esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
In the present study, the earthy odor molecules geosmin and 2-methylisoborneol were used to stimulate the olfactory system via inhalation in order to understand their effect on brain wave activity. The changes of 25 EEG power spectrum values were analyzed from 32 electrode sites located at the prefrontal, frontal, central, temporal, parietal, and occipital regions according to the International 10-20 System. In the results, we presented only significant changes in absolute and ratio values before and during the inhalation of both compounds with the exclusion of relative values (Tables 1-3). Figures 2 and 3 illustrate the t-mapping of significant changes of absolute power spectrum values. The results revealed that geosmin and 2-methylisoborneol showed significantly (\(p\)\(<\) 0.05) different EEG activity in women and men. In both genders, significant changes were observed in 16 and three indices during the inhalation of geosmin and 2-methylisoborneol, respectively. Table 1 shows significant changes of EEG power spectrum values before and during the inhalation of geosmin in both genders. A significant decrease (\(p\)\(<\) 0.05) of absolute waves such as fast alpha (4.1374-1.6438 \(\mu\)V\({}^{2}\)), beta (14.7287-10.4411 \(\mu\)V\({}^{2}\)), high beta (7.6548-5.3986 \(\mu\)V\({}^{2}\)), low beta (3.2775-2.1519 \(\mu\)V\({}^{2}\)), and gamma (9.4893-7.2170 \(\mu\)V\({}^{2}\)) was observed in both genders, specifically at the centroparietal (Cp6) region during the inhalation of geosmin (Figure 2). Furthermore, relative values of alpha, beta, fast alpha, slow alpha, low beta, and mid beta significantly decreased during the inhalation of geosmin compared to those before inhalation. In addition, the ratio of sensorimotor rhythm (SMR) to theta (RST), the ratio of mid beta to theta (RMT), and the ratio of SMR mid beta to theta (RSMT) significantly decreased during the inhalation of geosmin. Furthermore, spectral edge frequency 50% (SEF50) significantly decreased at the T8 region (12.3871-10.1593 \(\mu\)V\({}^{2}\)) and relative theta significantly increased at the Af4 region. In the case of 2-methylisoborneol, there was no significant change in absolute wave activity. However, RST (Af4 and Cp5), RMT (Cp5) and RSMT (Cp5 and AF4) significantly decreased during the inhalation of 2-methylisoborneol (Table 3).
## 2 Data Source and Experimental Methodology
### Data Source and Preprocessing  
In this paper, a total of 580 unconventional reservoir cores NMR T\({}_{2}\) spectrum and the corresponding movable fluid percentage were collected, all of which were obtained through the laboratory measurement. The movable fluid percentage of the reservoir was predicted based on the shape characteristics of the core NMR T\({}_{2}\) spectrum of unconventional oil reservoirs. By discretizing the NMR T\({}_{2}\) spectrum of the core, the horizontal axis T\({}_{2}\) relaxation time of each discrete point was fixed. At this time, all discrete points' T\({}_{2}\) distribution values could represent the shape characteristics of the core NMR T\({}_{2}\) spectrum [11]. The result of the discretization processing of different cores' NMR T\({}_{2}\) spectrum is shown in Figure 2. In the unconventional reservoir NMR T\({}_{2}\) spectrum, T\({}_{2}\) distribution values of discrete points with a relaxation time greater than 1000 ms is basically 0. Therefore, for the nuclear magnetic resonance T\({}_{2}\) spectrum of each core, collecting T\({}_{2}\) distribution values of the first 55 discrete points could completely extract the shape characteristics of the core NMR T\({}_{2}\) spectrum. Before model training, the data needed to be standardized. The specific standardized processing methods were as follows:  
\[\mathbf{a}_{i}=\frac{\mathbf{x}_{i}-\mathbf{\mu}}{\mathbf{\sigma}}\, \tag{1}\]  
where: \(\mathbf{a}_{i}\) and \(\mathbf{x}_{i}\) are the parameter value after standardization and the original parameter value, \(\mathbf{\mu}\) is the average value of input parameters, and \(\mathbf{\sigma}\) is the standard deviation of input parameters.  
The original data set was divided into a training set and a test set. The training set was mainly used for model learning, and the test set was used for evaluating the effect of model learning. The training set contained 500 cores NMR data, and the test dataset contained 80 cores NMR data.  
Figure 1: NMR T2 spectrum of cores with different tightness.  
Figure 2: Discretization of the core NMR T\({}_{2}\) spectrum. (1) Core sample 1; (2) Core sample 2.
