esa-sceva/satcom-chunk-collection huggingface dataset:
# Text-Based Emotion Recognition in English and Polish for Therapeutic Chatbot
## 3 Materials and Methods
### Creating the Polish Corpus Using Machine Translation  
Since we aim to create a sentiment and emotion classifier for a chatbot working in Polish, we faced the problem of lack of relevant dialogue datasets for the Polish language. As a cheap and fast solution, we proposed to take advantage of the available resources for English and obtain the desired Polish sentences via machine translation (MT). Such an approach has already turned out to be successful, e.g., in creating a corpus for virtual assistants [37].  
We tried several available MT solutions, translating from English into Polish and observed the correctness of the translations. Eventually we chose the Google Translation API ([[https://cloud.google.com/translate](https://cloud.google.com/translate)]([https://cloud.google.com/translate](https://cloud.google.com/translate)), accessed on 21 May 2021), a neural MT system, as it gave the highest level of correctness among the tested MT systems.  
The whole process of creating our corpus is shown in Figure 1. Eventually we created a parallel bilingual (English and Polish) corpus of emotional texts, designed to serve experiments on sentiment and emotion recognition. We named it CORTEX, or CORpus of Translated Emotional teXts.
###### Abstract
As the challenges of ensuring food security and sustainable agricultural practices become more complex there is a need for precise, data-driven solutions. Remote Sensing (RS) offers a powerful opportunity for monitoring, assessing, and optimizing agricultural activities across the heterogenous landscape of West Africa [1; 12; 18; 22; 23] as an increase in agricultural productivity is imperative [24; 25; 26].
## 4 Discussion  
The results of the first implementation of the proposed method indicate its high potential in identifying a relatively limited number of factor combinations that cover the realistic regions of the experimental design space. In addition to defining the design space for the given methods, the proposed method generated factor level combinations that cover the regions of the design space that are likely to be experienced by the turbine during operation based on the guidelines of the relevant standard, IEC 61400-1 [43]. In comparison to a full factorial DOE, which would result in over 78,000 factor level combinations to cover the design space at hand, the proposed method resulted in just under 3000 combinations. Table 5 shows the variety of DLCs that are responsible for reaching the extremities of the respective factor ranges, which in turn define the seven-dimensional design space in this case. This demonstrates the utility of performing several simulations in compliance with the IEC 61400-1 standard in order to reach a more complete definition of the design space for the planned experiments. The proposed method also generated level boundaries that split the majority of data points into even factor levels as listed in Table 6. In Figure 6, the proposed algorithms set level boundaries that clearly target the majority of the data points by generating a higher density of factor levels at the more populated regions of the distribution, while being robust against extreme, rare values, such as the extreme maximum value of Fz in Figure 6d. In turn, the proposed algorithms result in the parameter values to be tested at each factor level as listed in Table 7, which also take place with higher density at the more heavily populated regions of the respective factor range as shown in Figure 6. Additionally visible in Figure 6, a common observation among the majority of the factors, such as n, Fry, Fz, My, and Mz, is that a single peak significantly higher than the other peaks in a given factor distribution tends to be entirely or mostly within a single factor level. This is also visible in Table 8, which lists the most frequent combinations collectively accounting for 80% of the data. In the table, it can be seen that for the majority of factors a single level mainly dominates each factor across the 12 combinations. By splitting those dominant factors in the most frequent combinations, the proposed method further demonstrates its effectiveness in selectively targeting the most realistic regions of the design space that the WT at hand can be expected to encounter across all covered DLCs. Therefore, the results demonstrate that the proposed method is successful in achieving the intended goals of targeting the expected combinations of the WT rotor 6-DOF loads and rotational speed.
## 2 Laboratory Tests and Results
### XRD--X-ray Diffraction Analysis  
XRD is a versatile, nondestructive analytical technique that provides information on the crystallographic structure, chemical composition, and physical properties of materials. This technique is based on the observation of the scattered intensity of an X-ray beam, which strikes a sample as a function of the incident and scattered angle, of the polarization, and of the wavelength or energy [16]. The analysis was carried out in the laboratory of the Politecnico di Torino, with an XRD--Rigaku SmartLab SE. The selected marble sample was previously ground to a fine powder in order to obtain an almost infinite number of fine crystallites in random orientations. The incidence angles ranged from 4\({}^{\circ}\) (start angle) to 84\({}^{\circ}\) (stop angle), with a scan speed of 0.1\({}^{\circ}\)/s thanks to the continuous scan mode. The output of the analysis was an intensity vs. incidence angle chart, from which the minerals and their abundance could be identified, as each mineral has a specific angle of incidence. The intensity of the peaks, evaluated with the appropriate Rietveld model [17; 18], reflects the abundance of each element. The results are shown in Figure 4.  
