esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Effects of Air Route Alternation  
The results showed that compared with the air-route-as-planned condition, the score of the NASA-TLX scale and SPAM response accuracy under the air-route-altered condition was significantly decreased. Meanwhile, the SA scores of the 3D-SART were also observed as clearly reduced under the air-route-altered condition regarding Interfaces 1 and 3 (Figure 3a), indicating a notable decrease in the operator's SA when the air route changed suddenly. Further analysis about the dimensions of the 3D-SART scale showed that the ARS under the air-route-altered condition declined 79.54% on average compared to that under the air-route-as-planned condition (Figure 3b). These findings could also be verified from the eye movement data, as the fixation rate in all the AoIs and the average pupil diameter increased dramatically under the air-route-altered condition compared to the air-route-as-planned condition, suggesting that the unplanned air route alternation led to a higher demand of attention resource.  
Figure 3: Measurement indices in different display designs under conditions of air-route-as-planned and air-route-altered. (**a**) SA score of 3D-SART; (**b**) Attention resource surplus (ARS) of 3D-SART; (**c**) Overall operation accuracy; (**d**) Altitude operation accuracy; (**e**) Fixation rate in Ao1 (Overall); (**f**) Fixation rate in Ao1 (Altitude).  
Previous studies show that the deterioration in SA can be caused by an increase in task complexity [8; 60]. In the current study, the complexity of the flight attitude control task is varied by the unplanned air route alternation, and the SA degradation under the air-route-altered condition can be attributed to the fact that operators needed to consume more attention resources to cope with the comprehension and decision-making in the unplanned air route alternation situation, which results in the intensified imbalance between attention resource demand and supply. Some studies reported that a feasible approach to improve operators' SA in abnormal situations is to relieve the demand of cognitive resources for decision-making, such as adding planning aids, providing explanations or direct solutions by the system [18; 24; 25]. In terms of task performance, similarly, the deterioration in operation accuracy is initiated by the unexpected air route alternation, whether for the overall operation accuracy or a specific flight attitude parameter (i.e., altitude, speed and course). This is consistent with the findings of some previous literature, suggesting the decline in performance is usually caused by a degradation in SA [63; 64; 65]. The variation in performance can be used as one of the criteria to assess an operator's SA, and their performance can be promoted by an enhancement in SA.
## 4 Proposed Approach
### Design Methodology
#### 4.2.8 Anonymity  
The user's identity must not be leaked to any other party in the system, neither through the presentation itself nor through metadata, except if the user consented to reveal it. AURORA MINDS maintains user anonymity through advanced cryptographic methods including zero-knowledge proofs, homomorphic encryption, and attribute-based credentials, ensuring no personally identifiable information is revealed during authentication or data processing. All sensitive data remains encrypted and indistinguishable within large anonymity sets, with no identity-revealing metadata shared unless explicitly consented to by the user.
## 5 Experimental Setup
### Bin-Picking Robot  
In this user study, the objective was to interact with the robot though voice commands to indicate which cartridges would go to one of the containers of choice, whereas the rest were to be placed into the other container.  
In this case, the robot, depicted in Figure 4, was physically present in a manufacturing laboratory. Due to this, users were provided with an industrial headset with microphone, designed to prevent ambient noise to interfere with the voice captured and to protect them from said noise.  
Before the experimentation, users were instructed about the objectives of the study and minimal interaction restrictions. As in the previous user study, there were not wording restrictions in general, except for coordination of targets (e.g., \"Put the black ones into container 1 **and** the yellow ones into container 2\"). Furthermore, they were shown which cartridges were available and how they looked like, so they would see if the robot was correctly performing the action it was ordered to execute.  
During the experimentation, and for each interaction, users were asked to choose three or four cartridges, which were placed on the platform in front of the robot by the personnel in charge of the study. Then, they were asked to perform their commands using the app and the headset they were provided with from the position they were instructed to remain in. Figure 6 shows the conditions of the experimentation.  