# Capacity Analysis of Incentive Schemes in Opportunistic Networks
## 4 Capacity Analysis for Incentive Schemes
### Transmission Protocol  
If sources \(i\) and \(j\) want to send messages to destinations \(k\) and \(j\), respectively, for an arbitrary other node \(k\), the following equation should be satisfied [18]:  
\[|X_{j}-X_{i}|\geq\frac{\Delta}{2}(|X_{i}-X_{k}|+|X_{j}-X_{i}|)\,, \tag{16}\]  
where \(X_{i}\) is the position of \(i\) and \(|X_{i}-X_{j}|\) is the distance between them. The basic inequation is often used in research directly, and is associated with incentive schemes similar to the physical model in [18].  
To have transmissions, the incentive degree should have a lower bound, that is,  
\[\mathfrak{S}(\mu)\geq\mathfrak{S}\,. \tag{17}\]  
According to inequality properties, we have  
\[\frac{\mathfrak{S}_{k}}{\mathfrak{S}_{i}+1}\leq\frac{\mathfrak{S}}{\mathfrak{S }+1}\,. \tag{18}\]  
If we assume successful transmission probabilities between nodes are the same, the closest node has the highest cooperation degree. Moreover, \(+1\) in the left formula has little impact on the inequation. Therefore, from the previous formula we can infer that  
\[\frac{\mathfrak{S}\Big{(}\frac{h_{k}}{|X_{k}-X_{i}|^{a}}\Big{)}}{\mathfrak{S} \Big{(}\frac{h_{i}}{|X_{k}-X_{i}|^{a}}\Big{)}}\leq\frac{\mathfrak{S}}{ \mathfrak{S}+1}\,, \tag{19}\]  
Figure 5: Scheduling policy.  
where \(\alpha\) is the distance decay and \(\mu\) replaces entanglement \(\lambda\) in (3) to avoid conflicts with the throughput \(\lambda\). After expanding \(\$(\mu)\), we have  
\[\frac{|X_{i}-X_{j}|^{\alpha}}{|X_{k}-X_{j}|^{\alpha}}\frac{\mu_{k}}{\mu_{i}} \leq\frac{\$}{\$}\,. \tag{20}\]  
In the worst situation, \(\frac{\mu_{k}}{\mu_{i}}\) is equal to \(\frac{\mu_{-}}{\mu_{\perp}}\) where \(\mu_{\top}\) is the maximum feasible entanglement in the scene and \(\mu_{\perp}\) is the minimum feasible entanglement to lead to the emergence of communications in the scene which meets that \(\forall\mu>\mu_{\perp}\) implies \(q^{*}(\mu)>0\) and \(\$^{*}(\mu)>0\). Then, we have the inequation with the distance and incentive schemes:  
\[\begin{split}|X_{k}-X_{j}|&\geq\left(\frac{\mu_{ \top}}{\mu_{\perp}}\frac{\$+1}{\$}\,\right)^{\frac{1}{\alpha}}|X_{i}-X_{j}|\\ &=(1+\Delta)|X_{i}-X_{j}|\,,\end{split} \tag{21}\]  
where \(\Delta\coloneqq\left(\frac{\beta P_{\min}}{\text{P}_{\max}}\right)^{\frac{1} {\alpha}}-1\).
## 6 Implementation Scenarios
### Zero to a Few Obstacles  
In this scenario, there exists a LoS between BS1 and U3 but with few obstacles; in the case of fast movement or high winds, the network will suffer from high interference and the attenuation of the downlink signal. Although the BS's signals guarantee high signal quality and minimal latency, optimising the signal routes through the HRIS on top of B1 will increase communication reliability with respect to higher interference and attenuation.  
However, using the conventional T/R RIS will provide the user with the signal but at the cost of signal quality and latency since it can only serve one user at a time to transmit or reflect the signal. In the current example, there is only one user, but in the case of multiple users, this will result in a higher delay time.  
On the other hand, using the current BS handover mechanism will generate 10% higher latency, keeping in mind that the signal will be received with better quality.