Figure 4: Intensity vs. incidence angle chart (**a**) and mineralogical abundance (**b**).  
The associated minerals identified were 79.5% Calcite, 11.2% Quartz, 5.6% Muscovite, and 3.7% other minerals.
## 3 Medium Access Control (MAC) Layered UWAC Protocols
### Comparative Analysis of MAC Layer-Based UWAC Full-Duplex Protocols  
As shown in Table 5, we compare various MAC layered FD-UWAC protocols in this section. We compare the technologies used in various FD-MAC-UWAC protocols, as well as the implementation of these FD protocols in various environments, and their performances with respect to various metrics such as throughput, delay efficiency, energy efficiency, and transmission range. We found that FD-MAC-UWAC protocols outperformed in terms of collision avoidance, resolving the hidden and exposed terminal problem, and achieving higher throughputs and energy efficiency, etc. The performance comparison is explained below.  
Figure 6: Simultaneous transmission of control and data packets though E-FD-MAC.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Protocol Type**} & \multirow{2}{*}{**Name/Ref.**} & \multirow{2}{*}{**Network Type**} & \multirow{2}{*}{**Implementation**} & \multicolumn{5}{c}{**Performance Matrices**} \\ \cline{5-8}  & & & & **Throughput** & **Delay Efficiency** & **Energy Efficiency** & **Transmission Range** & **Data Rate** \\ \hline \multirow{3}{*}{Contention-free} & CDMA [46] & Ad hoc & Shallow water & Low & Low & Moderate & \(15\) feet & NA \\ \cline{2-8}  & Hybrid CDMA [46] & Ad hoc & Shallow water & Moderate & Low & Moderate & \(15\) feet & NA \\ \cline{2-8}  & FH-CDMA [46] & Ad hoc & Shallow water & High & Low & Moderate & \(15\) feet & NA \\ \hline \multirow{3}{*}{Hand-Shaking} & BD-FD-MAC [36] & Single hop & Simulation & High & High & Enhanced & NA & NA \\ \cline{2-8}  & HSPD-MAC [35] & NA & simulation & Medium & Low & Moderate & Very Low & Moderate \\ \cline{2-8}  & DA-CSMA [30] & Ad hoc & Shallow water & High & High & Low & Low & Low \\ \cline{2-8}  & FS-MACA [37] & distributed Ad hoc & simulation & Medium & Moderate & Very Low & High & High \\ \cline{2-8}  & \multirow{2}{*}{FDCA-MAC [34]} & Single hop & SEA-Swarm & \multirow{2}{*}{Moderate} & \multirow{2}{*}{Moderate} & \multirow{2}{*}{Enhanced} & \multirow{2}{*}{Moderate} & \multirow{2}{*}{Very Low} \\ \cline{2-8}  & Multi-hop & network & & & & & & \\ \cline{2-8}  & TTH-MAC [69] & NA & Simulation & High & Very Low & Enhanced & NA & NA \\ \cline{2-8}  & E-FD-MAC [38] & Single hop & Simulation & High & High & Enhanced & NA & NA \\ \hline Random Access & FDCP-MAC [39] & Multi-hop Ad hoc & Simulation & Low & Moderate & Moderate & Very High & High \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of various FD-MAC layer-based UWAC protocols.
# Opportunistic and On-Demand Network Coding-Based Solutions for LPWAN Forwarding
## 5 Design
### Network Coding
#### 5.3.1 Header Overhead Compression  
The header overhead in RLNC depends on the number of coefficients included in the packet combination. Therefore, we limit the number of possible packet combinations as \(m\) and use a subset of primitive pair elements from the \(GF\) as \(Q\)[30].  
Initially, we set \(m\) and \(Q\) as shared variables between the relay and the network server. Then, for every \(n\) packets, relays generate a \(k*n\) (\(k>n\)) sparse matrix of linearly independent combinations. The symbol \(k\) is an additional number of combinations, calculated via PDR estimation between the relay and gateway.  
For example, let us consider that there are six packets in the relays encoded by \(m=3\) and \(Q=[4,14]\) from \(GF(16)\) that represents coefficient \(Q=[\gamma,\alpha]\). The generated matrix is:  
\[E_{k}*_{6}=\begin{vmatrix}0&0&4&0&4&14\\ 14&0&4&14&0&0\\.&.&.&.&.&.\\ 0&4&4&0&4&0\end{vmatrix}\]  
The non-zero values represent the combination of \(m\) packets with the coefficient selected from \(Q\). Before transmission, the relay applies Compressed Sparse Row (CSR) format to identify and include only the non-zero element of each row vector. The header \(H=(I_{1}||I_{1}||S_{1}||...||I_{i}||J_{i}||S_{i})\) where \(I\mu\) is the coefficient index in \(Q[0,1]\), \(J\mu\) is the packet id, and \(S\mu\) is the packet sequence number.  
