esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Abstractive summarization is a technique that allows for extracting condensed meanings from long texts, with a variety of potential practical applications. Nonetheless, today's abstractive summarization research is limited to testing the models on various types of data, which brings only marginal improvements and does not lead to massive practical employment of the method. In particular, abstractive summarization is not used for social media research, where it would be very useful for opinion and topic mining due to the complications that social media data create for other methods of textual analysis. Of all social media, Reddit is most frequently used for testing new neural models of text summarization on large-scale datasets in English, without further testing on real-world smaller-size data in various languages or from various other platforms. Moreover, for social media, summarizing pools of texts (one-author posts, comment threads, discussion cascades, etc.) may bring crucial results relevant for social studies, which have not yet been tested. However, the existing methods of abstractive summarization are not fine-tuned for social media data and have next-to-never been applied to data from platforms beyond Reddit, nor for comments or non-English user texts. We address these research gaps by fine-tuning the newest Transformer-based neural network models LongFormer and T5 and testing them against BART, and on real-world data from Reddit, with improvements of up to 2%. Then, we apply the best model (fine-tuned T5) to pools of comments from Reddit and assess the similarity of post and comment summarizations. Further, to overcome the 500-token limitation of T5 for analyzing social media pools that are usually bigger, we apply LongFormer Large and T5 Large to pools of tweets from a large-scale discussion on the _Charlie Hebb_ massacre in three languages and prove that pool summarizations may be used for detecting micro-shifts in agendas of networked discussions. Our results show, however, that additional learning is definitely needed for German and French, as the results for these languages are non-satisfactory, and more fine-tuning is needed even in English for Twitter data. Thus, we show that a 'one-for-all' neural-network summarization model is still impossible to reach, while fine-tuning for platform affordances works well. We also show that fine-tuned T5 works best for small-scale social media data, but LongFormer is helpful for larger-scale pool summarizations.  
natural language processing; deep learning models; transformer models; abstractive summarization; social networks; opinion mining; Reddit; Twitter; pool summarization +
Footnote †: journal:  
0000-0001-9999-4600][PERSON]  
0000-0001-9999-4600][PERSON]  
0000-0001-9999-4600]A.
# A Study on the Stress Field Around an Elliptical Wellbore with an Eccentric Casing in Deep Wells
###### Abstract
[20] obtained the analytical solutions for the stress distribution in an elliptical hole using the complex stress function method. [PERSON] et al. [21] conducted research using the finite element method to explore the influence of wellbore ellipticity on casing stress. Currently, there is a lack of research results regarding the stress distribution in casing with an eccentricity in an elliptical wellbore. This is mainly because solving the stress distribution in the presence of an elliptical boundary is more complex, and there is no commonly used analytical method that directly calculates the stress distribution in the casing. To address this issue, researchers commonly employed conformal transformation techniques within the framework of complex functions. This approach initially transformed the elliptical boundary into a circular boundary on an auxiliary plane, subsequently utilizing the stress function method to derive the stress distribution expression for the casing. The complexity of this methodology necessitated a rigorous verification process to ensure the congruence of the derived solutions with engineering applications. Furthermore, in scenarios involving casing eccentricity, the analytical derivation of stress distribution became notably intricate [18]. To address this gap, this study utilized the principles of elastic theory and established a finite element model of the casing cement formation system under non-uniform stress and eccentricity using finite element methods. Through calculations and analysis, the influence of eccentricity and wellbore ellipticity on the overall stress distribution of the casing and the stress distribution at the casing outer wall is investigated. Furthermore, the impact of the non-uniformity of the stress field, the elastic modulus of the cement annulus, and the Poisson ratio of the cement annulus on the extreme values of the casing outer wall stress distribution is considered. The remaining sections of this article will be organized as follows. Section 2 defines and sets the necessary variables and parameters in detail. We will also establish a finite element model for the wellbore system under the eccentricity of the casing. In Section 3, we will utilize finite element software (Abaqus2022) to simulate the stress distribution in an eccentric elliptical wellbore under different conditions. We will consider the effects of varying ellipticity, eccentricity, and other parameters on the stress distribution. The final section will provide a summary of the entire study. We will conclude and summarize the stress distribution in eccentric elliptical wellbore casing under different conditions.
