esa-sceva/satcom-chunk-collection huggingface dataset:
# Cross-Lingual Cross-Domain Transfer Learning for
## 3 Multi-Task Cross-Lingual Learning for Rumor Detection  
### Definitions  
A scenario where someone posts a message on a social media platform is studied. This action can trigger various reactions, either replies to the original message or forward copies. Forwarding allows the message to be spread across the platform, potentially reaching a broad audience. In this context, we introduce the concept of \"Propagation Trees\" (PTs) [50]. A propagation tree is the structure of interactions triggered by a root message (the message that triggers the thread), denoted by \(m^{0}\), that gives rise to multiple responses \(m^{k}\), \(k>0\), from a set of users \(U\).  
To build a predictive model for rumor detection, we use a dataset comprising \(n\) propagation trees, represented as \(PT=\{pt_{1},pt_{2},...,pt_{n}\}\). With these definitions in place, we can outline the specific tasks that our approach will focus on:  
* **Rumor detection task**: This task aims to determine the veracity of a root message \(m^{0}_{i}\) of a propagation tree \(pt_{i}\). For doing so, it has been shown that it is helpful to consider the complete propagation tree; hence, \(pt_{i}\to y\). The labels used in \(y\) vary between works, but a minimal definition is \(y\in\{\textit{True},\textit{False}\}\).
* **Stance classification task**: This task focuses on classifying the position expressed by a user on the veracity of the root message. If \(m^{k}_{i}\) is the response given in the propagation tree \(pt_{i}\), then we can define \(m^{k}_{i}\to z\). The labels in \(z\) are generally with \(z\in\{Supporting\), _Denying_, _Questioning_, _Commenting_}, known as stance \(SDQC\).
* **Bot detection task**: This task classifies whether a user \(u_{j}\) that posts a message \(m^{k}_{i}\) is a human or a bot. That is, \(\forall u_{j}\in U,u_{j}\to w\) with \(w\in\{\textit{Bot},\textit{Human}\}\).
## 4 Results
### Evaluation of the ALDA Model
#### 4.2.1 Topic Coherence  
Topic coherence is mainly used to measure whether the words within a topic are coherent. So, how can these words be considered coherent? If the words support each other, then the group of words is coherent. In other words, if you put words from multiple topics together and cluster them with a perfect cluster, then words from the same topic should be in the same category. _PMI_ uses external text datasets to measure the coherence of a topic, which is a fair metric of evaluating the quality of topics extracted by each model. The PMI can be calculated by:  
\[PMI\big{(}w_{i},w_{j}\big{)}=\log\frac{p\big{(}w_{i},w_{j}\big{)}+\epsilon}{ p(w_{i})\cdot p\big{(}w_{j}\big{)}}\]  
where \(w_{i}\) and \(w_{j}\) are topic words, and \(\epsilon\) is a random disturbance term. The larger the value of PMI, the better the coherence between topic words.  
To further evaluate the PMI of randomly selected topics, Wikipedia articles downloaded from the official Wikipedia website were used as an auxiliary corpus. We selected the top 5, 10, and 20 words in each topic and calculated the average PMI score. Figure 3 shows the topic coherence results of selected topics learned by each topic discovery model. In the comparison of six models, it clearly shows that the PMI score of our ALDA model is significantly better than the other models. The results demonstrate that topics extracted by our ALDA are more coherent than other models. This is due to the fact that our ALDA conjointly models the topic intensities in the query sessions and the topic intensities in the webpages into the same document layer. The data sparsity of online query data is avoided by aggregating corresponding webpages to assist in learning users interested topics. Because Twitter-BTM and LDA can only model query documents and webpage documents separately, these two models perform worst. Twitter-BTM outperforms LDA because Twitter-BTM inherits BTM's excellent ability to deal with short texts. UCIT and UATM significantly outperforms Twitter-BTM and LDA. This is because UCIT and UATM not only extract topics from content generated by the user themself but also extracts topics from content generated by user clusters that are similar to them. HDLDA can generate more coherent topics than UATM, UCIT, Twitter-BTM, and LDA. This is because HDLDAmodels query the document and the webpage document in two hierarchical LDA processes. HDLDA can better capture the semantic relation between query and webpage.  
Unlike HDLDA, which models the topic intensities in the query sessions and the topic intensities in the webpages, our ALDA conjointly models the topic intensities in the query sessions and the topic intensities in the webpages into the same document layer. Thus, our ALDA obtained better results than HDLDA.