Table 4 shows demographic information regarding the study participants. As in the previous user study, all participants were familiar with technologies in general and they are also related to some degree to industrial processes.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**(a)** & **Gender** & **(b)** & **Age** & **(c)** & **Interaction with Everyday Devices** \\ \hline
**M** & 33\% & **24-34** & 58\% & **Never** & 33.3\% \\ \hline
**F** & 42\% & **35-44** & 17\% & **Sometimes** & 58.3\% \\ \hline
**N/D** & 25\% & **45-52** & 17\% & **Frequently** & 8.3\% \\ \hline  & & **N/D** & 8\% & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Demographic data for participants in the KIDE4 Guide user study. (**a**) Gender information. (**b**) Age information. (**c**) Frequency of voice interaction with everyday devices.  
Figure 6: User interacting with the bin-picking robot according to the conditions defined for the experimentation.
## 4 Analysis and Results
### Informal Security Analysis
#### 4.1.4 Secure Session Key  
The session key is a feature to ensure secure communication between two entities, where the key is used for only one session. It is then discarded, and a new key is randomly generated for the next session. In the proposed scheme, the session key K-SC_B is established by the C using the digest hash of the concatenation of the random number (R3) previously generated by B with a random number (R4) newly generated by C. The adversary is unable to extract R3 and R4 without obtaining the cipher C8, which is not transmitted or shared at all over the communication channel. Therefore, the session key cannot be computed by the adversary.  
Figure 11: Eavesdropping attack.
# Unlocking Wellness: Probiotics as Key Drivers in Functional Food Innovation and Health Promotion
## 3 The Function of Probiotics
### The Synergistic Effect of Probiotics and Prebiotics  
Prebiotics are food components that are either not digested or are challenging to digest. They selectively stimulate the growth or activity of one or more beneficial gut microbiota, positively impacting host health [68]. The main types include inulin, fructans, fructooligosaccharides, galactooligosaccharides, and resistant starch. When prebiotics are combined with probiotics, this synergistic effect is referred to as \"synbiotics\" [69]. Prebiotics provide probiotics with fermentation substrates, promoting their proliferation and fermentation metabolism. Probiotics, in turn, regulate intestinal health and positively affect the immune system and metabolic functions of the host [70]. Furthermore, several studies have demonstrated that synbiotics can enhance cognitive function by modulating gut microbiota. [PERSON] et al. [71] administered a symbiotic formulation containing xylooligosaccharides and _Lactobacillus paracasei HII01_ to obese insulin-resistant rats. The results indicated that rats receiving the symbiotic intervention exhibited significant improvements in the time taken to locate the platform and the time spent in the target quadrant during the Morris water maze test, suggesting restored cognitive function. The gut microbiota can produce or influence the precursors of neurotransmitters, such as tryptophan metabolites, which can be converted into neurotransmitters such as gamma-aminobutyric acid, serotonin, and dopamine. These neurotransmitters can subsequently influence brain function through the circulatory system or vagus nerve signaling. Additionally, a symbiotic formulation can indirectly protect the brain from cognitive decline by reducing both intestinal and systemic inflammation [72]. SCFAs, particularly butyrate, exhibit neuroprotective effects  
Figure 2: Probiotic regulation of the blood glucose process.  
by promoting the expression of brain-derived neurotrophic factor, inhibiting the over-activation of microglia; in this way, they reduce neuroinflammation and modulate the hypothalamic-pituitary-adrenal axis to regulate stress responses [73]. In a clinical trial involving Alzheimer's disease patients, researchers found that a symbiotic formulation (containing probiotics and selenium) significantly improved patients' cognitive abilities compared to selenium supplementation alone, as evidenced by higher Mini-Mental State Examination (MMSE) scores [74]. These findings suggest that synbiotics have significant potential in promoting overall health.
