esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 System Model
### Event Occurrence and Detection  
We assume the events occur randomly and independently in \(M\), subject to a Poisson process of Poisson rate \(\lambda\). The Poisson process is a stochastic process used to model random and independent events. Furthermore, it is assumed that the measurement is efficient, which means all the events occurring within the sensing range will be expediently detected. Therefore, the probability that \(q\) events processed by a sensor with sensing radius \(r\) in duration \(t\) is  
\[\Pr\left(C(t)=q\right)=\frac{\exp\left\{-\lambda^{\prime}\pi r^{2}t\right\} \left(\lambda^{\prime}\pi r^{2}t\right)^{q}}{q!}=\frac{\exp\left\{-\lambda t \right\}(\lambda t)^{q}}{q!}, \tag{1}\]  
where \(C(t)\) is the counting process of events and \(\lambda=\lambda^{\prime}\pi r^{2}\) represents the average Poisson rate in the circular sensing region of the sensor. By leveraging the properties of [PERSON]'s process, we have:  
\[\mathbb{E}[C(t)]=\hat{q}|_{[0,t]}=\lambda t, \tag{2}\]  
where \(\mathbb{E}[\cdot]\) is the expectation operator and \(\hat{q}|_{[0,t]}\) denotes the expected number of events processed in \([0,t]\).  
An event at a distance of \(d\) from the sensor has a probability of being detected:  
\[\Pr\left(d\right)=\begin{cases}1,&\text{if}\,d<r_{min}\\ \exp\left\{-\xi_{1}(d-r_{min})^{\xi_{2}}\right\},&\text{if}\,r_{min}<d<r_{max} \,,\\ 0,&\text{if}\,d>r_{max}\end{cases} \tag{3}\]  
where \(\xi_{1}\) and \(\xi_{2}\) are parameters determined by the sensor physical characteristics, which reflects the probability attenuation coefficients with distance.  
In sleep time, all events are considered missed. Let \(\Gamma_{S}\) be the average number of missed events, then  
\[\Gamma_{S}=\mathbb{E}[C(S)]=\lambda S. \tag{4}\]  
Figure 1: Tunable sensing radii.  
During working hours, the average number of missed events, \(\Gamma_{W}\), can be calculated as  
\[\Gamma_{W}=\mathbb{E}[C(W)](1-\Pr\left(d\right))=\lambda W(1-\Pr\left(d\right)). \tag{5}\]  
The event space is  
\[\Gamma=\mathbb{E}[C(W+S)]=\lambda(W+S). \tag{6}\]  
Now we define the detection accuracy \(\mu\) as a quantifiable indicator of event missing  
\[\mu=1-\frac{\Gamma_{S}+\Gamma_{W}}{\Gamma}. \tag{7}\]  
It follows from (3)-(6) that  
\[\mu=\begin{cases}\dfrac{W}{S+W},&\text{if}\,d<r_{min}\\ \dfrac{W}{S+W}\exp\left\{-\xi_{1}(d-r_{min})^{\xi_{2}}\right\},&\text{if}\,r_{ min}<d<r_{max}\\ 0,&\text{if}\,d>r_{max}\end{cases}. \tag{8}\]
## 2 Data and Methods
### Ancillary Data
#### 2.2.4 Sea Ice Thickness (SIT)  
Two separate SIT products are selected as ground truth data in the neural network, covering the respective ranges of thin sea ice up to 0.6 m, and full-range SIT. Hereby, daily SMOS Level-3 SIT maps (SIT\({}_{SMOS}\)) are used for thin SIT retrieval [12], and weekly composites of the merged SMOS and CryoSat-2 Level-4 SIT maps (SIT\({}_{CS2 SMOS}\)) were used to yield full-range SIT [21]. Both of the maps are obtained from SMOS L-band T\({}_{B}\) measurements on the basis of a thermodynamic and a radiative transfer model considering the variations of ice-physical properties [10]. In the latter product, daily SMOS-derived SIT is combined in an optimal interpolation scheme with the weekly CryoSat-2 SIT. The SIT\({}_{CS2 SMOS}\) product contains information on surface height and sea ice Fb included in the different modes of the SIRAL Level-1b data. Both of the datasets are available from 2010 onwards at Arctic scale from mid-October to mid-April on a 25 km EASE-Grid 2.0 and they are provided by the Alfred Wegener Institute (AWI) for Polar and Marine Research. The data can be downloaded from [[https://smos-diss.eeo.esa.int/socat/L3_SIT_Open](https://smos-diss.eeo.esa.int/socat/L3_SIT_Open)]([https://smos-diss.eeo.esa.int/socat/L3_SIT_Open](https://smos-diss.eeo.esa.int/socat/L3_SIT_Open)), (accessed on 7 February 2021) [48].  
