esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract
The efficacy of the proposed algorithm is evaluated using a sample of 1000 dialogue turns, randomly selected and annotated from the DailyDialog dataset, covering up to 18 distinct topics and 102 subtopics (Table 1). Furthermore, we tested this approach in real human-chatbot conversations obtained during our participation in the Alexa Prize Socialbot Challenge (SGC5) competition ([[https://www.amazon.science/alexa-prize/socialbot-grand-challenge/2022](https://www.amazon.science/alexa-prize/socialbot-grand-challenge/2022)]([https://www.amazon.science/alexa-prize/socialbot-grand-challenge/2022](https://www.amazon.science/alexa-prize/socialbot-grand-challenge/2022)) (accessed on 14 July 2023)). Second, we introduce an automated algorithm designed to identify inconsistencies or different semantic variabilities in chatbot responses. This algorithm uses clustering strategies to identify multiple semantic variations in chatbot responses to similar users' questions, therefore detecting the cases where the provided answers exhibit disparities (inconsistent responses). \begin{table}
\begin{tabular}{r l} \hline \hline  & \multicolumn{2}{c}{**Proposed Topics List**} \\ \hline
**Topics** & **Subtopics** \\ \hline animals & cats, dogs, pets. \\ art & ballet, cinema, museum, painting, theater. \\ books & author, genre, Harry Potter, plot, title. \\ education & history, mark, professor, school, subject, university. \\ family & parents, friends, relatives, marriage, children. \\ fashion & catwalk, clothes, design, dress, footwear, jewel, model. \\ finance & benefits, bitcoins, buy, finances, investment, sell, stock market, taxes. \\ food & drinks, fish, healthy, meal, meat, vegetables, dessert. \\ movies & actor, director, genre, plot, synopsis, title. \\ music & band, dance, genre, lyrics, rhythm, singer, song. \\ news & exclusive, fake news, interview, press, trending. \\ photography & camera, lens, light, optics, zoom. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: List of proposed topics to predict. The structure of this paper is as follows. Section 2 presents the State-of-the-Art on inconsistent responses and zero-shot classification. Section 3 delves into the comprehensive description of the proposed algorithms. Section 4 provides an elaborate account of the dataset used for the task of determining topics and subtopics, along with the process involved in designing the dataset used for the automatic detection of inconsistencies. Then, Section 5 describes the results obtained for both algorithms. Finally, Section 6 concludes this article by offering key insights and presenting avenues for future research.
# An Efficient Algorithm to Determine Probabilistic Bisimulation
## 3 A Partition Refinement Algorithm for Probabilistic Bisimulation (Outline)
### An Example
Block \(S_{3}\) can be ignored. For \(S_{4}\), both \(s_{1}\) and \(s_{2}\) have an \(a\)-transition into \(U_{2}\) as their only transition. Hence, block \(S_{4}\) is stable. Similarly, \(S_{5}\) is stable, as its states \(s_{3}\) and \(s_{4}\) both have an  
\begin{table}
\begin{tabular}{l l} \hline \hline Blocks of Actions States & Blocks of Probabilistic States \\ \hline \(S_{0}=\{t_{1},t_{3},t_{4},t_{6},t_{7},r_{1-5}\}\) & \(U_{0}=\{u_{1-6},v_{1-5}\}\) \\ \(S_{1}=\{s_{1-4}\}\) & \(U_{1}=\{u_{1},u_{3},v_{1-5}\}\) \\ \(S_{2}=\{t_{2},t_{5},t_{8},t_{9}\}\) & \(U_{2}=\{u_{2},u_{4}\}\) \\ \(S_{3}=\{t_{10}\}\) & \(U_{3}=\{u_{5},u_{6}\}\) \\ \(S_{4}=\{s_{1},s_{2}\}\) & \(U_{4}=\{u_{5}\}\) \\ \(S_{5}=\{s\(a\)-transition into \(U_{4}\cup U_{5}\) and no other transitions. Overall, in this iteration, no blocks require splitting to restore Invariant 3. Next, at Iteration 6, we split non-trivial constellation \(U_{4}\cup U_{5}\) into \(U_{4}\) and \(U_{5}\). For \(S_{0}\), \(S_{2}\), \(S_{3}\) and \(S_{4}\) we conclude stability in the same way as in the previous iteration. However, now we have for \(s_{3},s_{4}\in S_{5}\) on the one hand \(s_{3}\overset{a}{\rightarrow}U_{4}\) and \(s_{3}\overset{a}{\rightarrow}U_{5}\), but on the other hand \(s_{4}\overset{a}{\rightarrow}U_{4}\) and \(s_{4}\overset{a}{\rightarrow}U_{5}\). Hence, \(S_{5}\) needs to be split, yielding the singletons \(S_{6}=\{s_{3}\}\) and \(S_{7}=\{s_{4}\}\). Returning to constellations of actions states, at Iteration 7, we split \(S_{4}\cup S_{6}\cup S_{7}\) over \(S_{4}\) and \(S_{6}\cup S_{7}\). All probabilistic states have value 0 for both \(S_{4}\) and \(S_{6}\cup S_{7}\), hence no split of probabilistic blocks is needed. This is similar in Iteration 8, where the non-trivial constellation \(S_{6}\cup S_{7}\) is split, and none of the blocks become unstable. Now, all constellations are trivial and the algorithm terminates. According to the Stability Property, Lemma 3, the corresponding equivalence relation is a probabilistic bisimulation. Thus, the final partition is \(\{S_{0},S_{2},S_{3},S_{4},S_{6},S_{7},U_{1},\ U_{2},\ U_{4},\ U_{5}\}\). Moreover, the deadlock states \(t_{1},t_{3},t_{4},t_{6},t_{7}\) and \(r_{1}\) to \(r_{5}\) are probabilistically bisimilar, the states \(t_{2},t_{5},t_{8},t_{9}\) that have only a \(b\)-transition into a Dirac distribution to deadlock are probabilistically bisimilar, the states \(s_{1}\) and \(s_{2}\) are probabilistically bisimilar (which is clear when identifying states \(t_{7}\) and \(t_{8}\)), whereas the remaining action states \(s_{3},s_{4}\) and \(t_{10}\) have no probabilistically bisimilar counterpart. For the probabilistic states the states \(u_{1}\), \(u_{3}\) and \(v_{1}\) to \(v_{5}\) are identified by probabilistic bisimulation. This also holds for the probabilistic states \(u_{2}\) and \(u_{4}\). Probabilistic states \(u_{5}\) and \(u_{6}\) each have no probabilistically bisimilar counterpart.
## 4 Hybrid Control Based on Extension Theory
### Upper Extension Controller Design
#### 4.3.2 Correlation Degree Calculation  
The characteristic plane \(e_{1}Oe_{2}\) of extension set \(E(T)\) describes the state of the suspension system. The origin of characteristic plane \(O\) (0, 0) is supposed as the expected state of the suspension system, which desired body acceleration value and desired tire dynamic deformation value is 0.  
As illustrated in Figure 8, two-norm operator \(|OS|\) depicts the proximity to the expected state point as  
\[|OS|=\sqrt{k_{1}e_{x}^{2}+k_{2}e_{y}^{2}} \tag{29}\]  
where \(k_{1}\) and \(k_{2}\) are the influence factors. When \(k_{1}<k_{2}\), the extension set is more sensitive to the change of tire dynamic deformation. When \(k_{1}>k_{2}\), the extension set is more sensitive to the change of body acceleration.  
Where the classical domain boundary is \(A_{0}\), the extensive domain boundary is \(A_{1}\). \(A_{0}\) and \(A_{1}\) are expressed as  
\[A_{0}=\sqrt{e_{10\lim}^{2}+e_{20\lim}^{2}} \tag{30}\]  
\[A_{1}=\sqrt{e_{11\lim}^{2}+e_{21\lim}^{2}} \tag{31}\]  
In the light of characteristic plane \(e_{1}Oe_{2}\), the correlation function is defined as [39]  
\[k(S)=\left\{\begin{array}{ll}1-\frac{|OS|}{A_{0}}&0\leq|OS|<A_{0}\\ \frac{(A_{0}-|OS|)}{(A_{1}-A_{0})}&A_{0}\leq|OS|\end{array}\right. \tag{32}\]  
The correlation degree of the extensive set is calculated by the correlation function \(k(S)\), which represents the effect on ride comfort and driving safety performance. Different modes are divided based on correlation degree, corresponding to different parts of the extension set.
# A 1D-2D Coupled Lattice Boltzmann Model for Shallow Water Flows in Large Scale River-Lake Systems
## 4 Simulation of Surge Waves in the Reservoir of a Run-of-River Hydropower Station
### Simulation Results  
The steady flow simulation was conducted at first, and its results were used as the initial conditions for the transient simulation. The transient process simulation was caused by the load rejection of all units in two powerhouses simultaneously, and the discharge decreased linearly to zero within 40 s. The water depth and velocity at six typical moments are shown in Figure 9.  
