esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Tensile Behaviour of Fibres  
This section presents the results of the behaviour and tensile strength of the stems (Figure 6C). Through analysis of these results, it was possible to divide the stems into two distinct groups: medium yielding fibres and low yielding fibres. Tomato, pepper, cucumber and aubergine stems demonstrated an average tensile strength above 10 MPa, while zucchini stems exhibited an average tensile strength of 3.29 MPa. Conversely, cucumber stems exhibited the highest tensile strength, reaching 19.83 MPa. These values could be compared with other reference studies (Table 5) in which oil palm, tomato and Alfa fibres reached values in this range [57; 58; 59; 60]. It is also noteworthy that the tensile strength achieved in this study, as shown in Table 5, was significantly lower than that of high-performance fibres produced for specific industrial applications, such as jute and flax [58; 61; 62]. It is also important to note that these high-performance fibres do not support the circular economy, in contrast to fibres derived from by-products of intensive agriculture. A range of variables that may impact the mechanical properties of fibres was analysed by other  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Residue Type** & **Water Absorption of the Fibres (\%)** &
\begin{tabular}{c} **Weight Loss Due to** \\ **Degradation (\%)** \\ \end{tabular} \\ \hline Tomato & 197.42 \(\pm\) 1.57 & 32.52 \(\pm\) 0.04 \\ Pepper & 246.68 \(\pm\) 2.20 & 25.68 \(\pm\) 0.03 \\ Zucchini & 268.57 \(\pm\) 2.03 & 36.77 \(\pm\) 0.04 \\ Cucumber & 266.31 \(\pm\) 1.88 & 33.73 \(\pm\) 0.05 \\ Aubergine & 262.78 \(\pm\) 2.19 & 23.64 \(\pm\) 0.02 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Details of stem diameters after drying process.  
researchers. These include chemical composition, structural characteristics, growing conditions, extraction methods, harvesting timing and storage procedures [63]. In this regard, the studies from [PERSON] et al. (2002) [64] demonstrated a decline in strength within five days post-optimal harvest.  
It is important to highlight the linear tendency exhibited in the tensile strength-displacement graph (Figure 6C). As illustrated in Figure 6C, in the case of aubergine and tomato stems, the maximum resistance was reached quickly and with minimal displacement, which is less than 0.60 mm. In contrast, the behaviour exhibited by pepper and zucchini stems was more ductile, with maximum resistance being attained at displacements ranging from 1.41 mm to 2.11 mm, respectively. The displacement of cucumber was situated at an intermediate value of 1.02 mm when reaching its maximum value of tensile strength. A notable observation was the less linear behaviour exhibited by the cucumber stem graph, characterised by less continuous increases in tension and minor fluctuations.
# A Robust Image Watermarking Scheme Based on SVD in the Spatial Domain
## 5 Experimental Results and Performance Analysis
### Imperceptibility Test  
To further evaluate watermark imperceptibility, another evaluation metric, known as the peak signal-to-noise ratio (PSNR), is applied in this paper. For an 8-bit image with a size of \(M\times M\), the PSNR can be defined as:  
\[\text{PSNR}=10\log_{10}\frac{255^{2}\times M\times M}{\sum\limits_{i=1}^{M} \sum\limits_{j=1}^{M}\left[\mathbf{I}(i,j)-\mathbf{I}_{\text{w}}(i,j)\right]^{2}}\ (\text{dB}). \tag{18}\]  
In general, a higher PSNR value implies that the watermarked image has better visual quality. It also indirectly demonstrates that the watermarking scheme achieves better watermark invisibility. Figure 5 shows the watermarked images obtained by the proposed scheme. It can be observed that there is almost no distinction between the watermarked images and the original host images subjectively. Table 2 lists the PSNR and SSIM values of the corresponding watermarked images. As shown in Table 2, the PSNR values of the watermarked images are around 49 dB, and the SSIM values are more than 0.99, which further proves the good watermark invisibility of the proposed watermarking scheme.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Quantization Step** & \(\mathbf{\Delta=1}\) & \(\mathbf{\Delta=2}\) & \(\mathbf{\Delta=4}\) & \(\mathbf{\Delta=8}\) & \(\mathbf{\Delta=16}\) & \(\mathbf{\Delta=32}\) & \(\mathbf{\Delta=64}\) \\ \hline \multirow{2}{*}{Lena} & SSIM & 1 & 1 & 0.9991 & 0.9974 & 0.9917 & 0.9707 & 0.9226 \\  & NCC & 0.5014 & 0.4841 & 1 & 1 & 1 & 1 & 1 \\ \hline \multirow{2}{*}{Barbara} & SSIM & 1 & 1 & 0.9994 & 0.9983 & 0.9941 & 0.9793 & 0.9422 \\  & NCC & 0.4883 & 0.5021 & 1 & 1 & 1 & 1 & 1 \\ \hline \multirow{2}{*}{[PERSON]} & SSIM & 1 & 1 & 0.9996 & 0.9987 & 0.9957 & 0.9844 & 0.9532 \\  & NCC & 0.4910 & 0.5193 & 1 & 1 & 1 & 1 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Structural similarity (SSIM) index and normalized correlation coefficient (NCC) values of the proposed scheme under different quantization steps.
