esa-sceva/satcom-chunk-collection huggingface dataset:
# Numerical and Experimental Study of a Large-Scale Natural Circulation Helium Loop
## 3 Mathematical Model
### Cooler Model  
The mass (1) and momentum (2) equations also describe the flow inside the cooler. Nevertheless, the energy equations differ. The complex nature of the three-dimensional flow led to several simplifications. The transient behavior was reduced to heat accumulation in the solid parts of the heat exchanger. Thus, the energy equation took on the following form (5):  
\[Q_{acu} =-\dot{Q}_{f}-\dot{Q}_{ext}+\dot{Q}_{flux}\] \[m_{s}\,c_{p,s}\,\frac{\mathrm{d}T_{s}}{\mathrm{d}t} =-\dot{m}_{fl}\,c_{p,fl}(T_{out}-T_{in})-k_{A}\,A_{ext}(T_{out}-T_{ ext})+\dot{Q}_{flux} \tag{5}\]  
The signs used were the following: \(\dot{Q}_{acu}\)--heat accumulated in solid parts per unit of time, \(\dot{Q}_{f}\)--heat absorbed by a fluid per unit of time, \(\dot{Q}_{ext}\)--heat loss to the exterior per unit of time, \(\dot{Q}_{flux}\)--energy flux due to cooling/heating; \(m_{s}\)--mass of solid parts, \(c_{p,s}\)--specific heat capacity of solid parts, \(T_{s}\)--temperature of solid parts, \(\dot{m}_{fl}\)--mass flow rate of a fluid medium, \(c_{p,fl}\)--specific heat capacity of a fluid medium, \(T_{in}\)--heat exchanger inflow temperature, \(T_{out}\)--heat exchanger outflow temperature, \(k_{A}\)--coefficient of heat flux to the exterior, \(A_{ext}\)--heat flux to the exterior area, and \(T_{ext}\)--temperature of the exterior.  
The relation (5) represents the energy balance of a whole component. The heat exchanger is treated as a black box with energy inputs, outputs, and accumulation. The temperature of solid parts \(T_{s}\) is unknown and hard to estimate, so an assumption of \(T_{s}=T_{out}\) was made. The heat flux \(\dot{Q}_{flux}\) is calculated with the standard NTU-\(e\) and Bell Delaware methods.
# Shoulder-Elbow Joint Angle Prediction Using COANN with Multi-Source Information Integration
###### Abstract  
To address the precision challenges in upper-limb joint motion prediction, this study proposes a novel artificial neural network (COANN) enhanced by the Cheetah Optimization Algorithm (COA). The model integrates surface electromyography (sEMG) signals with joint angle data through multi-source information fusion, effectively resolving the local optima issue in neural network training and improving the accuracy limitations of single sEMG predictions. Experimental results demonstrate that the COANN achieves significant performance improvements: compared with RBF neural networks, it reduces the root mean square error (RMSE) by 24.32% (\(\Delta\)R\({}^{2}\) + 18.75%) with a 22.6% shorter system runtime; relative to conventional ANNs, it decreases the RMSE by 31.59% (\(\Delta\)R\({}^{2}\) + 12.15%) while reducing computational time by 35.1%; compared with CNN neural networks, it reduces the root mean square error (RMSE) by 14.9% (\(\Delta\)R\({}^{2}\) + 3.84%); and relative to conventional LSTM, it decreases the RMSE by 15.31% (\(\Delta\)R\({}^{2}\) + 4.86%). Multi-source integration enhanced elbow joint prediction accuracy by 5.7% and shoulder joint accuracy by 6.9% compared with single sEMG approaches. This methodology provides theoretical foundations for human-robot interaction systems in upper-limb rehabilitation robotics and motion-assistive devices.  