## 3 Results and Discussion
### Effect of Coir Fiber Addition on the Ultrasonic Pulse Velocity (UPV) of Geopolymer Specimens  
Table 5 shows the results of the ultrasonic pulse velocity (UPV) measurements performed on the metakaolin geopolymer samples and those with varying coil fiber content cured for 28 days. The addition of coir fibers into the matrices of the metakaolin-based geopolymer could influence the UPV values of samples as ultrasonic waves travel at a higher speed in fibers than in the geopolymer mortar itself [19]. The UPV result revealed the geopolymer samples' quality and homogeneity of pore distribution per ASTM C597-02 standard [61]. The geopolymer samples had UPV values varying from 2315 m/s to 2717 m/s. The control sample (MGP Control) had the least value of UPV (2315 m/s). The addition of 0.5 wt % caused a \(\approx\)15% increase in the UPV value (2717 m/s). This 0.5 wt % gave the highest UPV value. Conversely, the 0.5 wt % formulations also provided relatively high compressive and flexural strength, which indicated that a more homogeneous pore distribution within the composite for the 0.5% addition improved the compressive and  
Figure 10: Bulk density of geopolymer specimens at different coir fiber content cured at age 28.  
flexural strengths. At coir fiber weight percentages of 1.0, 1.5, and 2.0%, the recorded UPV values were 2688 m/s, 2577 m/s, and 2475 m/s, respectively. This study showed higher values of UPV values for reinforced samples than the unreinforced sample. The increased specimens' UPV with fiber content can be partially attributed to the bridging effect of the coir fiber, which leads to the reduction of micro-cracks in the geopolymer matrix [67]. This study showed higher values of UPV when compared with the results of the study conducted by [PERSON] et al. [68] that reinforced fly ash geopolymer with polypropylene (PP) fibers at 0.5 and 1.0% total volume of mixture, in which UPV values varied from 1800 m/s to 2000 m/s.  
Furthermore, compared to traditional concrete materials, the UPV values here are considerably low. In this regard, the lower UPV values of the geopolymer composites compared to traditional concretes are supported by the study of [PERSON] et al. [69], which noted that geopolymers often have less UPV values than conventional binder materials. Furthermore, the UPV values recorded in this study may have been strongly affected by the composite density, which is related to air voids and the porosity of the structure [19]. In addition, as reported by [PERSON] et al. [70] and [PERSON] et al. [71], UPV values tend to increase with the increase in compressive strength values and was the trend in this study.
# Kinematic In Situ Self-Calibration of a Backpack-Based Multi-Beam LiDAR System
## 2 Backpack-Based Sensor System  
The backpack-based MBL system used in this study consists of two LiDAR sensors, six optical cameras, and a Global Navigation Satellite System (GNSS)/Inertial Navigation System (INS) (Figure 1). The configuration of the involved sensors was determined to have minimum occlusion and maximum Field of View (FOV). The sensors are all integrated into a core computing system, which receives all sensor data and synchronizes into Universal Time Coordinated (UTC) timestamps. The Inertial Measurement Unit (IMU) was not described in Figure 1 since the unit is embedded in the computing system.
## 2 Channel Model and UOWC System  
### Underwater Optical Channel Model  
When the light signal propagates in the seawater, the photon suffers from absorption and scattering by the interactions with seawater. The absorption process will reduce the  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Optical OFDM Scheme** & **Spectral Efficiency** & **Power Efficiency** & **Detection Complexity** \\ \hline DCO-OFDM & high & low & low \\ ACO-OFDM & low & high & low \\ ADO-OFDM & high & medium & high \\ PEADO-OFDM & high & medium & low \\ \hline \hline \end{tabular}
\end{table}
Table 1: General performance comparisons between four optical O-OFDM schemes.  
energy of the photon while the scattering process will change the transmit direction of the photon. The absorption coefficient \(\mathrm{a}(\lambda)\) and scattering coefficient \(\mathrm{b}(\lambda)\) are utilized to evaluate the effects of absorption and scattering, respectively, which vary with water type and wavelength \(\lambda\). The total energy loss due to absorption and scattering can be described by the attenuation coefficient \(\mathrm{c}(\lambda)\), which is given by:  
\[\mathrm{c}(\lambda)=\mathrm{a}(\lambda)+\mathrm{b}(\lambda), \tag{1}\]  
The \(\mathrm{a}(\lambda)\), \(\mathrm{b}(\lambda)\) and \(\mathrm{c}(\lambda)\) for clear, coastal and harbor water used in this paper are listed in Table 2.  