After the load rejection, a pair of surge waves generated from two powerhouses and propagated upstream (\(t\) = 30 s). Then, the surge wave propagated back and forth between Dike 1 and Dike 2, causing the water depths near two dikes to fluctuate alternately. This phenomenon was particularly obvious in the preliminary stages of the transient simulation (\(t\) = 90 s and \(t\) = 120 s). At about \(t\) = 120 s, the surge waves reached the estuary of Branch 1 and Branch 2 for the first time, respectively. Meanwhile, it also reached the front of Channel 1, which caused an obvious increase in water depth (\(t\) = 120 s and \(t\) = 180 s). The surge wave kept propagating; at about \(t\) = 210 s, it reached the coupling interface. At about \(t\) = 420 s, it arrived at the end of Branch 1.  
Figure 8: Schematic of the reservoir region of the computational domain.  
Figure 9: Surge wave propagation and reflection in the reservoir after load rejection: water depth (**left column**) and velocity (**right column**).  
To analyze the fluctuation of water depth at each important building of the hydraulic project, Figure 10 shows the histories of water depth at some monitoring points. Many information could be extracted from the monitoring data, such as the first moment affected by the surge waves, the maximum and the minimum water depths, and the attenuation duration of the surge waves. Take point A, for example, the maximum and the minimum water depths were 68.77 m and 67.49 m, which occurred around 0.85 and 2 h, respectively. The time for the surge wave to attenuate completely was about 5 h.  
To study the propagation and reflection rules of the surge waves, we enlarged the histories of water depth at the inlet of the two powerhouses. As shown in Figure 11, the surge waves were composed of the low-frequency fundamental wave and high-frequency wave. The former was caused by the propagation and reflection of surge waves between two powerhouses and upstream boundaries, and the latter was caused by the propagation and reflection of surge waves between dikes and banks. The riverbed resistance attenuated these fluctuations gradually during the propagation process, which took about 1 h and 5 h to calm down the high-frequency wave and the low-frequency fundamental wave, respectively.  
Figure 11: Comparison of water depth at the inlets of Powerhouse 1 and Powerhouse 2.  
Figure 10: Water depth histories of some monitoring points.
# Predictive Path Following and Collision Avoidance of Autonomous Connected Vehicles
## 3 Controller Synthesis
### Collision Avoidance  
In order to account for collision avoidance of nearby vehicles, an NMPC is employed, due to its ability to handle time-varying nonlinear constraints by adding coupling constraints to the vehicle optimization problem with its neighbors to guarantee that the solution of the OCP provides a collision-free trajectory. The vehicles are assumed to be connected and they are exchanging information about their future predictions.  
Consider \(N_{o}\) encountered vehicles indexed by a superscript \([i]\), for instance, the state vector of each neighbor is denoted by \(\mathbf{x}^{[i]}\). Each of them is represented geometrically by an elliptic disk (see Figure 3) which is parameterized by the center position \((x,y)\), orientation (heading angle) \(\psi\), semi-major radius \(r_{1}\), and semi-minor radius \(r_{2}\). The elliptic disk is the closed type of conic section results from the intersection of a cone by a plane, and is expressed as  
\[\tilde{\mathcal{A}}\equiv\{X\ |\ X^{T}\mathcal{A}X\leq 0\}, \tag{13}\]  
where \(X=[x\ y\ 1]^{T}\) is the 3-D column vector containing the homogeneous coordinates,  
\[\mathcal{A}=\begin{bmatrix}A&-B&By_{p}-Ax_{p}\\ -B&C&Bx_{p}-Cy_{p}\\ By_{p}-Ax_{p}&Bx_{p}-Cy_{p}&Ax_{p}^{2}-2 Bx_{p}y_{p}+Cy_{p}^{2}-1\end{bmatrix},\]  
\(A=\left(\frac{\cos(\psi)^{2}}{r_{1}^{2}}+\frac{\sin(\psi)^{2}}{r_{2}^{2}}\right)\), \(B=\frac{\sin(2\psi)}{2}\left(\frac{1}{r_{1}^{2}}-\frac{1}{r_{2}^{2}}\right)\), and \(C=\left(\frac{\sin(\psi)^{2}}{r_{1}^{2}}+\frac{\cos(\psi)^{2}}{r_{2}^{2}}\right)\).  
Based on that, the collision avoidance is formulated in terms of coupling state constraints that represent the separation condition between the moving elliptic disk of the vehicle \(\bar{\mathcal{A}}\) and the neighbor vehicle \(\bar{\mathcal{A}}^{[i]}\). In the following, we recall a proposition that formulates the collision avoidance constraints.  