Keywords:COANN; sEMG; multi-information fusion; RMSE; R\({}^{2}\) +
Footnote †: journal: Applied sciences
## 5 Experiments and Results
### Enhancing Model Explainability and Transparency with SHAP and LIME
#### 5.5.1 SHAP--Global Behavior Analysis  
We used the SHAP approach to examine the global behavior of the XGBoost model, focusing on the features that have the most significant impact on predictions across the entire dataset. Figure 9 highlights the key features identified by SHAP, ranking them based on their overall influence on the model's predictions. The Y-axis lists these features in descending order of importance, with \"rst_count\" identified as the most significant and \"Protocol Type\" as the least influential. The X-axis indicates the mean absolute value for the SHAP method, with color coding to differentiate between the classes (0 for the Benign Class and 1 for the Attack Class). According to the findings, SHAP classifies just 20 of the 40 features as relevant and impactful, while ignoring others with little or no influence. Furthermore, the summary plot shows that the top five characteristics that substantially influence the model's predictions are \"IAT\", \"rst_count\", \"urg_count\", \"Header Length\", and \"flow_duration\".  
This thorough examination of model explainability through SHAP and LIME helps to present a comprehensive understanding of feature contributions. The insights gained from this analysis are crucial for developing trust in machine learning models and ensuring that decisions informed by these models are based on transparent and interpretable metrics.  
Figure 9: Summary plot using SHAP values and test set (Global Explanation).  
By elucidating the factors driving model predictions, we contribute to improved decision-making processes for users and stakeholders alike.
# SARS-CoV-2 Detection Using Optical Fiber Based Sensor Method
## 7 Results  
The experimental results are evaluated for a specimen from a person with and without COVID-19. Firstly, the results in Figure 6 reports the intensity measured for two different diameters of PoFs under test. In case of specimen with infection, it is observed that, for a smaller diameter of 200 \(\upmu\)m PoF, the intensity curve is much stronger as compared to the case when 500 \(\upmu\)m diameter PoF is utilized. This is in line with the explanation made in Section 6. The only reason for comparing two diameters PoF is to justify that smaller diameter is useful.  
Figure 6: Comparison of two POFs in terms of intensity with diameters 200 and 500 \(\upmu\)m are compared with specimen with a COVID-19 positive and negative result.  
In Figure 7, we have compared the results when swabs are taken from both oropharyngeal or nasopharyngeal (labeled as Swab (2)) and compare them with a single swab, either oropharyngeal or nasopharyngeal (labeled as Swab (1)). This confirms that the proposed method works in both orientations; however, the trends intensities are different. It should be noted that detection time window is similar for both tests, i.e., within the first 15 min.  
In Figure 8, a spectrometer measures the vibrations at the interatomic level that are produced once a laser diode sheds light on the specimen. Each virus has its own signature vibrations, which act as a sort of optical fingerprint that can distinguish the coronavirus from other specimens. When a laser diode via a U-shaped probe sheds the light on the specimen under test, COVID-19 infected specimen creates a distinctive optical fingerprint, or \"Raman peaks\" as shown in Figure 8. This leads to the conclusion that this setup will also be able to detect other variants of COVID-19 such as Omicron in a similar fashion.
## 3 Results and Discussion
### Physicochemical and Energetic Properties of the Briquettes  
According to standard UNE-IN 14774-1 (2010) [35; 41], the final moisture content of the briquettes made in this study ranged from 11.52-13.26\(\%\pm 0.57\), a value that is within acceptable parameters compared to other results reported in the literature, where moisture content was 14-16% [38; 53], and 8-10%. Those projects, however, employed electric machines to produce the briquettes, which accelerated the drying of the raw material [38]. In the present study, in contrast, the briquettes were left to dry in the open air after a production process that utilized a simple, innovative mechanical briquette-making apparatus (Figure 2).  
The calorific power of the briquettes analyzed fell into a range of 17.0-18.3 MJ/kg \(\pm\) 0.75, while in other studies briquettes made of splinter material had a range of 13.0-19.52 MJ/kg [64], and pellets made from similar material presented a range of 16-19 MJ/kg [38; 52; 65]. Clearly, the values we obtained are acceptable.  