Figure 2: Relationship between T\({}_{B}\) and sea ice thickness (SIT) as a function of T\({}_{Ph}\) of sea ice and sea ice types. Estimations are obtained using a radiative transfer model based on empirically determined values for salinity, density, and surface roughness. The curves represent circular-polarized emissions obtained with a nadir-pointing antenna at two frequencies, (**a**) at L-band (1.4 GHz, similar to T\({}_{B}\) observations of the FMPL-2 radiometer) and (**b**) at P-band (500 MHz).  
on 7 February 2021) and [[https://smos-diss.eo.esa.int/socat/L4_SIT_Open](https://smos-diss.eo.esa.int/socat/L4_SIT_Open)]([https://smos-diss.eo.esa.int/socat/L4_SIT_Open](https://smos-diss.eo.esa.int/socat/L4_SIT_Open)), (accessed on 7 February 2021).
## 3 System Models
### Network Model  
The network model of LDAKM-EIoT is presented in Figure 1. In this figure, we have IoT devices, an edge node, which is a gateway to the Internet, the cloud server(s), and a trusted authority (TA). IoT devices can be deployed as per their applications (for example, smart health care, smart manufacturing, smart cities, and smart homes). The task of each IoT device is to collect and process information about some activity (i.e., level of fumes in a industrial plant) and then to send the data to the cloud server(s) through the edge node. At the cloud server, this will be stored and utilized for other processing and decision making tasks. In this model, IoT devices are resource constrained, whereas the edge node and cloud server are resource rich as they are able to do intensive computations and have more storage capacity and battery backup. The \(TA\) is responsible for the registration of different devices (i.e., IoT devices, edge nodes, and cloud servers). In such a kind of communication environment, we have to secure the communication between the IoT device and edge node and the edge node and cloud server. Since most of IoT devices are resource constrained, we preferred to use lightweight cryptographic operations (i.e., hash and XOR operations) in different exchanged messages' computation. Consequently, to secure such kinds of communication, we urge a new \"authentication and key management\" protocol with lightweight operations. Using this scheme, the communicating entities can securely access the resources of other entities and also communicate securely.
# QESIF: A Lightweight Quantum-Enhanced IoT Security Framework for Smart Cities
## 4 Simulation Setup and Experimental Results
### Ablation Study  
To evaluate the individual contribution of each core component in the QESIF, an ablation study was conducted by selectively disabling three key modules: the QKD layer, the Q-IDS, and the hybrid protocol stack. Each variant was simulated under identical conditions across the TON_IoT, Edge-IIoTset, and Bot-IoT datasets. Figure 7 presents a clustered heatmap of the normalized performance metrics--ADR, latency, throughput, and energy consumption--for all the QESIF variants. Each metric is computed as an average over 100 independent simulation runs, and corresponding standard deviation values are reported to quantify the variability. The full QESIF configuration achieved the best overall results, with an ADR of 98.7%, latency of 20.3 ms, throughput of 868 kbit/s, and energy consumption of 13.4 mJ. When the QKD layer was disabled (QESIF--QKD),  
Figure 5: Throughput comparison of QESIF and baseline approaches across datasets and communication scenarios.  
