esa-sceva/satcom-chunk-collection huggingface dataset:
# Opportunistic Network Algorithms for Internet Traffic Offloading in Music Festival Scenarios
## 3 A Festival Mobility Model
### Simulating Festival Mobility Behavior
This is achieved by assigning a goal to each node. A goal is simply a cell on the simulation grid, which acts as a final destination, and it is selected by computing a metric called social attractivity for every grid cell and choosing the one with the highest value. This metric is measured as the strength of social relationships with the nodes within that cell. For this model to work, a social network is required as an input. This Home-cell Community-based Mobility Model [18] (HCMM) takes the CMM model one step further and introduces the idea that users are attracted by particular physical locations [18], in which they tend to preferentially spend their time, such as the workplace, their home after work, etc. CMM and HCMM serve as a great inspiration when implementing a mobility model. HCMM was the mobility model already implemented in the simulator used for this project, MobEmu. The shortcomings which led to a custom implementation of a mobility model, with the ideas of HCMM as a starting point, were that HCMM:Has no support for a custom social network;
* Measures cell attractivity differently than CMM--the social attraction towards a cell is evaluated based on the social relationships with nodes having that cell assigned as their home cell; the idea of a home cell is not relevant in a festival scenario, but it is of great importance when simulating a workday in a city;
* Allows for a single group to be placed in a cell regardless of cell size;
* Has no crowd density mechanism.
# Hydrodynamic Investigation on a Land-Fixed OWC Wave Energy Device under Irregular Waves
## 4 Results and Discussions
### Characteristic of the Wave Height and Pneumatic Pressure
Thus, the wave power transmitted into the chamber reduces with the increase of the incident wave frequency. Consequently, the fluctuation of the free surface elevation inside the chamber diminishes with the increase of incident wave frequency. Additionally, the maximum wave height \(H_{\text{max}}\), was influenced most by the incident wave frequency, with its greatest value reaching approximately 2.65 times the incident significant wave height \(H_{1/3}\) at \(k_{p}h\) = 0.75 and its lowest value 0.70 times the incident significant wave height \(H_{1/3}\) at \(k_{p}h\) = 3.00. The characteristic values of pneumatic pressures at different input peak periods are presented in Figure 8b. In contrast to the free surface displacement, the pneumatic pressures initially increase with \(k_{p}h\) to their peak value and then decrease with further increasing \(k_{p}h\). This phenomenon is similar to the observed behavior of the device in regular wave conditions [25]. This trend can be explained by the fact that the pneumatic pressures at the chamber interior are caused by the fluctuation of the free surface inside the chamber and are proportional to the variation of the free surface displacement according to Equation (4). To further analyze this behavior, the variation rate of the free surface elevation \(H_{1/3}/T_{p}\) inside the chamber at different wave frequencies are calcula  
Figure 8: Comparison of different characteristic values of (**a**) wave heights at chamber center and (**b**) pneumatic pressures in the chamber versus \(k_{p}h\). Figure 7: Comparison of the (**a**) surface elevation at the chamber center and (**b**) pneumatic pressure in the chamber for three different values of \(k_{p}h\) in the OWC chamber. frequencies \(k_{p}h=1.402\), \(1.037\), \(0.750\) are selected, including the frequency at which the significant pneumatic pressure \(P_{1/3}\) reaches its peak value, i.e., \(k_{p}h=1.037\). According to Figure 8a, it is determined that \(H_{1/3}/T_{p}=0.587\), \(0.591\) and \(0.55\) correspond to \(k_{p}h=1.402\), \(1.037\), \(0.75\). That is to say, the surface-elevation variation rate reaches its peak at \(k_{p}h=1.037\). Consequently, the significant pneumatic pressure \(P_{1/3}\) achieves its peak value at \(k_{p}h=1.037\) as shown in Figure 8b.
