esa-sceva/satcom-chunk-collection huggingface dataset:
# Deep Learning for Information Triage on Twitter
## 3 Preliminary Study: Types of Information Found on Twitter
### Analysis of Election Tweets
In secondary information, there were official RT which copied the poster of the elections, and tweets containing phrases like \"They say that there is an election campaign speech at...\". Moreover, out of a total number of 1503 tweets, 997 contained some kind of URL information, and information about candidates which could be influential on the choice of a candidate. For example, there were 197 tweets containing links to a list of election candidates divided by each district. This kind of information is often included in primary information tweets and often contains positive or negative opinions about political parties. Next, from the total of 1317 primary information tweets, we randomly extracted 1000 and classified them into three categories: positive, negative, or neutral. Objective opinions were considered as neutral. For the results of the classification, out of 1000 tweets, there were 68 (7%) positive tweets, 771 (77%) neutral tweets, and 161 (16%) negative tweets (see Figure 3). Figure 2: Ratio of primary and secondary information in Lower House election tweets. In positive tweets, there were many direct positive expressions about particular political parties, such as, \"I support the party of...!\". In negative tweets, there were many expressions, such as, \"I will never vote for the party of...\". There was also a great deal of neutral tweets which contained lists of candidates for each city district. Primary, Secondary and \"Sesquiary\" Information. Based on the analysis results of tweet logs from both the Great East Japan Earthquake and the general election tweet logs, we redefined our classification criteria. It was not possible to apply to the general election tweets the classification criteria from the Great East Japan Earthquake alone, due to the fact that in tweets about the general elections, different information is often considered as important. In the disaster tweets, the factual information was the most important. In the election tweets, users often wrote about their political preferences; thus, it was also important to take into consideration information from the borderline of pure fact and rumor, such as opinions, or emotional attitudes. This kind of information in the Earthquake tweets is mostly considered as noise. However, in election tweets, private opinions and emotional comments could become useful as a reference. Therefore, it is important to distinguish this kind of information from the rest and annotate it separately. To do this, we have defined a third kind of information which is neither primary nor secondary, though keeping a structure of its own, namely, \"sesquiary\" (\"sesqui-\" = 1.5) information (see Table 4). Many tweets represent mixed information. In order to solve this conflict, we applied the following heuristic rules. 1. If a tweet contains different kinds of information, priority is given to the kind of lower density;
2. In cases of only sesqutiary and secondary information appearing in a tweet, priority is given to secondary information.
## 5 Conclusions and Future Perspectives  
Marine microalgae represent a sustainable, versatile, and underexploited reservoir of bioactive compounds with wide-ranging applications across the health, cosmetic, and functional food sectors. This review has highlighted the structural diversity, biological activities, and therapeutic relevance of major compound classes, including PUFAs, pigments, peptides, sterols, polysaccharides, phenolics, phycobiliproteins, vitamins, mycosporine-like amino acids, and alkaloids, underscoring their multifunctionality and market potential.  
Bioprospecting studies and advances in omics technologies have significantly expanded our understanding of microalgal metabolism, revealing novel compounds with antioxidant, anti-inflammatory, anticancer, antimicrobial, photoprotective, and neuroprotective properties. Many of these compounds exhibit equal or superior bioactivity compared to their synthetic or terrestrial counterparts and fulfil increasing consumer demand for clean-label, plant-based, and functional products.  
However, realising the full biotechnological potential of marine microalgae still requires overcoming key challenges. These include optimising cultivation systems, improving biomass and metabolite yields, reducing downstream processing costs, and navigating complex regulatory landscapes. Additionally, standardisation of bioactive content, efficacy validation, and scalability remain bottlenecks for widespread industrial adoption.  
Future research should prioritise multidisciplinary strategies that combine systems biology, synthetic biology, and bioprocess engineering to enhance compound production and enable precision cultivation. Co-cultivation, stress modulation, metabolic engineering, and biorefinery models are emerging as promising avenues to maximise biomass valorisation while reducing environmental impact.  
Strategic investments in infrastructure, international collaboration, and policy support, particularly through frameworks aligned with the blue bioeconomy, will be critical to facilitate the translation of research into commercial innovation. Furthermore, public engagement and safety assessments are essential to build consumer trust in microalgae-derived products.  
This research was funded by Fundacao para a Ciencia e a Tecnologia grants (Lisbon, Portugal) UIDB/00276/2020 to CIISA and LA/P/0059/2020 to ALAAnimals.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** No new data were created or analysed in this study. Data sharing is not applicable to this article.  