# Photoacoustic Imaging with a Finite-Size Circular Integrating Detector
## 3 Algorithms
### Coordinate Transformation  
The algorithm proposed in this study is based on coordinate transformation and interpolation techniques. The detailed process and algorithm for this transformation are described as follows:  
The Cartesian image \(I(x,y)\), with dimensions \([N\times N]\), assumes that the origin \((0,0)\) is located at the center of the image. For any Cartesian coordinate \((x,y)\), its mapping to the polar coordinate \((r,\theta)\) is defined as:  
\[r=\sqrt{(x-x_{c})^{2}+(y-y_{c})^{2}},\,\theta=\arctan 2(y-y_{c},x-x_{c}) \tag{27}\]  
where \((x_{c},y_{c})\) is the center of the image, typically \(x_{c}=\frac{N}{2}\) and \(y_{c}=\frac{N}{2}\). The radial coordinate \(r\) represents the distance of a point from the center, while the angular coordinate \(\theta\) denotes the angle between the point and the x-axis, usually ranging from \([0,2\pi]\).  
For the target polar coordinate image \(I(r,\theta)\), the discrete resolutions of \(r\) and \(\theta\) are \(R\) and \(\Theta\), respectively. The inherent lack of direct one-to-one correspondence between polar grid points \((r,\theta)\) and Cartesian pixels \(I(x,y)\) in polar coordinate systems necessitates pixel value estimation through interpolation. The bicubic interpolation method adopted here uses 16 adjacent pixels to maintain superior detail preservation and conversion accuracy.  
The bicubic interpolation formula can be expressed as:  
\[I(r,\theta)=\sum\limits_{i=-1}^{2}\sum\limits_{j=-1}^{2}\omega(i,\Delta x) \cdot\omega(j,\Delta y)\cdot I(x_{i},y_{j}) \tag{28}\]  
In the bicubic interpolation formula, \((x_{i},y_{j})\) represents the coordinates of neighboring pixels in the Cartesian image, which are used to calculate the intensity value at the interpolation point. The offsets of the interpolation point are defined as \(\Delta x\) and \(\Delta y\), the offsets of the interpolation point; \(\omega(i,\Delta x)\) and \(\omega(j,\Delta y)\) are the weight functions that depend on the offsets \(\Delta x\) and \(\Delta y\), respectively.  
The weight function \(\omega(k,\Delta)\) is defined as [34]:  
\[\omega(k,\Delta)=\begin{cases}\begin{array}{c}1.5|\Delta|^{3}-2.5|\Delta|^{2 }+1,&if|k|\leq 1\\ -0.5|\Delta|^{3}+2.5|\Delta|^{2}-4|\Delta|+2,if1<|k|\leq 2\\ 0&if|k|>2\end{array}\end{cases} \tag{29}\]  
The error of bicubic interpolation converges at a rate of O(d\({}^{4}\)), meaning that, as the sampling spacing d decreases, the interpolation error rapidly approaches zero [34]. Therefore, bicubic interpolation provides a reliable approximation of pixel values during the coordinate transformation process.  
The algorithm for converting from polar to Cartesian coordinates involves reversing the mapping process used for Cartesian to polar conversion.
# On Correlated Equilibria in Marinatto-Weber Type Quantum Games
## 5 Bimatrix Representation of the MW Scheme
In the seminal Eisert-Wilkens-Lewenstein scheme [2], restricting strategies to some type of one-parameter unitary operators yields the classical game. Similarly, a proper limitation of the sets \(B_{1}\) and \(B_{2}\) in \(\Gamma_{cMW}\) leads to the game that is equivalent to (1). That is the content of the following proposition:  
**Proposition 2**. : _If_  
\[ \begin{split} B_{1}^{\prime}&=\left\{(V_{i},V_{(m- 1)+=i},V_{(m-2)+=i}\ldots,V_{1+=i});i\in\{0,1,\ldots,m-1\}\right\},\\ B_{2}^{\prime}&=\left\{(V_{j},V_{(n-1)+n},V_{(n-2)+n }]\ldots,V_{1+nj});j\in\{0,1,\ldots,n-1\}\right\};\end{split}  \tag{68}\]  