# Metabolic Reaction Network-Based Model Predictive Control of Bioprocesses
## 3 Results and Discussion
### Numerical Results
#### 3.2.3 Influence of Horizon Length  
Firstly, the influence of the horizon length on the tracking performance is studied by selecting the same horizon for estimation and prediction (control): \(H=H_{e}=H_{p}\). Four different horizons have been simulated, assuming that a sample is taken every hour: \(H=2\) h, \(H=4\) h, \(H=12\) h, \(H=24\) h. The absolute values of the absolute tracking error for all states are depicted in Figure 9.  
Globally, it can be observed that the MPC control performance obtained with the true flux model and the linear flux model are more similar for a small horizon length, i.e., \(H=1\) h. However, no clear global trends can be observed for the increase of the horizon length. As both the estimation and prediction horizon have been modified, it is difficult to interpret these graphs. Therefore the effect of only changing the prediction horizon or solely changing the estimation horizon is addressed in the next paragraphs.  
Figure 8: MPC control actions obtained with the different flux models.  
Figure 9: _Cont._  
Influence of Only Changing the Prediction Horizon  
In this paragraph the estimation horizon is set to \(H_{e}=4\) h and the prediction horizon has been varied as in the previous paragraph. Four different horizons have been simulated, assuming that a sample is taken every hour: \(H_{p}=2\) h, \(H_{p}=4\) h (4 h), \(H_{p}=12\) h, \(H_{p}=24\) h. In Figure 10, the absolute values of the absolute tracking error are displayed.  
Figure 10: Absolute values of the absolute tracking error in case of \(H_{e}=4\) h for the four states, i.e., the extracellular metabolite concentrations.  
Figure 9: Absolute values of the absolute tracking error in case of \(H=H_{e}=H_{p}\) for the four states, i.e., the extracellular metabolite concentrations.  
Increasing the prediction horizon results in a slight reduction of the tracking error of all states using the true flux model. For the other flux models, such clear observations cannot be made.  
Influence of Only Changing the Estimation Horizon  
Contrary to the previous paragraph, the estimation horizon is varied and the prediction horizon is kept constant at 4 h. Following estimation horizons have been studied for a sampling rate of 1 h: \(H_{e}=2\) h, \(H_{e}=4\)h (4 h), \(H_{e}=12\) h, \(H_{e}=24\) h. In Figure 11, the absolute values of the absolute tracking error are displayed.  
The estimation horizon has a small effect on the MPC tracking performance, except for the linear flux model, in which the estimation horizon of 24 h clearly results in a higher on the MPC tracking error for all extracellular metabolites except \(\mathrm{F_{ext}}\).