## 4 Experiments
### Method Analysis Results and Discussion  
#### 4.3.1 Ablation Study  
To verify the effectiveness of each design of the PCC module, we conducted an ablation study. We removed or replaced each part of the PCC module, including removal of the two submodules, differential representation inside the state correction network, and inverting the order of the two submodules. The experiments used SegRNN as the backbone model with a look-back window of 96 and prediction horizon of 96. As demonstrated in Table 5, the PCC module with default settings achieves the best performance. Removing the State Correction module leads to the most significant performance degradation, particularly for longer prediction horizons. This indicates that the SC module plays a crucial role in stabilizing predictions and achieving improvement, given the distinct unstable fluctuations and strict physical value constraints among the weather time series.
# Estimation of Impact Loads Transmitted to Vibro-Ripper Housing Using Transfer Path Analysis
## 2 Vibration Experiments with a Vibro-Ripper
### Acquisition of Transfer Function in the Modal Experiment  
Analysis of the transfer path of vibration-based load requires a transfer function, which is a unique characteristic of the target system. The frequency response function (FRF) matrix obtained through the modal experiment provided the mass, strength, and damping matrices of the structure, enabling the derivation of characteristics of the dynamic system [12].  
The modal experiment was undertaken to set the position of excitation in FRF matrices along the Y direction at the bottom end of the housing of the vibro-ripper. The experiments confirmed the unique characteristics of the housing in setting the excitation position (Figure 9a). The transfer function obtained in the modal impact experiment was applied to the assembly position of the link module, which makes a large contribution to load transfer  
Figure 8: Installation sites of triaxial accelerometers in the modal experiment.  
Figure 7: Positions of key paths in the vibro-ripper and triaxial excitation jig for frequency response function acquisition.  
in the vibro-ripper (Figure 9a); the shank and teeth for rock striking are operated via the gearbox and link module.  
Examination of the major modal characteristics of the housing, including the position of the link module (Figure 9b), indicated that the bending mode occurred often in all frequency bands due to the overall shape of the housing. Mode shapes and their respective frequencies are provided in Table 4. The transfer function obtained by excitation of all paths (left and right) was applied in the TPA.  
Figure 9: Results of the modal experiment. (**a**) frequency response function; (**b**) mode shapes of the ripper housing.
## 4 IoMT-Related Technologies  
Wireless Sensor Networks (WSN) and Radio-Frequency IDentification (RFID) systems are two key IoMT technologies. In recent years, their integration with smart objects has offered new communication capabilities. Due to their benefits and use in IoT, we present them in brief in this section.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Layer** & **Communication Protocols** & **Range Type** & **References** \\ \hline Perception Layer & RFID, NFC, Bluetooth/BLE, Z-Wave, UWB & Short-Range & [26, 27] \\ \hline  & IPV4, IPV6 protocols (for network addressing) & \\  & For routing RPL, CARP, and CORPL protocols (for network routing) & N/A & \\  & TCP, UDP, 6 LoWPAN, WIA-PA & & \\ \cline{2-4} Network Layer & NFC, RFID, IEEE 802.11 (Wi-Fi), IEEE 802.15.1 (Bluetooth), IEEE 802.15.4 (ZigBee), & & [28, 29] \\ \cline{2-4}  & LPWAN (LoRaWAN and LTE-M) & Long-Range & \\ \hline Application Layer & HL7, CoAP, DSS, MQTT, HTTP, ITIPS, TLS & N/A & [30, 31] \\ \hline \hline \end{tabular}
\end{table}
Table 2: The primary protocols used in IoMT systems at various levels.
