esa-sceva/satcom-chunk-collection huggingface dataset:
## 8 Conclusions and Future Work
#### Future Work  
Several promising directions will guide the next iterations of Edge-FLGuard, as follows:  
* Adaptive Detection via Reinforcement Learning: Future implementations may integrate online learning techniques to dynamically adjust anomaly thresholds and response strategies in real-time, based on evolving device behavior or contextual feedback.
* Robust Federated Defenses: We aim to enhance resilience to adversarial clients by adopting Byzantine-resilient aggregation algorithms (e.g., Krum and median) and incorporating client reputation scoring to detect and mitigate malicious behavior during FL rounds.
* Deployment in Smart City Environments: A critical step will be field deployment in live urban IoT ecosystems--such as smart traffic infrastructure, utility monitoring, or edge video analytics platforms--to validate Edge-FLGuard's robustness under real-world data and network variability.
* Model Explainability for Edge Decisions: Integrating explainability techniques (e.g., SHAP) will be crucial for enhancing the transparency of anomaly decisions, particularly in regulated or human-supervised domains. This will support auditability, trust, and adoption in safety-critical applications.
* Energy and Thermal Profiling for Ultra-Low-Power Devices: While Edge-FLGuard operates within acceptable memory and latency constraints, future work will include profiling power consumption and thermal behavior across ultra-constrained hardware platforms (e.g., ARM Cortex-M series and battery-powered sensors). This will inform model compression strategies and sustainable edge AI deployment at scale.  
From our experiments on representative edge devices (Raspberry Pi 4 and Jetson Nano), we observed that lightweight deep models can operate comfortably within memory and latency constraints for real-time inference. However, scaling to large edge fleets introduces cumulative training and update overheads. Training rounds in federated settings required 3-4 s per edge node and added communication cost (~180-250 KB per round), which are acceptable for periodic training, but may challenge battery-operated or bandwidth-constrained nodes. Furthermore, although hardware platforms with 1-4 GB of RAM support current models, deployment on microcontrollers or ultra-low-power IoT sensors will require additional work on model compression, power profiling, and thermal constraints. These insights reinforce the need for adaptive orchestration strategies and energy-aware learning pipelines in future implementations.  
This research received no external funding.  
**Institutional Review Board Statement: Not applicable.**  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data supporting this study's findings are available upon reasonable request from the corresponding author. Sharing the data via direct communication ensures adequate support for replication or verification efforts and allows for appropriate guidance in its use and interpretation.  
**Conflicts of Interest:** The author declares no conflicts of interest.
## 2 Materials and Methods
### Validation  
The mathematical model was validated using the wind tunnel testing on NACA airfoils from the literature [23]. The study under consideration comprises experiments conducted in a wind tunnel with varied density. Through the variable-density wind tunnel, experimental data can be obtained for high-Reynolds-number flows. For data validation, CFD analysis was performed with a 3D single-wing geometry with the NACA 4415 airfoil. At a 20.7 atm, analyses were conducted at 21.1836 m/s using a Reynolds number of 3,110,000. The turbulence model SST \(k\)-\(\omega\) (with wall function) was used to model eddy viscosity.  
In order to establish the boundary layer's mesh structure, the value of \(y^{+}\) was intended to be 10. This parameter is a dimensionless value and is used to determine the height of the first layer of the mesh elements to be created for the boundary layer. Depending on the dimensionless \(y^{+}\), the first layer thickness \(y\) is defined as [24]  
\[y=\frac{u_{\tau}y^{+}}{v}, \tag{5}\]  
where \(v\) is the kinematic viscosity and \(u_{t}\) is the friction velocity at the closest wall, defined as [24]  
\[u_{\tau}=\sqrt{\frac{\tau_{\omega}}{\rho}}, \tag{6}\]  
where \(\rho\) is the density at the wall and \(\tau\omega\) is the wall shear stress, calculated by [18]  
\[\tau_{\omega}=\frac{\rho C_{f}V^{2}}{2}, \tag{7}\]  
where \(V\) is the velocity and \(C_{f}\) is the skin friction coefficient, calculated by [25]  
\[C_{f}=\frac{0.027}{Re^{1/7}}, \tag{8}\]  
In order to achieve the goal of \(y^{+}=10\), a boundary layer mesh structure consisting of 20 layers with 0.01 mm height for the first layer's elements was constructed. Figure 3 depicts the boundary layer mesh structure.  