**Conflicts of Interest:** The author declares no conflicts of interest.
# Quadratic Multilinear Discriminant Analysis for Tensorial Data Classification
## 2 Notation and Background Material
The dimensions of the core \(\mathcal{G}\) can be chosen to be smaller than those of \(\mathcal{X}\), making this decomposition useful for dimension reduction (among other things). The main result from invariant theory we will use is the Kempf-Ness theorem [19]. Over the real numbers, it first appeared in ([28], Theorem 4.3). We will describe the general setup of [28], which is technical, but note that it applies to some concrete situations of interest. Let \(\mathrm{GL}_{n}=\mathrm{GL}_{n}(\mathbb{R})\) (resp. \(\mathrm{GL}_{n}(\mathbb{C})\)) be the group of invertible \(n\times n\) matrices with real (resp. complex) entries. Suppose that \(G_{\mathbb{C}}\) is a reductive, Zariski closed subgroup of \(\mathrm{GL}_{n}(\mathbb{C})\) that is closed under complex conjugation. Let \(G\subseteq G_{\mathbb{C}}\cap\mathrm{GL}_{n}(\mathbb{R})\) be a subgroup that contains the connected component of the identity in \(G_{\mathbb{C}}\cap\mathrm{GL}_{n}(\mathbb{R})\). The orbit \(G\cdot v\) of a vector \(v\in\mathbb{R}^{n}\) with respect to \(G\) is defined as the set of vectors \(g\cdot v\) as \(g\) varies in the group \(G\). Finally, define \(K=G\cap\mathrm{O}_{n}\), where \(\mathrm{O}_{n}\) is the group of orthogonal \(n\times n\) matrices. This setup applies to the following situations that are of interest to us:  
* \(G=\mathrm{SL}_{n}\), the group of matrices with determinant \(1\) and \(K=\mathrm{SO}_{n}\), the special orthogonal group acting on \(\mathbb{R}^{n}\) in the usual way;
* The group \(G=\mathrm{T}_{n}\) of \(n\times n\) diagonal matrices of determinant \(1\) with positive diagonal entries, and \(K=\{I_{n}\}\) is the trivial group;
* The group \(G=\mathrm{SL}_{n_{1}}\times\mathrm{SL}_{n_{2}}\times\mathrm{SL}_{n_{3}}\) acting on the space \(\mathbb{R}^{n_{1}\times n_{2}\times n_{3}}\) of tensors of size \(n_{1}\times n_{2}\times n_{3}\) and \(K=\mathrm{SO}_{n_{1}}\times\mathrm{SO}_{n_{2}}\times\mathrm{SO}_{n_{3}}\), where \(n=n_{1}n_{2}n_{3}\);
* The same as the previous example, but where \(G\) and \(K\) acts on \(m\)-tuples of \(n_{1}\times n_{2}\times n_{3}\) tensors, or equivalently, on \(n_{1}\times n_{2}\times n_{3}\times m\) tensors (and \(n=n_{1}n_{2}n_{3}m\)). We can now state the real Kempf-Ness theorem:  
Theorem 1 (Kempf-Ness): _Suppose that \(G\subseteq\mathrm{GL}_{n}(\mathbb{R})\) be as in the setup above, and \(v\in\mathbb{R}^{n}\) is nonzero. The Euclidean norm function \(v\mapsto\|v\|^{2}\) has a critical point on the orbit \(G\cdot v\) if and only if the orbit is closed. If a critical point exists, then it is an absolute minimum and the set of critical points is unique up to the action of \(K\), i.e., the set of critical points is a unique \(K\)-orbit._
# Data-Free Area Detection and Evaluation for Marine Satellite Data Products
## 3 Endmember of Turbid Water, Clean Water and Sea Ice
### 3.3 Optimal Threshold Automatic Acquisition via the Improved SAM (ISAM) Algorithm
The obtained angular thresholds are theoretically applicable to all data under the same sensor. It improves the efficiency of the subsequent identification of a large number of data. As the SAM classification method is pixel-based, the spectral vectors are calculated pixel by pixel, and the recognition classification results are usually fragmented (Figure 8a). In this study, high-pass filtering was performed on the SAM recognition classification results to enhance the texture information, followed by Clump clustering [52]. By using the mathematical morphology operator, the classification results with the enhanced texture information can be clustered and merged with the adjacent fragmented but similar classification regions. After the initial classification, the individually classified pixels can be merged by the ISAM method. This improves spatial continuity and classification accuracy. The improved classification result is shown in Figure 8b. The comparative classification results clearly show that the images were fragmented before the processing of the Clump algorithm (Figure 8a). The number of pixels in a separate category is high and does not correspond to the actual situation. By contrast, Figure 8b shows that some fragmented similarity areas (patches) can be combined to achieve good spatial continuity. Figure 8: Diagram of the processing result of the Clump method (Red for clouds, green for green tide, blue for clean water): (**a**) pre-method results and (**b**) post-method results. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Radians (R)** & **GOCI** & **MODIS** & **OLCI** \\ \hline Open Sea & 0.235 & 0.325 & 0.260 \\ Turbid Water & 0.175 & 0.240 & 0.265 \\ Green Tide & 0.360 & 0.295 & 0.320 \\ Sea Ice & 0.125 & 0.155 & 0.110 \\ Cloud & 0.585 & 0.630 & 0.525 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Threshold values for each type of object.