## 2 EMTR and Correlation Estimator Methods
### Correlation-Estimator-Based Fault Location Method
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Height above the ground & 15 m \\ Line length & 10 km/50 km \\ Wire diameter & 18 mm \\ Wire conductivity & \(3.7\times 10^{7}\) S/m \\ Air conductivity & \(5\times 10^{-15}\) S/m \\ Dielectric constant of air & \(8.8419\times 10^{-12}\) \\ Air permeability & \(1.2566\times 10^{-6}\) \\ Relative ground permittivity & 10 \\ Ground resistivity (conductivity) & 100 \(\Omega\)m \\ Load impedance’s & 10 k\(\Omega\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Transmission line parameters. Figure 4: Fault-generated transients recorded at x = 0 due to a fault at \(x_{f}\) = 8 km. In addition to being time-efficient, the correlation estimator outperforms conventional EMTR energy metric, as shown in Figure 5. This is because, when using the EMTR energy metric, there is an error in the fault location, and for the lossy case, this error increases as the fault moves away from the observation point. More importantly, when using the EMTR energy metric, it is hard to distinguish between falsely and truly estimated fault location candidates, because it uses the normalised to the maximum value criterion for the fault locations, whereas, in the correlation estimator method, the CCv corresponding to the guessed fault locations verifies the true candidate for the fault position. Therefore, the presented analysis in this paper is focused on the correlation estimator method. As the lossy transmission line case is considered throughout the entire study, the frequency-dependent \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Line Length**} & \multicolumn{2}{c}{**EMTR Energy**} & \multicolumn{2}{c}{**Correlation Estimator Metric**} \\  & **Metric** & & **Pre-Fault** \\ \hline  & **Post-Fault** & **Processing Time** & **Processing Time** \\ \hline
10 km & 2 min & 30.96 min & 2.4 s \\
50 km & 18.3 min & 2.8 h & 11.59 s \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Computational efficiency of the EMTR energy metric and correlation estimator metric, while considering a spatial step = 10 m and a frequency bandwidth of 100 kHz. Figure 5: Correlation coefficient value and normalised signal energy as a function of GFL (\(x_{g}\)). The test fault location is 8 km. Figure 6: Surge compression by inverse filtering of the transient signal presented in Figure 4.  
line model is employed, whose accuracy is very robust and whose simulation findings were consistent with the field test results [28].
## 2 Mobile M2M Literature Review
### Device Mobility, Autonomy, and Security  
Devices that are able to connect to multiple different networks will have significant signalling traffic overheads for vertical handovers. Furthermore, devices in vehicles might face constant vertical handovers originated from the vehicles' mobility. Discussion on the necessity of improving vertical handovers in M2M communications are presented in [35]. [PERSON] _et al_. [36] propose an energy efficient algorithm for vertical handovers between an IEEE 802.11 and a 3 GPP network for M2M communications. The connectivity and cross-platform networking originated from the vehicles' mobility and positional distribution should also be a concern.  
M2M communications should operate seamlessly without human intervention, and therefore self-configuration, self-management, and self-healing are important challenges [12]. Envisioned applications for mobile M2M communications require autonomous data collection and aggregation, transmission and distribution of aggregated data, and storing and reporting of information [37]. Furthermore, the total absence of, or limited, human intervention in many M2M applications can occasion physical attacks from malicious attackers, disrupting communications. Security for M2M communications is discussed in [22,25,37]. Security is a major aspect for, as an example, vehicular collision-avoidance applications [37] or for healthcare applications [15].
# Spectrum and Energy Efficiency of Uplink Massive MIMO System with D2D Underlay
## 1 Introduction
### Related Works  
Compared with the traditional MIMO system, the large-scale antennas can bring in the higher spectrum efficiency (SE) and energy efficiency (EE) [1; 2; 3]. Lower capacity bounds for linear detection are derived from the uplink massive MIMO systems [3]. Conjugate and Zero-Forcing precoders are compare in terms of SE and EE for the downlink of massive MIMO systems [4]. [PERSON] et al. compared massive MIMO and small cells in terms of user-average SE by using stochastic geometry theory [5]. [PERSON] et al. analyzed MC-CDMA systems adopting an adaptive detection technique based on threshold orthogonality restoring combining [6; 7]. [PERSON] et al. derived the asymptotic sum-rate of the multiuser (MU) MIMO uplink channel in the large-system regime [8].  
D2D underlaying cellular network (CN) is regarded as a means to increase the data rate, and decrease the load of CNs [9; 10]. However, the coexistence of the D2D and the CN causes the mutual interference. To combat the interference from the D2D to the CN, [PERSON] et al. proposed a mechanismto limit the maximum transmit power of the D2D transmitter [11]. However, the interference from the CN to the D2D link is not considered. In [12], the cellular user equipments (CUEs) are scheduled for the communication if they are not in the interference-limited area so as not to generate harmful interference to the D2D receiver. [PERSON] et al. analyzed the performance of D2D enabling V2V Beaconing [13]. [PERSON] et al. developed a framework for design and analysis of relay-assisted diversity communications accounting [14].  
