esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Antioxidant Capacity  
We determined the antioxidant capacity using ABTS, DPPH, and FRAP assays, which are known to be the most commonly used methods to evaluate total antioxidant capacity (Table 2). For ABTS radical scavenging activity, the BuOH fraction showed the highest ABTS radical scavenging capacity among the five fractions of ISE, with 286.25 \(\pm\) 14.61 mg VCE/g of dry weight, followed by EtOAc (120.32 \(\pm\) 8.14), H\({}_{2}\)O (109.26 \(\pm\) 8.94), CHCl\({}_{3}\) (25.40 \(\pm\) 3.86), and Hex (18.21 \(\pm\) 4.43). Furthermore, ISE showed the third highest ABTS radical scavenging capacity, with 119.26 \(\pm\) 9.28 mg VCE/g of dry weight compared to the five fractions of ISE. The DPPH radical scavenging capacity was the highest in BuOH (82.14 \(\pm\) 12.13), followed by EtOAc (53.29 \(\pm\) 6.84), ISE (44.85 \(\pm\) 5.96), H\({}_{2}\)O (36.76 \(\pm\) 3.49), CHCl\({}_{3}\) (12.21 \(\pm\) 1.36), and Hex (11.63 \(\pm\) 0.38). In the case of FRAP activity, BuOH (0.40 \(\pm\) 0.05) had the highest, followed by EtOAc (0.15 \(\pm\) 0.01), H\({}_{2}\)O (0.16 \(\pm\) 0.01), ISE (0.12 \(\pm\) 0.01), CHCl\({}_{3}\) (0.05 \(\pm\) 0.01), and Hex (0.04 \(\pm\) 0.00). We further conducted the Pearson correlation analysis to estimate the association between the TPC and three antioxidant assays (Table 3). The TPC assay was significantly positively correlated with the ABTS (\(r\) = 1.00, \(p\) \(<\) 0.01), DPPH (\(r\) = 0.98, \(p\) \(<\) 0.01), and FRAP (\(r\) = 0.99, \(p\) \(<\) 0.01) assays, which indicated that polyphenol compounds in ISE have strong total antioxidant capacity.
## 3 Methodology
### Iterative Elastic Bin Refinement
#### 3.2.2 Iterative Refinement Process  
The iterative refinement process gradually narrows the depth search space by adjusting the bin widths based on the estimated uncertainty. Depth predictions are refined over multiple stages. For each pixel, the depth prediction is updated based on the current bin centers and predicted probabilities. The depth estimate for pixel \(p\) is given by  
\[D_{t}(p)=\sum_{n=1}^{N}C_{n}\cdot P_{n}(p) \tag{10}\]  
where \(C_{n}\) is the center of bin \(n\) and \(P_{n}\) (\(p\)) is the predicted probability for bin \(n\) at iteration \(t\). The uncertainty for each pixel is estimated using the variance of the predicted depth distribution. The bin widths are adjusted based on the uncertainty estimates. Pixels with higher uncertainty are assigned wider bins, while pixels with lower uncertainty are assigned narrower bins. The depth prediction is refined by focusing on the adjusted bins in the next iteration. The process is repeated for a fixed number of iterations \(T\), typically between 3 and 5 stages. The fixed number of iterations \(T\) is determined empirically based on validation set performance. During preliminary experiments, we evaluated the performance of the DI-MDE framework for T = {3,4,5,6}. We found that T = 5 balanced computational efficiency and depth prediction accuracy, with diminishing returns observed for higher iteration counts. By iteratively refining the depth predictions and adjusting the bin widths dynamically, the network can progressively improve the accuracy of its depth estimates, particularly in regions where the initial prediction is uncertain. After several iterations of refinement, the final depth prediction for each pixel is obtained by selecting the bin with the highest probability and using the corresponding bin center as the predicted depth (see Equation (10)). The iterative elastic bin refinement improves the accuracy of the final depth predictions. This is especially true in regions with high uncertainty or large depth discontinuities, such as object boundaries.  
The IEBins module is a core component of the DI-MDE framework that enhances depth estimation accuracy by refining predictions iteratively. It discretizes the depth range into several bins, treating depth prediction as a classification-regression problem. At each iteration, depth predictions are progressively refined by dynamically adjusting the width of these bins, based on uncertainty estimates. This iterative process allows the network to focus more precisely on regions with higher depth uncertainty. The DI-MDE framework successfully integrates iterative depth refinement and dynamic scale alignment to address the challenges of monocular depth estimation in dynamic scenes. The interaction between the IEBins and DSA modules ensures accurate and consistent depth predictions for both static and dynamic regions. This reduces depth prediction errors and improves overall performance.