_then the game determined by \(\Gamma_{cMW}=\left(|\Psi\rangle,(B_{1}^{\prime},B_{2}^{\prime}),(U_{1},U_{2})\right)\) can be identified with a bimatrix game \(m\times n\)._  
Proof. : Let us consider first a strategy profile consisting of a strategy \((V_{0},V_{m-1},V_{m-2},\ldots,V_{1})\) of player 1 and \((V_{0},V_{n-1},V_{n-2},\ldots,V_{1})\) of player 2. Keeping in mind that \(a\equiv 0\pmod{a}\), we can write final state (60) in the form of  
\[ \begin{split}\rho_{\rm f}&=\sum_{i=0}^{m-1}\sum_{j =0}^{n-1}|\alpha_{ij}|^{2}(V_{m-i}\otimes V_{n-j})|ij\rangle\langle ij|(V_{m- i}\otimes V_{n-j})^{\dagger}\\ &=\sum_{i=0}^{m-1}\sum_{j=0}^{n-1}|\alpha_{ij}|^{2}|i+_{m}(m-i),j +_{n}(n-j)\rangle\langle i+_{m}(m-i),j+_{n}(n-j)|\\ &=\sum_{i=0}^{m-1}\sum_{j=0}^{n-1}|\alpha_{ij}|^{2}|00\rangle \langle 00|.\end{split}  \tag{69}\]  
In general, adding \(i\) modulo \(m\) and \(j\) modulo \(n\) to the subscripts of \((V_{0},V_{m-1},V_{m-2},\ldots,V_{1})\) and \((V_{0},V_{n-1},V_{n-2},\ldots,V_{1})\) respectively, yields  
\[\rho_{\rm f}=\sum_{i=0}^{m-1}\sum_{j=0}^{n-1}|\alpha_{ij}|^{2}|ij\rangle \langle ij|. \tag{70}\]  
This implies pure payoff profiles of the bimatrix game:  
\[(U_{1},U_{2})((V_{i},V_{(m-1)+=i},V_{(m-2)+=i}\ldots,V_{1+=i}),(V_{j},V_{(n-1) +=i},V_{(n-2)+=i}\ldots,V_{1+=j}))=(a_{ij},b_{ij}). \tag{71}\]  
Note that \(|B_{1}^{\prime}|=m\) and \(|B_{2}^{\prime}|=n\), which completes the proof. It was shown in Example 2 that a Nash equilibrium in the MW scheme may not necessarily mean that the initial state generates a correlated equilibrium in the classical game. The strategy profile \((V_{0},V_{0})\) is a Nash equilibrium in the MW scheme, even though the probability distribution based on squared amplitudes of the initial state is not a correlated equilibrium. In the case of scheme (58), the correlated equilibrium and the existence of some specific Nash equilibria are equivalent. **Example 3**. : _Consider the scheme \(\Gamma_{cMW}\) associated with (22). Let the initial state \(|\Psi\rangle\) be given by (41). Since \(m=2\) and \(n=3\) for bimatrix game (22), a typical strategy of player 1 is \((V_{k_{0}},V_{k_{1}})\), and a typical strategy of player 2 is of the form \((V_{l_{0}},V_{l_{1}},V_{l_{2}})\).
# A Survey on Recent Advanced Research of CPS Security
## 4 Discussion
### MADC Types
#### 4.3.3 Defense  
Most devices in CPS only have limited storage, battery, and transmission ranges. Therefore, it is a challenging mission to build lightweight warning systems for abnormal data or intrusion behaviors. In different structure layers, researchers have utilized different approaches to detect and block attacks. They used the system's operating characteristics in the perception layer, analyzed traffic in the network layer, and formulated defense policies in the application layer.  
In the perception layer, researchers have created methods to detect abnormal status with operation features, such as cycle per instruction (CPI) [95], radio frequency (RF) [10], sensor-device-user interactions [31], and long-term expected values [87]. In the network layer, researchers modeled regular traffic first and then leveraged different methods to identify abnormal sequences, such as model checking [91], packet observation against long-term expected values [87], and artificial neural networks (ANNs) [89]. In the application layer, game theory [72; 85] can be used to judge the reliability of distributed CPS, generate invariant rules from ICS operational logs [76], and check abnormal behaviors by transforming behavior rules to a state machine [17].