The combination of massive MIMO and D2D is investigated in [15; 16; 17]. [PERSON] et al. adopt the partial zero-forcing receiving technique to cancel the uplink interference from the nearby CUEs and D2D links, and investigate the spectral efficiency with both the perfect and imperfect channel state information (CSI) [15]. The precoding and power allocation are jointly optimized for D2D underlaying massive MIMO multicasting networks [16]. [PERSON] et al. derived average sum rate and EE for massive MIMO system with D2D links underlay [17].
# Double-Diffusive Effects on the Onset of Rayleigh-Benard Convection of Water-Based Nanofluids
## 4 Conclusions  
Natural convection in a rectangular enclosure filled with nanofluid and heated from below is studied numerically using a two-phase model based on the double-diffusive approach, considering that the slip mechanisms between liquid and solid phases are only due to the Brownian diffusion and thermophoresis.  
Simulations have been performed using Al\({}_{2}\)O\({}_{3}\) + H\({}_{2}\)O for different values of the average volume fraction of the suspended solid phase, and for different aspect ratio of the cavity, with the scope to investigate how the onset of convection is affected by the presence of the nanoparticles dispersed into the base fluid, also taking into account the lateral confinement effect.  
The results may be summarized as follows:  
1. The value of the difference in temperature \(\Delta T\) between the heated and cooled horizontal walls corresponding to the onset of convection increases as the average volume fraction is increased, which is a consequence of the growth of the dynamic viscosity;
2. The nanoparticles migration from hot to cold results in a pronounced reduction in the critical condition for the onset of convection in comparison with the case of the single-phase approach;
3. The value of the difference of temperature \(\Delta T\) corresponding to the onset of convection decreases with decreasing the aspect ratio of the cavity;
4. The onset of convection is anticipated for TiO\({}_{2}\) + H\({}_{2}\)O than for CuO + H\({}_{2}\)O and Al\({}_{2}\)O\({}_{3}\) + H\({}_{2}\)O, due to the higher value of thermophoresis parameter \(S_{T}\).  
The above results represent the first analysis of a wider investigation bearing on the instability of nanofluid layers in a cavity. In the future, further numerical works will be proposed in order to extend the present research to three-dimensional cases.  
Conceptualization, methodology, software, validation, data curation, writing, review and editing, A.Q. and M.C. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.  
Figure 10: Distribution of \(h_{av}\) vs. \(\Delta T\) for a \(A=1\), using different nanoparticle materials.
## 2 Related Work
### Hierarchical Architectures for Scalability  
HFL frameworks have been proposed to improve scalability and efficiency in distributed systems by organizing the network into multiple tiers, such as IoT devices, edge servers, and cloud servers [10; 11]. By enabling localized aggregation at the edge level, these architectures reduce communication overhead and mitigate bottlenecks caused by stragglers. For example, hierarchical designs like HierFAVG [12] and FLAIR [13] have demonstrated improved adaptability in large-scale networks.  
Despite these advancements, existing HFL solutions often prioritize architectural scalability over privacy and resource efficiency. Many rely on computationally intensive security measures that are unsuitable for resource-constrained IoT devices [41]. Moreover, the lack of robust asynchronous mechanisms limits their ability to handle dynamic participation and device heterogeneity effectively [42].  
In this work, we build upon existing research by proposing an integrated hierarchical federated-learning framework that incorporates lightweight encryption and asynchronous aggregation. Unlike previous approaches, our framework is specifically designed to address the privacy, scalability, and heterogeneity issues in IoT-Edge-Cloud environments, providing a practical solution for real-world IoT applications such as human activity recognition. By leveraging lightweight encryption and a hierarchical architecture, we aim to strike a balance between security, efficiency, and adaptability, overcoming the limitations identified in existing approaches.