# Performance Evaluation of MPTCP on Simultaneous Use of 5G and 4G Networks
## 2 An Overview of The MPTCP Schedulers and Congestion Control Algorithms
### MPTCP Schedulers
#### 2.1.3 Redundant  
This scheduler sends the same data through all SFs with space in CWND [29]. This approach of sending the same data through multiple communication interfaces helps reduce the HoL blocking at the receiver, thus ensuring low data delivery latency. However, sending the same packet wastes a significant bandwidth (BW) which could be utilized for sending other essential data by implementing an intelligent scheduling scheme.
## 3 Results and Discussion
### Selective Adsorption of Molecularly Imprinted Stir Bars  
In order to better study the adsorption characteristics of BPA-MIPs-SBSE and BPA-NIPs-SBSE for BPA in aqueous solutions, the equilibrium experimental data of BPA adsorbed by BPA-MIPs-SBSE and BPA-NIPs-SBSE were fitted to the Langmuir adsorption isotherm equation, and the correlation coefficient (R\({}^{2}\)) was used to evaluate the fitting degree of each adsorption process to the equation. The fitting results are shown in Figure 11.  
Figure 11: Fitted curves of the Langmuir adsorption isotherm model.  
Figure 10: The impact of desorption time on desorption efficiency.  
From Figure 11 and Table 1, it can be observed that both BPA-MIPs-SBSE and BPA-NIPs-SBSE exhibit good fitting to the Langmuir adsorption isotherm for BPA (with R\({}^{2}\) values of 0.960 and 0.941, respectively). The Langmuir adsorption coefficients were determined to be 0.031 and 0.015 L/\(\upmu\)mol for BPA-MIPs-SBSE and BPA-NIPs-SBSE, respectively. The maximum adsorption capacity of BPA-MIPs-SBSE was found to be 35.19 \(\upmu\)mol/g, while for a BPA aqueous solution of 500 \(\upmu\)g/L, the maximum adsorption capacity was 3.545 \(\upmu\)mol/g. On the other hand, the maximum adsorption capacity of BPA-NIPs-SBSE was determined as 18.92 \(\upmu\)mol/g, with a maximum adsorption capacity of 1.963 \(\upmu\)mol/g for a BPA aqueous solution of 500 \(\upmu\)g/L.  
In conclusion, based on the correlation coefficient R\({}^{2}\), it can be seen that within the studied concentration range, BPA-MIPs-SBSE and BPA-NIPs-SBSE exhibit the best fit to the Langmuir adsorption isotherm equation for BPA. This indicates that the adsorption capacity of BPA-MIPs-SBSE and BPA-NIPs-SBSE for BPA is uniform across their surfaces, suggesting a monolayer adsorption process. This also indirectly suggests that the imprinting sites on the surface of BPA-MIPs-SBSE are evenly distributed. When the adsorbate concentration on its surface does not continue to increase, it can be determined that adsorption has reached saturation. The maximum adsorption capacity of BPA-MIPs-SBSE for BPA is twice that of BPA-NIPs-SBSE. Therefore, it can be observed that BPA-MIPs-SBSE exhibits good binding ability and selective adsorption characteristics towards BPA. Comparison with non-imprinted materials confirms that the imprinting process indeed enhances the adsorption performance of BPA-MIPs-SBSE for BPA.
## 2 Materials and Methods
### Dataset
#### 2.2.2 Histogram Equalization (HE)  
HE is used to enhance image contrast by redistributing pixel intensity values. It spreads out the most frequent intensity levels, allowing for areas with low contrast to gain more definition and visual clarity. The process involves computing the cumulative distribution function (CDF) of the image histogram and using it to map the original pixel intensities to new values. This results in a more uniform brightness distribution, making hidden details more visible. The HE is calculated as shown in Equation (2):  
\[\mathrm{T(r_{k})}=(\mathrm{L-1})\sum_{j=0}^{k}\frac{n_{j}}{n} \tag{2}\]  
\(\mathrm{r_{k}}\) is the original intensity;  
\(\mathrm{n_{j}}\) is the number of pixels with intensity \(\mathrm{r_{j}}\);  
\(\mathrm{n}\) is the original intensity;  
\(\mathrm{L}\) is the number of possible intensity levels;  
\(\mathrm{T(r_{k})}\) is the new intensity value.
## 3 Data Collection and Exploratory Data Analysis  
Three main data sources are utilized in this study. The first source is individual surveys, which are used to model train consideration. These surveys also collected information on respondents' socio-demographic characteristics, perceptions of transit services, first- and last-mile connectivity, and walkability. The second source comprises automated fare collection (AFC) data from 32 metro stations in Chennai that capture weekday footfall. The third source is secondary data, including official timetables from Chennai Metro Rail Limited and the Metropolitan Transport Corporation to obtain metro and bus service frequency and headway details [32; 33]. Additionally, secondary data from the Chennai City Master Plan and Google Maps Points-of-Interest (Pols) are used to determine population density and land-use variables [34]. A detailed discussion of the data and exploratory analysis is presented next.