The values calculated for the density of the briquettes varied from 930-1240 kg/m\({}^{3}\). Data reported in other studies oscillate between 810 and 1060 kg/m\({}^{3}\)[66] and from 890 to 1080 kg/m\({}^{3}\)[56]. Thus, the density of our briquettes did not differ significantly from those produced in other studies and can be deemed acceptable [38].  
Table 3 presents the results of the chemical analysis of the wood waste; that is, the cellulose, hemicellulose, and lignin content obtained from the material collected in the study community.  
The analysis of the samples of _Pinus_ spp. with respect to hemicellulose content produced an average of 14.5%, while for cellulose the average was 53.3%, and for lignin 25.8%, with 6.53 of other components (extractives and soluble sugars, among others). These results are also similar to those reported in earlier research, where values ranged from 68.1-74.7% for holocellulose and 24.7-28.5% for lignin [5; 34; 67]. Since lignin is the main structural element for generating energy, our study obtained an average similar to other reports for species of the genus _Pinus_ spp., which indicate a figure of 24.59% for lignin [68; 69]. Published studies mention hemicellulose and cellulose values of 12.37% and 54.81%, respectively, for the species _Abies Religioas_, giving a total of 67.18% of holocellulose, and an average lignin content of 24.68% [50]. These results indicate the potential of the materials suggested in this study for exploitation as sources of bioenergy.  
Figure 8: Images of the finished briquettes.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Material** & **Hemicellulose** & **Cellulose** & **Lignin** & **Other** \\ \hline \multirow{2}{*}{_Pinus_ spp.} & 14.5 & 53.3 & 25.8 & 6.53 \\  & \(\pm\)0.50 & \(\pm\)0.30 & \(\pm\)0.50 & \(\pm\)0.70 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Chemical composition of the main structural components of the wood of _Pinus_ spp (%).
# Integrating Cognitive Radio with Unmanned Aerial Vehicles: An Overview
## 5 Developing a Simple and Low-Cost CR-Based UAV Testbed
### Outdoor Radio Data Collection of a Jamming Attack  
The experiment we conducted in this work comprises executing a typical jamming attack to the CR-based UAV prototype outdoor. The CR should detect the jammed frequency range as busy and then switch to a backup frequency, thus avoiding the jamming attack consequences.  
In this experiment, we used a jammer device capable of jamming the frequency 432 MHz and the CR-based UAV testbed. First, we deployed the CR-based UAV sensing the 432 MHz frequency. Second, we used the jammer device to execute the jamming attack to that frequency. The CR-based UAV then sensed the high signal level in that frequency and it autonomously switched to its backup frequency. Although it was an outdoor experiment, it was important to isolate it, keeping it away from other variables. Therefore, we experimentally defined its backup channel as 440 MHz and its level threshold to \(-40\) dB. It was necessary to avoid that other PUs and/or SUs interfered in the experiment, and to reduce the incidence of false alarms.  
Figure 9 shows that the CR-based UAV prototype performed as expected under a jamming attack.  
Figure 8: The CR-based UAV prototype developed for this work.  
Figure 9: Data collected using the CR-based UAV. It kept using the 432 MHz frequency until it sensed a transmission level over the threshold. It then switched to its backup channel at 440 MHz.  
For a clearer understanding of the radio spectrum environment within the frequency range from 432 MHz to 441 MHz, we used the CR platform to make a data collection during a jamming attack, in similar circumstances to the CR-based UAV testbed experiment. Figure 10 shows the data collected. It highlights the 432 MHz frequency level discrepancy under the jamming attack, related to other frequencies within the measured range.  
These results show that even switching to a channel around the same frequency bands could be enough to avoid a jamming attack, considering the attacker does not have access to the new channel. Furthermore, note that we used a simple reactive algorithm based on static channel noise threshold, but especially proactive algorithms may benefit from such data collections, as Machine Learning techniques could be trained and tested over real-scenario data.