# Domain Adaptation for Satellite-Borne Multispectral Cloud Detection
## 3 Domain Adaptation Tasks for EO Mission
### Cloud Detection on PhiSat-1  
The aim of the PhiSat-1 nanosatellite mission is to demonstrate the feasibility and usefulness in bringing AI on board a satellite [2]. It involves the use of a CNN called CloudScout [5] to perform cloud detection on data cubes captured by the hyperspectral imaging payload. More formally, preprocessing is first performed on the output of the hyperspectral imager (e.g., radiometric and geometric corrections, stacking and alignment, as well as band selection and normalisation) to yield a data cube \(x\). The CNN-based cloud detector can be formalised as the function \(y=f(x;\theta)\) such that the assigned label  
\[y=\begin{cases}1&\text{if $x$ contains significant cloud coverage;}\\ 0&\text{otherwise.}\end{cases} \tag{1}\]  
In the case where \(y=1\), \(x\) is discarded (precluded from being transmitted to ground), the weights \(\theta\) define the function implemented by \(f\); details of the CNN architecture are provided in Section 5. In PhiSat-1, the CNN is executed on the EoT AI board, particularly the embedded Intel Myriad 2 VPU, which was experimentally proven to be able to withstand the harshness of the space environment [72].
## 3 Results
### Method Validation  
The quantification of the identified compounds was implemented under the multiple reaction monitoring (MRM) condition and by a comparison of peak areas with calibration curves from corresponding standard compounds. The results showed that the calibration curves of most standards exhibited the regression coefficients with a high R\({}^{2}\)-value (R\({}^{2}\)\(\geq\) 0.997). The limit of detection (LOD) of most analytes was in the range of 1.0-5.0 \(\mu\)g/L and limit of quantification (LOQ) ranged 3.0-15.0 \(\mu\)g/L, except for gallic acid and betulinic acid which exhibited the limit of detection at 10 \(\mu\)g/L. The values expressed a high sensitivity of analytical method. The recovery values of most compounds were ~100%, except for quercetin and gallic acid with ~80% (Supplementary Table S2).
# The Influence of Vertical Ground Motion on the Design of Common R/C Frames
## 3 Evaluation of Frames for Vertical Ground Motion
### Evaluation Through Spectral Analysis
The absolute maximum bending moments were amplified by up to 45% and 114% at the ends and the mid-points, respectively. In several cases, the longitudinal reinforcement of both the bottom and top fibers of the beams was not able to resist these additional moments. Figure 11: Acting shear force and shear resistance (kN) of joint J22. Figure 12: Maximum and minimum bending moments (kNm) of beam B12: (**a**) ends; (**b**) mid-span. Regarding the shear resistance of beams, it is worth noting that all inadequacies that occurred had to do with non-critical regions. On the contrary, in critical regions, the shear forces produced by H+V excitation did not exceed the initial design forces calculated on the basis of the capacity design rule. Finally, from Table 6 and Figures 6-12, it becomes clear that the higher the seismic intensity, the more influential the vertical seismic component.
# Study on Components Determination and Performance Evaluation of LS Pre-Maintenance Agent
## Materials  
According to asphalt pavement maintenance requirements, the LS pre-maintenance agent requires good penetration and bonding performance, which will ensure the penetration effect after spraying. By restoring and activating the aged asphalt by supplying lightweight components, this improves the asphalt's performance. Strong light screening abilities are able to inhibit the light aging damage in strong UV areas. Quick-drying and long-term storage stability reduces traffic closure time and meets the storage requirements for large-scale maintenance engineering. Finally, the construction economy, convenience, safety, and environmental protection must be guaranteed. In summary, the raw materials of the LS pre-maintenance agent include:  
1. Asphalt with low viscosity and consistency, high penetration, and ductility to ensure permeability.
2. Organic reducing agent with a high light component content and low viscosity to impregnate the asphalt layer.
3. Penetrant to balance the solute and solvent molecular weight and ensure the long-term stability.
4. Solvent oil to speed up the evaporation and curing process.
5. Dispersant to reduce the asphaltene precipitation and extend material storage time.
6. Light-shield agent (LS agent) with high dispersibility, hiding power, and UV shielding capabilities.