The experiments of the literature research were conducted at different angles of attack, and the analyses of the present study were carried out accordingly. A comparison of the experimental study's data and the CFD analysis's resultant data is presented in Figure 4. The experimental results and the mathematical model diverge at angles of attack of \(12^{\circ}\) and above. At small angles of attack, the results do, however, appear to accord. In the swarm motion analysis, which is the main subject of the present study, small angles of attack were used, corresponding to high performance, so the mathematical model is suitable for the analysis of small angles of attack.  
Figure 3: The mesh structure of boundary layer.
###### Abstract  
Cr(VI) detoxification by biotereatment is considered one of the most practical detoxification methods, especially at low-to-medium concentrations. Although the capabilities of chromium-reducing bacteria and related enzymes in removing Cr(VI) have been explored, little is known about their differences in engineering applications. In this study, _Ochroductrum_ sp. CUST210-1 was isolated and its chromate reductase identified and separated as biological elements in biosystems developed for Cr(VI) removal. Results indicate that intracellular Cr(OH)\({}_{3(\theta)}\) accounted for 88.01% of Cr(VI) reduction product, and a possible reduction mechanism was exposed. The chromate reductase in _Ochroductrum_ sp. CUST210-1 was ChrR protein, and its crystal structure was revealed. The toxicity of Cr(VI)-containing wastewater was decreased by 57.8% and 67.0% (at minimum) by the CUST210-1 strain and ChrR, respectively. The _Ochroductrum_ sp. CUST210-1 biosystem demonstrated good adaptability to pH (7-9), and the ChrR biosystem exhibited high removal efficiency (>98.2%) at a wide range of temperatures (25 \({}^{\circ}\)C-40 \({}^{\circ}\)C). The outlet Cr(VI) concentration of the CUST210-1 biosystem met the industrial discharge limit of 0.5 mg L\({}^{-1}\) when the inlet Cr(VI) concentration in the actual Cr(VI)-containing wastewater was \(<\)430 mg L\({}^{-1}\). The stricter water quality standard of 0.05 mg L\({}^{-1}\) could be complied with by the immobilized ChrR biosystem when \(<\)150 mg L\({}^{-1}\) Cr(VI) concentration was introduced. These developed biosystems can be used in the bioremediation of various Cr(VI)-contaminated wastewaters. Regarding capital costs, those of the CUST210-1 biosystem were higher. To our knowledge, this is the first report comparing differences in the economic and operational characteristics of bacteria and enzyme biosystems for Cr(VI) removal.  