# Survey of Dense Video Captioning: Techniques, Resources, and Future Perspectives
## 3 Techniques for Dense Video Captioning  
Recent years have seen remarkable progress in video captioning: the automated generation of descriptive text for video content. Central to these advancements are attention-based methodologies, which enable models to dynamically focus on the most relevant spatiotemporal features within videos. This review provides a structured analysis of cutting-edge techniques in dense video captioning, emphasizing three methodological pillars: attention mechanisms, transformer architectures, and encoder-decoder frameworks. This study highlights innovations in modeling long-range temporal dependencies and integrating multimodal cues (visual, auditory, textual) to enhance caption relevance and fidelity. These technical breakthroughs are particularly valuable given the growing demand for video captioning in human-computer interaction systems, content-based retrieval platforms, and automated video indexing pipelines. As visualized in Figure 1, our analysis systematically dissects current approaches through three complementary lenses-attentiondriven strategies, transformer-based paradigms, and encoder-decoder architectures-to offer a comprehensive roadmap for researchers and practitioners in this dynamic field.
## 4 Genomic Instability, Fe-S Clusters and Iron Chelators  
The physical structure of the genome consists of DNA molecules wrapped around histone proteins that are then assembled in chromosomes. DNA divides in a carefully monitored process controlled by the DNA damage response (DDR) machinery. To maintain genomic stability there needs to be accurate replication and repair; if this cannot be achieved, it can lead to cancer development. As described earlier, aberrant levels of iron can increase ROS via Fenton chemistry. The increased ROS can cause DNA damage which implicates iron in genomic instability. In addition, iron is also involved in DNA replication and repair as Fe-S clusters are integral parts of enzymes involved in the DDR. Key enzymes involved include DNA polymerases (Pol \(\alpha\), Pol \(\delta\) and Pol \(\epsilon\)), the DNA helicases, primases and glycosyles, which all have Fe-S clusters. Notably, the redox activity of Fe-S clusters in these enzymes has also been proposed to play crucial roles in the biochemical and cellular functions [77]. Fe-S clusters are vulnerable to oxidation due to their redox ability [63]. Fe-S clusters can play a structural or active role in DNA damage repair, and their abundance in DDR suggests that they are vital for their function.  
Recent studies have shown possible links between Fe-S metabolism and cancer. One possible link is evidence that the overexpression of microRNA miR-210 is involved in suppressing ISCU in breast cancer solid tumours [78]. ISCU and frataxin are controlled at the transcription level by p53, which controls the volume of ROS in cells. Therefore, changes in Fe-S metabolism could be crucial in cancer progression. Other Fe-S cluster proteins that have shown links to cancer are NEET proteins, such as nutrient-deprivation autophagy factor-1 (NAF-1), which are overexpressed in cancer and are also thought to make the cancer cells resistant to oxidative stress [79]. It is clear that iron is essential in genomic stability through the enzymes involved in the DDR and that aberrant iron levels could also lead to tumorigenesis and cancer. Further, as iron levels are elevated in cancer cells and DNA is required at a higher volume, the ability to remove chelate-able iron in cancer could restrict DNA replication. Even so, as some iron chelators can induce ROS, it could also cause DNA damage to the cancerous cells by inhibiting the volume of iron  
Figure 4: **(A)** The labile iron pool in a normal healthy cell. Iron enters via the transferrin receptor before getting reduced and used for storage or the labile iron pool (**A**). (**B**) How the labile iron pool is altered in cancer cells, upregulated TFR1, STEAP3 and DMT1, while a downregulated Ferroportin results in a larger labile iron pool.  
available or potentially by inactivating the iron enzymes involved in the DNA damage response. More recently, iron chelators have been shown to inhibit the DNA repair protein ALKBH2 by removing available iron that is critical for catalytic function [80].
# Multi-Agent Reinforcement Learning: A Review of Challenges and Applications
## 3 The Limits of Multi-Agent Reinforcement Learning
### Varying Learning Speed  
The effect of environmental non-stationarity is to make the reward information of state-action pairs related to past iterations obsolete, and this effect grows as time progresses. Several approaches tackle this challenge by adopting varying learning rates with the aim of guiding the training to the most efficient joint policy. In the context of cooperative games, hysteretic Q-learning [26] is an algorithm that improves the performance of standard independent learners approaches. The reward is shared between agents and conditioned by the joint action; thus, an agent can be punished even if it select the optimal option due to the bad actions of other teammates, who might be exploring. The algorithm applies a different learning rate if the update would cause a decrease of the Q-value using the update Equation (15) under the condition that \(\beta<\alpha\).  