# Can Gestural Filler Reduce User-Perceived Latency in Conversation with Digital Humans?
## 2 Related Work
### Reducing Perceived Latency in Conversation  
Latency in conversation refers to the gap between the question and the answer. _Delay_ is often used interchangeably with latency. Research has been conducted on the effect of latency on user perception in human-agent and human-robot interactions. [PERSON] et al. [49]reported that the frustration and anger of the user increased when a time delay occurred during a task-solving situation with robots. Similarly, the time delay reduced the users' satisfaction and willingness to use the system in the future [34]. Furthermore, researchers compared the effect of time delay (0, 1, 2, and 3 s) in human-robot interaction [36] and found that users considered a delay of 1 s more natural than no delay.  
Attempts to mitigate the negative effects of time delay in conversation with digital humans have been continually conducted. The researchers in [39] studied the conversational fillers (CFs) of a robot controlled with the Wizard of Oz method and compared non-CF and CF conditions in open-ended conversation. The users evaluated robots with CF conditions as more alive, human-like, and likable than robots with non-CF conditions. Furthermore, lexical CFs such as \"Let me think\" were rated higher than non-lexical CFs such as \"Hmm.\" or \"Aha.\". The researchers in [41] compared uncontextualized and contextualized fillers. With uncontextualized fillers, the digital human says, \"Hold on a minute,\" regardless of the context. With contextualized fillers, the digital human recognizes and reflects on the user's questions to determine what he or she says. For example, if the user asked about the amount of an ingredient, the digital human would say, \"Let's see how much you need\". In the results, contextualized fillers could mitigate the user-perceived latency more effectively than uncontextualized fillers. Researchers also examined a filler that accompanied both verbal and gestures [50]. In the study, participants watched videos of two digital humans having conversations, in which momentary silents were included. In one condition, digital humans said \"Ummm\" and touched their own chin during the silence, while exhibiting nothing in another condition. The participants evaluated the silences as less embarrassing when the filler was used.
## 3 Results and Discussion
### Workability and Mechanical Properties of Fiber-Reinforced SCC at Ambient Temperature  
In order to maintain the same water-to-binder ratio and an acceptable slump flow for SCC (slump flow of 60 cm), a larger volume of superplasticizer is used for the fiber-reinforced SCC, especially for mixtures containing PF, as illustrated in Table 1. Table 3 presents the workability test results of the fresh fiber-reinforced SCC and the mechanical properties of the hardened fiber-reinforced SCC at room temperature. Moreover, Figure 6 illustrates pictures of the workability test for fresh SCC. Table 3 and Figure 6 show that all parameters related to workability satisfy the SCC's requirements in accordance with EFNARC [49].  
Table 3 shows that the fiber amounts utilized in the present study affect the compressive strength and flexural tensile strength. On the one hand, brittle concrete can be converted into a ductile material with the incorporation of different fibers. The inclusion of SF can slightly enhance the compressive strength of concrete at room temperature. In comparison to reference specimens, the compressive strength increases only by 5% after adding 50 kg/m\({}^{3}\) SF in the concrete matrix, and no significant effect is observed with the combination of SF and PF. However, adding a single PF exhibits a negative effect on the compressive strength of SCC. On the other hand, incorporating SF can affect flexural tensile strength significantly. In comparison to plain SCC, adding 20 kg/m\({}^{3}\), 40 kg/m\({}^{3}\), and 50 kg/m\({}^{3}\) SF to the concrete can increase the flexural tensile strength by 33%, 47%, and 42%, respectively. This is because the fibers' contribution to the flexural tensile strength is dominated by the number of fibers crossing the crack rather than their weight. It should be indicated that the advantageous effect of SF on the tensile strength of the concrete is due to the sum of the tensile strength brought about by each fiber [50], while for specimens with PF, no significant impact on the flexural tensile strength can be observed.
