esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Context-Aware Positioning Model
### Context-Aware Positioning
#### 3.3.1 Request Self-Positioning  
If a device using the model has positioning capabilities of its own, it is very likely that the recommendation for positioning will be to perform self-positioning, i.e., accessing a strategy available in the scenario itself. In that case, the model would send a request to use the respective peripherals, and estimate its position on its own. For example, devices with GPS transceivers or RFID readers could activate them, while Android-based smartphones could use their embedded Wi-Fi positioning system in order to estimate their positions.  
Dead reckoning (Figure 4a), also known as deduced reckoning or DR, a special type of self-positioning, is the process of calculating one's current position by using a previously determined position, or fixing and advancing that position based upon known or estimated speeds, over elapsed time and course [28]. In the context of our model, dead reckoning is applied using the list of known positions, in conjunction with the navigation peripherals, in order to smooth transitions between different positioning strategies, since there could be a \"blind\" positioning elapsed time between using different strategies, or between two consecutive estimations. However, DR is subject to cumulative errors, and it requires means to correct its estimations by using a proper positioning strategy [28]. It is by no means reliable for long periods, and is used only to offer positioning consistency during \"blind gaps\" when the device does not have access to a proper positioning strategy. The proposed model strictly ignores all nodes using dead reckoning during the collaborative positioning process.  
It is important to note that the actual self-positioning conducted by a device is transparent to our model, _i.e._, the model recommends using a positioning strategy, but it is up to the device's peripherals to actually perform the position estimation.
## 3 Results
### Performances of Nonstationary and Stationary Models  
Figure 3 shows the performance of the non-stationary and stationary models evaluated using the AIC for the eighth month (D series fitted with GAMLSS for the spring-summer season) and the second month (D series fitted with GAMLSS for the fall-winter season). The spatial distribution of the different models is consistent across the months, consistently showing a trend of decreasing AIC values from the southeast to northwest, which implies the optimization of the model performance.  
The AIC values are in the range of 300-600 for both the non-stationary and stationary models, proving that nonstationary models have qualified simul  
Figure 3: Performances of nonstationary and stationary models under AIC assessment. (**a**) and (**b**) are the stationary and nonstationary models, respectively; (**1**) and (**2**) are the AIC values for the seventh and fifth-months, respectively.  
Figure 2: Spatiotemporal distribution of the long-term trends of P, PET, and D series. Black dots indicate confidence levels of 95% or more.  
those of the stationary model. The simulation performance of the nonstationary model in Southeast China is similar to that of the stationary model; it is slightly better in Northeast China, especially in the CRB. Combined with the long-term change of the D series, Northwest China reacts strongly to climate change, and a nonstationary model with precipitation and temperature climate covariates is needed for a better evaluation of the drought conditions.