## 3 Results
### Model Validation  
The comparisons of simulated NPP and NEP against observations were shown in Figure 3. The square of the Pearson correlation coefficient suggested that the Biome-BGC could realistically assess the effect of climate on NPP across various vegetation types in Central Asia. The daily values of measured NEP can be largely explained by the model (\(R^{2}\) = 0.64, slope of the regression = 1.09, \(p\) < 0.01, Figure 3b). NPP was predicted with some values being somewhat overestimated (\(R^{2}\) = 0.83, slope of the regression = 0.87, \(p\) < 0.01, Figure 3a). Parameterization and validation of NPP/NEP is a crucial task for correct predictions of water and carbon fluxes by models. This is valid especially for arid and semi-arid ecosystems.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Longitude (\({}^{\circ}\))** & **Latitude (\({}^{\circ}\))** & **Value** & **References** \\ \hline
90.3 & 43.76 & 323.14 & [44] \\
90.01 & 46.54 & 337.54 & [44] \\
81.61 & 44.6 & 347.19 & [44] \\
86.65 & 47.13 & 358.35 & [44] \\
84.37 & 43.
# Broadband Dual-Polarized 2 \(\times\) 2 MIMO Antenna for a 5G Wireless Communication System
## 3 MIMO Antenna
By using Equation (2), the ECC can be calculated as expressed in Equation (3) [24]. Figure 14 shows the envelope correlation coefficient (ECC) measurements based on the simulated and measured S-parameters:  
\[\frac{D_{1}}{4\pi}\iint\left|F_{1}(\theta,\phi)\right|^{2}d\Omega=1-\left(\left| S_{11}\right|^{2}+\left|S_{21}\right|^{2}\right) \tag{2}\]  
\[\rho_{e}=\frac{\left|S_{11}*S_{12}+S_{21}*S_{21}\right|^{2}}{\left(1-\left( \left|S_{11}\right|^{2}+\left|S_{21}\right|^{2}\right)\right)\left(1-\left( \left|S_{22}\right|^{2}+\left|S_{12}\right|^{2}\right)\right)} \tag{3}\]  
Figure 14: Simulated and measured ECC (envelope correlation coefficient) of the proposed broadband dual-polarized 2 \(\times\) 2 MIMO antenna. Figure 7:0 Simulated and measured \(S_{21}\) values of the proposed broadband dual-polarized 2 \(\times\) 2 MIMO antenna. Figure 15 shows the simulated and measured peak gain. The antenna exhibited a gain of over 5 dB in the 2-6 GHz band. The MRDA exhibited a peak gain of 9.6 dB at 5 GHz and the CMA exhibited a peak gain of 7.07 dB at 4.5 GHz. In addition, it exhibited average measured radiation efficiency (gain to directivity ratio) of 87.19%. Figure 16 shows the simulated and measured CMA radiation pattern in (**a**) 3.5 GHz XY-plane; (**b**) 3.5 GHz XZ-plane; (**c**) 5.5 GHz XY-plane; and (**d**) 5.5 GHz XZ-plane. Figure 7:1 Simulated and measured CMA radiation pattern in (**a**) 3.5 GHz XY-plane; (**b**) 3.5 GHz XZ-plane; (**c**) 5.5 GHz XY-plane; and (**d**) 5.5 GHz XZ-plane. Figure 7:2 Simulated and measured peak gain of the proposed broadband dual-polarized \(2\times 2\) MIMO antenna. Figure 17 shows the simulated and measured YZ-plane (E-plane) and XY-plane (H-plane) radiation pattern of the MRDA at 3.5 GHz and 5.5 GHz. For MRDA, vertical polarization is cross-polarization and horizontal polarization is co-polarization. The figure shows that the simulated and measured MRDA has a quasi-directional radiation pattern at both 3.5 and 5.5 GHz. Table 1 presents a comparison among various existing wide band MIMO antennas and the proposed broadband MIMO antenna. It is observed that the proposed antenna provides the widest bandwidth. The proposed MIMO antenna has a bandwidth of 2.3-7 GHz (101.07%) and low envelope correlation coefficient under 0.1 at the operating frequency. It also has a peak gain of 9.69 dBi at 5 GHz. Figure 7:3 Simulated and measured MRDA radiation pattern in (**a**) 3.5 GHz XY-plane; (**b**) 3.5 GHz YZ-plane; (**c**) 5.5 GHz XY-plane; and (**d**) 5.5 GHz YZ-plane.