# Emotional Dynamics in Social Interactions: A Multi-Strategy Synergy Model
## 3 Stability Analysis
### Local Asymptotic Stability Analysis  
**Lemma 1**.: _For any \(\beta,\kappa,\gamma>0\), the quadratic equation_  
\[\beta\kappa E^{2}-(2\beta\kappa+\gamma)E+\beta\kappa=0 \tag{6}\]  
_has two real roots, where the smaller root \(E_{-}^{*}\in(0,1)\) and the larger root \(E_{+}^{*}>1\)._  
Proof.: We calculate the discriminant:  
\[\Delta=(2\beta\kappa+\gamma)^{2}-4(\beta\kappa)^{2}=\gamma^{2}+4\beta\kappa \gamma>0\quad(\because\beta,\kappa,\gamma>0).\]  
Thus, the Equation (6) has two real roots:  
\[E^{*}=\frac{(2\beta\kappa+\gamma)\pm\sqrt{\Delta}}{2\beta\kappa} \tag{7}\]  
Next, we analyze these two roots separately:  
* Larger root \(E_{+}^{*}\): From the inequality \[\sqrt{\gamma(\gamma+4\beta\kappa)}>\gamma\quad\Rightarrow\quad E_{+}^{*}= \frac{(2\beta\kappa+\gamma)+\sqrt{\Delta}}{2\beta\kappa}>1\] which exceeds the physically valid range and is therefore discarded.
* Smaller root \(E_{-}^{*}\): From the inequality chain \[\sqrt{\gamma(\gamma+4\beta\kappa)} =\sqrt{(\gamma+2\beta\kappa)^{2}-(2\beta\kappa)^{2}}\] \[<\gamma+2\beta\kappa\quad(\text{always true for a difference of squares})\] we obtain \[E_{-}^{*}=\frac{(2\beta\kappa+\gamma)-\sqrt{\Delta}}{2\beta\kappa}>\frac{(2 \beta\kappa+\gamma)-(\gamma+2\beta\kappa)}{2\beta\kappa}=0\] In addition, \[E_{-}^{*}=\frac{2\beta\kappa+\gamma-\sqrt{\Delta}}{2\beta\kappa}<\frac{2 \beta\kappa+\gamma-\gamma}{2\beta\kappa}=1\] Thus, \(E_{-}^{*}\in(0,1)\).  
Next, we present the theorem for local asymptotic stability.  
**Theorem 1**.: _When all control strategies fail (\(\mu=\
u=\xi=0\)) and the parameters satisfy \(\beta,\kappa,\gamma>0\), the system (5) has a unique physically valid equilibrium point:_  
\[(E^{*},\,G^{*},\,S^{*})=\left(\frac{(2\beta\kappa+\gamma)-\sqrt{\gamma(\gamma+ 4\beta\kappa)}}{2\beta\kappa},\,\kappa(1-E^{*}),\,0\right) \tag{8}\]  
_with \(E^{*}\in(0,1)\), and this equilibrium point is locally asymptotically stable._  
Proof.: When \(\mu=\
u=\xi=0\), we have \(S_{1}=S_{2}=S_{3}=0\), and the system degenerates to  
\[\left\{\begin{aligned} \frac{dE}{dt}&=\beta\kappa(1-E)^{2}- \gamma E\\ G(t)&=\kappa(1-E)\\ S(t)&=0\end{aligned}\right. \tag{9}\]Setting \(\frac{dE}{dt}=0\), we obtain  
\[\beta\kappa(1-E)^{2}-\gamma E=\beta\kappa E^{2}-(2\beta\kappa+\gamma)E+\beta \kappa=0. \tag{10}\]  
Using Lemma 1, Equation (10) has a unique valid equilibrium point \(E^{*}\in(0,1)\), corresponding to \(G^{*}=\kappa(1-E^{*})\) and \(S^{*}=0\).  
We compute the eigenvalue of the Jacobian matrix at the equilibrium point:  
\[J=\left.\frac{\partial}{\partial E}\left[\beta\kappa(1-E)^{2}-\gamma E\right] \right|_{E=E^{*}}=-2\beta\kappa(1-E^{*})-\gamma.\]  
Since \(E^{*}\in(0,1)\) and \(\beta,\kappa,\gamma>0\), we have \(J<0\). Therefore, the equilibrium point is locally asymptotically stable.