ADR decreased to 94.1% despite slightly lower latency (19.8 ms), indicating compromised cryptographic robustness. The removal of the Q-IDS module led to the lowest ADR (88.4%), confirming its critical role in real-time threat detection. Meanwhile, eliminating the QESIF hybrid stack significantly increased latency to 27.6 ms and energy usage to 16.8 mJ, underscoring its importance in adaptive communication switching.  
Figure 6: Energy consumption trends across datasets and scenarios for QESIF and baseline approaches.  
Figure 7: Heatmap illustrating the normalized performance metrics of the QESIF and its ablated variants across key indicators—Attack Detection Rate (ADR), latency, throughput, and energy consumption. Each metric represents an average over 100 simulation runs.
# Assessment of _Bacillus pumilus_ Isolated from Fresh Water Milieu for Bioflocculant Production
## 3 Results and Discussion
### Optimization of Flocculating Activity of Purified Biofleculant  
#### 3.7.1 Effect of Biofleculant Dosage on Flocculating Activity  
The effect of dosage on the flocculating activity of the purified biofleculant was investigated (Figure 9). The main objective of the biofleculant dosage experiment was to determine the lowest amount of biofleculant to be used while attaining the highest flocculating efficiency.  
Figure 8: Scanning Electron Microscopy Imaging (SEM) of: biofleculant (**a**); kaolin clay before flocculation (**b**); and kaolin clay flocculated by the biofleculant (**c**).  
As depicted in Figure 9, the flocculating activity was above 90% within a dosage range of 0.1-0.5 mg/mL, and the maximum flocculating activity (96.5%) was achieved at an optimum biofoculant dosage of 0.1 mg/mL. The flocculating activity was almost linear between 0.2 and 0.5 mg/mL, showing that there was no major difference in the flocculating activity as the biofoculant dosage increased. The results in this study showed that the biofoculant produced by bacterial strain exhibited high flocculating activity at very low dosage (0.1 mg/mL) which could be cost containing for industrial-scale applications. A lower dosage of biofoculants with concomitant high flocculating activity will definitely reduce treatment cost [54]. The biofoculant produced by _Paenibacillus mucilaginousus_ achieved the highest flocculating activity at a dosage of 2.0 mg/L, and similar to the present study, higher or lower dosages reduced flocculating activity [64].
## 3 Results and Discussion  
### Soft Soil Settlement  
In order to test the reliability of the HC-LSSVM model proposed in this study, the cumulative settlement of the embankment center of an expressway in eastern China is selected as the research object. The locations of the expressway and the study area in this paper are shown in Figure 3a. With a total length of 237 km, the expressway undertakes the important task of transportation in the east and west of China. The whole line of the expressway is designed according to the standards of full closure, full interchange and two-way four-lane. The design speed is 120 km/h and the embankment width is 28 m. The expressway is divided into five sections A, B, C, D, and E according to administrative regions. The location of the data collected in this study is section A [25; 26].  
The embankment in the area of concern of this study is constructed of soft soil. The thickness of soft soil \(L\) in this area is 3.9-9.0 m, mainly composed of silt and clay according to the classification of the Code for Investigation of Geotechnical Engineering (GB 50021-2001 (2009)) [27], with a void ratio \(e\) of 1.2-2.1, a Poisson's ratio \(n\) of 0.40-0.50, a natural moisture content \(w_{n}\) of more than 50% and locally as high as 80%, a liquid limit \(w_{L}\) of generally 40-60%, a consolidation coefficient \(C_{v}\) of 0.2 \(\times\) 10\({}^{-3}\)-0.7 \(\times\) 10\({}^{-3}\) cm\({}^{2}\)/s, an over-consolidation ratio \(OCR\) < 1, a coefficient of permeability \(k\) of 1 \(\times\) 10\({}^{-4}\)-1 \(\times\) 10\({}^{-8}\) cm/s, a compression index \(C_{c}\) of 0.8-0.9, the rebound index \(C_{r}\) of 0.09, the unconfined compressive strength \(q_{u}\) of 25-50 kPa, a Young's modulus of elasticity \(E\) of 2.0-5.0 MPa, and a groundwater level \(h\) of 1.0-2.0 m [26].  