\[\delta\gets r-Q_{i}(a_{i},s_{i}) \tag{15}\] \[Q_{i}(a_{i},s_{i})\leftarrow\begin{cases}Q_{i}(a_{i},s_{i})+ \alpha\delta\text{ if }\delta\geq 0\\ Q_{i}(a_{i},s_{i})+\beta\delta\text{ else }\end{cases}\]  
This heuristic has the positive effect of implementing \"optimistic\" agents that are able to ignore the bad rewards caused by the actions of others, improving the performance compared to traditional IL approaches in cooperative MGs. In [27], the authors argue that in order to obtain robustness against incorrect future reward estimation, in the early iterations of the reinforcement learning algorithms, the agents need to show some sort of leniency towards others. In particular, the future reward for a performed action can be assessed as the maximum reward received over a number of different actions chosen by the other agents. The proposed algorithm is called lenient multi-agent reinforcement learning (LMRL) and implements varying leniency, lowering the amount that each agent exhibits in the later stages of learning. Agents decide the amount of leniency to apply by associating a temperature with each state-action pair. The level of leniency is inversely proportional to the temperature and decreases as those state-action pairs are selected.
## 3 Proposed Methodology
### A Hybrid Boundary Tour
This process tries for all nodes and then sufficiently learns based on the natural bound of each node, until it progressively reaches the best location for the AUV to sense information from all sensor nodes. Accordingly, a new tour can be constructed with the same sequence as the \(NT\) tour estimated by MACO. Therefore, a boundary tour (BT) estimation algorithm is proposed to optimize the path trajectory for the AUV to visit the nodes with minimum tour distance, which in turn affects AUV energy consumption. For simplicity, we change the vector space direction participant in changing the direction of the line when the AUV sails from the \(ith\) node to the \(jth\) node. Hence, along with the aforementioned strategy, the proposed algorithm tries to construct a new tour \(NT\) randomly inherited from the best previously allocated coordinates and thus generates unexpected coordinates for the AUV. The inheritance process can be expressed as follows [23]:\[L(\delta)_{j}^{\delta+1}= \begin{cases}L(\delta)_{j}^{\delta}&\quad\text{if }U<0.1,\\ NT_{j}^{\delta}&\quad\text{otherwise}.\end{cases}  \tag{13}\]  
\[NT^{\delta+1}\ =\ \left\{ \begin{array}{ll}L(\delta)^{\delta+1}&\quad\text{if }C\left(L(\delta)^{\delta+1}\right)<C(NT^{\delta}),\\ NT^{\delta}&\quad\text{otherwise}.\end{array} \right. \tag{14}\]  
where \(g\) represents the current iteration. The inheritance mechanism updates some direction in the tour balance exploration (new random directions) and exploitation (using the best previously allocated coordinates so far). In addition, the updated directions ensure diversity during the search trip. Thus, comparing the new tour \(L(\delta)^{\delta+1}\) with the current best tour helps the algorithm to focus on improving their solutions progressively. Algorithm 2 summarizes the process of BT construction. ```
Begin
1:\(g=1\); \(nAnt=60\); \(n=SN\);
2:for m from 1 to \(nAnt\)do
3: Calculate \(kth\) points \(P_{k}\) within the node bounds;
4: Calculate direction vectors \(D\overset{\rightarrow}{V}_{p}\);
5: Determine the best locations for the AUV using \(\delta\) and direction vectors;
6: Determine \(C(L\delta)\);
7:endfor
8:while\(g<Maxg\)do
9:for m from 1 to \(nAnt\)do
10:for j from 1 to \(n\)do
11: Calculate \(P_{k}\) and \(D\overset{\rightarrow}{V}_{p}\) for each \(SN\) using Equations (9) and (10);
12: Create new locations for the AUV using \(\delta\) and direction vectors;
13:if\(U\ \leq\ 0.1\)then
14: Create new locations for the AUV using \(\delta\) and direction vectors;
15:else
16: Use the best previous AUV location from the current population;
17:endif
18:endfor
19:if\(C\left(L(\delta)^{\delta+1}\right)<C(NT^{\delta})\)then
20:\(NT^{\delta+1}\ =L(\delta)^{\delta+1}\);
21:else
22:\(NT^{\delta+1}\ =\ NT^{\delta}\);
23:endif
24:endfor
25:\(g\)\(=g+1\);