# Grey-Box Fuzzing Based on Reinforcement Learning for XSS Vulnerabilities
## 5 Evaluation  
In this section, we evaluate the effectiveness of the proposed method and compare it with four state-of-the-art web scanners. We propose the following standard research questions. These questions can help us comprehensively evaluate the proposed method's performance detecting XSS vulnerabilities.  
* **RQ1.** Effectiveness: Is the proposed method able to detect XSS vulnerabilities?
* **RQ2.** Comparison with other tools: Does the proposed method perform better than other tools? What is the reason?
* **RQ3.** Resource consumption: Is the proposed method consuming excessive system resources?
# A Thermopile-Based Colorimetric Temperature Measurement Method for Arbitrary Bandwidth
###### Abstract  
Colorimetric temperature measurement is an essential technique in radiometric thermometry. Traditional colorimetric methods determine temperature by comparing the ratio of radiative energies within two narrow bands at specific wavelengths, effectively mitigating the effects of the emissivity of the measured object and ambient conditions. However, these methods typically approximate integration using area calculations when calculating radiative energy. This article eliminates this approximation and calculates the radiative energy with accurate integration. Based on the principle of monotonicity, this article demonstrates for the first time that when two narrow-band infrared radiations are selected, as long as their wavelength ranges do not overlap, the ratio of radiative energies within these bands maintains a monotonic relationship with the measured temperature. This allows the temperature to be inferred from the energy ratio. Furthermore, this conclusion is extended to arbitrary widths of bands as long as their wavelength ranges do not overlap. Building on this foundation, a thermopile-based colorimetric temperature measurement method for arbitrary bandwidth is proposed. Simulation experiments validate this method, showing that the energy ratio maintains a monotonic relationship with the measured temperature as long as the infrared radiation wavelength ranges absorbed by the thermopile do not overlap. The simulation results are consistent with the mathematical proof.  
temperature measurement; colorimetric temperature measurement; thermopile; infrared radiation +
Footnote †: journal: Applied Sciences
## 2 Materials and Methods  
This section gives insights into the datasets, analytical tools, and methodologies employed to establish the tectono-stratigraphic framework and assess the hydrocarbon potential of the Albert Rift, Uganda. This study integrates seismic interpretation, well-loganalysis, geochemical characterization, and Basin and Petroleum System Modeling (BPSM) to evaluate key petroleum system elements. The primary datasets include 2D seismic data, well logs, and geochemical analyses of shale samples, which were processed and interpreted using industry-standard software. Schlumberger Petrel 2018 was used for seismic and well-log interpretation, Schlumberger PetroMod 2018 for BPSM, and Midland Valley's Move 2015 for structural restoration. Additionally, Rock Eval 7 Pyrolysis was conducted on shale samples to determine source-rock quality, thermal maturity, and hydrocarbon-generation potential. The results from Rock Eval were integrated with burial history and thermal maturity models to refine the basin's petroleum system evaluation. The workflow in this study follows a systematic approach, incorporating seismic stratigraphic analysis, structural restoration, source-rock evaluation, thermal maturity modeling, and hydrocarbon charge assessment, ensuring reliable predictions based on the petroleum history of this rift.