## 6 Analysis of the Findings of This Review
### Effectiveness of Existing Cobot Program and Control Methodologies  
Cobot tasks programmed with script-based methods require some expertise in formal programming, an understanding of the specific cobot functionality and a sound knowledge of robotics in general. Proprietary Teach Pendants, however, are typically marketed as user-friendly tools designed for programming a cobot without the need for robotic or programming skills. While the graphical interface of the teach pendant is easier to use than a script-based alternative, this study has found that some foundation skills in robotics and programming are still required. Basic programming and robotics knowledge will contribute to a more accurate and safer outcome, even when programming a relatively simple cobot task. Knowledge of coordinate frames, for example, can allow more positional precision of joints and an understanding of efficient joint and linear movements, along with motor speed and acceleration settings, could help with collision avoidance. Furthermore, programming fundamentals such as sequences and loops can add vital insights into program structure and process iteration. When marketing their teach pendants, cobot vendors tend to highlight the ease of use for non-experts without emphasising the extent to which task complexity may complicate the process. The teach pendant, therefore, does require both robotic and programming skills, which increase as the task becomes more complex. It is, however, a significantly more intuitive cobot programming tool than a script-based language, which requires a high level of competency in robotics and formal programming. Some computer networking knowledge may also be required, depending on the method of connection between a terminal and its host.
# Review of Magnetic Properties and Texture Evolution in Non-Oriented Electrical Steels
## 5 Effect of Other Elements
### Influence of B Content  
The influence of B content in NGO electrical steels is correlated to the Al and nitrogen content. [PERSON] reported that over the composition range of 0.033-0.053 wt.% Al, the addition of 0.0007-0.0038 wt.% B could lead to BN precipitation instead of AlN [76]. This may increase the grain size and lead to an ideal texture. In the range of 0.0075-0.053 wt.% B content for Fe-6.5 wt.% electrical steels, [PERSON] observed that an increase of B content might also have a grain-refining effect, and also improve bending strength and ductility at room temperature [77]. The influence of B in Fe-1.35 wt.% electrical steels has also been studied [78], as illustrated in Figure 9. As shown in Figure 9a, with the increase of B content up to 0.004 wt.%, the grain size increased. When the B content is higher than 0.004 wt.%, the correlation is inverted [70]. As shown in Figure 9b, the correlation between core loss, flux density, and B content also changed at 0.004 wt.% B content. The best magnetic properties were achieved with 0.004 wt.% B content, with core loss 3.616 W/kg and magnetic induction 1.792 T [70].  
Figure 8: Optical microstructure of Fe-Al TD section samples with different Al content: (**a**) 0.53 wt.% Al, (**b**) 1.86 wt.% Al, (**c**) 2.64 wt.% Al, (**d**) 4.68 wt.% Al, (**e**) 6.54 wt.% Al, and (**f**) 9.65 wt.% Al after annealing at 1000 \({}^{\circ}\)C for 5 min [5].  
The opposite effect (at a B content higher than 0.004 wt.%) in electrical steels may be caused by boron segregation at austenite grain boundaries. This segregation decreases the boundary energy and delays the \(\gamma\rightarrow\alpha\) phase transformation in compositions that are not fully ferritic. It also promotes a favorable texture during the recrystallization process [79; 80]. Boron is unique and, in contrast with AlN, hurts drawing textures and in some cases may be beneficial to magnetic properties. However, boron forms carbides which are difficult to control in processing.
# Ising-Based Kernel Clustering +
## 3 Ising-Based Kernel Clustering
### Qibo Formulation for Ising-Based Kernel Clustering  
The kernel function transforms the elements of the Gram matrix into the degree of similarities among data points. Thus, combinatorial clustering using the Gram matrix provides global optimal clustering assignments. The elements of the Gram matrix are already transformed as distances in the high-dimensional data. Since the objective function of combinatorial clustering is the sum of intra-cluster similarities among data points, Equation (10) is the objective function of combinatorial clustering using the kernel method.  
\[H_{objective}=-\sum_{ij=0}^{N-1}g_{ij}\sum_{a=0}^{K-1}q_{a}^{i}q_{a}^{j}. \tag{10}\]  
Equation (10) is a similar form to the first term of Equation (5). Although the distance matrix \(\mathbf{D}\) with \(d(x_{i},x_{j})\) elements has zero diagonal components, the Gram matrix \(\mathbf{G}\) with \(g_{ij}\) elements has non-zero diagonal components. Thus, the QUBO function for combinatorial clustering using the Gram matrix needs to be defined differently from the QUBO function for conventional combinatorial clustering using the Euclidean distance matrix.  