For the purpose of illustration, let us consider that the header of the first row vector is to be formatted as per the previous example; if \(\theta\) is assumed to be the packet sequence number, the compressed header will be \(h=[0||010||\theta||0||100||\theta||1||101||\theta]\). Hence, the relay transmits both the header \(H\) and the linear combination \(P^{\prime}\) to the gateway.  
If we assume the packet id represents the end-device index position in a shared list between the relay and the network server, packet id size is \(log_{2}\)\(n\) bits. In combination with the 16 bits of the packet sequence number [3] and 2 bits of \(Q\) size, the header overhead is \(m(17+Log_{2}N)\) bits.
# Hybrid Representation of Sensor Data for the Classification of Driving Behaviour
## 2 Background
### RNNs
#### 2.2.1 LSTMs  
LSTMs were originally proposed by [PERSON] and [PERSON] [14], whereas several variants followed, including the variant of [PERSON] [26], which is described here. Unlike the recurrent unit which simply computes a weighted sum of the input signal and applies a nonlinearity, each \(j\)-th LSTM unit maintains a memory \(\mathbf{c_{t}^{j}}\) at time \(t\). The activation \(\mathbf{h_{t}^{j}}\) of the LSTM unit is:  
\[\mathbf{h_{t}^{j}}=\mathbf{o_{t}^{j}}\mathrm{tan}\,\mathrm{h}\left(\mathbf{c_ {t}^{j}}\right) \tag{4}\]  
where \(\mathbf{o_{t}^{j}}\) is an output gate that modulates the amount of memory content exposure. The output gate is computed by:  
\[\mathbf{o_{t}^{j}}=\sigma(\mathbf{W_{o}x_{t}}+\mathbf{U_{o}h_{t-1}}+\mathbf{V _{o}c_{t}})^{j} \tag{5}\]  
where \(\sigma\) is a logistic sigmoid function and \(\mathbf{V_{o}}\) is a diagonal matrix.  
The memory cell \(\mathbf{c_{t}^{j}}\) is updated by partially 'forgetting' the existing memory and adding a new memory content \(\mathbf{\bar{c_{t}^{j}}}\):  
\[\mathbf{c_{t}^{j}}=\mathbf{f_{t}^{j}c_{t-1}^{j}}+\mathbf{i_{t}^{j}c_{t}^{j}} \tag{6}\]  
where the new memory content is:  
\[\mathbf{\bar{c_{t}^{j}}}=\mathrm{tanh}(\mathbf{W_{c}x_{t}}+\mathbf{U_{c}h_{t- 1}})^{j} \tag{7}\]  
The extent to which the existing memory is 'forgotten' is modulated by a forget gate \(\mathbf{f_{j}^{j}}\) and the degree to which the new memory content is added to the memory cell is modulated by an input gate \(\mathbf{i_{t}^{j}}\). Gates are computed by:  
\[\mathbf{f_{t}^{j}}=\sigma(\mathbf{W_{f}x_{t}}+\mathbf{U_{f}h_{t-1}}+\mathbf{V _{f}c_{t-1}})^{j} \tag{8}\]  
\[\mathbf{i_{t}^{j}}=\sigma(\mathbf{W_{i}x_{t}}+\mathbf{U_{i}h_{t-1}}+\mathbf{V _{i}c_{t-1}})^{j} \tag{9}\]  
Note that \(\mathbf{V_{f}}\) and \(\mathbf{V_{i}}\) are diagonal matrices.  
Unlike the traditional recurrent unit which overwrites its content at each time-step (Equation (3)), an LSTM unit is able to decide whether to keep the existing memory via the introduced gates. Intuitively, if the LSTM unit detects an important feature from an input sequence at an early stage, it easily carries this information much later, capturing potential long-term dependencies.