## 7 Results and Discussion
### Maintenance Scheduling with Imperfect RUL Prognostics (PdMI) on the Aircraft Engine Use Case
In contrast, the predicted RUL with the safety margin \(RUL_{s}\) suggests replacing the engine immediately, avoiding the cost \(C_{c}\) associated with a failure and ensuring that the engine does not fail during the flight. Having the safety factor calculated dynamically allows a better exploitation of the useful  
Figure 10: Costs per working cycle: comparison of the proposed PdMI with the minimum maintenance cost that can be achieved. Figure 9: Results of the k-LSTM-GFT for the 20 test engines. (**a**) RUL predictions over time for the 20 test engines in FD001. (**b**) RMSE of the RUL predictions for the 20 test engines. life of components because it assumes higher values near the point of failure, ensuring the safety of components, and lower values far from the point of failure, according to the GFT failure probability estimation, optimizing the exploitation of the useful life of components.
## 2 System Description
### Experimental Setup for the Absorption Cooling System
Moreover, two additional auxiliary systems were used during the experimental assessment for the ACS: one of them was used to remove the heat delivered from the absorption and condensation processes; the last auxiliary system is used for supplying a mass flow rate of water that is going to be chilled in the evaporator. The system was designed to provide a mass flow rate of water to the evaporator at a constant temperature. Instrumentation of the Absorption Cooling System  
In order to assess the ACS performance, a complete set of different sensors was installed on the main components of the absorption system. The pressure was registered in the inlet ports of the solution and refrigerant, in the main components (generator, condenser, evaporator, and absorber), with that porpoise, Ashcroft(r) piezoelectric transducers were utilized. To measure the mass flow rate of refrigerant and diluted solution, a couple _Micro Motion(r)_ Elite Coriolis flowmeters were used. The concentrated solution was registered by utilizing an _Omega(r)_ rotameter; the mass flow rates of water in the three auxiliary systems were obtained by the use of turbine flow meters. The temperatures were measured in each inlet and outlet port of every plate heat exchanger, with the use of _PT1000_ resistance temperature detectors (RTD). The uncertainties related to each measuring unit utilized during the assessment of the ACS is presented in Table 3.  
A schematic diagram of the ACS showing the different internal and external streams is presented in Figure 2. \begin{table}
\begin{tabular}{c c} \hline
**Measuring Instrument** & **Uncertainty** \\ \hline Elite Coriolis flowmeter & \(\pm\)0.10\% \\ Omega rotameter & \(\pm\)1\% \\ Turbine flowmeter & \(\pm\)1\% \\ Pressure transducer & \(\pm\)1\% \\ RTD & \(\pm\)0.3 \({}^{\circ}\)C \\ \hline \end{tabular}
\end{table} 
Table 3: Uncertainties reported by the instruments’ manufacturers.
# An ECC-Based Anonymous and Fast Handover Authentication Protocol for Internet of Vehicles
## 1 Introduction
The proposed protocol is lightweight, with few message exchanges between entities, which effectively reduces communication and computational overhead and the risk of privacy leakage. 2. The proposed protocol uses the time tree technology in trusted computing [5] to share some private data of vehicles, ensuring the security of vehicle authentication while improving the efficiency of authentication. When a vehicle moves across domains, it does not need to re-register and authenticate, reducing the cost of the vehicle in the authentication process. 3. The proposed protocol implements \"non-full key escrow\". The private key of the vehicle is generated by the TA and the vehicle separately. The TA only knows part of the vehicle's private key. Even if the TA is malicious, the security of the vehicle's private key can be guaranteed. 4. Performance analysis shows that compared with existing protocols, the proposed protocol greatly reduces the communication overhead in the authentication process and the computational overhead when switching the authentication process. The rest of the paper is structured as follows. Section 2 reviews existing authentication protocols. Section 3 introduces the preliminaries, including the system architecture, threat assumptions, and security requirements. Section 4 details the proposed protocol. Sections 5 and 6 present the security and performance evaluations, respectively. Finally, Section 7 concludes the paper.
# Optimizing Flight Delay Predictions with Scorecard Systems
## 2 Literature Review
### Conventional Forecasting Methods
It should be noticed that the forecasting of delays in air transportation has evolved through the integration of traditional statistical methods and advanced machine learning techniques. The combination of ARIMA models with external variables, the use of ensemble methods, and the adaptation of forecasting approaches in response to external shocks such as COVID-19 are crucial in enhancing the accuracy and reliability of delay predictions. Future research should continue to explore these hybrid methodologies and their applicability in various operational contexts within the aviation industry.