# Reduction Potential of Gaseous Emissions in European Ports Using Cold Ironing
## 1 Introduction  
Globalization, concentration, and the specialization of production increase the focus on the global supply chain, where seaports play a key role as transshipment hubs. In the last few decades, ports have become more and more important as global logistic actors [1]. A vital part of port efficiency and port value proposition is seaside and landslide connectivity and services. Moreover, the role of hinteland logistics and transportation is receiving increasing attention, and ports' strategies and management focus more and more on incorporating and coordinating hinterland logistic activities with the activities of the seaport [2]. Ports are often publicly owned but often privately operated, normally by means of concessions. Ports are an interesting focal point since their activities, directly and indirectly, heavily affect the local and regional traffic system [3]. Ports have the potential for the internalization of both social and environmental externalities by means of differentiated fees and other incentives. Indeed, sea transport has traditionally been seen as a green form of transportation due to the low emission of carbon dioxide per t-km compared to any other transport mode. However, given that around 90% of world trade is carried by international shipping and the sector is experiencing significant year-on-year growth, it nevertheless represents a major source of carbon emissions. In this framework, the cold ironing solution, consisting of shore electricity supply to ships, should play an increasing role in favor of the global decarbonization process.
## 1 Introduction  
Wireless sensor networks (WSN) have gained popularity due to its applications and ease of installation [1, 2]. These networks are used for the collection and transmission of various parameters depending upon its application. WSNs are self-organized and self-maintained due to its powerful operating system and consume less power and operational time. These networks reorganize by pre-programmed instructions based on the predefined parameter's quantity and place. A new DEC network with its structure and other parameters have been investigated in this work. A task-specific application has been proposed with a new routing protocol. The proposed protocol will have random nodes, which will be placed at different distance locations within the radio signal range. The radio range will depend on the environment and the life expectancy of the node. The power generated by the node is adjusted according to the required radio range and data bandwidth. One of the most important factors is to increase the life span of the WSN by utilizing the data arriving from the network system without considering neighboring nodes--for example, leftover vitality of individual hubs. The model experiences comparable issues of unreliable group head decisions per round when compared with similar models. As per the previous study, LEACH protocol presented an optimal environment that could ensure the best execution utilizing the stochastic model they proposed, yet more often than not the outcome could be imperfect because of the vulnerabilities in the bunch head race process [3; 4]. The WSN performance is dependent on the routing protocol as it needs to have a protocol, which has less queuing and congestion. The congestion consumes power, which limits the lifetime of the sensors and thus networks. The proposed work is expected to overcome the issues of efficiency and other shortcomings. The network classifications and other parameters are being explained for providing simplicity.
# A Probabilistic VDTN Routing Scheme Based on Hybrid Swarm-Based Approach
###### Abstract  
The probabilistic Delay Tolerant Network (DTN) routing has been adjusted for vehicular network (VANET) routing through numerous works exploiting the historic routing profile of nodes to forward bundles through better Store-Carry-and-Forward (SCF) relay nodes. In this paper, we propose a new hybrid swarm-inspired probabilistic Vehicular DTN (VDTN) router to optimize the next-SCF vehicle selection using the combination of two bio-metaheuristic techniques called the Firefly Algorithm (FA) and the Globworm Swarm Optimization (GSO). The FA-based strategy exploits the stochastic intelligence of fireflies in moving toward better individuals, while the GSO-based strategy mimics the movement of glowworm towards better area for displacing and food foraging. Both FA and GSO are executed simultaneously on each node to track better SCF vehicles towards each bundle's destination. A geography-based recovery method is performed in case no better SCF vehicles are found using the hybrid FA-GSO approach. The proposed FA-GSO VDTN scheme is compared to ProPHET and GeoSpray routers. The simulation results indicated optimized bundles flooding levels and higher profitability of combined delivery delay and delivery probability.  