# Fast Image Reconstruction Technique for Parallel Phase-Shifting Digital Holography
## 3 Experiments
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Bilinear (3 \(\times\) 3 Kernel)**} & \multicolumn{2}{c}{**Bicubic (7 \(\times\) 7 Kernel)**} & \multicolumn{2}{c}{**Lanczos-3 (11 \(\times\) 11 Kernel)**} \\ \cline{2-6}  & **Conventional** & **Proposed** & **Conventional** & **Proposed** & **Conventional** & **Proposed** \\  & **[ms]** & **[ms]** & **[ms]** & **[ms]** & **[ms]** & **[ms]** \\ \hline Object wave generation & \(116\pm 5\) & \(18\pm 1\) & \(167\pm 8\) & \(18\pm 1\) & \(274\pm 13\) & \(18\pm 1\) \\ Diffraction calculation & \(345\pm 14\) & \(398\pm 17\) & \(350\pm 14\) & \(399\pm 17\) & \(349\pm 15\) & \(395\pm 17\) \\ Total & \(461\pm 19\) & \(416\pm 18\) & \(518\pm 22\) & \(417\pm 18\) & \(623\pm 28\) & \(413\pm 18\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Calculation time for the conventional and proposed techniques. Figure 5: Reconstructed images using (**a**–**c**) the conventional technique and (**d**–**f**) the proposed technique using the interpolation algorithms (**a**,**d**) bilinear, (**b**,**e**) bicubic, and (**c**,**f**) Lanczos-3. much as that required for the bilinear interpolation. However, in our experiments, no differences were observed in the reconstructed images obtained for the three interpolation algorithms because the line spacings were much larger than the theoretical minimum resolvable distance of the experimental system.
# Continuous-Wave THz Imaging for Biomedical Samples
## 5 The Application of CW Imaging with Biological Tissues
### Tumour Tissues
#### 5.1.5 Liver Cancer  
There are no obvious symptoms in the early stage of liver cancer, and by the time the patient feels pain or obvious discomfort of the liver, the condition of the liver cancer has mostly entered into the middle or late stages. Therefore, the early detection and treatment of liver cancer are necessary for high-risk groups of liver cancer patients [92]. In 2015, [PERSON] et al. used the in-line CW TDH method shown in Figure 8 for the first time to image frozen liver cancer tissue slices with sizes of 3.2 mm \(\times\) 2.3 mm \(\times\) 30 \(\upmu\)m thick [93]. The digital photo of the sample is shown in Figure 15a. This method proposed the phase reconstruction algorithms of in-line digital holography optimised from the terms of the constraint conditions and initial value optimisation, which improved the calculation efficiency. They also used the subpixel micro-shift and extrapolation algorithms to improve the reconstruction accuracy. With the THz images, they obtained the absorption and phase contrast distribution of the tissue slices as shown in Figure 15c,d. The tissue fibrosis and the signs of cirrhosis could be clearly observed from the intensity images. The micro vessels in the tissue slices could be viewed from the phase contrast image, which indicated that more detailed tissues information was reconstructed in the THz phase distribution image, and that was of great significance for the detection and treatment of early liver cancer.  
Figure 14: Two datasets corresponding to different tissue samples. Photographs of normal and cancerous colon tissue and cross-polarised THz image. “N” and “C” represent normal and cancerous respectively: (**a**,**d**) Photographs of normal and cancerous colon tissue; (**b**,**c**) The cross-polarised THz image of the normal and cancerous colon tissue. Reproduced from [41] CC BY 4.0.  
Figure 15: The CW THz in-line holography of human liver cancer sample: (**a**) Photo of the liver cancer sample; (**b**) In-line THz hologram; (**c**) The reconstructed amplitude and (**d**) phase images. The phase image contains more structural information. The tissue fibrosis arrows point to a sign of cirrhosis that was untreated and developed into liver cancer. Adapted from [93] under the terms of the Creative Commons Attribution 4.0 License.
# An Integrated Neural Network and SEIR Model to Predict COVID-19
## 3 Methodology
### An Integrated SEIR and Neural Network Framework  
We included a neural network model to calculate the quarantined population at a specific time. A neural network is a combination of artificial neurons [22]. The gradient descent process [23] was used to resample the value of all parameters in the opposite direction. This process continues until the cost function is minimized. The flow chart of our integrated system is illustrated in Figure 2.  
Here, the part of the population that is supposed to be infected with this disease can be placed in the susceptible compartment. Those who are in the incubation period and have not been diagnosed are placed in the exposed compartment. The infected compartment contains patients who are infected by COVID-19. The patients who have died or recovered from this disease are in the removed compartment.  
Figure 2: SEIR model with a neural network.  
To calculate the quarantined population figure at a specific time \(t\), the population figure in S, E, I, R, and Q (quarantined) at time \(t-1\) is sent to the neural network. The network generates the quarantined population figure.  