26:endwhile
27:End
```  
**Algorithm 2**Pseudo-code of _boundary tour_ (\(BT\)) algorithm.
## 4 Methodology
### Deep Reinforcement Learning Setup
#### 4.1.1 Observation Description  
The observation space--as mentioned in Section 3--consists of four components: \(O_{i}^{t}=\left[o_{e}^{t},o_{o}^{t},o_{l}^{t},o_{aux}^{t}\right]\). \(o_{e}^{t}\) is the relative goals' positions in the ego-robot's polar coordinates. \(o_{e}^{t}\) is a 3D tensor \(\left(o_{e}^{t}\in\mathbb{R}^{N-1\times q\times c\times N}\right)\). The first dimension \(q\) represents the values of the past consecutive number of frames of this observation. The second dimension \(b\) represents the goal's distance and heading from the ego-robot in the ego-robot's polar coordinates. Thus, \(b=2\). The third dimension \(N\) is the number of goals in the environment. Since \(o_{e}^{t}\) encompasses the information of all the goals relative to the ego-robot, it enables the robot to choose which goal position to aim towards. \(o_{o}^{t}\) is the relative goals' positions in the other robots polar coordinates. \(o_{o}^{t}\) is a 4D tensor \(\left(o_{e}^{t}\in\mathbb{R}^{N-1\times q\times c\times N}\right)\). The first dimension is the number of the other robots \(N-1\), while the third dimension \(c\) represents the goal's distance and heading from each of the other robots in its polar coordinates. Since \(o_{o}^{t}\) encompasses the information of all the goals relative to the other robots, it enables the ego-robot to predict the intents of other robots and which robot is aiming towards which goal; this aids the ego-robot to choose a unique goal position to aim towards. \(o_{l}^{t}\) is the 360 deg laser scanner data of the ego-robot, and it is a 2D tensor \(\left(o_{l}^{t}\in\mathbb{R}^{q\times 360}\right)\). The second dimension represents all 360 distance values. \(o_{l}^{t}\) enables the robot to detect both static and dynamic obstacles and helps the robot to navigate without collisions. Finally, \(o_{aux}^{t}\) is a 1D vector \(\left(o_{aux}^{t}\in\mathbb{R}^{3}\right)\) representing the time spent since the ego-robot started to move and the previous action, which consists of: the linear velocity \(v_{l}^{t-1}\) and rotational velocity \(v_{r}^{t-1}\) of the ego-robot at the last time step. Since the policy is required to train to minimize the expectation of the total arrival time of all robots to their goals successfully, the policy for each robot requires an input that informs it of the time it is spending while moving. Having the previous action as an input informs the robot about its intent in the last time step.
## 4 Results and Discussion
### Multi-Step-Ahead Prediction by the Autoencoder  
For predicting a multi-step prediction horizon, we experimented with two different LSTM autoencoder models. In the first model, the encoder and decoder have three LSTM layers with 75, 50, and 25 units, respectively, and in the second model, the encoder and decoder have two LSTM layers consisting of 50 and 25 units each. The size of the latent space was set to 15 in each variant. The input window size \((w)\) and the prediction horizon \((h)\) were both set to 20. To evaluate prediction performance by the model, root mean square error was used as the performance metric, which is defined in the following equation:  
\[RMSE=\sqrt{\frac{\sum_{t=1}^{h}\left(\tilde{Y_{i}}-Y_{i}\right)^{2}}{h}} \tag{12}\]  
Out of the 17,000 data points, the first 14,000 data points were used to train the model, and the rest were used for testing. The training and testing segments are presented in Figure 8.  
Several experiments were performed by varying the batch size and the number of epochs, as listed in Table 3. The lowest test RMSE of 0.0410 was obtained for a batch size of 64 when the model was trained for 100 epochs. The model was trained using normal sensor signals only. After the model was trained, faulty sensor signal samples are provided as input to predict 20 future data points.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & & **Model 1** & & \\ \hline
**Input Window** & **Prediction Horizon** & **Batch Size** & **Epochs** & **MSE** \\ \hline
20 & 20 & 64 & 200 & 0.0588 \\
20 & 20 & 32 & 200 & 0.0572 \\
20 & 20 & 20 & 200 & 0.0582 \\ \hline \multicolumn{5}{c}{**Model 2**} \\ \hline
20 & 20 & 64 & 100 & **0.0410** \\
20 & 20 & 32 & 100 & 0.0596 \\
20 & 20 & 16 & 100 & 0.0531 \\ \hline \hline \end{tabular}
\end{table}
Table 3: List of parameters for model training.  
Figure 7: SHAP summary plot for (**a**) bias faults, (**b**) drift faults, and (**c**) stuck faults.  
The prediction performance of the autoencoder model for the test data segment is presented in Figure 9, where it is evident that the prediction by the model follows the general pattern of the test data. At this point, the trained model is employed to detect faults by utilizing its ability to predict a window of 20 time steps in the future.