## 3 Results and Discussion
### Effect of Flow Rate on Oil Production with Injection of Colloidal Dispersions  
We confirmed that injection of colloidal dispersions containing a zwitterionic surfactant on silica NPs adsorbed by PSS-_co_-MA significantly increased the flow rate and oil recovery from Berea sandstone (Figure 7a). A possible explanation for this result is that the elevated flow velocity detached the NPs from rock, due to the increased hydrodynamic force; the NPs then readily adsorbed to oil-water interfaces, thus improving oil recovery. Indeed, NP retention fell as the flow rate increased, and the normalized breakthrough NP level rose (Figure 7a). We calculated the relative permeability of the oil-water phase, to characterize fluid flow by flow rate, using both the Corey correlation and our experimental data. The relative permeability is calculated as follows: \(k_{r\text{o}}=k^{0}r_{\text{o}}\left(\frac{S_{\text{op}}-S_{\text{ov}}}{1-S_{ \text{virt}}-S_{\text{svr}}}\right)^{m}\), \(k_{r\text{w}}=k^{0}r_{\text{w}}\left(\frac{S_{\text{op}}-S_{\text{virt}}}{1-S_{ \text{virt}}-S_{\text{svr}}}\right)^{n}\) where \(k_{r\text{o}}\) and \(k_{r\text{w}}\) are the relative permeabilities of oil and water, respectively, \(S_{\text{virt}}\) is the irreducible (minimum) water saturation, and \(S_{\text{or}}\) is the (experimental) residual (minimum) oil saturation of porous rock. \(m\) and \(n\) are adjusted using a parameter estimation method (here, both values were 2). \(k^{0}r_{\text{o}}\) and \(k^{0}r_{\text{w}}\) are the relative permeabilities of oil and water, respectively, at the experimental endpoints (\(S_{\text{virt}}\) and \(S_{\text{or}}\)). As the colloidal dispersion flow rate increased, the residual oil saturation (\(S_{\text{or}}=1-S_{\text{w}}\), \(max\)) fell and the relative water permeability at the endpoint (\(k^{0}r_{\text{w}}\)) increased; \(k^{0}r_{\text{w}}\) and \(S_{\text{or}}\) were 0.024 and 0.275, respectively, at a flow rate of 0.1 mL/min, but 0.203 and 0.177 at a flow rate of 10 mL/min (Figure 7b). Thus, silica NPs readily flowed through the pore throat, increasing water permeability when residual oil saturation was achieved.  
Figure 6: (**a**) Particle aggregation at sandstone injects on injection of colloidal dispersions containing a cationic surfactant. Reduced (**b**) fluid injectivity and (**c**) oil recovery attributable to aggregation.  
Figure 7: (**a**) Ultimate oil recovery and particle retention. (**b**) Relative permeability as a function of flow rate.
# Survey on Unmanned Aerial Vehicle for Mars Exploration: Deployment Use Case
## 2 Space Exploration through Different Methods/Vehicles
### Space Exploration through UAVs
#### 2.1.3 Titan UAVs  
The NASA Johns Hopkins University Applied Physics Laboratory [42] proposed a mission concept for Titian UAV exploration in February 2017. Early conceptualization began in 2008 when NASA and the ESA developed the Montgomeryie style balloon concept. While a pathfinder would explore the surface of Titan, the balloon would act as a flagship. The idea of using a UAV was presented after adjustment to the initial concept. The UAV used an eight-bladed rotor design with vertical take-off and landing capabilities, labelled the Dragonfly. The Dragonfly will be able to explore both Titan's surface and the atmosphere [42].
## 2 Underwater Wireless Sensor Network
### Communication Architecture  
The network topology can be divided into two categories including static and mobile UWSNs based on nodes' movement underwater [21; 32].
# Exploring ISAC: Information-Theoretic Insights
## 5 Network ISAC with Sensing Distortion
### One-to-Many Communication (Broadcast Channels) with Tx Sensing
#### 5.1.3 Example  
Consider the physically degraded broadcast channel with binary input and output alphabets \(\mathcal{X}=\mathcal{Y}_{1}=\mathcal{Y}_{2}=\{0,1\}\) and two-bit state alphabet \(\mathcal{S}=\{0,1\}^{2}\), i.e., the state \(S\) can be written as \(S=(S_{1},S_{2})\) with binary \(S_{1}\) and \(S_{2}\). To describe the channel, let \(Y_{k}^{\prime}=S_{k}\cdot X\) for each Rx \(k\in\{1,2\}\), where the joint state pmf is:  
\[P_{S_{1}S_{2}}(s_{1},s_{2})=\begin{cases}1-q,&\text{if }(s_{1},s_{2})=(0,0)\\ 0,&\text{if }(s_{1},s_{2})=(0,1)\\ q\gamma,&\text{if }(s_{1},s_{2})=(1,1)\\ q(1-\gamma)&\text{if }(s_{1},s_{2})=(1,0),\end{cases} \tag{39}\]  
for a real number \(\gamma,q\in[0,1]\). The generalized feedback signals are \(Z=(Y_{1}^{\prime},Y_{2}^{\prime})\) and the Rx outputs \(Y_{k}=(Y_{k}^{\prime},S_{k})\), which means that each Rx is informed of its corresponding state. Distortion is measured in terms of Hamming distortion between \(S_{1}\) and an optimal estimator of \(S_{1}\) based on \((X,Z)\).  