Equation (10) can be expressed as Equation (11) by eliminating redundancy. This is because the Gram matrix is symmetric.  
\[H_{objective} = -\sum_{i=j}^{N-1}g_{ij}\sum_{a=0}^{K-1}q_{a}^{i}q_{a}^{j} \tag{11}\] \[-\sum_{j<1}^{N-1}g_{ij}\sum_{a=0}^{K-1}q_{a}^{i}q_{a}^{j}-\sum_{i<j }^{N-1}g_{ij}\sum_{a=0}^{K-1}q_{a}^{i}q_{a}^{j}\] \[= -\sum_{i=0}^{N-1}g_{ii}\sum_{a=0}^{K-1}q_{a}^{i}q_{a}^{j}-2\sum_{i< j}^{N-1}g_{ij}\sum_{a=0}^{K-1}q_{a}^{i}q_{a}^{j}.\]  
When the two data are in the same cluster, the elements of the Gram matrix are calculated. Equation (11) adds the element of the Gram matrix between the two data to the Hamiltonian when \(q_{a}^{i}\) and \(q_{a}^{j}\) are both 1.  
However, Equation (11) has a minimum value when all binary variables are 0. This means that all the data do not belong to any cluster. To avoid this problem, the one-hot constraint that all the data should belong to only one cluster is given as Equation (12).  
\[\exists \eta_{a}^{i}\in\{q_{a}^{0},\ldots,q_{a}^{N-1}\}\text{ s.t. }q_{a}^{i}=1. \tag{12}\]  
To satisfy Equation (12) for \(\forall q_{a}^{i}\in\{q_{0}^{i}\ldots q_{K-1}^{i}\}\), the one-hot constraint function based on the QUBO model is shown in Equation (13).  
\[H_{constraint}=\sum_{i=0}^{N-1}(\sum_{a=0}^{K-1}q_{a}^{i}-1)^{2}. \tag{13}\]  
The one-hot constraint function has also been used for combinatorial clustering based on the Euclidean distance. Therefore, combinatorial clustering based on the Ising model using the kernel method is represented in Equation (14).  
\[H = -\sum_{i=0}^{N-1}g_{ii}\sum_{a=0}^{K-1}q_{a}^{i}q_{a}^{i}-2\sum_{i <j}^{N-1}g_{ij}\sum_{a=0}^{K-1}q_{a}^{i}q_{a}^{j} \tag{14}\] \[+\sum_{i=0}^{N-1}\lambda(\sum_{a=0}^{K-1}q_{a}^{i}-1)^{2}.\]  
\(\lambda\) is given by the method of the Lagrange multiplier. Violation of the constraint is avoided by providing a sufficiently large Lagrange multiplier.
## 5 Discussion  
An analysis of datasets containing information about the vertical movements of the Earth's crust in the territory of Poland and the resulting models revealed that datasets based on leveling data were anisotropic. The anisotropic direction differed across the evaluated datasets, and most datasets had a northwestern orientation. Anisotropy was confirmed using statistical analyses [34] of datasets A and B, which revealed unidentified disruptions. Nugget effects were identified in the analysis performed using a directional variogram. Nugget effects were detected in datasets where neighboring points had significantly different values (datasets F and D). These findings indicate that vertical movements calculated based on GNSS data differed from those determined based on leveling data (for proximal points), or even differed considerably within dataset D containing GNSS data only. The above could point to data errors or external influences, including anthropogenic or geophysical factors [35]. The number of lags significantly affected the anisotropy angle (Figure 7). The values of RMS and \(\sigma\) were stabilized when the length of both semi-axes of the ellipse increased several-fold. Models developed based on simultaneously adjusted data (C and E) were characterized by a better distribution of \(\sigma\) (Figure 9).  
Models C and E were characterized by smaller variations and a smaller number of isolines with gentle curves, which could be attributed to data fitting during the adjustment procedure. In the remaining models that were generated using co-kriging, isolines differed from those noted in the reference models (C and E). The above was particularly visible in the models developed based on dataset A (models A and AB). Even greater variations were noted in the models created from datasets that had been merged before interpolation. These models were characterized by considerable nugget  
Figure 9: Standard error of the estimate for dataset E.  
effects. The values of the standard error of the estimate for the analyzed datasets are presented in Table 4.  
The average standard error of the estimate was the highest in datasets D, F, and G. These datasets contained points that deviated from the remaining values of \(\sigma\). The presence of outliers could mainly be attributed to differences in the values of proximal points, which was confirmed by the presence of nugget effects.