esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Energy-Saving Route Planning Algorithm for Marine Vehicles
## 2 Formulation of the Energy-Saving Problem
### Ocean Current Model  
This paper built a two-dimensional hydrodynamic numerical model to calculate the velocity and setting of ocean current. The incompressible Reynolds-averaged Navier-Stokes equation is solved by the finite volume method. Boussinesq approximation and assumption of shallow water and hydrostatic pressure are considered [26]. The governing equations are as follows.  
\[\frac{\partial h}{\partial t}+\frac{\partial hu}{\partial x}+\frac{\partial hv }{\partial y}=hS \tag{1}\]  
\[\frac{\partial hu}{\partial t}+\frac{\partial lu^{2}}{\partial x}+\frac{ \partial lvu}{\partial y}=fvh-gh\frac{\partial\eta}{\partial x}-\frac{h}{ \rho_{0}}\frac{\partial P_{a}}{\partial x}-\frac{gh^{2}}{2\rho_{0}}\frac{ \partial\rho}{\partial x}+\frac{\tau_{xx}}{\rho_{0}}-\frac{\tau_{bx}}{\rho_{0 }}+F_{u}+hu_{s}S \tag{2}\]  
\[\frac{\partial hv}{\partial t}+\frac{\partial luv}{\partial x}+\frac{ \partial lv^{2}}{\partial y}=fuh-gh\frac{\partial\eta}{\partial x}-\frac{h}{ \rho_{0}}\frac{\partial P_{a}}{\partial y}-\frac{gh^{2}}{2\rho_{0}}\frac{ \partial\rho}{\partial y}+\frac{\tau_{yy}}{\rho_{0}}-\frac{\tau_{yy}}{\rho_{0 }}+F_{v}+hv_{s}S \tag{3}\]  
where \(t\) is the time. \(x\) and \(y\) are Cartesian coordinates. \(u\) and \(v\) are the average velocity components in the \(x\) and \(y\) directions, respectively. \(h\) is the total water depth. \(S\) is the source-sink term. \(\eta\) is the water surface elevation. \(f\) is the Coriolis force parameter. \(g\) is the acceleration of gravity. \(\rho_{0}\) is the actual water density. \(\rho=1\) g/cm\({}^{3}\). \(\tau_{xx}\) and \(\tau_{sy}\) are the wind shear stress components on the water surface. \(\tau_{bx}\) and \(\tau_{yy}\) are the shear stress components of the bed. \(P_{a}\) is the air atmospheric pressure. \(P_{a}\) and \(v_{s}\) are the velocity components of the source. \(F_{u}\) and \(F_{v}\) are the horizontal eddy viscous stress forces.
# Innovative Approach Integrating Machine Learning Models for Coiled Tubing Fatigue Modeling
## 6 Results Analysis and Interpretation  
Each model was assessed based on its mean absolute error (MAE), R\({}^{2}\) score, and mean squared error (MSE). Additionally, a neural network was developed and trained to further explore deep learning's potential in predicting the N cycle, as shown in Table 2. The analysis is focused on the best four models that demonstrated good results (namely CatBoost, XGBoost, Random Forest, and Decision Tree). The other models (SVR, LR, and ANN) will be ignored as they fail to learn patterns because of the following potential reasons:  
* Model overfitting (occurs when a model is overly complex in training data or fails when tested).
* The dataset might be too small or noisy.  