VDTN; next-SCF vehicle selection; ProPHET; probabilistic DTN routing; Glowworm Swarm Optimization; Firefly Algorithm; swarm-based approaches +
Footnote †: journal:  
_Future Internet_
## 3 Results
### CSF Dynamics Analysis  
In recent study conducted an intensive investigation on intracranial pressure signals and CT scans using correlation-based feature selection [47]. Their method achieved 85.7% classification accuracy (12 of 14 patients correctly classified) using just three features: Skewness of Single-Wave Amplitude, Skewness of P1 subpeak absolute amplitude, and Skewness Leading Edge Slope. The high accuracy achieved with this minimal feature set demonstrates the diagnostic power of CSF pressure dynamics for NPH classification. The method showed higher specificity (88.89% for non-NPH patients) than sensitivity (80% for NPH patients), indicating it may be more reliable for ruling out than confirming NPH. While limited by small sample size (14 patients), these promising results suggest that incorporating CSF pressure dynamics into AI diagnostic systems could complement traditional imaging-based approaches.  
[PERSON] et al. assessed the cerebrospinal fluid tap test as a prognostic tool for shunt surgery in NPH patients [36]. Their comparison of different predictive models revealed a clear advantage for multimodal approaches: the combined clinical + imaging model substantially outperformed both clinical-only and imaging-only models (out-of-sample accuracy: 0.70 vs. 0.57/0.63, ROC-AUC: 0.83 vs. 0.54/0.59). The statistically significant improvement (\(p\) = 0.028) achieved by combining modalities demonstrates the value of integrating diverse data types for NPH prediction. This finding has important implications for AI development, suggesting that models incorporating both imaging and clinical data will likely achieve higher diagnostic performance than those limited to a single data type. The impressive performance metrics of their integrated model (ROC-AUC of 1.0 [0.99-1.0] on the whole dataset) further supports the potential of sophisticated multimodal ML approaches for NPH management.
# Numerical Simulation of the Tip Leakage Vortex Characteristics in a Semi-Open Centrifugal Pump
## 2 Computational Model and Method
### Numerical Method  
In this study, ANSYS CFX software was used for the numerical simulation. The internal flow field information was obtained by solving the continuity equation and the momentum equation of incompressible fluid. So, the turbulence model was a key issue. The Shear Stress Transport (SST) k-\(\omega\) two-equation turbulence model, which combines the standard k-\(\epsilon\) and k-\(\omega\) model, was designed to produce highly accurate predictions of flow separation phenomenon under the adverse pressure gradient, which was also applicable to the numerical description of the tip leakage flow. The closure mode of the SST k-\(\omega\) was expressed as,  
\[\frac{\partial(\rho k)}{\partial t}+\frac{\partial}{\partial x_{j}}\Big{(} \rho u_{j}k\Big{)}=\frac{\partial}{\partial x_{j}}\Bigg{[}\left(\mu+\frac{\mu_ {t}}{\sigma_{k}}\right)\frac{\partial k}{\partial x_{j}}\Bigg{]}+P_{k}-\beta^{ \ast}\rho k\omega \tag{1}\]  
\[\frac{\partial(\rho\omega)}{\partial t}+\frac{\partial}{\partial x_{j}}\Big{(} \rho u_{j}\omega\Big{)}=\frac{\partial}{\partial x_{j}}\Bigg{[}\left(\mu+\frac {\mu_{t}}{\sigma_{\omega 3}}\right)\frac{\partial\omega}{\partial x_{j}}\Bigg{]}+ \alpha_{3}\frac{\omega}{k}P_{k}-\beta^{\prime}\rho k\omega+(1-F_{1})2\rho \frac{1}{\sigma_{\omega 2}\omega}\frac{\partial k}{\partial x_{j}}\frac{\partial \omega}{\partial x_{j}} \tag{2}\]  
\[\mu_{t}=\frac{\rho\alpha_{1}k}{\max(\alpha_{1}\omega,SF_{2})} \tag{3}\]  
\[F_{1}=\tanh\Big{(}\arg_{1}{}^{4}\Big{)} \tag{4}\]  
\[\arg_{1}=\min\Bigg{(}\max\Bigg{(}\frac{\sqrt{k}}{\beta^{\prime}\omega y^{\prime }},\frac{500\
u}{y^{2}\omega}\Bigg{)}\frac{4\rho k}{CD_{kw}\sigma_{\omega}2y^ {2}}\Bigg{)} \tag{5}\]  
\[F_{2}=\tanh\Big{(}\arg_{2}{}^{2}\Big{)} \tag{6}\]  
\[\arg_{2}=\max\Bigg{(}\frac{2\sqrt{k}}{\beta^{\prime}\omega y^{\prime}},\frac{ 500\
u}{y^{2}\omega}\Bigg{)} \tag{7}\]  
where \(k\) and \(\omega\), respectively, represent the turbulence kinetic energy and turbulence eddy frequency; \(P\) and \(\rho\) are the pressure and density; \(F_{1}\) and \(F_{2}\) represent the blending function; \(P_{k}\) is the turbulence production due to viscous forces; and \(\alpha_{1}\), \(\alpha_{3}\), \(\beta^{\ast}\), \(\beta^{\prime}\), \(\sigma_{k}\), \(\sigma_{\omega 2}\), and \(\sigma_{\omega 3}\) are the model constants.  