Note that \(S_{2}\) is a degraded version of \(S_{1}\), which together with the transition law, ensures the Markov chain \(X\)\(\
eg\)\(\
eg\)\((S_{1},Y_{1}^{\prime})\)\(\
eg\)\(\
eg\)\((S_{2},Y_{2}^{\prime})\) and the physically degradedness of the BC. For physically degraded BCs, the presented inner and outer bounds coincide [64] and thus, we can obtain the exact characterization of the capacity-distortion tradeoff of this example, which is shown numerically in Figure 9.  
Figure 9: Capacity–distortion region for proposed example.  
We observe a tradeoff between the two rates \(R_{1}\) and \(R_{2}\) and the permissible distortion \(D\). Moreover, resource-sharing strategies are highly suboptimal as for the point-to-point case.
# Screening of Sourdough Starter Strains and Improvements in the Quality of Whole Wheat Steamed Bread
## 2 Results and Discussion
### Selection of Starters
#### 2.3.2 Effects of Different Yeast Combinations on the Quality of Whole Wheat Steamed Bread  
Specific volume is an important apparent characteristic index to evaluate the quality of steamed bread and is closely related to CO\({}_{2}\) production by yeasts [26]. The specific volumes of whole wheat steamed bread made by different yeast fermentation combinations are shown in Figure 4A. The larger the specific volume of whole wheat steamed bread, the better quality characteristics of the product. The specific volumes of whole wheat steamed bread fermented by _Saccharomyces cerevisiae_ and _Wicherhammyces anomalies_ alone were significantly higher than that of the control, and the former was superior to the latter, which is consistent with the results of previous studies [26]. However, when _Saccharomyces cerevisiae_ and _Wicherhammyces anomalies_ were used in combination, the effect was not as good as _Saccharomyces cerevisiae_ fermentation alone, because the resource competition between _Saccharomyces cerevisiae_ and _Wicherhammyces anomalies_ may not be conducive to the growth of _Saccharomyces cerevisiae_[19]. When fermented with _Saccharomyces cerevisiae_, the specific volume of whole wheat steamed bread increased by 7.6% when compared with the control.  
The textural parameters of the steamed bread were explored, including hardness, cohesiveness, springiness, chewiness, and gumminess (Table 3). Hardness is an important parameter affecting the sensory quality of steamed bread and a main index of aging during storage [27]. The whole wheat steamed bread fermented by Y2 had the lowest hardness (802.3 g) and the highest springiness (0.90) values among all treatment groups, indicating a better product quality. Therefore, Y2 was selected for the following experiments.  
Figure 4: (**A**) Effects of different yeast combinations on the quality of whole wheat steamed bread. (**B**) Effects of different LAB combinations on the quality of whole wheat steamed bread. Note: control—commercial dry yeast; Y2—_Saccharomyces cerevisiae_; Y6—_Pichia kudriavzevii_; Y9—_Wicherhammyces anomalous_; L1—_Lactobacillus johnsonii_; L2—_Levilactobacillus brevis_; L5—_Pediococcus pentosacus_; L12—_Lacticaseobacillus paracasi_; L16—_Lacticaseobacillus case_; L18—_Pediococcus acidilactici_. Different superscript letters (a–e) above bars indicate significant difference (\(p<0.05\)).