# Understanding and Enhancement of Internal Clustering Validation Indexes for Categorical Data
## 4 Internal Clustering Validation Index: CUBAGE
### Inter-Cluster Separation Measure: AGE  
Our measure of inter-cluster separation--averaged information gain of isolating each cluster, henceforth _AGE_--was based on the idea of information gain in information theory. Before presenting _AGE_, we will review the concept of information gain.  
The mutual information is a measure of the shared information of two discrete variables \(X\) and \(Y\)[52]:  
\[I(X;Y)=H(X)-H(X\,|Y)=\sum\limits_{y\in Y}\sum\limits_{x\in X}p(x,y)\log(\frac{p( x,y)}{p(x)p(y)}). \tag{37}\]  
In machine learning, the mutual information \(I(X;Y)\) is the expected information gain; that is, the reduction in the entropy of \(X\) that is achieved by learning the state of \(Y\)[53]. In general terms, the expected information gain is the change in entropy from a prior state (\(X\)) to a state that takes some information (\(X\,|\,Y\)):  
\[IG(X,Y)=H(X)-H(X\,|Y). \tag{38}\]  
Given a partition \(P=\{C_{l}\}\) (\(l\) = 1,..., \(k\)) of dataset \(U\), which is described by \(V\) = \(\{A_{1}\),..., \(A_{m}\}\), we define the information gain of separating cluster \(C_{l}\) from other clusters as:  
\[GE(C_{l})=H(V)-H(V\,|P_{l}), \tag{39}\]  
where \(P_{l}\)= \(\{C_{l}\), \(U-C_{l}\}\) is the partition that separates \(C_{l}\) from other clusters, and \(U-C_{l}\) is the complementary set of \(C_{l}\).  
\(GE(C_{l})\) is the information gain of \(V\) from the unpartitioned state to the state where the objects are divided into \(C_{l}\) and \(U-C_{l}\); in other words, the degree of certainty that we can gain by separating the objects in \(C_{l}\) from other objects. Therefore, \(GE(C_{l})\) equals the dissimilarity between \(C_{l}\) and other clusters (\(U-C_{l}\)); a higher value of \(GE(C_{l})\) indicates that more separation is achieved by separating \(C_{l}\) with other clusters.  
As Figure 2 illustrates, we average the value of \(GE\) over all the scenarios of isolating each cluster to measure the overall separation:  
\[AGE(P)=\frac{1}{k}\sum\limits_{l}^{k}GE(C_{l})=\frac{1}{k}\sum\limits_{l}^{k} [H(V)-H(V\,|P_{l})\,]. \tag{40}\]  
Explicitly, \(AGE(P)\) is calculated as:  
\[AGE(P)=\frac{1}{k}\sum\limits_{l}^{k}[H(V)-p(C_{l})\cdot H(V_{C_{l}})-p(U-C_{l })\cdot H(V_{U-C_{l}})], \tag{41}\]  
where \(V_{C_{l}}\) is the set of attributes describing the objects in \(C_{l}\), \(V_{U-C_{l}}\) is the set of attributes describing other objects, and:  
\[\left\{\begin{array}{l}H(V)=-\sum\limits_{j=1}^{m}\sum\limits_{i=1}^{d_{j}} p(a_{j}{}^{(i)})\log(p(a_{j}{}^{(i)})),\\ H(V_{C_{l}})=-\sum\limits_{j=1}^{m}\sum\limits_{i=1}^{d_{j}}p(a_{j}{}^{(i)}\,|C _{l})\log(p(a_{j}{}^{(i)}\,|C_{l})),\\ H(V_{U-C_{l}})=-\sum\limits_{j=1}^{m}\sum\limits_{i=1}^{d_{j}}p(a_{j}{}^{(i)}\,| U-C_{l})\log(p(a_{j}{}^{(i)}\,|U-C_{l})).\end{array}\right. \tag{42}\]