The total pressure was used and set to 101,325 Pa at the inlet. The discharge at the vaneless diffuser was set for the outlet boundary condition. In addition, no-slip wall condition in the solid walls was also specified. The frozen rotor was used as the interface between the rotating and fixed domain. Water at normal temperature was used as a working fluid.
# Performance of Dense Wireless Networks in 5G and beyond Using Stochastic Geometry
## Appendix B Derivative of Mean Aggregated Interference  
The mean of the aggregated interference can be derived readily from its CF as:  
\[\mathbb{E}[I]=(-j)^{-1}\left.\frac{d\Psi_{I}(\omega)}{d\omega}\right|_{\omega=0}. \tag{10}\]  
Here, we start with the CF at (19). Note that the \(|\omega|\) and \(sign(\omega)\) here are decomposed deliberately to demonstrate the analogy with the standard alpha-stable distribution with parameter \(\beta=\pm 1\). Here, one can combine them, and (19) can be re-written as:  
\[\begin{split}\Psi_{I}(\omega)&=\exp\biggl{(}- \frac{\pi\lambda}{C_{\alpha}}|\omega|^{\alpha}(1-j\text{sign}(\omega)\tan\frac{ \pi\alpha}{2})P_{G_{c}}(\omega,\alpha,h)+\pi\lambda h^{2}\biggr{)}\\ &=\exp\biggl{(}-\pi\lambda\Gamma(1-\alpha)|\omega|^{\alpha}(\cos \frac{\pi\alpha}{2}-j\text{sign}(\omega)\sin\frac{\pi\alpha}{2})P_{G_{c}}( \omega,\alpha,h)+\pi\lambda h^{2}\biggr{)}\\ &=\exp\biggl{(}-\pi\lambda\Gamma(1-\alpha)\omega^{\alpha}(-j)^{ \alpha}P_{G_{c}}(\omega,\alpha,h)+\pi\lambda h^{2}\biggr{)}.\end{split} \tag{11}\]  
This is a differentiable function. The derivative of (11) can be achieved by chain rules as:  
\[\left.\frac{d\Psi_{I}(\omega)}{d\omega}\right|_{\omega=0}=\Psi_{I}(\omega). \biggl{(}\upsilon\omega^{\alpha-1}P_{G_{c}}(\omega,\alpha,h)\biggr{)}.\biggl{(} \upsilon\omega^{\alpha}\frac{dP_{G_{c}}(\omega,\alpha,h)}{d\omega}\biggr{)} \right|_{\omega=0}, \tag{12}\]  
where \(\upsilon=-\pi\lambda\Gamma(1-\alpha)(-j)^{\alpha}\). Considering the derivative of normalized lower incomplete Gamma function \(\partial P(s,x)/\partial x=x^{s-1}e^{-x}/\Gamma(s)\) and expanding the Taylor series of the incomplete gamma function (noting that only the first two terms exist at \(\omega=0\)), after some calculus it yields (26).