanks to this method, the DC-link voltage is increased as required only under imminent over-modulation situation.
# The Role of Data-Driven Methodologies in Weather Index Insurance
## 4 Methodology
### Data
#### 4.1.3 Data Transformation  
Flood damage is not directly caused by precipitation but by actions caused by the flow of water. Thus, even if floods are related to rainfall, rainfall by itself is not the best predictor of the intensity of a flood [31]. Therefore, a transformation is adopted that seeks, in a simplified way, to emulate the process by which floods are caused due to rainfall. This is based on an approach proposed by [PERSON] [31].  
First, because part of the rainfall water is absorbed by the land, a parameter is adopted that represents the daily infiltration rate. Then, the rainwater that is not absorbed (run-off) is defined for the cell \(g_{j}\) as:  
\[R_{t}(g_{j})=max\big{\{}X_{t}(g_{j})-u,0\big{\}} \tag{9}\]  
In addition, the flow of water over the surface accumulates due to excess rainfall. For this reason, this behaviour is represented by a weighted moving average, which is restricted to 3 days. The cumulative surface flow (cumulative run-off) for the cell \(g_{j}\) during days \(t\), \(t-1\) and \(t-2\) is given by:  
\[R_{t}^{*}(g_{j})=\theta_{0}R_{t}(g_{j})+\theta_{1}R_{t-1}(g_{j})+\theta_{2}R_{ t-2}(g_{j}) \tag{10}\]  
where \(\theta_{0},\theta_{1},\theta_{2}>0\) and \(\theta_{0}+\theta_{1}+\theta_{2}=1\).  
Finally, a summation is performed over the region of interest to summarize the information in a single variable with a daily time period.  
\[Y_{t}=\sum_{j=1}^{J}R_{t}^{*} \tag{11}\]
## 3 Experimental Result and Accuracy Evaluation
### Validation with TCCON Measurements  
To ensure the spatial consistency of ground-based data and XCO2 estimates, we defined circular geographical regions centered on TCCON sites with diameters of 1\({}^{\circ}\), 3\({}^{\circ}\), and 5\({}^{\circ}\) as the validation areas (Figure S5). The averages of XCO2 estimates for each period within different validation regions were calculated. Meanwhile, the mean values for each period of the TCCON sites were obtained by first calculating the daily means and then calculating the monthly mean values. The standard deviations of the measurements relative to the monthly means at Hefei and Xianghe sites are in the ranges of 0.26-2.64 ppm and 0.67-2.9 ppm, respectively. The comparison between the two datasets was conducted to validate the accuracy of the interpolated data, as illustrated in Figure 6.  
The validation results of three geographic ranges of each site prove that the model-estimated XCO2 data is highly consistent with TCCON data. The validation accuracy of the Hefei site ranges from 0.961 to 0.968 for \(R^{2}\), 0.75 to 0.833 ppm for \(RMSE\), and 0.628 to 0.688 ppm for \(MAE\), which is higher than that of the Xianghe site. Taking the 3\({}^{\circ}\) region as an example (Figure 6d), the biases between model-estimated XCO2 and TCCON XCO2 reveal a general underestimation at the Xianghe site. On the one hand, this discrepancy may be attributed to the close proximity of the Xianghe site to Beijing, China, which is affected strongly by human activities. On the other hand, the 3\({}^{\circ}\) and 5\({}^{\circ}\) regions of the Xianghe site include a portion of the maritime area for which corresponding XCO2 estimates are unavailable. As a result, the omission of XCO2 values over the marine airspace near the Xianghe site from the calculation of regional average concentrations may have contributed to the relatively large biases at the Xianghe site. In general, the experimental results fit well with the TCCON data, with an overall accuracy of about \(R^{2}\) of 0.954, \(RMSE\) of 0.898 ppm, and \(MAE\) of 0.741 ppm.