**Proposition 1**.: _Consider a pair of moving elliptic disks \(\{\bar{\mathcal{A}}(t),\bar{\mathcal{B}}^{[i]}(t)\}\) in the Euclidean plane \(\mathbf{E}^{2}\) represented by the matrices \(\mathcal{A}\) and \(\mathcal{B}^{[i]}(t)\), respectively. Let \(\mathcal{P}^{[i]}(\lambda,t)=\mathrm{C}_{3}^{[i]}(t)\lambda^{3}+\mathrm{C}_{2 }^{[i]}(t)\lambda^{2}+\mathrm{C}_{1}^{[i]}(t)\lambda+\mathrm{C}_{0}^{[i]}(t)\) be their characteristic polynomial and \(\Delta^{[i]}(t)\) denote the discriminant of \(\mathcal{P}^{[i]}(\lambda,t)\) with respect to \(\lambda\). Suppose that every pair \(\{\bar{\mathcal{A}}(T_{0}),\bar{\mathcal{B}}^{[i]}(T_{0})\}\) is separate, then the motions of them are collision-free for all \(t\in[t_{k},t_{k}+T_{p}]\) if_  
1. \(\mathrm{C}_{2}^{[i]}(t)<0\)__
2. \(\Delta^{[i]}(t)>0\)_,_ \(\forall t\in[t_{k},t_{k}+T_{p}]\)_._  
Proof.: See [17].
## 4 Results and Analysis
### Horizontal Earth Pressure  
The variation in the horizontal earth pressure as a function of the height over time is represented in Figure 9. During the construction of the reinforced backfill, the pressure increases with the weight of the soil, which is the largest at the second layer. Even after the completion of the reinforced backfill, the horizontal earth pressure increases moderately without any increase in the overburden load. The increase in earth pressure generated during the backfill stabilization process was similar to that of the RSR using sand soil as a backfill [23]; however, this value was considerably smaller for the backfill using cement-treated gravel.  
Figure 7: Settlements of RSR over time.  
Upon the completion of the rigid facing wall, the horizontal earth pressure tends to increase at the 2 nd layer, whereas the horizontal earth pressure tends to decrease at the 10 th and 16 th layers. Notably, the RSR supports the concrete pouring pressure by connecting the net of welded rebars to the formwork. Considering this fact, the decrease in the horizontal earth pressure presumably occurred because the concrete pouring pressure applied to the net of welded rebars resulted in the horizontal displacement of the reinforced backfill.  
The horizontal earth pressure at the 16 th layer increased immediately after the completion of the rigid facing wall. Presumably, these effects, owing to rainfall and the overburden pressure exerted by the reinforced roadbed and track on the 16 th layer, were prominent  
Figure 8: Net settlement of reinforced backfill and rainfall over time.  
Figure 9: Variation of the horizontal earth pressure over time.  
because of this layer's proximity to the surface. In the interim between the construction of the rigid facing wall and the reinforced roadbed, the horizontal earth pressure increases to 5.1 kPa in the 2 nd layer, 2.8 kPa in the 10 th layer, and 21.7 kPa in the 16 th layer, which was 4.2-7.8 times larger than the pressures of other layers.  
For the period between the completion of the reinforced backfill and the initiation of the rigid face wall, the variation in the distribution of the horizontal earth pressure with respect to the height is plotted in Figure 10. The horizontal earth pressure in the second layer (1.0 m) increases from 41% to 54% of the Rankine's active earth pressure considering the friction angle of 35\({}^{\circ}\) used in an existing design. Moreover, it increases from 45% to 63% and from 87% to 148% of the Rankine's active earth pressure in the 10 th layer (4.2 m) and 16 th layer (6.6 m), respectively. Except for the 16 th layer, which is heavily influenced by the external environment, such as construction works, the horizontal earth pressure in all layers is similar to that of granular soil with a friction angle of 45-50\({}^{\circ}\). This entails an increase in the shear strength of the backfill in the RSR using cement-treated gravel. Notably, the higher the shear strength, the higher the stiffness encountered in the soil will be [28; 29], and, accordingly, the low compressibility of the RSR can be confirmed indirectly.