Figure 2: Block diagram of HC-LSSVM model for soft soil settlement prediction. The orange box is the starting position of the block diagram, and the yellow box is the end position of the block diagram. The arrows in different colors represent the sequence of the model building process, with the red sorted as 1, the light blue sorted as 2, and the green sorted as 3.  
The embankment cross section and embankment settlement monitoring location of expressway are shown in Figure 3b. The embankment height is \(\sim\)6000 mm (relative to horizontal plane), the embankment width is 28,000 mm, the berm width is 3000 mm, and the embankment slope is 1:1.5. Three embankment settlement monitoring points are set in the embankment center, and the settlement is measured by landmark method (settlement plate). The settlement plate is composed of steel bottom plate, reinforced concrete plate, metal measuring rod, and protective sleeve [25,28]. The following is the monitoring and calculating data of central settlement of soft soil embankment of the expressway (Table 1) [26]. The observation data with cumulative settlement time from 0 to 587 days are taken as training samples, and the observation data from 618 to 742 days are taken as test samples.
# Numerical Investigations on the Blade Tip Clearance Excitation Forces in an Unshrouded Turbine
## 2 Numerical Setup
### Computational Method and Boundary Conditions  
The commercial software ANSYS-CFX 16.1 was applied to solve the compressible Reynolds-averaged Navier-Stokes (RANS) equations. To obtain the full set of the frequency-dependent rotordynamic coefficients, transient analysis along with the multi-frequency elliptical whirling model were performed. The corresponding steady solutions were also found using the initial flow fields of the transient solutions. The convective terms and turbulence were found using high-order discretization. The desired converged target was the root mean square (RMS) residuals of each variable reaching a value of \(10^{-4}\) or lower, the imbalance of the mass and momentum being less than 0.1%, the response forces caused by the rotor motion approaching periodic vibration, and the difference of results between two adjacent vibration periods being lower than 0.1%. The RNG (Re-Normalization Group) \(k\)-\(\varepsilon\) turbulence model, as well as the scalable wall function, were used to solve the turbulence characteristics. On one hand, RNG \(k\)-\(\varepsilon\) model has a higher precision and a faster convergence speed compared to the standard k-\(\varepsilon\) model, and it has been validated in many studies [20; 21]. On the other hand, it demands a relatively high y+ value compared to the SST (Shear Stress Transfer) model, which is more suitable for mesh movement.  
As for the boundary conditions, the absolute total pressure and total temperature were given at the inlet and the static pressure was applied at the outlet. The fixed temperature and no-slip wall conditions were applied. It should be noted that [PERSON] et al. [19] did not take the rotational effects into consideration in their experiment. Therefore, the inlet total pressure (16.613 kPa) with no rotational speed was applied to simulate the experiment for the validation part. For the rest of the analysis, the inlet total pressure (243.5 kPa) and rotational speed (10000 rpm) was applied to keep the mass flow rate same as that in the experiment. Other detailed information is listed in Table 2.  
\begin{table}
\begin{tabular}{c c c} \hline
**Parameters** & **Units** & **Values** \\ \hline Blade number & - & 52 \\ Average radius, \(R_{m}\) & mm & 225.44 \\ Axial chord, \(C_{\text{ax}}\) & mm & 35.91 \\ Pitch & mm & 27.24 \\ Blade height, \(h\) & mm & 24.97 \\ Stagger angle & \({}^{\circ}\) & 58.38 \\ Inlet flow angle, \(\alpha\) & \({}^{\circ}\) & 48.36 \\ Normalized mean tip clearance, \(t_{m}/h\) & \% & 2.0 \\ \hline \end{tabular}
\end{table}
Table 1: Blade geometrical parameters.  
Figure 1: Three-dimensional computational model.