# GCE: An Audio-Visual Dataset for Group Cohesion and Emotion Analysis
## 5 Experimental Results
### Discussion
The research endeavored to achieve a thorough comprehension of the interaction between group cohesion and group emotion. The GCE dataset and its corresponding model represent significant assets for future research within the field of group dynamics and psychology. After a thorough examination of the data, significant connections wereobserved between group cohesion and group emotion in various scenarios. Positive emotions consistently resulted in higher cohesiveness scores, whereas negative emotions were linked to lower cohesion values. The level of cohesiveness varied across different settings. For instance, in argument scenarios, a low level of cohesion was observed for negative emotions. On the other hand, casual conversations, meetings, and interviews indicated a high level of cohesion. The scenario of the conversation exhibited a diverse range of cohesiveness scores, ranging from 1 to 6, which can be attributed to the likely presence of agreement or disagreement among the participants during the discussions. It is worth noting that the discussions and interviews often exhibited a dearth of unpleasant feelings. The expected influence of the participant quantity on group cohesion exhibited negligible variability within the range of two to five individuals. A foundational multimodal system was proposed using cutting-edge deep learning methods to simultaneously forecast the group cohesiveness scores and emotional states. The evaluation findings demonstrated that the spectrogram of audio representation and facial expressions in visual representation exhibited a superior performance compared with the other unimodal extractors. The utilization of transfer learning techniques, specifically focusing on facial expression characteristics, has been shown to significantly improve the accuracy of group-level emotion recognition. Although spectrograms have been proven to be effective in recognizing emotions, fusion models did not demonstrate substantial enhancements compared with unimodal techniques. The lack of an equal distribution of classes in the GCE dataset posed a challenge for deep models to achieve a full comprehension of the underlying classes, leading to unaltered results. Given these circumstances, we propose the promotion of innovative data resampling or punishment algorithms as viable approaches to mitigating the challenges associated with class imbalance.
## 3 Established Connection Phase
### Information Data Stage and Detection  
Having described the process related to interference and channel information acquisition, we now detail the data transmission stage, lasting \(T_{d}\), as shown in Figure 1. Before symbol emission, bit-loading is performed. In this regard, we do not provide further details, since proposing a new mechanism was not our aim. However, it is fundamental to highlight that channel knowledge (and its reliability) is a key element to realizing bit-loading, especially when procedures based on _waterfilling-like_ algorithms are considered [51]. Furthermore, channel knowledge is also necessary for signal detection. Specifically, we can express the OFDM data symbol as:  
\[g(t)=\sqrt{E_{\mathrm{s}_{0}}}\sum_{k=0}^{N_{\mathrm{SC}}-1}G(k)e^{j2\pi kt \Delta f},\quad 0\leq t\leq T_{x} \tag{19}\]  
where \(T_{x}\) is the above-mentioned signal length without the insertion of CP. Moreover, the \(G(k)\) term is related to the inverse-DFT of the symbol emitted on the \(k\)-th sub-channel. Note that symbols on different sub-channels may belong to different constellations (that is, the modulation order employed on sub-carriers may be different) if bit-loading is considered. The OFDM symbol is completed by CP insertion before transmission. If the CP length has been suitably adapted to completely avoid ISI, it follows that echoes related to the previously emitted symbol fall into the current symbol CP window, and, thus, do not affect the detection of carried data. Under such an assumption, the received analog signal related to an OFDM symbol after CP removal can be written as:  
\[r(t)=\sum_{d=1}^{P}\beta_{d}(t)a_{d}g(t-\tau_{d})e^{j2\pi\
u_{d}t}+z(t),\quad 0 \leq t\leq T_{x} \tag{20}\]  
that collects the component \(z(t)\) related to background noise and other external interference, and the signal echoes coming from the propagation over \(P\) paths. Regarding this latter aspect, according to Equation (8), multipath was initially characterized by \(\rho\) paths. However, since delay spread may be longer than the OFDM symbol duration \(T_{x}\), it is likely that the late echoes fall within the next symbol CP window, while early arrivals are, instead, superposed to the currently received signal, thus giving rise to _auto-interference_. So, in Equation (20), we refer to \(P\leq\rho\) as the number of secondary paths acting as interference on the current symbol.  