# A Novel Alternating \(\mu\)-Law Companding Algorithm for PAPR Reduction in OFDM Systems
## 4 Proposed Scheme
### Proposed Alternating \(\mu\)-Law Companding Method (Aulc)
```
The first stage: Step 1 Set position vector \(\mathbf{p}\) and index vector \(\mathbf{z}\) to zero vector. Step 2 Sort \(\mathbf{x}\) signal by amplitude, denoted as \(\mathbf{x}_{sort}\), update \(\mathbf{z}\) to its original position. Step 3 Divide \(\mathbf{x}_{sort}\) and \(\mathbf{p}\) into two groups each, as follows: \[\mathbf{x}_{sort1} :=\Big{[}x_{sort}[0],x_{sort}[1],\dots,x_{sort}[\beta_{rough}-1] \Big{]},\] \[\mathbf{x}_{sort2} :=\Big{[}x_{sort}[\beta_{rough}],x_{sort}[\beta_{rough}+1], \dots,x_{sort}[N-1]\Big{]},\] and \[\mathbf{p}_{1} :=\Big{[}p[0],p[1],\dots,p[\beta_{rough}-1]\Big{]},\] \[\mathbf{p}_{2} :=\Big{[}p[\beta_{rough}],p[\beta_{rough}+1],\dots,p[N-1]\Big{]}.\] Step 4 Do companding for \(\mathbf{x}_{sort1}\) by the original \(\mu\)-law and set position vector \(\mathbf{p}_{1}\) to \(\mathbf{1}\). The second stage: Step 1 Compute the PAPR of \(\mathbf{x}_{sort2}\), denoted as \(PAPR_{\mathbf{x}_{sort2}}\). Step 2 Set \(\mathbf{x}^{\prime}:=\mathbf{x}_{sort2}\) Step 3 For \(n=\beta_{rough}:N-1\) \[\rho=\frac{\beta_{rough}}{N}=0.54. \tag{20}\]0  Compute the PAPR of \(\mathbf{x}^{\prime}\), denoted as \(PAPR_{\mathbf{x}^{\prime}}\)  If \(PAPR_{\mathbf{x}^{\prime}}<PAPR_{\mathbf{x}_{sort2}}\) then  Set \(p[n]\) to \(1\)  Updata \(PAPR_{\mathbf{x}_{sort2}}\) to \(PAPR_{\mathbf{x}^{\prime}}\)  Else  Break  End If  End For Step 4 Set \(\mathbf{y}_{AULC}:=\mathbf{x}^{\prime}\)
```  
**Algorithm 2** Proposed AULC algorithm
## 5 Conclusions  
In this study, we built unsupervised NMT systems for pairs of Korean-Japanese and Korean-English and analyzed the performances of the NMT systems according to the properties of the language pairs.  
We proposed a new method to analyze cross-attentions in terms of alignments. Using this analysis method, we were able to draw insights about the differences in word order between a source and a target language in an NMT model.  
Korean and Japanese are known to be very similar in word order, morphology, and syntax; however, there has been no way to measure their similarities. In this study, we measured the similarities between the two languages by alignments of cross-attentions. The high similarity between the two languages allows unsupervised NMT systems of the languages to achieve high performance.  
Before building an unsupervised NMT system, we can estimate the differences between a language pair using the proposed method, and thereby predict whether an unsupervised NMT system between the language pair can be trained well.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Cross Attentions** & **Models** & \(AttnI_{\rightarrow K}^{K}\) & \(Attn_{E\rightarrow K}^{EK}\) \\ \hline \multirow{3}{*}{\begin{tabular}{c} Aligned in the reverse order \\ \end{tabular} } & Pre-trained Model & 0.992\% & 3.284\% \\  & Unsupervised NMT & 0.703\% & 2.069\% \\  & Supervised NMT & 0.992\% & 7.256\% \\ \hline \multirow{3}{*}{\
\begin{tabular}{c} Non-aligned \\ \end{tabular} } & Pre-trained Model & 2.112\% & 8.126\% \\  & Unsupervised NMT & 10.078\% & 18.917\% \\  & Supervised NMT & 12.099\% & 16.095\% \\ \hline \multirow{3}{*}{
\begin{tabular}{c} Aligned in the reverse order \\ + Non-aligned \\ \end{tabular} } & Pre-trained Model & 4.446\% & 14.202\% \\  & Unsupervised NMT & 12.055\% & 25.027\% \\ \cline{1-1}  & Supervised NMT & 14.651\% & 41.279\% \\ \hline \hline \end{tabular}
\end{table}
Table 7: The cross-attentions between Japanese–Korean and English–Korean.  