Referring to the close-form expression of the gamma-gamma function given in [8], the impulse response of UOCM is:  
\[h(t)=C_{1}\Delta te^{-C_{2}\Delta t}+C_{3}\Delta te^{-C_{4}\Delta t} \tag{2}\]  
where \(\Delta t\) represents the duration of the impulse response.  
By using the nonlinear least square criterion:  
\[(C_{1},C_{2},C_{3},C_{4})=\mathrm{arg}min\left(\int[h(t)-h_{mc}(t)]^{2}dt\right) \tag{3}\]  
where \(h(t)\) and \(h_{mc}(t)\) are the impulse response of double-gamma functions and Monte Carlo simulation results, respectively, and the operator \(\mathrm{arg}\;min\;(\cdot)\) is used to return the argument of the minimum.  
In the simulation, the Monte Carlo simulations are firstly carried out to obtain the Monte Carlo impulse responses for three kinds of water. Then, Equation (3) can be realized by the curve fitting approach in MATLAB to compute the parameter sets \((C_{1},C_{2},C_{3},C_{4})\). By this method, we can build up various impulse responses of clear, coastal and harbor waters. The normalized impulse responses of clear, coastal and harbor waters are shown in Figure 1. We can observe that the time delay spread of the harbor water channel is much larger than that of the other two, which means it will cause more serious ISI.  
Figure 1: The normalized impulse response in (**a**) clear, (**a**) coastal and (**b**) harbor water using double-gamma functions.
# Task-Aware Feature Composition for Few-Shot Relation Classification
## 5 Experiments
### The Impact of Word Embeddings  
BERT can provide richer contextual representations of words by pretraining with a large-scale corpus, and many works have shown that BERT embeddings are better than classical word embeddings, such as GLOVE [34]. In this section, we investigate whether better pretrained embeddings will further improve the effectiveness of our proposed model. Specifically, we conduct experiments by replacing BERT embeddings with GLOVE embeddings. The experimental results are shown in Table 4.  
From Table 4, we find that the classification performance decreases dramatically when using word embeddings instead of BERT embeddings. This is because BERT is trained  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **5-Way 1-Shot** & **5-Way 5-Shot** & **10-Way 1-Shot** & **10-Way 5-Shot** \\ \hline TFC+Mode 1 (BERT) & 83.27 & 91.13 & 68.58 & 81.11 \\ TFC+Mode 1 (WE) & 80.73 & 89.26 & 64.30 & 79.15 \\ \hline TFC+Mode 2 (BERT) & 83.33 & 91.13 & 69.02 & 80.37 \\ TFC+Mode 2 (WE) & 80.71 & 89.17 & 63.97 & 79.32 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Accuracy (%) of all models on FewRel validation dataset.WE means using word embeddings instead of BERT embeddings.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **5-Way 1-Shot** & **5-Way 5-Shot** & **10-Way 1-Shot** & **10-Way 5-Shot** \\ \hline Proto-ADV (CNN) & 42.21 & 58.71 & 28.91 & 44.35 \\ Proto (BERT) & 40.12 & 51.50 & 26.45 & 36.93 \\ Proto-ADV (BERT) & 41.90 & 54.74 & 27.36 & 37.40 \\ BERT-PAIR & 56.25 & 67.44 & 43.64 & 53.17 \\ \hline TFC+Mode 1 & **67.33** & **79.86** & 54.31 & 69.35 \\ TFC+Mode 2 & 67.12 & 78.13 & **54.86** & **70.36** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Accuracy (%) of all models on FewRel2.0 under four different settings.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **5-Way 1-Shot** & **5-Way 5-Shot** & **10-Way 1-Shot** & **10-Way 5-Shot** \\ \hline GNN & 70.21 & 81.25 & 49.22 & 67.29 \\ GNN+Mode 1 & **72.48** & 82.12 & 54.67 & **69.10** \\ GNN+Mode 2 & 72.13 & **82.35** & **55.44** & 69.01 \\ \hline Proto & 75.25 & 86.02 & 59.37 & 77.18 \\ Proto+Mode 1 & **75.97** & **87.49** & 61.28 & 78.03 \\ Proto+Mode 2 & 75.62 & 87.33 & **61.41** & **78.22** \\ \hline Proto (BERT) & 78.31 & 89.18 & 63.51 & 78.77 \\ Proto (BERT)+Mode 1 & **80.22** & 88.60 & 65.73 & 79.22 \\ Proto (BERT)+Mode 2 & 80.03 & **89.79** & **65.92** & **79.83** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Improvement after incorporating TFC into existing methods on FewRel validation dataset.  