# Efficient Multi-Objective Simulation Metamodeling for Researchers
## 4 Results
### Student Services Simulation Case Study  
As mentioned in Section 3.1.1, the goal of this case study is to optimize the operations of three student service centers that handle stochastic student arrivals and offer both general and specialized services. Figure 2 shows the relationship between normalized hypervolume and time limits for the Student Services problem. The symbols between the two lines of each MO pair represent the mean value, while the lines represent the limits of the associated confidence interval. The graph suggests that the Student Services problem is relatively easy to solve, as all MO pairs, even with low time limits, can achieve a high normalized hypervolume (above 0.98). Figure 2: Student services experiments—hypervolume evaluation—time series. Table 1 summarizes the results for the Student Services experiments based on the mean normalized hypervolume performance (\(\mu_{norm}\)s) of each MO pair with the best-performing one at each time limit. The results show that at the lowest time limit of 50 s, GR-B turns out to be the best-performing approach while GR-GA delivers a similar performance based on the statistical test. As the time limit is increased to 75 s, NN-GA becomes the best approach, where the performance difference is statistically significant with \(p\)-values being less than 0.05 when compared to both GR-based methods. NN-B delivers a similar, but slightly worse performance than NN-GA. As the computational budget is increased further to 100 s and 125 s, NN-B outperforms the other MO pairs, while NN-GA ranks the second best method with a similar performance to NN-B, except for 100 s. The GR-B and GR-GA pairs once again turn out to be the worst methods of all. The performance difference between NN-B and GR-based methods is statistically significant at the 95% confidence level for the time limits greater than 50 s. Regardless of the given time limit, all methods produce a \(\mu_{norm}\) about 0.99. One interesting detail is that for the 75 s time limit case, the best Pareto front (Figure 3) and the worst Pareto front (Figure 4) are both from the NN-B pair, which has the largest standard deviation across all MO pairs and time limits. The reason for the difference between them is that the metamodel in the worst case has underestimated the performance of solutions that would have otherwise been located in the top left corner of the Pareto front, where wages are low and student inquiry time is high. In this case, that means it had an excessively high estimate for student time cost or employee wages. The explanation for this is that the Basic optimizer uses the metamodel evaluations to check every possible solution and does not identify them as possible candidates for inclusion. The general pattern of slow improvements in the two Gaussian regression-based pairs, leading to them being out-performed by the neural network-based pairs is also reflected in the normalized Hausdorff distance plot in Figure 5.
## 3 Results and Discussion
### Nadir Points of the Moon-Based Observatory and Satellites on Earth  
The nadir point is the point on the Earth's surface directly beneath a satellite or celestial body at a given moment in time, while the nadir track refers to the path it follows as it orbits the Earth. By analyzing the nadir points and tracks, we can determine the relative position and attitude between the Moon-based observatory, the satellite, and the Earth, allowing for further analysis of the observation characteristics of different bistatic configurations for Earth observation.  
Since the distance between the Moon and the Earth is significantly larger than the radius of the Moon, the difference between the nadir point of the Moon-based observatory and the Moon itself is negligible. Therefore, in this section, we refer to the nadir point of the Moon-based observatory as the sublunar point and the nadir point of the satellite platform as the subsatellite point.
## 1 Introduction
### Related Works
In more detail, some studies have examined the effect of DL on secure routing [30; 31]; others have studied the design of secure multicast routing against eavesdropped attack [24; 30]; very few works have explored the effect of DL techniques in the secure multicast routing protocol specifically against wormhole attacks on security, scalability, and stability performance, which is a critical performance metric in 5G wireless networks.
## 6 Limitation and Future Works
To address the limits of scalability, our planned future works will distribute decryption across multiple trusted nodes using threshold cryptography. Each node will handle only its assigned subsets using partial key shares, without access to the complete AES key. In addition, we plan to offload selective stages of decryption, such as AES pre-processing or partial decryption, to slave nodes under controlled policies that restrict key visibility, enforce task isolation, and prevent unauthorized reconstruction. This enables parallel execution while maintaining strict key confidentiality.
# An Emerging Role of Long Noncoding RNAs as Novel Biomarkers for Breast Cancer Metastasis
## 2 Review Methods  
To conduct a comprehensive review on the role of lncRNAs as biomarkers for BC metastasis, we searched the literature using PubMed and Google Scholar databases. The search included articles published between January 2013 and March 2024. We utilized specific keywords such as \"lncRNA\", \"biomarker\", \"breast cancer\", \"metastasis\", \"prognosis\", and \"diagnosis\" to identify relevant studies. Filters were applied to only include articles published in English within the specified date range. We focused mostly on original research articles that discussed lncRNAs as biomarkers for BC metastasis, provided mechanistic insights, clinical correlations, or potential therapeutic implications. Articles not related to BC or metastasis, non-peer-reviewed articles, and editorials, as well as commentaries studies, were excluded. Each selected article was meticulously assessed based on its abstract, which served as the primary criterion for exclusion during the initial screening. Full-text articles were then reviewed to confirm the relevance and quality of the study, with particular attention given to their clinical implications. A total of 111 articles were ultimately selected for inclusion in the review
## 3 Results and Discussion
### Desorption Studies  
The recyclability of the sorbent is one of the most important factors in wastewater treatment [52]. In this work, the regeneration and reuse of the ion exchangers were investigated. For the elution of Pb from G-26 and MTS9570 resins, various electrolytes volumes of HCl, HNO\({}_{3}\), CH\({}_{3}\)COOH, and C\({}_{2}\)H\({}_{2}\)O\({}_{4}\) were tested at room temperature. It was observed that the best elution result was obtained using 40 mL volume of HCl with 15% (\(v/v\)) concentration and 20 min of elution time for the 0.5 g loaded resin, as shown in Figure 13.  