Figure 6: The cross-attentions between Japanese–Korean and English–Korean.  
**Author Contributions:** Conceptualisation, S.Y., S.-H.K. and [PERSON]-J.L.; methodology, Y.-S.C. and [PERSON]-J.L.; software, Y.-H.P.; validation, Y.-H.P.; writing--original draft preparation, [PERSON]-J.L. and [PERSON]-S.C.; writing--review and editing, [PERSON]-J.L. and [PERSON]-S.C. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by Electronics and Telecommunications Research Institute(ETRI) grant funded by the Korean government. [21 ZS1100, Core Technology Research for Self-Improving Integrated Artificial Intelligence System].  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** All data generated or analysed during this study are included in this published article.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Two Low-Level Feature Distributions Based No Reference Image Quality Assessment
## 3 TLLFD for NR IQA
### Low-Level Feature Distribution
#### 3.2.3 Nonlinear Regression Model  
In the next step of feature extraction, the regression function is used to establish the complex nonlinear relationship between feature space and quality evaluation. In [27], [PERSON] et al. proposed SVM, which can be regarded as a special type of single hidden layer feed forward network, i.e., support vector network. SVM is a machine learning method based on statistical theory and can transform low-dimensional original feature space into high-dimensional feature space by using kernel function. SVM is generally noted for being able to handle small sample, non-linear, and high-dimensional data. In our implementation, LIBSVM [28] package is used to implement a nonlinear regression model SVR with a radial basis function (RBF) kernel. Considering a set of training data \(\left\{\left(x_{1},y_{1}\right),\left(x_{2},y_{2}\right),\ldots,\left(x_{l},y_{ l}\right)\right\}\), where \(x_{i}\in R^{n}\) is the extracted quality aware feature and \(y_{i}\) is the corresponding difference mean opinion score (DMOS). Given regularization constant parameters \(C\) and constant deviation parameters \(\varepsilon\), the standard form of SVR can be represented as [29]:  
\[\begin{split}\min\limits_{\omega,b,\xi,\xi^{*}}& \frac{1}{2}\|\omega\|^{2}+C\bigg{\{}\sum\limits_{i=1}^{l}\xi_{i}+\sum \limits_{i=1}^{l}\xi_{i}^{*}\bigg{\}}\\ s.t&\left\{\begin{array}{l}\omega^{T}\phi(x_{i})+b- y_{i}\leq\varepsilon+\xi_{i}\\ y_{i}-\omega^{T}\phi(x_{i})-b\leq\varepsilon+\xi_{i}^{*}\\ \xi_{i}\geq 0,\xi_{i}^{*}\geq 0,i=1,2,\ldots,l.\end{array}\right.\end{split} \tag{14}\]  
where \(\xi_{i}\) and \(\xi_{i}^{*}\) are the relaxation variable. \(\omega\) and \(b\) are the weights and biases, respectively. The parameters \(C\) and \(\varepsilon>0\) are found by searching the optimal values from the sets \(\left(2^{-3},2^{-2},\ldots,2^{10}\right)\) and \(\left(2^{-10},2^{-9},\ldots,2^{6}\right)\).