\begin{table}
\begin{tabular}{l c c c c c c c c c c c c} \hline \hline  & **R\({}^{2}\)** & **MSE** & **MAE** & **MAPE** & **R\({}^{2}\)** & **MSE** & **MAE** & **MAPE** & **R\({}^{2}\)** & **MSE** & **MAE** & **MAPE** \\  & **Train** & **Train** & **Train** & **Test** & **Test** & **Test** & **Test** & **Val** & **Val** & **Val** & **Val** \\ \hline Linear & & & & & & & & & & & & \\ Regens- & 0.79 & 71,043.3 & 162.86 & 41.6 & 0.74 & 137,571 & 179.6 & 95.2 & 0.17 & 238,488 & 227 & 102.6 \\  & & & & & & & & & & & & \\ Decision & 0.98 & 5325.93 & 34.49 & 13.93 & 0.90 & 52,927.47 & 140.34 & 31.18 & 0.96 & 12,851.32 & 81.48 & 34.31 \\ Random & & & & & & & & & & & & \\ Forest & & & & & & & & & & & & \\ CatBoost & 0.98 & 5676.25 & 42.42 & 16.16 & 0.95 & 26,838.36 & 94.67 & 26.61 & 0.95 & 13,412.97 & 83.05 & 36.38 \\ XGBoost & 0.98 & 5326.35 & 34.72 & 13.99 & 0.94 & 32,084.48 & 109.97 & 27.97 & 0.95 & 13,372.35 & 83.24 & 34.29 \\ SVR & \(-\)0.11 & 3802.9908 & 344.41 & 92.22 & \(-\)0.18 & 6717.00 & 4053.79 & 114.22 & \(-\)0.07 & 308,363.33 & 320.95 & 132.93 \\ ANN & \(-\)2297.66 & 786,764,411.21 & 230,403.33 & 10,138.22 & \(-\)1366.43 & 714,863,1233.33 & 22,284.46 & 9517.33 & \(-\)3439.14 & 991,063,768.08 & 23,647.49 & 16,981.80 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Summary of ML results.
## 2 Principles
However, in the far field, the SNR is not enough to maintain the VLC with the highest data rate, resulting in a nonlinear decrease achievable information rate (AIR) with the distance increases. Therefore, the SNR and the AIR should be nonlinear correlated with the distance, and the SNR table should be interpolated with a nonlinear function. With such a distance slicing interpolation method, the SNR table is ready for all the points within the supporting distance range. In the testing phase, a PS bitloading DMT signal is generated with the SNR from the SNR table according to the distance information from the radar to achieve the flexible data rate. At the transmitting side, the entropy of the transmitting signal is calculated with the Shannon Limit as  
\[S=\log_{2}(1+SNR). \tag{4}\]  
Then, a Maxwell-Boltzmann distribution approaching the source entropy is determined for the generation of the PS signal. The form of the distribution is  
\[P_{X}(x_{i})=\frac{1}{\sum_{j=1}^{M}e^{-\left|x_{j}\right|^{2}}}e^{-\left|x_{i }\right|^{2}} \tag{5}\]Ref. [15], in which \(x_{i}\) denotes the constellation coordinate of the \(i\)-th symbol in the complex plane, and \(v\) is adjusted to match the entropy. The original data stream is processed with constant composition distribution matching (CCDM) [16], forward-error correction (FEC) coding [17], and loaded to each subcarrier by quadrature amplitude modulation (QAM) mapping according to the matched distribution. Finally, the signal is DMT modulated to be transmitted in the VLC system. At the receiving side, the generalized mutual information (GMI) and the normalized generalized mutual information (NGMI) are calculated to evaluate the performance of the communication. GMI is calculated as  
\[GMI\approx\frac{1}{N}\sum_{k=1}^{N}-P_{X}(x_{k})\log_{2}P_{X}(x_{k})-\frac{1}{N }\sum_{k=1}^{N}\sum_{i=1}^{m}\log_{2}\left(1+e^{(-1)^{b_{k,i}}\Lambda_{k,i}} \right), \tag{6}\]  
where \(b_{k,i}\in\{0,1\}\) denotes the \(i\)-th bit of the \(k\)-th symbol [18]. In addition, the NGMI is calculated as  
\[NGMI=1-(H(P_{X})-GMI)/m, \tag{7}\]  
where \(H(P_{X})\) is the constellation entropy, and \(m\) is the most bits that each symbol carries. When the calculated NGMI exceeds the rate \(R_{c}\) of an ideal binary FEC code, an error-free communication is guaranteed [18]. Therefore, when the NGMI condition is satisfied, the AIR that deducts the expenses of FEC code can be calculated as  
\[AIR=BW(H(P_{X})-m(1-R_{c})), \tag{8}\]  
where \(BW\) is the overall bandwidth.