The adsorption-desorption cycle was repeated three times using the same resin, and the results are presented in Table 6.  
Figure 13: Elution of G-26 and MTS9570 at different HCl concentrations (\(v/v\)) at 0.5 g of resin dosage, 20 min agitation time, 40 mL volume, and at room temperature.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Resin**} & \multicolumn{5}{c}{\(\Delta\)**G (kJ/mol)**} \\ \cline{2-9}  & **293 K** & **303 K** & **313 K** & **353 K** & \(\Delta\)**H (kJ/mol)** & \(\Delta\)**S (kJ/mol)** & \(\mathbf{R^{2}}\) \\ \hline G\(-\)26 & \(-\)38.34 & \(-\)40.96 & \(-\)43.58 & \(-\)46.20 & 42.30 & 131.30 & 0.9642 \\ MTS9570 & \(-\)27.93 & \(-\)29.73 & \(-\)31.60 & \(-\)33.50 & 32.07 & 95.44 & 0.9266 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Thermodynamic parameters for the adsorption of Pb(II) on G-26 and MTS9570.  
Table 6 shows excellent stability of G-26 resin in all the three cycles, with 100% for adsorption and 100% for elution of Pb(II) ions. While, for the MTS99570 resin, the adsorption percentage was 100, 97, and 96 in the first cycle, second cycle, and third cycle, respectively, the elution efficiency was 90%, 86%, and 76% in the first cycle, second cycle, and third cycle, respectively. This result agrees with the earlier results of [PERSON] [53]. The performance degradation of MTS9570 resin might be due to its macroporous S-DVB structure (low surface area) and negative swelling factor (\(-\)5%) as well. Swelling is an important parameter for the completion of adsorption reaction [54]. Negative swelling factor means volume shrink, which leads to specific surface area adsorption-site decrease.
## 3 Results
### Validation of the Degradation Areas and Degradation Level  
#### 3.2.1 Validation with Ground Survey Data  
We compared the forest degradation area from remote sensing data and those from provincial ground survey data (Figure 7). In general, a good linear relationship exists between the forest degradation areas from satellite observations data and those from ground survey data. R\({}^{2}\) between degradation area from MODIS data and ground survey data was 0.48, and RMSE was 7.37 \(\times\) 10\({}^{4}\) hectares. A good agreement with ground survey data demonstrated that the method could be utilized to detect forest degradation for large forest areas using MODIS time series data. R\({}^{2}\) between degradation area from MODIS-Landsat and ground survey data was as high as 0.62, and RMSE was only 4.73 \(\times\) 10\({}^{4}\) hectares. A higher R\({}^{2}\) and lower RMSE demonstrated that forest degradation from MODIS-Landsat data achieved a better precision and indicated the applicability and reliability of detecting the forest degradation from integrated time series MODIS and Landsat data. From Figure 7, we could find that scatter plots of degraded forest areas from integrated MODIS-Landsat images were located closer to the 1:1 line, especially at the slight degraded forest. Besides, forest degraded area from time series MODIS indictors were obviously larger than those from integrated MODIS-Landsat time series data. The main reason may be that single pixel-time series MODIS data did not contain additional and useful information to differentiate forest degradation [15]. Some non-forest land-cover types existed in a coarse MODIS pixel (500 m), which lead to the overestimation of degraded forest areas.  
Figure 5: Forest degradation area from remote sensing data and ground survey data. Degradation area from ground survey data is lower than that from remote sensing data in most province.  
Figure 6: Forest degradation maps (30 m in typical areas (a) Maowusu; (b) Hulun Buir; (c) Khorchin; (d) Loess Plateau. These maps were derived from the degradation rate from Landsat data.  
Figure 7: Validation of degradation area from remote sensing data against ground survey data. (a) is the validation from MODIS data, and (b) is the validation from MODIS-Landsat data. Forest degradation from MODIS-Landsat data achieved a better precision.