## 3 Results
### Orangina Fruit Juice Preservation  
#### 3.4.1 Effect of Varying Dose of EGEO  
As EGEO was active to inhibit different food-borne spoilage bacteria and fungi in _in vitro_ methods, its effect in a fruit juice matrix (Orangina juice) was also studied (Figure 2). The reduction in viability of _Saccharomyces cerevisiae_ due to EGEO use in a concentration-dependent way (0.8, 2 and 4 \(\upmu\)L/mL) and a time-dependent manner (i.e., 0, 1, 2, and 6 days) was evaluated. Complete growth inhibition was observed in Orangina fruit juice only when a high concentration of EGEO (4 \(\upmu\)L/mL) was used. However, the doses of 2 and 4 \(\upmu\)L/mL did not show an important decrease in the final amount of yeasts (2.8 log CFU/mL and 2 log CFU/mL, respectively) in comparison to untreated Orangina juice (2.3 log CFU/mL).
## 2 Materials and Methods
### Study Design  
The data collected during preliminary testing were utilized to design training sets with the following characteristics: (a) Set LT consisted of eight repetitions of 200 m (8 \(\times\) 200 m) performed at an intensity corresponding to the speed at LT, with 30 s recovery intervals between repetitions [23]; (b) set MAS comprised eight repetitions of 100 m (8 \(\times\) 100 m) at an intensity corresponding to MAS with 30 s recovery periods [24]; and (c) set SPR included eight repetitions of 25 m (8 \(\times\) 25 m) sprint swimming (maximum effort), with 2 min recovery intervals, corresponding to a, i.e., 1:8 swimming-to-recovery ratio [25]. The LT and MAS intensities were chosen, since they represent the boundaries between different exercise intensity domains frequently used in swimming training [26], while the swimming distance in each training set was used based on previous reports proposing a training volume of about 1600-2000 m in heavy intensity (e.g., LT) and 800-1000 m in severe intensity (e.g., MAS) to improve aerobic endurance [1]. The rest intervals were short enough (i.e., 30 s) to maintain increased aerobic contribution in the LT and MAS sets and long enough (i.e., 2 min) to allow adequate PCr resynthesis in the SPR set [27; 28].  
In a crossover design, four training sessions performed in a randomized order, incorporating the aforementioned training sets: (i) set LT (session LT), (ii) set SPR, followed by set LT (session SPR-LT), (iii) set MAS (session MAS), and (iv) set SPR, followed by set MAS (session SPR-MAS). A 10 min passive recovery period was implemented between sets in sessions SPR-LT and SPR-MAS, allowing adequate but not complete recovery to ensure ecological validity of real training conditions. The design of the study is illustrated in Figure 1.
## 2 Related Work  
The development of educational dialogue classification in low-resource contexts shares similarities with the evolution of relation extraction (RE) [11]. Early approaches in classification tasks primarily relied on rule-based systems [12, 13]. While these methods were foundational, they lacked scalability and were heavily dependent on manual efforts, making them unsuitable for large-scale or dynamic educational datasets [14]. Subsequently, machine learning methods introduced greater flexibility, yet they still required substantial amounts of annotated data, which remains a significant challenge in low-resource educational contexts [15]. Deep learning brought advancements by capturing intricate patternsin classroom interactions, but these models continued to depend on large datasets, limiting their applicability in education.  
Distant supervision has been widely explored to alleviate data scarcity by aligning unannotated text with predefined knowledge bases [16]. While this method has successfully generated labeled data, it often introduces noise due to oversimplified assumptions, such as rigid text-label alignment. In the educational domain, this manifests as misaligned dialogue annotations, which compromise model accuracy. Recent advances in few-shot learning offer promising alternatives for addressing data scarcity. [PERSON] et al. [17] demonstrated that linguistic cues could enhance few-shot methods, compensating for limited annotated data. However, these methods often struggle to generalize across diverse educational scenarios due to their reliance on narrow training data [18].  
The emergence of LLMs has significantly impacted low-resource tasks, including educational dialogue classification. Studies such as those by [PERSON] et al. [19] and [PERSON] et al. [20] have shown the ability of LLMs to handle complex linguistic structures with minimal fine-tuning. Despite their strengths, LLMs face limitations in domain-specific contexts, such as nuanced educational settings where deep pedagogical knowledge is required [21]. Nonetheless, LLM-driven approaches to labeling unlabeled text highlight their ability to extract meaningful patterns and assign accurate annotations, which is essential for educational dialogue analysis.  
To address these challenges, our EduDCM framework leverages the strengths of LLMs while introducing heuristic techniques for enhanced label alignment and semantic disambiguation. By mitigating the noise typically associated with distant supervision and improving context-aware label generation, EduDCM establishes a reliable foundation for constructing high-quality educational dialogue classification datasets.