Finally, concerning detection, the receiver computes the DFT of the signal passed through analog-to-digital conversion. Then, by processing each sub-carrier component, the symbol \(\hat{G}(k)\) (with \(k=0\), \(1\), \(\ldots\), \(N_{\mathrm{SC}}-1\)) is decided according to the Maximum Likelihood criterion (by remembering that, on different sub-channels, we can have different modulation formats) [18].
## 3 Test Results and Analysis
### Analysis of XRD Test Results  
Figure 4 shows the XRD patterns of the two types of coal samples, in which the X-ray diffraction peak was positively correlated with the amount of minerals, and the higher the diffraction peak, the higher the corresponding mineral content. As can be seen from the figure, the main mineral components of AB coal samples were muscovite, gypsum, and quartz, among which muscovite had four peaks, gypsum had two peaks, and quartz had one peak. The main mineral components of PE coal samples were kaolinite and montmorillonite, in which kaolinite had five peaks and montmorillonite had one peak. The higher the diffraction peak, the more mineral components there were. Gypsum and quartz minerals in AB coal samples were hydrophilic to some extent, and montmorillonite minerals in PE coal samples were also hydrophilic, but kaolinite minerals showed weak hydrophilicity. Combined with the test results, the minerals in coal had an important influence on the wettability of coal samples.  
Figure 3: Micropore size distribution and cumulative micropore volume of AB and PE coal samples.
# A UAV-Assisted Stackelberg Game Model for Securing IoMT Healthcare Networks
## 3 System Model
### Proposed Stackelberg Security-Based Game Theory Algorithm for Securing IoMT Networks Using UAVs
#### 3.2.1 Stackelberg Game Theory Scheme
Similarly, the attacker will choose a strategy (\(a\)) that maximizes their utility over any alternative approach (\(a^{\prime}\)) available to them as Equation (9) indicates. \[U_{d}(M,a)>U_{d}(M,a^{\prime}) \tag{9}\]  
In the Stackelberg security game, the victim or defender UAV decides first, and the attacker node chooses its strategy based on the defender's action. The function \(\mathrm{g}(M)=\mathrm{a}\) represents the attacker's response to the defender's choice. The strong Stackelberg equilibrium (SSE) can be formulated by analyzing the game in this manner. Based on SSE the defender will make its best response strategy can be calculated in Equation (10). \[U_{d}(M,g(M))\geq\big{(}M^{\prime},g(M^{\prime})\big{)} \tag{10}\]  
Here \(U_{d}(M,g(M))\) is the best response strategy for all \(M^{\prime}\); moreover, attackers best response strategy would be  
\[T=\{r1,r2,r3,\ldots.\ldots rn\} \tag{2}\]0  
Here Equation (11) shows the best response of the attacker. In our scenario, the victim or defender will choose its best response from the set of strategies to recover its target as Equation (12) presents. \[T=\{r1,r2,r3,\ldots.\ldots rn\} \tag{2}\]1  
We can observe in Figure 3(a) IoMT system based on UAV ad hoc networks where UAVs operate as an intermediate relay to forward data between a data center and end users. UAVs are adopted to provide efficient and secure data delivery; though they have limited resources and advanced algorithms, they are hot spots, black hole attackers. In Figure 3(b), a black hole attacker finds and attacks UAV, damages the user's data, and destroys the network. In Figure 3(c), we can see that when a black hole attacker tries to access the UAV's data, the victim UAV tries to respond with the help of a game-theoretic algorithm that defends itself to ensure users' security. The Stackelberg game theory ensures that in both cases when a black hole attacker tries to attack whole resources or find some weak spots, it succeeds. Furthermore, if a black hole attacker attacks data, the game theory utilizes its optimal strategy to recover the data. Finally, the defender UAV blocklists the attacker and informs all end devices about the attacker, making sure other users share any information with the black hole attacker by giving a black flag to it; on the other hand, the cooperative users have a white flag that describes the behavior of users in the network.