# The Usage of Virtual and Augmented Reality in Underwater Archeology
## 5 Use of Augmented and Virtual Reality in Underwater Archeology--Example Solutions
### BLUMED and BLUMED PLUS Projects  
The projects are a continuation and extension of the VENUS project [119]. The BLUMED project was implemented in the years 2016-2021 by a consortium consisting of 14 organizations from the following countries: Italy, Cyprus, Spain, France, Greece, Croatia, Malta, Portugal, and Slovenia [128]. The projects did not create new virtual solutions, but previously developed solutions were implemented in new places, including archaeological sites. The focal points of the BLUMED project were as follows [128]:  
* Underwater archaeological park--Baiae, Italy, where existing VR solutions have been improved;
* Marine protected area--Capo Rizzuto, Italy, including the underwater archaeological site of Punto Scifo D, where existing VR solutions have been improved and stands in the local museum have been additionally adapted to allow the introduction of VR systems as exhibition elements;
* Underwater museum--Alonissos/Sporades, Greece, where a VR site was created allowing for diving to the Peristera wreck (a wreck dating back to the 5 th century BC);
* The Underwater Museum of Western Pagasetic Gulf, Greece, where virtual sites were created for the Kikinthos, Akra Glaros and Telegrafos shipw;
* Underwater archaeological sites of Cavtat, Croatia, where VR systems have been implemented for the local archaeological site.  
In this case, virtual applications were only aimed at increasing the tourist attractiveness of selected places, using existing underwater archaeological sites for this purpose. The system did not develop original solutions in this area, but used applications developed as part of other projects, adapting them to the needs of the given archaeological sites [128].
###### Abstract  
The Echaurren Norte Glacier is a reference glacier for the World Glacier Monitoring Service (WGMS) network and has the longest time series of glacier mass balance data in the Southern Hemisphere. The data has been obtained by the direct glaciological method since 1975. In this study, we calculated glacier area changes using satellite images and historical aerial photographs, as well as geodetic mass balances for different periods between 1955 and 2015 for the Echaurren Norte Glacier in the Central Andes of Chile. Over this period, this glacier lost 65% of its original area and disaggregated into two ice bodies in the late 1990s. The geodetic mass balances were calculated by differencing digital elevation models derived from several sources. The results indicated a mean cumulative glacier wide mass loss of \(-\)40.64 \(\pm\) 5.19 m w.e. (\(-\)0.68 \(\pm\) 0.09 m w.e. a\({}^{-1}\)). Within this overall downwasting trend, a positive mass balance of 0.54 \(\pm\) 0.40 m w.e. a\({}^{-1}\) was detected for the period 2000-2009. These estimates agree with the results obtained with the glaciological method during the same time span. Highly negative mass change rates were found from 2010 onwards, with \(-\)1.20 \(\pm\) 0.09 m w.e. a\({}^{-1}\) during an unprecedented drought in Central Andes of Chile. The observed area and the elevation changes indicate that the Echaurren Norte Glacier may disappear in the coming years if negative mass balance rates prevail.  
g pacs: 2019, 11, 260; 10.3390/rs11030260 www.mdpi.com/journal/remotesensing
## 2 MIMO Antenna Design Approaches
### Envelope Correlation Coefficient (ECC)  
The envelope correlation coefficient (ECC) is the diversity parameter which indicates the correlation between the adjacent MIMO antenna elements. It can be calculated from the radiation patterns or S-parameters. However, the ECC value evaluated using the far field radiation pattern is highly preferred because the ECC states how multiple radiating elements in MIMO systems are independent in their radiation pattern. Moreover, it is seen that most of the planar antennas suffer from loss; therefore, the method for determiningECC using the S-parameters should be avoided. The mathematical expression for ECC using the radiation pattern data of MIMO design is given by Equation (1) [11]:  
\[\text{ECC}_{qp}=\frac{\left|\int_{0}^{2\pi}\int_{0}^{\pi}\left(E_{\theta p}^{*}E_{ \theta q}P_{\theta}XPR+E_{qp}^{*}E_{\varphi q}P_{\varphi}\right)d\Omega\right|^{ 2}}{\alpha\times\beta} \tag{1}\]  
here,  
\[\alpha=\int\limits_{0}^{2\pi}\int\limits_{0}^{\pi}\left(E_{\theta q}^{*}E_{ \theta q}P_{\theta}XPR+E_{qp}^{*}E_{\varphi q}P_{\varphi}\right)d\Omega\]  
\[\beta=\int\limits_{0}^{2\pi}\int\limits_{0}^{\pi}\left(E_{\theta p}^{*}E_{ \theta p}P_{\theta}XPR+E_{qp}^{*}E_{\varphi p}P_{\varphi}\right)d\Omega\]  
where \(XPR\) is the cross-polarization level defined as the ratio of average power along the phi and theta directions. In a practical environment, the acceptable limit of ECC must be <0.5.