bioremediation; chromium-reducing bacteria; chromate reductase; hexavalent chromium-Containing Wastewater
## 3 Control Methods Used in Agricultural Vehicle Path Tracking Control
### Summary of Control Methods  
The path-tracking control method directly affects the operation accuracy and efficiency of autonomous agricultural machinery. Path tracking control methods such as PID, fuzzy algorithm, pure pursuit model algorithm, and model predictive algorithm have been used in agricultural vehicles' automatic navigation. The summary of path-tracking control methods for agricultural vehicles in different operating scenarios is shown in Table 5. Different from structured scenes, the agricultural machinery operation environment is complex and subject to interference from many uncertain factors. The characteristics of different agricultural machinery working scenes are different, such as paddy field mud, easy obstruction in orchards, and limited space in the greenhouse. In such cases, the accuracy, stability, and robustness of agricultural vehicles' path tracking will be reduced.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Working Scenario** & **Agricultural Vehicle** & \multicolumn{2}{c}{**Control Method**} & \multicolumn{2}{c}{**Improvement of Control Methods**} & \multicolumn{2}{c}{**Practical Difficulties**} \\ \hline \multirow{7}{*}{Dry field} & & \(\bullet\) & Fuzzy control & \(\bullet\) & Optimizing Stanley controller & \multirow{7}{*}{\(\bullet\)} & \multirow{7}{*}{\(\bullet\)} \\  & \(\bullet\) & \(\bullet\) & Stanley model & & parameters through GA & & \\  & \(\bullet\) & Four-wheel tractor & & control & & & Developing a SOSM controller & \(\bullet\) \\  & \(\bullet\) & Corn harvester & \(\bullet\) & Sliding model & & to suppress chattering & & \\  & \(\bullet\) & Peanut harvester & & control & & & Adjusting the look-ahead & \(\bullet\) \\  & \(\bullet\) & Rape Seeder & \(\bullet\) & Optimal control & & distance by PSO, fuzzy & & \\  & \(\bullet\) & \(\bullet\) & Pure pursuit & & algorithm, DRL, SOA & & \\  & & & model control & & & Adjusting the PID parameters & & adaptability \\  & & & \(\bullet\) & PID control & & by a single neuron & \\ \hline \multirow{7}{*}{Paddy field} & & \(\bullet\) & & & \(\bullet\) & Adjusting the look-ahead & \multirow{7}{*}{\(\bullet\)} \\  & \(\bullet\) & Rice transplanter & \(\bullet\) & Pure pursuit & & algorithm & \\ \cline{1-1}  & \(\bullet\) & Rice seeder & \(\bullet\) & model control & & & \\ \cline{1-1}  & \(\bullet\) & Sprayer & & control & & & \\ \cline{1-1}  & \(\bullet\) & Tracked tractor & \(\bullet\) & Sliding model & & observer & \\ \cline{1-1}  & & & & control & & \(\bullet\) & Improving robustness by & \\ \cline{1-1}  & & & & & combining fuzzy algorithm & & \\ \cline{1-1}  & & & & & and MPC & & \\ \hline \hline \end{tabular}
\end{table}
Table 5: Summary of path tracking control methods for agricultural vehicles.  
Figure 12: The tractor-trailer system.
## 3 Materials and Methods
### M-I Encoder  
The M-I encoder first utilizes an interest perceptron to detect potential interests embedded in each item in the session, then designs a decentralized GRU-based interest extraction network that uses the hidden state of the last step as a representation of each interest, and finally utilizes additive attention to obtain the user's final multi-interest representation vector. The interest perceptron uses multiple self-attention head vectors to detect the interest implied in the items clicked at each time step. Each self-attention header vector represents one interest, e.g., represents the first interest. As in Equations (12)-(14), \(W_{i}^{Q},W_{i}^{K}\) and \(W_{i}^{V}\in R^{d\times d}\) are parameter matrices used to map inputs to queries, keys, and values, respectively; and \(h_{\gamma_{i}}^{g}\) is the item clicked at each time step.  
\[S_{i}=[h_{\gamma_{1}}^{g},h_{\gamma_{2}}^{g},\dots,h_{\gamma_{m}}^{g}] \tag{12}\]  
\[head_{k}=Attention(W_{i}^{Q}h_{\gamma_{m}}^{g},W_{i}^{K}h_{\gamma_{m}}^{g},W_{ i}^{V}h_{\gamma_{m}}^{g}) \tag{13}\]  
\[E_{head}=[head_{1},head_{2},\dots,head_{i},\dots,head_{k}] \tag{14}\]  
The decentralized GRU-based interest extraction network consists of multiple recursive interest channels, and the number of channels of interest is the same as the number of self-attention heads. Multiple potential interests for each item are obtained through an interest perceptron, and then each channel models the sequential dependency of session items within each potential interest. For the ith channel, the interest representation \(head_{i}\) for each step is taken as input and the GRU credentials are updated as in Equations (15)-(18), where \(p_{t}\) and \(q_{t}\) are the reset and update gates in the GRU, respectively. \(\sigma_{g}\) and \(\sigma_{z}\) are the \(sigmoid\) and \(tanh\) activation functions, respectively. \(head_{i,t}\) is the ith potential interest representation embedded in the item clicked at time t, and \(x_{t-1}\) is the last state of interest in the ith channel.  