# A Review of the Network Arch Bridge
## 3 Network Arch Bridges of Different Structural Systems around the World
### Stuttgart Stadtbahn Bridge  
The Stuttgart Stadtbahn Bridge, Figure 15, is the world's first railway network arch bridge, with hangers made of carbon fiber-reinforced plastic (CFRP) [31]. CFRP elements have the advantage of having a very small cross-section, only a quarter compared to the steel solution. Due to the light weight, the installation of the hangers required only construction workers and no crane. As stated in refs. [32; 33], the use of CFRP changes entirely the boundary conditions for the selection of hangers. CFRP has very good resistance to fatigue; therefore, the criterion to be considered during design is utilization of the tensile load capacity. This is the reason behind being able to reduce the cross-sections of the hangers to a quarter compared to similar hangers made in steel. Also, [34] states another advantage of CFRP hangers: the reduced cross-section in relation to the small Young's modulus (180,000 N/mm\({}^{2}\)) leads to larger elongations. With the possibility of increasing the pre-stress in the hanger cross-section, the natural frequency of the hangers will shift upward. [PERSON] also states [34] that wind-induced vibrations of CFRP tendons above 10 Hz do not reduce the service life in case of a suitable design of the connections. In [33; 34; 35], the fabrication process for such tendons is explained, and simulation of the behavior of the hangers after 100 years of train operation is shown. For the Stuttgart Stadtbahn Bridge, a life-cycle assessment (LCA) was not required, but [PERSON] in ref. [36] performed an LCA for another competition for a railway crossing with a span of 130 m. The CO\({}_{2}\) emissions for the variant with steel hangers were almost three times higher than those of the bridge with CFRP hangers, and the energy expenditure for steel was twice as high. Stuttgart Bridge was the first NAB with CFRP tendons as hangers to win in a competition based on initial cost against steel hangers. It was opened in 2021, and in 2022 won the German Civil Engineer Award [37] for being an \"outstanding example of engineering design and answers current questions in civil engineering\". The construction of the Stuttgart Stadtbahn Bridge opens the possibility for new materials such as CFRP to be used in the design of future network arches.  
Figure 14: Hanger crossing device in the form of a needle eye; from Google Street view.
## 4 System Modelling Methodology
### Basic Signal Model  
Figure 1c shows that the wireless signal incident on a given element of the conventional T/R RIS is divided into transmitted and reflected signals. To characterise this RIS feature, let \(S_{m}\) denote the signal incident on the \(m^{th}\) element of the T/R RIS, where \(m\in M\triangleq 1,2,\ldots,M\). The signals transmitted and reflected by the \(m^{th}\) element can be modelled as \(x_{m}^{t}=(\sqrt{\beta_{m}^{t}}e^{\beta_{m}^{t}})S_{m}\) and \(x_{m}^{r}=(\sqrt{\beta_{m}^{r}}e^{\beta_{m}^{t}})S_{m}\)[37], where \(\sqrt{\beta_{m}^{t}}\in[0,1],\theta_{m}^{t}\in[0,2\pi)\), and \(\sqrt{\beta_{m}^{r}}\in[0,1],\theta_{m}^{r}\in[0,2\pi)\) are the amplitude and phase shift response of the \(m^{th}\) element's transmission and reflection coefficients, respectively. In practical scenarios, the phase shifts (i.e., \(\theta_{m}^{t}\) and \(\theta_{m}^{r}\)) can be chosen independently of each other. On the other hand, the adjustments for the amplitude coefficient (i.e., \(\sqrt{\beta_{m}^{t}}\) and \(\sqrt{\beta_{m}^{r}}\)) are coupled. Thus, the sum of the transmitted and reflected signal powers must be equal to the power of the incident signal, which should satisfy each element's condition in (1).  
\[\beta_{m}^{t}+\beta_{m}^{r}=1,\quad\forall m\in\mathcal{M} \tag{1}\]  
Accordingly, the conventional operating protocol for a T/R RIS splits the power of the incident signal between the transmitted and reflected signals, with a splitting ratio of \(\beta_{m}^{t}:\beta_{m}^{r}\). In this case, the transmission and reflection coefficient matrices are given as in (2) and (3), respectively, in which \(\beta_{m}^{t},\beta_{m}^{r}\in[0,1]\) and \(\theta_{m}^{t},\theta_{m}^{r}\in[0,2\pi],\forall m\in\mathcal{M}\). As can be noticed, both coefficients can be optimised, resulting in a higher degree of flexibility in the communication system's design. However, the conventional protocol wastes half of the signal power, resulting in less coverage and higher power loss.  