# Modelling and Assessment of a New Triple-Frequency IF1213 PPP with BDS/GPS
## 1 Introduction
[10] investigated the performance of GPS/Galileo/BDS-2 for triple-frequency PPP AR. Different studies have shown that multi-GNSS positioning performance is not only better than single GNSS, but further improves position estimation and can be applied to complex environmental conditions. In summary, the short- and long-term characteristics of the satellite PIFCB have been studied, as well as the triple-frequency PPP in the case of satellite PIFCB correction. In this study, we focus on the precision modeling of the dual-system triple-frequency IF1213 PPP and validate it using GPS and BDS observations. First, the IF1213 PPP models with different treatments for the PIFCB are presented, namely, triple-frequency PIFCB (TF-C) with the PIFCB estimation, triple-frequency IFB (TF-F) ignoring the PIFCB, and the triple-frequency PIFCB-IFB (TF-CF) model without the full estimation of PIFCB, which only estimates the PIFCB in one system. Additionally, we also present the IF1213 PPP model using the IGMAS [30] product corrected by the satellite PIFCB, namely, the triple-frequency IFB-product (TF-FP). The relationships between these models are also analyzed. Next, the deduced models are validated using BDS and GPS triple-frequency observations, respectively. GPS triple-frequency observations are also used to validate the influence of GPS PIFCB. BDS triple-frequency observations are also used to validate the influence of BDS-2 PIFCB in BDS. Static and kinematic experiments are then conducted using BDS/GPS triple-frequency data. Finally, the main conclusions are given.
# Find You: Multi-View-Based Location Inference for Twitter Users
###### Abstract  
Location information on Twitter plays a critical role in emergency detection, event recommendation, and disaster warning. However, only a limited amount of Twitter data are geotagged. Previous research has presented various models for inferring location based on text, social relations, and contextual data, yielding highly promising results. Nonetheless, these existing methods have certain limitations that need to be addressed. Firstly, most of the existing methods overlook the role of local celebrities (well-known users in the local community) as indicators of location within the social network. Secondly, they fail to consider the associations between words in tweets, resulting in insufficiently rich features extracted from the tweets. We propose a multi-view-based location inference model called MVGeo to overcome these limitations. In the network view, our approach employs the Gaussian Mixture Model (GMM) to identify and retain local celebrities, thereby strengthening user location associations. In the tweet view, we construct a heterogeneous graph based on the co-occurrence relationship between words in tweets and the user's mentioned relationship with the words. This allows us to fully leverage the local correlation between words and the global correlation to extract tweet features more comprehensively. Finally, we employ a modified multi-layer graph convolutional network, called Gate-GCN, to fuse the network and tweet information. This expansion of the feature space enables us to extract sample features from multiple perspectives. To demonstrate the effectiveness of MVGeo, we conduct exhaustive experimental evaluations on a publicly available dataset and compare its performance against several state-of-the-art benchmark models. The results confirm the superior performance of the proposed model.  