\[p_{t}=\sigma_{g}(W_{1}[head_{i,t},x_{t-1}]) \tag{15}\]  
\[q_{t}=\sigma_{g}(W_{2}[head_{i,t},x_{t-1}]) \tag{16}\]  
\[u^{\prime}=\sigma_{z}(W_{3}[head_{i,t},x_{t-1}\odot p_{t}]) \tag{17}\]  
\[u_{t}=(1-q_{t})\odot x_{t-1}+q_{t}\odot u^{\prime} \tag{18}\]  
Until the last step t, the user's current ith interest representation \(u_{t}\) is obtained. To further distinguish the different importance of interests and generate more informative representations for the user, we utilize additive attention to aggregate the above interest vector representations, as in Equations (19) and (20), where \(q^{T}\) is the query vector, \(v\) and \(b\) are the training parameters, and \(\beta_{i}\) is the attentional weight of the ith interest in the user's potential interests.  
\[\beta_{i}=q^{T}tanh(V\times u_{t,i}+b) \tag{19}\]  
\[\beta_{i}=\frac{\exp(\beta_{i})}{\sum_{p=1}^{k}exp(\beta_{p})} \tag{20}\]  
In addition, the \(u_{t}\) of all k channels are integrated together to obtain the final composite interest representation of the user: \(T^{s}=\sum_{i=1}^{k}\beta_{i}u_{t,i}\).
# Recommendation-Based Trust Evaluation Model for the Internet of Underwater Things
## 5 Recommendation Evaluation Process
### Belief Process  
Based on the assumptions we highlighted in Section 4.1, we introduce the belief function, outlined in Algorithm 2, which evaluates the credibility of recommendations by integrating several key factors: freshness, similarity, trustworthiness, and decay. Each factor contributes to the overall belief score, which is used to assess the belief in recommendations.  
The freshness factor, \(\Gamma_{kj}\), emphasizes the importance of the recency of the recommendation. It ensures that more recent recommendations are given higher weight. This interval adheres to a power-law distribution, expressed as [25]:  
\[\Gamma_{kj}=\eta_{sc}(t-t_{Tij})^{-\varepsilon}, \tag{9}\]  
where \(\eta_{sc}\) is a predetermined scaling constant, and \(\varepsilon\) is the exponent of the power law. This formula is used to underscore the significance of the recency and freshness of the trust evaluation made by a node.  
The similarity factor, \(s_{ik(t)}\), quantifies the extent of similarity between the trustor's previous interactions with recommenders, as detailed in Section 5.2.3.  
The decay factor, \(d\), accounts for the degradation of the trust score over time. This is particularly important to adjust the weight of the trust score based on how recent the interactions are. The decay is computed using the technique provided by MATMU.  
The overall belief score, \(B_{ij\gets k}\), is computed by integrating the aforementioned factors. The integrated formula is:  
\[B_{ij\gets k}=\frac{s_{ik(t)}\times\Gamma_{kj}\times(1-d_{Tij})\times T_{ ki(t)}}{\sum_{k^{\prime}}\left(s_{ik^{\prime}(t)}\times\Gamma_{k^{\prime}j}\times(1-d_{T \bar{t}^{\prime}j})\times T_{k^{\prime}i(t)}\right)},k\
eq k^{\prime} \tag{10}\]  
where \(s_{ik(t)}\) is the similarity factor, \(\Gamma_{kj}\) is the freshness factor, \(d_{Tij}\) is the decay factor for the trust score that the trustor \(i\) has towards the trustee \(j\), and \(T_{ki(t)}\) is the trustworthiness of the recommender \(k\) at time \(t\). Given this linear computational complexity of the proposedbelief function and the relatively small number of metrics involved, the computational cost is often negligible in practical applications.  