The following differential equations are used to change the status of each compartment at time \(t\) (\(t>0\)).  
\[\frac{dS}{dt}=-\beta S(t)I(t) \tag{7}\]  
\[\frac{dE}{dt}=\beta S(t)I(t)-\varepsilon E(t) \tag{8}\]  
\[\frac{dI}{dt}=\varepsilon E(t)-\gamma I(t)-NNI(t) \tag{9}\]  
\[\frac{dR}{dt}=\gamma I(t) \tag{10}\]  
\[\frac{dQ}{dt}=NNI(t) \tag{11}\]  
where \(dQ/dt\) denotes the quarantined compartment and \(NN\) is the output of the neural network. The basic reproduction rate, \(R_{0}\)[8], for this diseases is calculated using Equation (12).  
\[R_{0}=\frac{\beta}{NN+\gamma} \tag{12}\]
# Eigenmode Wavefront Decoupling Algorithm for LC-DM Adaptive Optics Systems
## 3 Materials and Methods
### Eigenmode Correction Principle  
After the eigenmodes are established, the wavefront can be reconstructed. This reconstruction process is similar to that of the Zernike mode. Because \(\varphi\) and Mi are two-dimensional matrices in Formula (6), for convenience of expression, the matrices \(\varphi\) and Mi are converted into columns \(\overline{\varphi}\) and \(\overline{M}_{i}\), respectively (the function of Matlab \(\overline{\varphi}\) = reshape (\(\varphi\), [], 1)), then Formula (7) can be expressed as follows:  
\[\overline{\varphi}=M\cdot m \tag{7}\]  
where \(M=[\overline{M}_{1},\overline{M}_{2},\cdots,\overline{M}_{n}]\) is the DM eigenmode response matrix, and \(m=[m_{1},m_{2},\cdots,m_{n}]^{T}\) is the eigenmode coefficients. The corresponding eigenmode coefficients of \(\overline{\varphi}\) can be obtained using the least square method:  
\[m=M^{-1}\cdot\overline{\varphi} \tag{8}\]  
where \(\overline{\varphi}\) is the measured distorted wavefront, and \(M^{-1}\) is the pseudo-inverse of the eigenmode matrix \(M\). Moreover, the ith DM eigenmode and the DM voltage signal vector can also be obtained using the least square solution:  
\[\overline{M}_{i}=\overline{R}\cdot v_{M_{i}}\Rightarrow v_{M_{i}}=\overline{ R}^{-1}\cdot\overline{M}_{i} \tag{9}\]  
where \(\overline{R}=[\overline{R}_{1},\overline{R}_{2},\cdots,\overline{R}_{n}]\)\(\overline{R}_{i}\) is the column vectors of the response matrix of each actuator, and \(v_{M_{i}}\) is the drive voltage vector corresponding to the ith eigenmode. Combining Formulas (7)-(9), the voltage vector \(v\) corresponding to the arbitrary wavefront \(\overline{\varphi}\) expressed in the eigenmodes is obtained as follows:  
\[v=v_{M}\cdot m=v_{M}\cdot M^{-1}\cdot\overline{\varphi} \tag{10}\]  
where \(v_{M}=\left[v_{M_{1}},v_{M_{2}},\cdots,v_{M_{n}}\right]\) is the driving voltage matrix corresponding to the eigenmode response matrix \(M\). For Formula (10), \(v_{M}\) and \(M^{-1}\) can be obtained in advance. The voltage vector \(v\) is sent to the DM for wavefront correction. Figure 4 shows an original wavefront with the PV and RMS of 3.66 and 0.52 \(\upmu\)m, respectively. After eigenmode correction, the PV and RMS decreased to 0.41 and 0.03\(\upmu\)m, respectively.
# Substring Position Search over Encrypted Cloud Data Supporting Efficient Multi-User Setup +
## 8 Conclusions  
In this work, we present a new Substring Position Searchable Symmetric Encryption scheme (SSP-SSE) that allows efficient substring search on encrypted documents outsourced to the cloud. Specifically, our solution efficiently finds the occurrences and positions of a substring over encrypted cloud data. We formally define the leakage functions and security notions of SSP-SSE. We show that our scheme is secure against chosen-query attacks executed by an adaptive adversary. We also present a multi-user SSP-SSE scheme that supports a distributed setup, where data users choose their own secret key rather than receive the key from a trusted authority. As future work, we plan to focus on enhancing query privacy in SSP-SSE, while keeping all of the good properties in the current design. Furthermore, we plan to expand the SSP-SSE scheme to support dynamic updates on the document collection that will allow query execution when the document corpus is modified.  
This work was partially supported by the U.S. National Science Foundation under Grant No. 0905232. We are grateful to anonymous referees for their constructive comments and valuable suggestions that helped to improve this paper.  
[PERSON] contributed to the design of the proposed scheme, literature survey and manuscript preparation; [PERSON] implemented the scheme and performed the experiments; [PERSON] supervised this research work.  
The authors declare no conflict of interest.