Twitter user localization; social information fusion; graph neural network; semi-supervised learning; word co-occurrence +
Footnote †: journal: Applied  
Article
## 3 Results and Discussion
### Huanglongbing Effects on the Volatile Profile of 'Ray Ruby' Grapefruit juice
PCA was performed to project the volatile juice components on a two-component plot (Figure 6). The PCA discriminated HLB+ from HLB\(-\) samples on Component 2, explaining 24.9% of the variation (Figure 6). Most juice volatiles were associated with HLB\(-\) samples, located on the positive side of PC2, while the VOCs compounds of HLB+ samples were on the negative side of PC2 (Figure 6). HLB caused increases in sesquiterpenes and decreases in valencene and nootkatone in both peel oil and juice samples (Figure 7). Valencene is a major sesquiterpene in oranges and some mandarins, but it was not detectable in other mandarins [12; 21; 49; 51]. Valencene existed in grapefruit juice samples, although it was not the major sesquiterpene (Table 3 and Figure 7). However, valencene was not detected in the peel oil samples, although there were other sesquiterpenes in the samples (Table 2 and Figure 7). [PERSON] et al. [52] reported that Cstps1 encodes valencene synthase, and [PERSON] et al. [51] confirmed that the gene expression level of Cstps1 and the abundance of valencene were closely correlated. The opposite responses to HLB between total sesquiterpenes and valencene in juice samples confirm the above observation again, suggesting a possible mechanism that valencene biosynthesis may be independent of other sesquiterpenes (Figure 7). The enzymatic steps from valencene to nootkatone in planta remain unclear [50; 53]. However, in recent years, the integration of heterologous metabolic pathways indicates that regioselective oxidation of valencene at C-2 to \(\beta\)-nootkatol through cytochrome P450 was coupled with P450 reductase (CPR) and dehydrogenase-catalyzed oxidation of \(\beta\)-nootkatol to nootkatone [53; 54]. Our data indicate that in peel tissue, nootkatone could be converted from nonvalencene sesquiterpenes, or that valencene is not accumulated in peel oil but is directly converted to nootkatone (Table 2 and Figure 7). Monoterpenes are the major components in peel oil (about 97-98%) and juice (about 50%) samples (Tables 2 and 3). HLB caused an increase of monoterpenes in peel oil samples but a decrease in juice samples (Figure 7, Tables 2 and 3). We focused on how HLB affects the volatile profiles in peel oil and juice. However, more complicated interactions mediated by volatiles exist among plants (including fruit), pathogens (including CLas), and insects (including the vector of CLas, ACP) [56; 57; 58; 59]. Nootkatone, valencene, and many VOCs in citrus are insect repellents and/or antimicrobial agents [53; 60; 61]. The volatile profiling method has been used to compare the HLB tolerance of citrus cultivars [35; 58; 62]. [PERSON] et al. [57] observed that the responses of the volatile profile in the citrus tree to ACP were more significant than to CLas infection. A better knowledge of the interactive effects of plant hosts and the insects and microbials on the volatile profile in the fruit may also help us better understand and estimate the tolerant plant selection, insect and disease control, and fruit quality control [56; 59; 63].
## 4 V2H and V2G
### Prospects  
Prospects for future V2G and V2H development were provided from a commercial and technical perspective as follows. In general, the development of future V2G and V2H technologies includes two main directions, namely integration with smart electricity grids and retired EV batteries.
# Development of a New Test-Rig for Wheel-Rail Contact Experiments under Large Slip Conditions
## 1 Introduction
### Rolling Contact Theories  
It is well known that longitudinal creep force, lateral creep force and spin moment respectively oppose longitudinal creepage, lateral creepage and spin creepage at the wheel-rail contact interface. The rolling contact theory is to describe the relationship between them. For the two-dimensional case, [PERSON] and [PERSON] conducted mathematical analyses of rolling contact in the 1920s [4]. In the 1950s, [PERSON] [5; 6] proposed the experiment and modeling work of using the equivalent plane to contact a three-dimensional sphere, distinguishing longitudinal, lateral and spin creepage. The theory showed the interactions between the creepage and creep force. Then, [PERSON] made a significant contribution to rolling contact theory, putting forward the linear theory [7], the simplified theory (FAST-SIM) [8; 9] and the full theory of three-dimensional rolling contact (CONTACT) [10; 11; 12]. These traditional theories indicate that the creep force increases rapidly within small creepage (microslip) and then saturates at the Coulomb maximum value.  
[PERSON]'s full theory solved the wheel-rail contact problem with small creepage under clean and dry surface conditions. However, the rolling contact theory of large creepage, especially involving surface contaminants, is still in development. Therefore, a special experimental device for theoretical work verification is required.