```
0: List of Received Recommendations \(R_{j}(t)=\{T_{k_{1}j},T_{k_{2}j},\ldots,T_{k_{n}j}\}\)
0: Weight of each Recommenders
1: Initialize evidence collection variable
2:for Each \(T_{kj}\) in \(R_{j}(t)\)do
3: computes \(s_{ik(t)}\)
4: compute \(\Gamma_{kj}\)
5: compute \(d_{T_{lj}}\)
6: find \(T_{ik}\)
7:\(B_{ij\gets k}=s_{ik(t)}\Gamma_{kj}T_{ik}(1-d_{Tij})\)
8: scale the results
9:endfor
```  
**Algorithm 2** Belief Process
## 3 Results and Discussion
### Adsorption Dynamics  
Adsorption under flow through conditions is carried out on a filter bed at specified parameters. Deterioration of the assumed adsorbent adsorption capacity is defined as the breakthrough of the bed. It ceases to absorb molecules, and the adsorbate concentration increases in the filtrate. The process continues until the bed is fully saturated and reaches the point of exhaustion. The adsorption capacity of the adsorbent layer to the breakthrough point and exhaustion is determined by such process factors as: Adsorbent type, initial concentration \(C_{0}\) of the adsorbed component and contact (retention) time \(T_{R}\). The breakthrough time for a given adsorbent decreases with decreasing \(T_{R}\) and increase in \(C_{0}\) while the adsorption capacity increases for higher initial concentrations of \(C_{0}\) adsorbate, as illustrated by adsorption isotherms (Figures 4 and 5).  
The filtration process to the point of bed depletion for WDA and WD (HCl) carbons, with contact time \(T_{R}=420\) s, filtration rates \(v=2\) m/h and initial concentration \(C_{0}=1\) mg/dm\({}^{3}\) is illustrated by Figure 6.  
bed breakthrough point fluctuated in range \(C\) = 0.000-0.008 mg/dm\({}^{3}\). The adsorption capacity at the bed breakthrough point (adopted after filtering 50 L of solution) was \(\mathrm{P_{bWDA}}\) = 1.38 mg/g.  
The WD (HCI) bed worked much more efficiently. The filtration cycle to the bed breakthrough point lasted twice as long, and the chromium compounds were removed almost completely (\(C\) = 0.000-0.002 mg/dm\({}^{3}\)). At the bed breakthrough point, adopted after filtration of 90 litres of adsorption, adsorption capacity \(\mathrm{P_{bWD(HCI)}}\) = 2.25 mg/g as obtained. For comparison, the adsorption capacity for another bed (mixed activated carbon) was set at 6.67 mg/g but with a much higher Cr(VI) concentration (40 mg/dm\({}^{3}\)) [30].  
The calculated adsorption capacities after exhaustion of the bed were, respectively: \(\mathrm{P_{eWDA}}\) = 4.02 mg/g and \(\mathrm{P_{eWD(HCI)}}\) = 4.35 mg/g. Referring these values to the results obtained under static conditions (\(C_{0}\) = 10 mg/dm\({}^{3}\)) (Figure 2), it was found that dynamic adsorption capacities are much higher. This relationship is also confirmed by other studies of the adsorption process [31]. To increase the efficiency of dynamic conditions, tests should be continued, optimizing the retention time for various initial concentrations, because on a technical scale granular activated carbon is used in the filtration process (as an adsorption bed).