# Adaptive Impedance-Based Fault Location Algorithm for Active Distribution Networks
###### Abstract  
Modern fault location methods are robust; however, they depend strongly on the availability of the measurements given by Distributed Energy Resources (DER). If the communication or synchronism of this information is lost, the fault location is not possible. This paper proposes an adaptive impedance-based fault location algorithm for active distribution systems. The proposal combines information provided by Intelligent Electronic Devices (IEDs) located at the substation, the knowledge of the network topology and parameters, as well as the distributed power sources, to estimate the fault location. Its adaptive feature is given by the use of a Distributed Energy Resources (DER) electrical model. This model is used to estimate the DER current contribution to the fault, in case the information provided by a local IED is not available. The method takes two types of DER technologies into account: Inverter non-interfaced DER (INIDER) and Inverter-interfaced DER (IIDER). The proposed method is validated on a modified IEEE 34-node test feeder, which was simulated with ATP/EMTP. The results obtained using the IEDs information, presented a maximum error of 0.8%. When this information is not available, the method's performance decreases slightly, obtaining a maximum error of 1.1%. The proposed method showed better performance when compared with two state of the art methods, indicating potential use for real-life applications.  
Keywords:impedance-based fault location; active distribution networks; distributed energy resources +
Footnote †: journal: Applied Soft. 2018
## 2 Methods and Principles
### Equivalent Linear Dynamic Time-History Analysis Method
#### 2.2.1 Viscoelastic Artificial Boundary  
The consideration of dynamic artificial boundaries is a key aspect in the study of the seismic performance of underwater immersed tunnels. The fundamental principle of the  
Figure 3: Schematic diagram of equivalent linear method.  
Figure 2: Longitudinal seismic response analysis model.  
viscoelastic artificial boundary involves creating a new physical element by combining dampers and springs in parallel, which can be positioned at the foundation boundary to simulate the radiation damping effect of a semi-infinite foundation. The seismic waves encountered at the model boundaries are transformed into equivalent loads for each node at the boundary. When the scattered waves propagating towards the boundary are absorbed by the viscoelastic artificial boundary, the respective nodes at the boundary will exhibit free-field motion in response to the seismic load. A schematic diagram illustrating the boundary conditions is presented in Figure 4. In order to facilitate the analysis of three-dimensional seismic responses of complex structures such as submarine immersed tube tunnels, a secondary development based on Fortran was carried out using the UPFs provided by ANSYS. This involved writing a subroutine for equivalent viscoelastic boundary elements embedded in the software. The tangential and normal spring stiffness, denoted as K, and damping coefficient, denoted as C, are calculated using Equations (1) and (2) [30], respectively.  
\[\mathrm{K}=\alpha\frac{\mathrm{G}}{\mathrm{r}}\sum_{i=1}^{1}\mathrm{A_{i}} \tag{1}\]  
\[\mathrm{C}=\rho\mathrm{c}\sum_{i=1}^{1}\mathrm{A_{i}} \tag{2}\]  
In the formula, G represents the shear modulus, \(\rho\) denotes the mass density, r is the distance from the scattering source to the nodes on the artificial boundary, and c is the wave speed. The parameter \(\alpha\) stands for the boundary parameters of different directional boundaries, while A represents the control area of the nodes at the outer boundary of the foundation region.