###### Abstract  
Background:We require an quantitative imaging technique for the diagnosis and assessment of chronic kidney disease (CKD). Renal elastography has been widely used in recent years in different studies; however, the results across them are not consistent and, as a result, we conducted a meta-analysis of the published literature on this topic. **Methods:** The databases of PubMed, Medscape, Medline were searched for all studies published in English from 2010 until November 2021 that evaluated kidney shear wave speed (SWS) by elastography in patients with CKD. Trial design, methodological information, patient characteristics, interventions, results, and outcome data were all collected from each study according to a set protocol. **Results:** We found 37 publications, yet only 18 studies that utilized point shear wave elastography (Virtual Touch Quantification--VTQ system) were compared because the values achieved using different types of elastography are not evaluable. Finally, 1995 attendees (1241 patients with CKD versus 781 healthy subjects as the control group) were included. When comparing mean values of kidney SWS between studies we found increased heterogeneity Q = 513.133; DF = 10; \(p<0001\), P\({}^{2}\) (inconsistency) = 98.12% (95% CI for I\({}^{2}\) 97.52-98.57%). With a standardized mean difference of \(-0.216\), patients with CKD have a lower kidney SWS than healthy controls. A positive association between kidney SWS and eGFR was also discovered across the presented studies, with a pooled correlation coefficient of 0.38 (Z = 10.3, \(p<0.001\)), Q = 73.3, DF = 5, \(p<000.1\), I\({}^{2}\) = 93.18% (95% CI for I\({}^{2}\) 87.86 to 96.18). The pooled area under the ROC curve for kidney SWS to predict chronic kidney disease was 0.831 (95% CI, \(p<0.001\)), Q = 28.32, DF = 6, \(p=0.0001\), I\({}^{2}\) = 78.8% (95% CI for I\({}^{2}\) 56.37 to 89.72). In the four articles that used the Elast-PQ method, the data presented were insufficient for statistical analysis: area under the curve (AUC) values are used to compare distinct characteristics (differentiating kidney SWS between mildly and moderately impaired kidneys, between non-diabetic/predicabetic/diabetic patients, or kidney SWS between the CKD and control group), therefore not being suitable for further evaluation. **Conclusions:** The results show that patients with CKD have a lower kidney SWS than healthy controls. However, the number of studies involving renal elastography that have been published is limited and show an increased heterogeneity. Further research is needed to determine which factors actually influence kidney SWS in CKD patients and, as a result, to specify the role and indication of renal elastography in clinical practice.  
chronic kidney disease; stiffness; shear wave elastography; ultrasound
## 4 The Role of Metallurgy Slags in Sustainable Construction
### Metallurgy Slags in Concrete Production
Copper slag, for example, is generated during the smelting process of copper, and contains high levels of iron, silicon, and aluminum oxides, making it suitable as a substitute for aggregates in concrete. These slags enhance abrasion resistance and the density of concrete, and have been successfully used in applications such as pavements and building blocks. However, their use is limited by the potential leaching of heavy metals, which requires strict environmental controls [250]. Copper slag specifically has demonstrated credibility for partial utilization in concrete, either as a binding material or as sand. Studies have shown that the optimal percentage of these slags in concrete can significantly impact its strength, with typical substitution rates ranging from 50 to 60% by weight of fine aggregates [251]. This variability in optimal use is often due to differences in the slag's source. Despite its ability to enhance the mechanical properties of concrete, copper slag usage faces limitations in tensile strength. Nickel slag is another valuable by-product that can be used in construction. Rich in silicate and magnesium, nickel slag has been employed as a partial replacement for fine aggregates in concrete, improving wear resistance and thermal stability [252]. Additionally, nickel slag has been shown to increase the concrete's resistance to aggressive environments, such as marine conditions, due to its dense microstructure and reduced permeability [253]. [PERSON] et al. [254] demonstrated that the highest flexural and compressive strength for concrete using nickel slag sand was achieved at an optimal dosage of 30%. Phosphorous slag, a by-product of phosphoric acid production, also shows promise in concrete applications. Due to its high glass content and latent hydraulic properties, it can be used as a supplementary cementitious material, similar to ground granulated blast furnace slag. Phosphorous slag is currently utilized in concrete industries with a low cement replacement rate of 20-30%, due to its low pozzolanic activity [255]. But this material has been found to improve the durability of concrete, particularly its resistance to sulfate attack and alkali-silica reaction, making it a suitable material for infrastructures exposed to harsh chemical conditions [256]. These alternative slags expand the range of sustainable materials available for concrete production, helping to reduce the environmental footprint of construction while enhancing the performance of concrete in various applications. In contrast to cellulose and tailings, slags show the highest potential for widespread use due to their relatively uniform composition and established history in cement and asphalt applications. However, issues like expansion (due to free lime) require careful pre-treatment to ensure structural integrity.