# Untargeted 1H-NMR Urine Metabolomic Analysis of Preterm Infants with Neonatal Sepsis
## 3 Results
### Pathway Analysis  
The identified significant metabolites from multivariate and univariate analysis were implemented into MetaboAnalyst pathway analysis module to determine a qualitative aspect of all the possibly affected metabolic pathways. A separate analysis for the EOS and LOS group was selected, as except myo-inositol, betaine, gluconate, taurine, lactose, creatinine and hyppurate which were statistically significant for both groups, glucose and \(N\), \(N\)-Dimethylglycine were characterized as significant only for EOS and LOS group, respectively. A metabolic pathway analysis depicted ten (n = 10) altered metabolic pathways for the EOS (Figure 8a, Supplementary materials Table S2) and nine (n = 9) for the LOS group (Figure 8b, Supplementary materials Table S3). The representation of all the identified pathways was based on the pathway impact (\(x\)-axis) and the calculated \(p\)-value (\(y\)-axis). Among the nine metabolic pathways for EOS, ascorbate/ alternate metabolism and taurine/hypotaurine metabolism were the most significant, with \(p\)-value \(<\) 0.05. Taurine/hypotaurine metabolism pathway had the largest impact factor (0.42), followed by inositol phosphate pathway (0.13). Myo-inositol and taurine were the metabolites involved in these pathways (Supplementary materials Table S2). Regarding the LOS, significant metabolites, glycine/serine, and threonine metabolism pathway, where betaine and \(N\),\(N\)-Dimethylglycine are involved, had the lowest \(p\)-value (0.01). The pathway impact scores did not present large differences from EOS pathway analysis.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Metabolites** & \(p\)**-Value** & **Septic Group** \\ \hline EOS versus & Taurine & 0.004 & \(\downarrow\) \\ healthy non NICU preterms & Gluconate & \(8\times 10^{-5}\) & \(\uparrow\) \\  & Lactose & \(1\times 10^{-4}\) & \(\downarrow\) \\  & Hippurate & \(7\times 10^{-5}\) & \(\downarrow\) \\ \hline  & Gluconate & 0.0007 & \(\uparrow\) \\  & Lactose & 0.01 & \(\downarrow\) \\  & Betaine & 0.006 & \(\downarrow\) \\  & \(N\), \(N\)-Dimethylglycine & 0.005 & \(\downarrow\) \\  & Hippurate & 0.03 & \(\downarrow\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Statistically significant metabolites and their \(p\)-value for EOS and LOS groups.  
Figure 8: Graphically representation of the pathway analysis. Each cycle represents a metabolic pathway, while the color and the size are based on \(p\)-value and pathway impact, respectively. (**a**) Pathway analysis of EOS group significant metabolites. (**b**) Pathway analysis of LOS significant metabolites.
###### Abstract
The results showed that a value in the higher range of the examined WDC (0.06 and 0.07) and TI (12% and 14%) values represented a better comparison to the observed data. [PERSON] et al. [56] developed the actuator disc (AD) model which was the most common simplified wind turbine model, based on the Open FOAM open-source software. Results demonstrated that values for low and high wake impact situations were improved with 2.5% and 1.3%, respectively. [PERSON] et al. [57] introduced a novel method called the geometrical pattern-inspired placement methodology to find the layout of turbines with maximum total power output at Kutch-India. The enhanced passing vehicle search (PVS) algorithm was used in this study. The results showed that the power output was improved by 4.29%. The purpose of this original research is to optimize the 3D layout of turbines at the Valfajr Wind site in Manjil, Iran. In other words, this is an attempt to identify the best placement of turbines and their heights as the power plant reaches its optimal operating level [58]. Initially, by considering the wind and geographic information of the region, the wake effect is analyzed with the [PERSON] method [59; 60]. Next, the objective function, which is the cost of total power generation [40; 61; 62], will be estimated. Finally, the objective function will be optimized using the particle swarm optimization algorithm.
# A Review of Medical Diagnostic Video Analysis Using Deep Learning Techniques
## 5 Publicly Available Datasets for Medical Video Analysis
### Nerthus Dataset  
The Nerthus dataset [33] consists of 21 videos, showing a gastrointestinal tract with different degrees of bowel cleansing. The dataset has four classes showing bowel cleansing quality. The number of videos per class varies from 1 to 10 with different durations. The dataset consists of videos with a resolution 720 \(\times\) 576.