# Survey on Q-Learning-Based Position-Aware Routing Protocols in Flying Ad Hoc Networks
## 6 Lessons Learned, Open Issues, and Research Challenges
### Lessons Learned
#### 6.1.1 Precise Link SINR Calculation  
To make a routing decision, SINR needs to be calculated precisely. In FANETs, the SINR depends on the effect of channel medium, transmission power, inter-UAV distance, node density, mission environment, and UAV mobility. The precise calculation of SINR can boost up the performance of the MAC and routing layers. Thus, to improve the SINR performance, joint mobility control and resource allocation need to be allocated optimally [12; 35]. Better SINR ensures high link quality and low packet error rate, which improves the routing performance significantly [23]. Thus, cross-layer design is required in FANETs.
## 4 Results and Analysis
### Results of Marshall Stability Test  
Marshall stability tests were carried out on NHMA, 40%HMA, 40%RHMA, 40%RHMA-1C, 40%RHMA-1.5C and 40%RHMA-2.5C, and the results are shown in Table 7. As can be seen, the residual stability of 40%HMA was the smallest, only 73.2%, which did not even meet the standard requirements. This is because the adhesion performance between the old asphalt binder and aggregate in RAP was poor. Water easily invaded the gap between the asphalt binder and aggregate and gradually produced cracks and asphalt stripping. This weakened the anti-deformation ability of the recycled asphalt binder, and the Marshall stability decreased significantly. After adding waste engine oil regenerant into 40%HMA, the adhesive property of the recycled asphalt binder obviously improved, and the residual stability increased by 13.9%. But there was still a difference between the Marshall stability of 40%RHMA and that of NHMA. After adding carbon nanotubes into 40%RHMA, carbon nanotubes strengthened the adhesion between the waste engine oil, recycled asphalt binder and aggregate. Therefore, the waste engine oil, recycled asphalt binder fell off the asphalt surface less easily. Carbon nanotubes also increased the ductility of the waste engine oil, recycled asphalt binder, making cracks between 40%RHMA less likely to occur via water invasion. The residual stability of 40%RHMA-1.5C was restored to the level of the new asphalt mixture, and the Marshall stability significantly improved.
# Rheological Properties of Lunar Mortars
## 4 Results and Discussion
### Rheology of Lunar Micro-Mortars (Series with w/c = 0.265)  
When examining the consistency of micro-mortars, which are cement pastes supplemented with an aggregate of the micro fraction of lunar regolith simulant, an attempt was made to reduce water and lower the w/c ratio in relation to that of the abovementioned 0.50 due to a different degree of filling of such mixes. At this stage of the research, the starting point was determining the proportion between water and cement at which the so-called standard consistency (according to EN 196-3 standard) is obtained. For the used cement CEM 142.5 R the w/c ratio for standard consistency was experimentally determined at the level of 0.265 (Figure 5 shows the results of these tests). This w/c was then used to determine the compositions of the tested lunar micro-mortars with the LRS. Table 6 contains their compositions--LRS was added to the cement paste in increasing amounts from 2% to 6% of the cement mass.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**No** & **Water** & **Cement** & **Aggregate** & **W/C** & **Admixture** & **QP** & **LRS** & **D** \\
**[g]** & **[g]** & **[g]** & **[g/g]** & **[\% of c.m.]** & **[\% of agg.]** & **[\% of agg.]** & **[mm]** \\ \hline
1 (ref.) & & & & & 0.0 & 0.0 & 0.0 & 206 \\
2 & 225 & 450 & 1350 & 0.50 & 1.0 & 20.0 & 0.0 & 208 \\
3 & & & & & 1.0 & 0.0 & 20.0 & 245 \\ \hline \end{tabular}
\end{table}
Table 5: Compositions of mortars with/without micro-filler (QP—quartz powder; LRS—lunar regolith simulant) and flow table test results (D—mean value of the flow diameter measured in two perpendicular directions).  
Table 6 contains results of testing the consistency of lunar micro-mortars performed in the Vicat apparatus (measurement of the bolt intensder penetration depth in the mix). To assess the plasticity of the micro-mortars, they were also tested on the flow table according to the modified flow table test method, in which a smaller volume of the sample is used (as described in Section 3). The results of the tests are shown in Figure 7.  