\[\mathbf{\Theta}_{t}^{\text{ES}}=\text{diag}\bigg{(}\sqrt{\beta_{1}^{t}}e^{ \beta_{1}^{t}},\sqrt{\beta_{2}^{t}}e^{\beta_{2}^{t}},\ldots,\sqrt{\beta_{M}^{ t}}e^{\beta_{M}^{t}}\bigg{)} \tag{2}\]  
\[\mathbf{\Theta}_{r}^{\text{ES}}=\text{diag}\Big{(}\sqrt{\beta_{1}^{t}}e^{ \beta_{1}^{t}},\sqrt{\beta_{2}^{t}}e^{\beta_{2}^{t}},\ldots,\sqrt{\beta_{M}^{ r}}e^{\beta_{M}^{t}}\Big{)} \tag{3}\]
# A Community-Based Construct Method for an Inter-Satellite Communication Network of Satellite Swarm
## 5 Simulation Analyses
### Simulation Analyses of Dynamic Connection of ICNS  
We designed simulations on the dynamic connection of ICNS, which was based on the results of simulation group 1 and simulation group 3 in Section 5.1. We planned dynamic paths from each satellite to all other satellites during each slot, that is, there was a simulation case of \(DP(v_{i},v_{j},k)\) for each possible combination of a source node \(v_{i}\in V\), a destination node \(v_{j}\in V\) (\(v_{i}\
eq v_{j}\)), and a plan slot \(k\) (\(1\leq k\leq\Gamma\)). Then, we computed DCD for each slot according to the results of planning dynamic paths. If \(DP(v_{i},v_{j},k)\) did not exist, DCD for slot \(k\) was 0.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Simulation Group Number** & **Improved or Not** & **Number of Slots** \\ \hline
1 & Yes & 20 \\
2 & No & 20 \\
3 & Yes & 40 \\
4 & No & 40 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Introductions of simulation groups.  
Figure 4: Results of the constructions of ICNS: (**a**) Simulation group 1; (**b**) Simulation group 2; (**c**) Simulation group 3; (**d**) Simulation group 4.  
Figure 5: Structural entropies of CCLC within each slot.  
The results of simulation group 1 are shown in Figure 7.  
As the blue, grey, and green lines in Figure 7 show, DCDs for the same slot in state period 1, state period 2, and state period 3 were the same. However, in state period 4, some S&Ds did not have available dynamic paths from slot 14 to slot 20, and correspondingly, the ICNSs were not able to be dynamically connected from slot 14 onwards. The reason for this phenomenon was that a dynamic path whose plan slot was at the back of a state period may have finished until the next state period. For S&Ds that had planned dynamic paths from slot 14, if they were in the first three state periods, their dynamic paths may have finished in the next state period, which is still within the task time interval; nevertheless, if they were in the last state period, their dynamic paths may not have finished at the end of task time interval, so these dynamic paths are believed to have been non-existent in the task time interval. The results of simulation group 3 are shown in Figure 8, which are similar to simulation group 1 and will not be introduced in this paper.
### Our Contribution  
We constructed the first lattice-based GS-MR scheme from lattice assumption. In the GS-MR scheme, the message will be recovered in full while the signature is verified as being correct. Therefore, in the rest of this paper, we will use the verification parameter to represent the total size of the message-signature required for the verification phase. The specific contributions are as follows:  
1. We construct a GS-MR based on the Abe-Okamoto signature scheme [25] (ASS) combined with the sign-hybrid-encrypt framework. In the key generation phase, we combine a ring version of [PERSON]'s signature scheme (BSS) with an algorithm for generating ring trapdoors to distribute private signing keys to the group members. In the signature generation phase, the member's identity ID is first encrypted into cipher text using a double encryption algorithm with CCA-security [26] (LPR encryption scheme) to ensure the anonymity and traceability of the group member's identity; The encrypted result is then used as part of the input to the LSS combined with the ASS to generate the final signature.
2. We prove that the GS-MR scheme satisfies correctness (with message recoverability), full anonymity, and traceability under the random oracle model (ROM). In addition, the anonymity of GS-MR relies on ring learning with errors assumption (RLWE), and the traceability of GS-MR relies on the short integer solution assumption (RSIS).
3. We have experimentally performed some simple evaluations of the proposed GS-MR scheme, which include a comparison of the key and verification parameters, respectively. Then, we compare three existing lattice-based group signature schemes [19; 24; 27] with the proposed GS-MR scheme and perform an exhaustive verification parameter size analysis. According to the results of the analysis, the proposed GS-MR scheme reduces the verification parameter size by an average of 39.17%.  
The structure of this paper is as follows. In Section 2, we introduce the symbols, lattice, the RSIS and RLWE problems, and some algorithms. In Section 3, we introduce the definition and security model of the GS-MR scheme. Then, we introduce the proposed scheme in Section 4. The security analysis is shown in Section 5. Section 6 presents the efficiency analysis. The last section is a summary of the paper.