based on a considerable corpus scale, and its network structure is based on the transformer, which has stronger semantic modeling capability. BERT encodes more context semantic information through multi-task learning. This information can be used as an additional knowledge base to remedy limited training examples in the few-shot learning setting. This finding is consistent with the work of [PERSON] et al. [35].
## 4 Results
### Sabratha and Metaponto Case Studies  
In order to assess the capability of single date analysis from COSMO-SkyMed in different contexts and environmental conditions, we selected two significant test cases: one in Sabratha in Libya,  
Figure 9: Soil and damp marks of the Eastern-Han to Northern-Wei Dynasty Luoyang city site (highlighted by the green polygon in Figure 4) on the acquisition image of 25 July 2013 after a flood disaster. Rectangular and linear features highlighted by red arrows correspond to an ancient palatial structure and settlement foundations of walls or buildings, respectively, as interpreted by a local archaeologist. Green and blue polygons highlight the samples of natural dry soil and damp archaeological marks. The Northeast Inner City Wall is marked by a dotted blue line.  
and the second in Metapontum (Italy, Europe). The first is mainly characterized by micro-relief, visible thanks to shadow marks; the second shows the presence of soil, damp and crop marks depending on the moisture content and vegetation cover, which change with the seasons. For each test site, one COSMO-SkyMed reading, acquired in Enhanced Spotlight Mode (for additional details see [27]) was used, to detect unrecorded information close to known and unknown archaeological and palaeoenvironmental features. The Spotlight image was first compared with a multi-temporal image set available from Google Earth, then processed in order to enhance the information content, _i.e._, to maximize the signal/noise ratio. To this aim, noise was reduced using different approaches including one based on adaptive filtering, in particular the Lee enhanced algorithm, and the square of the summation (Sum\({}^{2}\)).
## 3 Materials and Methods
### Quantitative Analysis of Aroma Compounds  
#### 3.6.1 Free-Form Aroma Compound Analysis by HS-SPME-GC-MS  
The extraction and detection methods of aroma compounds in grape berries were performed according to our previous studies [38]. First, 50 grape berries were deseeded and then mixed with 1.0 g of PVPP and 0.5 g of d-gluconolactone. This mixture was ground to a powder in liquid nitrogen, macerated at 4 \({}^{\circ}\)C for 4 h and immediately centrifuged at 8000\(\times\)\(g\) for 15 min to obtain a clear juice. A glass vial, containing 1 g of NaCl, 5 mL of clear juice and 10 \(\upmu\)L of internal standard (1.0086 g/L 4-methyl-2-pentanol), was placed on a CTC CombiPAL autosampler (CTC Analytics, Zwingen, Switzerland). The sample vials were equilibrated at 40 \({}^{\circ}\)C for 30 min. Subsequently, DVB/CAR/PDMS 50/30 \(\upmu\)SPME fibres (Supelco, Bellefonte, PA, USA) were inserted into the headspace of the vials and stirred at 500 rpm for 30 min at 40 \({}^{\circ}\)C to extract volatile compounds. The SPME fibre underwent desorption in the injector for 8 min.  
An Agilent 6890 gas chromatograph coupled with an Agilent 5975C mass spectrometer (Agilent Technologies) was used to perform GC-MS analysis. Volatile compounds were separated using an HP-INNOWAX capillary column (60 m \(\times\) 0.25 mm \(\times\) 0.25 \(\upmu\)m, J&W Scientific, Folsom, CA, USA) with helium as the carrier gas at 1 mL/min flow rate. Injection was done in splitless mode with the injector temperature set at 250 \({}^{\circ}\)C. The column chamber was held at 50 \({}^{\circ}\)C for 1 min and then gradually heated at a rate of 3 \({}^{\circ}\)C/min until it reached 220 \({}^{\circ}\)C, at which point it was held for 5 min. The mass spectrometer utilizes electron ionization (EI) at 70 eV to ionize molecules of volatile compounds. The temperatures for the ion source and quadrupole were maintained at 250 \({}^{\circ}\)C and 150 \({}^{\circ}\)C, respectively. Full scan mode (\(m/z\) 30-350) was set for the mass detector.