# Robustness Analysis for Multi-Agent Consensus Systems with Application to DC Motor Synchronization
## 2 Multi-Agent Consensus System Preliminaries
### Information Flow Topology  
The information flow between agents is defined by the employed topology. In [4], it is stated that cyclic and chain topology, shown in Figure 1, can be combined to model all kinds of complex multi-agent information flow topologies. It is important to recall that the forced consensus of multi-agent systems of arbitrary length communicating over networks with cyclic and chain topologies are controllable and observable. Details on controllability and observability properties of leader-based multi-agent systems can be found in [4].  
The state space representation of a multi-agent system with cyclic topology is the following:  
\[\left[\begin{array}{c}\dot{\xi}_{1}\\ \dot{\xi}_{2}\\ \vdots\\ \dot{\xi}_{n}\end{array}\right]=\left[\begin{array}{ccccc}-1&1&0&0\\ &-1&1\\ &&\ddots&\ddots\\ &&&-1&1\\ 1&&&&-1\end{array}\right]\left[\begin{array}{c}\xi_{1}\\ \xi_{2}\\ \vdots\\ \xi_{n}\end{array}\right]+\left[\begin{array}{c}1\\ 0\\ \vdots\\ 0\end{array}\right]u \tag{6}\]  
\[y=\left[\begin{array}{ccccc}1&0&\cdots&0\end{array}\right]\left[\begin{array} []{c}\xi_{1}\\ \xi_{2}\\ \vdots\\ \xi_{n}\end{array}\right]\]  
The state space representation of a multi-agent system with the chain topology is the following:  
\[\left[\begin{array}{c}\dot{\xi}_{1}\\ \dot{\xi}_{2}\\ \vdots\\ \dot{\xi}_{n}\end{array}\right]=\left[\begin{array}{ccccc}-1&1&&&\\ &1&-2&1&\\ &\ddots&\ddots&\ddots&\\ &&1&-2&1\\ &&&1&-1\end{array}\right]\left[\begin{array}{c}\xi_{1}\\ \xi_{2}\\ \vdots\\ \xi_{n}\end{array}\right]+\left[\begin{array}{c}1\\ 0\\ \vdots\\ 0\end{array}\right]u \tag{7}\]  
\[y=\left[\begin{array}{ccccc}1&0\cdots&0\end{array}\right]\left[\begin{array} []{c}\xi_{1}\\ \xi_{2}\\ \vdots\\ \xi_{n}\end{array}\right]\]  
Figure 1: Information flow configuration: (**a**) cyclic topology; (**b**) chain topology. The gray circle represents the leader agent and white circles are followers.  
For simplicity, Equations (6) and (7) can be rewritten as  
\[\left[\begin{array}{c}\dot{\xi}_{1}\\ \dot{\xi}_{2}\\ \vdots\\ \dot{\xi}_{n}\end{array}\right]=-\mathcal{L}\left[\begin{array}{c}\xi_{1}\\ \xi_{2}\\ \vdots\\ \dot{\xi}_{n}\end{array}\right]+B\,u \tag{8}\] \[y=C\left[\begin{array}{c}\xi_{1}\\ \xi_{2}\\ \vdots\\ \xi_{n}\end{array}\right]\]
# Noise-Free Security Assessment of Eviction Set Construction Algorithms with Randomized Caches
## 4 Eviction Set Construction Algorithms
### Eviction Set Construction Algorithms
#### 4.2.1 Single Address Elimination Algorithm  
The _single address elimination_ algorithm (SAE) was introduced by [PERSON] et al. in [31] to perform Prime+Probe attacks on last-level caches (see Algorithm 1). The algorithm takes a virtual address \(x\) and a candidate set \(C\) as input. For each element \(c\in C\), the algorithm tests whether the candidate set is still an eviction set without \(c\) (lines three and four). When the test succeeds, the address \(c\) is removed from \(C\) (line six). When the test does not succeed, the address \(c\) is considered necessary for \(E\) to be an eviction set. Then, the address is added to \(E\) and the algorithm keeps the iteration, as long as the eviction set \(E\) is smaller than the number of ways \(W\) (line two). The single address elimination method optimizes an eviction set in \(\mathcal{O}(n^{2})\) memory accesses, where \(n\) is the candidate set size. This implies that the number of memory accesses grows quadratically with the size of the candidate set.  