# Considering Inter-Frequency Clock Bias for BDS Triple-Frequency Precise Point Positioning
## 3 Results
### Performance Evaluation of Triple-Frequency PPP
The RMS values of phase residuals for all BDS-2 satellites are also given in each panel. B1/B2 and B1/B3 phase observation residuals appear at the same level and no significant systematic errors can be found in the B1/B3 phase residuals. It is suggested that we have properly handled the issue of satellite clocks in triple-frequency PPP model. For the purpose of further analysis, the B1/B3 phase observation residuals are re-computed when neglecting the IFCB, which indicates larger phase residuals with RMS statistics of 0.018 m. Although the difference between B1/B3 phase residuals of triple-frequency PPP with and without IFCB consideration is only 4 mm, the latter ones show systematic errors. The robustness of position solutions is also very important in PPP besides the positioning accuracy. When the observations on B2 frequency are poorly tracked or contaminated, the benefits will be more significant after adding B3 observations to the B1/B2 PPP processing. In this paper, we only consider the ambiguity-float PPP solutions. It is expected that the ambiguity resolution will benefit from the extra frequency. Given that high-accuracy and reliable float solutions can be achieved by the triple-frequency PPP developed here, the triple-frequency PPP with AR can be investigated based on this. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Item** & **Direction** & **B1/B2** & **B1/B2 + B1/B3 (with IFCB)** & **B1/B2 + B1/B3 (no IFCB)** \\ \hline \multirow{3}{*}{Positioning Accuracy (cm)} & East & 1.2 & 0.8 & 1.4 \\  & North & 0.4 & 0.3 & 0.4 \\  & Vertical & 1.6 & 1.5 & 2.6 \\ \hline \multirow{3}{*}{Convergence Time (min)} & East & 23.5 & 17.5 & 31 \\  & North & 7 & 5 & 15 \\ \cline{1-1}  & Vertical & 132.5 & 130 & 134.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Positioning accuracy and convergence time for three different cases at JFNG on 12 September 2016. Figure 11: Phase observation residuals for triple-frequency PPP at JFNG on 12 September 2016. Figure 10: PPP positioning errors for three different cases at JFNG on 12 September 2016. For completeness, the datasets used for accuracy statistics are re-computed after adding \(P_{1}/P_{3}\) ionospheric-free combined code observations to triple-frequency PPP processing. The code-specific IFCB is obtained using the DCB products, which are provided by German Space Operations Center. The average positioning accuracy and convergence time for all 24-h cases are calculated and listed in Table 8. For comparison, the results for the triple-frequency PPP developed here are also given in Table 8. After adding \(P_{1}/P_{3}\) code observations, the convergence time shows a decrease of 1.1-3.6 min in three directions, but no significant change is found for the positioning accuracy. Greater benefits from \(P_{1}/P_{3}\) code observations for triple-frequency PPP can be expected as long as the code-specific IFCB is thoroughly investigated and precisely estimated.
## 3 The Proposed EFAFN Model
### Feature Classification  
Inspired by the VGG16 [32] network structure, we designed a Multilayer Perceptron (MLP) and Softmax layers for feature classification, as shown in Figure 5. The activation function of the hidden layer and output layer is LeakyReLU, and the loss function uses cross entropy.  
\[J=-\sum_{i=1}^{N}[y_{i}\log\hat{y}_{i}+(1-y_{i})\log\left(1-\hat{y}_{i}\right)]+\lambda R \tag{11}\]  
where \(J\) is the cost function. \(\hat{y}_{i}\) is the prediction result of our model for sample \(i\), and \(y_{i}\) is the true label for sample \(i\). \(N\) is the size of the training data. \(R\) is the standard L2 regularization, and \(\lambda\) is the weight of \(R\).  
Figure 4: Adaptive feature fusion strategy diagram. Where \(\alpha\) and \(\beta\) are the parameters of the subnetworks \(\varphi(\cdot;\alpha)\) and \(\phi(\cdot;\beta)\), \(\oplus\) represents the join operation, and \(u_{a}^{*}\) and \(u_{b}^{*}\) are the subnetworks \(\varphi(\cdot;\alpha)\) and \(\phi(\cdot;\beta)\) output.  
Figure 5: Multilayer perceptron block diagram. It consists of a three-layer fully connected neural network. The number of neurons in the input layer and output layer is 3099 and 2, respectively, and the number of neurons in the hidden layer is 328.