# Speech Act Theory as an Evaluation Tool for Human-Agent Communication
## 5 Evaluating Human-Agent Communication Using SAT  
In this study, we aim to use SAT concepts as a tool to evaluate agent verbal communication and its ability to empower human-agent collaboration toward achieving a common task. Each act of an agent's message (locutionary, illocutionary, and perlocutionary) should be evaluated individually to reach a final analysis of the agent's overall communication. Each act will be evaluated as follows:  
Locutionary acts: equivalent to uttering a certain sentence with building structures. A locutionary act could be verified by asking the interlocutor his/her impression about the structure of the utterances. The interlocutor could be asked whether the sentences are clear, easy to understand, and natural. Another way to evaluate the locutionary act is to ask a third person to review and evaluate the form of the sentences.  
Illocutionary acts: the intention the speaker meant from his/her locutionary act. The illocutionary force could be informing, ordering, requesting, warning, undertaking, etc. The aim of evaluating this act is to determine whether the illocutionary force is clear to the interlocutor. The illocutionary act could be verified by considering an appropriate ratio of using different classes of illocutionary act so that the conversation will have various tones and surveying whether the interlocutor perceives the intention behind the locutionary acts uttered by the speaker.  
Perlocutionary acts: what is achieved by saying something, such as convincing, persuading, deterring, surprising, or misleading. Evaluating both the locutionary act and the illocutionary act requires surveying the interlocutor's impression and opinion about the form of the utterances and the meaning behind it. Evaluating the perlocutionary act should go beyond asking the interlocutor to investigate the actual effect of the locutionary act as demonstrated from the behaviour performed (see Table 1). To track the behaviour of the participants in a virtual world, actions should be carefully logged as part of the design and implementation. The next sections will describe the materials used for implementation, the scenario designed, the procedure followed in the study, and the data collected.
## 3 Simulation Results of Unit Cell and Theory Analysis of LCPC
 \tag{12}\]  
Therefore, based on Equation (12), the following equation can be demonstrated [31]:  
\[\frac{r_{xx}}{r_{yx}}=\frac{r_{yy}}{r_{xy}}=\frac{1+e^{-j\Delta\varphi_{uv}}}{ 1-e^{-j\Delta\varphi_{uv}}}=\frac{1+cos(\Delta\varphi_{uv})-j\dot{sin}(\Delta \varphi_{uv})}{1-cos(\Delta\varphi_{uv})+j\dot{sin}(\Delta\varphi_{uv})}= \frac{j\dot{sin}(\Delta\varphi_{uv})}{1-cos(\Delta\varphi_{uv})} \tag{13}\]  
Equation (13) depicts that the ratio between the co- and cross-polarized reflection coefficients is a purely imaginary number for both \(x\)- and \(y\)-polarized incidences. This implies that the absolute value of the phase difference between them will always be equal to \(90^{\circ}\), which helps explain why the absolute value of the phase difference \(\Delta\varphi_{xy}\), as shown in Figure 3b, the absolute value remains approximately \(90^{\circ}\) within certain frequency bands of the spectrum. The proposed unit cell was simulated under illumination by a \(45^{\circ}\) linearly polarized (LP) wave, as illustrated in Figure 7a,b. For \(\varepsilon_{r}=2.8\), the reflection coefficients \(r_{uu}\) and \(r_{vv}\) exhibit near-equality (\(r_{uu}\approx r_{vv}\)) within two frequency bands: 8.15-8.5 GHz and 9.84-12.54 GHz, with magnitudes exceeding 0.96.Additionally, the phase difference \(\Delta\varphi_{uv}=\pm 90^{\circ}\) with a deviation of \(\pm 10^{\circ}\). This provides additional evidence supporting the mechanism of LCPC. Figure 6: \(e_{r}=2.8\), incident wave \(\varphi=90^{\circ}\), \(\theta=0^{\circ}\), (**a**) surface current distribution in the MS unit cell at 8.24 GHz, and (**b**) 11 GHz, both with different resonance phases varying from \(0^{\circ}\) to \(270^{\circ}\).