Figure 4: The decomposition of a virtual address into a physical cache location.  
```
Input:\(E\)=candidate set, \(x\)=target address Output:\(E\)=minimal eviction set Output:\(E\)=minimal eviction set
1 while\(|E|>W\)do
2\(c\leftarrow\) pick one address from \(C\) if\(E\cup C\backslash\{c\}\) does not evict \(x\)then
3\(E\leftarrow E\cup\{c\}\)
4\(C\leftarrow C\backslash\{c\}\)
5 end if
6 return\(E\)
```  
**Algorithm 1**The single address elimination algorithm
## 4 Results and Discussion
### In Vitro Release Study  
To observe the release mechanism of \(\alpha\)-M from ChAlg HF, an in vitro release study of ChAlg/\(\alpha\)-M HF was performed in PBS solution (pH 7.4) at 5, 15, 30, 45, 60, 90, and 120 min. The concentrations of \(\alpha\)-M were measured by UV spectrophotometry at wavelength of 246 nm [24]. The Q value determines the mechanism of drug release. When Q approaches 0.5, the release mechanism overtakes Fickian diffusion, where the value of Fickian diffusion is 0.5 \(<\) Q \(<\) 1.0 [25]. Higuchi regression parameters of drug release percentage and quadratic curve of the \(\alpha\)-M release time from ChAlg/\(\alpha\)-M HF in 2 h confirmed that slope (% min-0.5) and correlation coefficient (Q) were 2.21 \(\pm\) 0.02 and 0.48 \(\pm\) 0.09, respectively. The release mechanism of \(\alpha\)-M from ChAlg/\(\alpha\)-M HF was a matrix type based on Fickian diffusion. As we can see in Figure 7A, the burst release in 2 h was necessary for RAS therapy to provide quick onset of action in oral mucous. Previous study reported that the release of \(\alpha\)-M from Ch-Alg microparticle after being immersed in simulated gastric fluid (SGF) and simulated colon fluid (SCF) were burst released for 2 h. The release of \(\alpha\)-M in acidic media decreased in the higher Alg concentration, but in neutral condition (in SCF and simulated intestinal fluid/SIF) the presence of Alg increased the \(\alpha\)-M release [30]. This phenomenon was caused by the chain relaxation in the electrostatic interaction from the carbonyl group in Alg and amino group in Ch [30,44]. Based on the release properties of \(\alpha\)-M, ChAlg/\(\alpha\)-M HF is suitable to give an enough time to release the \(\alpha\)-M while the ChAlg HF covered the mucous layer.  
Figure 6: Swelling ratio of ChAlg/\(\alpha\)-M HF. Each value represents the mean \(\pm\) SEM of three experiments. * \(p<\) 0.05, compared to the Ch HF. # \(p<\) 0.05, compared to the ChAlg HF.
# Method of Calculating Desynchronization of DVB-T Transmitters Working in SFN for PCL Applications
## 2 Passive Radar Geometry  
Passive radar operates on a different basis from active radar. In traditional active radar, the signal used for illumination is generated by the transmitting part of the radar system. Most commonly, a single antenna is used for both transmission and reception, switching between those two states. This configuration is described as monostatic. In a passive solution, the transmitter, or in this case the IO, works independently of the radar system. This creates a receiver-transmitter (Rx-Tx) pair working in a bistatic configuration.  
The independence of the exploited transmitter forces each receiver to gather two signals simultaneously: the reference signal gathered from the directional antenna facing the IO; and the measurement signal composed of all echo signals (see Figure 1). Both signals are then cross-correlated, providing the bistatic range and velocity of targets. In terms of localization (with 2D geometry assumption), instead of forming a circle (as in a monostatic configuration), the bistatic pair (Rx-Tx) forms an ellipse on which the target may be found (see Figure 2a). By adding more receivers and/or transmitters, one can form more ellipses. The target is located at the point of their intersection (Figure 2b).  
Figure 1: Passive radar principle visualization.  
Target localization precision is highly dependent on the accuracy of the transmitter and receiver position data.