# MultiAspect Graphs: Algebraic Representation and Algorithms
## 2 MultiAspect Graph (MAG)
### MAG Sub-Determination
#### 2.2.1 Sub-Determined Composite Vertices  
Let \(\zeta\), with \(1\leqslant\zeta\leqslant 2^{p}-2\), be an index for one of the possible ways to construct a proper nonempty sublist of aspects. From this, we can define a canonical representation of the sub-determination directly defined by \(\zeta\). For any given \(\zeta\), we consider the \(p\)-bit binary expansion of \(\zeta\) that is used as an indicator showing which aspects of the original MAG are present on the sub-determination. More specifically, the least significant bit indicates the presence or absence of the first aspect and the most significant bit indicates the presence or absence of the last aspect. By this convention, in a MAG with \(p=3\) aspects, we have that \(\zeta=001_{2}\) corresponds to the sub-determination where only the first aspect is present, \(\zeta=010_{2}\) corresponds to the sub-determination where only the second aspect is present, \(\zeta=101_{2}\) corresponds to the sub-determination where both the first and the third aspects are present, and so on. By using this convention, we can directly associate a given \(\zeta\) to its corresponding aspect sublist.  
Therefore, for each \(\zeta\), we have a unique sublist \(A_{\zeta}(H)\) of aspects, such that \(p_{\zeta}=|A_{\zeta}(H)|\) is the order of the sub-determination \(\zeta\). We now define the set  
\[\mathbb{V}_{\zeta}(H)=\underset{n=1}{\overset{p_{\zeta}}{\times}}A_{\zeta}(H) [n], \tag{8}\]  
where \(\mathbb{V}_{\zeta}(H)\) is the cartesian product of all the aspects in the sublist \(A_{\zeta}(H)\) of aspects, according to the index \(\zeta\). We call \(\mathbf{u}_{\zeta}\in\mathbb{V}_{\zeta}(H)\) a sub-determined vertex, according to the sub-determination \(\zeta\).  
We can now define the function  
\[\begin{split} S_{\zeta}:\mathbb{V}(H)&\to\mathbb{V }_{\zeta}(H)\\ (a_{1},a_{2},\ldots,a_{p})&\mapsto(a_{\zeta_{1}},a_{ \zeta_{2}},\ldots,a_{\zeta_{m}}),\end{split} \tag{9}\]  
where \(m=p_{\zeta}\). \(S_{\zeta}\) maps a composite vertex \(\mathbf{u}\in\mathbb{V}(H)\) to the corresponding sub-determined composite vertex \(\mathbf{u}_{\zeta}\in\mathbb{V}_{\zeta}(H)\), according to the sub-determination \(\zeta\). As \((a_{\zeta_{1}},a_{\zeta_{2}},\ldots,a_{\zeta_{m}})\in\mathbb{V}_{\zeta}(H)\), it follows that \(a_{\zeta_{1}}\in A_{\zeta}(H)[1],\ldots,a_{\zeta_{m}}\in A_{\zeta}(H)[m]\). From the definition, it can be seen that the function \(S_{\zeta}\) is not injective. Hence, the function \(S_{\zeta}\) for a given sub-determination can be used to define a equivalence relation \(=_{\zeta}\) in \(\mathbb{V}(H)\), where for any given composite vertices \(\mathbf{u},\mathbf{v}\in\mathbb{V}(H)\), we have that \(\mathbf{u}=_{\zeta}\mathbf{v}\) if and only if \(S_{\zeta}(\mathbf{u})=S_{\zeta}(\mathbf{v})\).