Based on the analysis of the above data, it is obvious that with increasing LRS content in the micro-mortar mix, the consistency becomes denser. The application of LRS more than 6% of cement mass resulted in the inability to perform the tests--the mixes were too dense to obtain reliable test results. Both tests showed that the relation between the consistency (regardless of whether it was the penetration depth in the Vicat apparatus or the flow diameter) is directly proportional to the content of LRS. Moreover, it indicated that both tests correlated very well. Figure 5 (right) shows the relation between the results obtained with both methods for lunar micro-mortars with identical compositions and the correlation coefficient is high (0.91).
## 2 Research Methodology  
### Governing Equations  
When the lateral spread of an avalanche is much larger than the depth of the avalanche, a depth-averaged avalanche dynamics model can be implemented by integrating the three-dimensional Navier-Stokes equations along the depth of the avalanche. To develop a shock-capturing scheme, conservative governing equations are mainly adopted, ensuring the conservation of the mass and momentum of such a model. Hence, by neglecting the second-order terms (viscous forces), the governing equations presented using a three-dimensional rectilinear coordinate system (\(x,y,z\)) take the following form [2; 19]:  
\[\frac{\partial\overset{\rightarrow}{Q}}{\partial t}+\frac{\partial\overset{ \rightarrow}{f}}{\partial x}+\frac{\partial\overset{\rightarrow}{g}}{ \partial y}=\overset{\rightarrow}{S}+\overset{\rightarrow}{H}, \tag{1}\]in which \(\overrightarrow{Q}\) is the conservative variables vector; \(\overrightarrow{f}\) and \(\overrightarrow{g}\) are the flux vectors along the \(x\)- and \(y\)-directions, respectively; and \(\overrightarrow{S}\) and \(\overrightarrow{H}\) are the source terms. More specifically, the vectors can be expressed as  
\[\overrightarrow{Q}=\left(\begin{array}{c}h\\ l\overline{u}\\ h\overline{\overline{v}}\end{array}\right), \tag{2a}\] \[\overrightarrow{f}=\left(\begin{array}{c}\overline{u}h\\ \overline{u}^{2}h+g_{z}h^{2}K_{x}/2\\ \overline{u}\overline{v}h\end{array}\right),\] (2b) \[\overrightarrow{g}=\left(\begin{array}{c}\overline{v}h\\ \overline{v}\overline{u}\\ \overline{v}^{2}h+g_{z}h^{2}K_{y}/2\end{array}\right)\] (2c) \[\overrightarrow{S}=\left(\begin{array}{c}0\\ g_{x}h-\frac{\overline{u}}{\left|\overline{u}\right|}\mu_{b}g_{z}h\\ 0\end{array}\right),\] (2d) \[\overrightarrow{H}=\left(\begin{array}{c}0\\ 0\\ g_{y}h-\frac{\overline{v}}{\left|\overline{u}\right|}\mu_{b}g_{z}h\end{array}\right) \tag{2e}\]  
where \(\overline{u}\) and \(\overline{v}\) are the depth-averaged flow velocities along the \(x\)- and \(y\)-axes, respectively; \(h\) is the local depth of the granular flow; \(g_{x}\), \(g_{y}\), and \(g_{z}\) are the gravitational accelerations along the \(x\)-, \(y\)-, and \(z\)-axes, respectively; \(\mu_{b}\) is the coefficient of basal friction; and \(K_{x}\) and \(K_{y}\) are the earth pressure coefficients, which are the ratios of the vertical normal stress to the horizontal stresses along the \(x\)- and \(y\)-axes, respectively. In hydrodynamic models [20; 21; 22; 23], \(K_{x}=K_{y}=1\), whereas in the S-H theory, the Mohr-Coulomb yield criterion is used to calculate these coefficients [2; 5; 24].  
The first array of the equation represents the mass conservation equation. In the second and third array of the equation, the first term in the source vectors stands for the gravity component parallel to the inclined plane, and the second is the term of basal friction stress. The first term in the flux vectors represents the stress linked to the depth gradient, and the second term is the contribution from the lateral pressure.