esa-sceva/satcom-chunk-collection huggingface dataset:
# TSCH and RPL Joining Time Model for Industrial Wireless Sensor Networks
## 6 Simulations and Results
\begin{table}
\begin{tabular}{c c c} \hline \hline EB Period in TSCH Simulations & Number of Nodes & DIO Period in RPL Simulations \\ \hline
4 s & 1 & 4 s \\
8 s & 2 & 8 s \\
16 s & 5 & 16 s \\
32 s & 7 & 32 s \\ - & 10 & - \\ - & 15 & - \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Simulation parameters for TSCH and RPL simulations. Figure 3: Network deployment in Cooja Simulator for \(N=5\). modified in the RPL simulations. The combination of all these configuration parameter variations gives a total of 24 simulated configuration variations, and 30 iterations were performed for each of them. Figure 5 shows the results obtained in the simulations carried out for the RPL part. The model and the results obtained have been represented using six different graphs depending on the number of neighbours, showing the results in the same way as in the case of the TSCH synchronization time. These graphs show how the time it takes the new node to obtain the information about the RPL tree increases in a linear way as the period between DIO messages increases, showing a higher dispersion of the standard deviation at the highest values of the period. It should be noted that the periodic sending of RPL signaling messages is not the normal operation of the protocol, but in stable situations and without events that reset the value of the Trickle Timer, the period between DIO messages would reach much higher values. Moreove,r the behavior between the different nodes would not necessarily follow the same pattern, since the state of the Trickle Timer is individual for each node. However, through this individualized analysis, the impact that a certain period between DIO messages has on obtaining the information from the RPL tree can be demon  
Figure 4: TSCH simulation results. strated. The behavior of this message sending compromises between the traffic congestion produced by the RPL control plane and the speed at which a node is fully deployed and configured, so it is interesting to be able to control this behavior when deploying devices. Another aspect to emphasize is the impact that the time that it takes to connect the node to the RPL tree has compared to the time that it takes the node to synchronize. Observing the scale of the graphs in Figure 4 and comparing them with the graphs in Figure 5, it is clear that the impact of the RPL part in the display and configuration of a node is lower than that of the TSCH synchronization time.
# Assessment of the Bearing Capacity of Bridge Foundation on Rock Masses
## 5 The Influence of the Self-Weight on Bearing Capacity  
In the absence of a coefficient that takes into account the increase in the bearing capacity due to the material's self-weight in rock mechanics, the classical soil mechanics self-weight coefficient \(\left(\frac{\gamma\cdot B\cdot N_{\gamma}}{2}\right)\) was considered.  
The coefficient \(N_{\gamma}\) is recommended by [PERSON] [53]. \(N_{\gamma}\) depends on the value of the friction angle of the material, to apply to a rock mass, and the authors of [54] recommend considering the angle (\(\rho_{man}\)) of friction from the harmonic mean of the sine of the instantaneous friction angles acting on the two boundaries as described in Section 2 (\(2/sen\rho_{mnm}=1/sen\rho_{1}+1/sen\rho_{2}\)).  
To compare the values of bearing capacity obtained using the \(W_{F}\) factor with those factors of the classical soil mechanics equation, the bearing capacity was calculated with the [PERSON] et al. method [15] (\(P_{h1 SGO}\)). The load increase, due to the self-weight of the rock, was added both by the correction factor presented in this paper (14) (\(P_{h1}\)) and the classical solution of the soil mechanics (\(P_{h2}\)).  
\(P_{h1}\) was estimated by applying \(W_{F}\) to Equation (14), then verified in Figure 15 that for the case of \(m_{i}=12\), \(B=22\) m, \(UCS=5\) MPa and \(GSI=10\), \(50\), and \(85\) the self-weight affects the bearing capacity in more than \(5\%\).  
\[P_{h1}=(1+W_{F})\cdot P_{h_{8 AO}} \tag{15}\]  
\(P_{h2}\) is the result obtained using classical soil mechanics [10]:  
\[P_{h2}=P_{h_{8 AO}}+\frac{\gamma\cdot B\cdot N_{\gamma}}{2} \tag{16}\]  
Table 5 shows the results of the bearing capacity considering the self-weight of the material through the two methods.  
The term defined by the bearing capacity considering the self-weight that uses the soil mechanics equation significantly overestimates the bearing capacity. This is due to the fact that every fractured rock mass (low quality) present high friction angles which is the main  
Figure 17: Correlation obtained between the numerical results applying the self-weight coefficient Equation (14).  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multicolumn{2}{c}{**Cases**} & & & & & & \\ (\(m_{i}=12\), \(B=22\) m, \(UCS=5\) MPa) & \(GSI\) & \(\rho_{1}\) (\({}^{\circ}\)) & \(\rho_{2}\) (\({}^{\circ}\)) & \(\rho_{mean}\) (\({}^{\circ}\)) & \(P_{h1}\) (MPa) & \(P_{h2}\) (MPa) & \(\varepsilon=\frac{|P_{h2}-P_{h1}|}{P_{h2}}\) \\ \hline
1 & 10 & 64 & 28.8 & 38.8 & 2.2 & 26.2 & 0.92 \\
2 & 50 & 62.6 & 22.2 & 32.1 & 8.4 & 14.6 & 0.42 \\
3 & 85 & 53.3 & 19.6 & 28.2 & 30.6 & 30.9 & 0.01 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Bearing capacity considering the self-weight of the material.  
influential parameter. In rocks the parameters that qualitatively define and influence the bearing capacity are the \(GSI\) and the \(UCS\), which are used in the coefficient \(W_{F}\); in cases with low \(GSI\) the influence of \(W_{F}\) is noticeable.
## 2 Laser-Generated Ultrasound
### Laser-Generated Ultrasound Applications  
AOUSI is one of the most important applications of LGUS transmitters. (i) The intrinsic broad bandwidth facilitates the investigation of frequency-dependent responses in tissue, allowing the application to \"virtual biopsy\" [76]. (ii) Especially, when implemented based on fiber optics, AOUSI could open up a broad range of applications. A probe with a diameter of <0.84 mm for AOUSI is fabricated [77]. Axial resolution of 64 \(\upmu\)m and lateral resolution of 88 \(\upmu\)m at a depth of 3.5 mm are measured. Ex vivo vascular tissue imaging is demonstrated, as shown in Figure 6. More details about AOUSI are presented in Section 3.  
Figure 5: Measurement setup for the LGUS characterization. The laser bean is expanded and then irradiated on the CNT lens. The generated ultrasound is optically detected by scanning the fiber-optic hydrophone. Inset: two types of CNTs–PDMS focused LGUS transmitters. Reproduced with permission from [30], Nature Publishing Group, 2012.  
Besides AOUSI, the LGUS approach offers a valuable tool for nondestructive testing and biomedical applications. (i) Characterization of the resonant frequencies of a cured PDMS thin film is presented using a fiber-optic LGUS transmitter and an FP ODUS detector [78]. The received acoustic pulse is used to acquire the resonant frequencies of the PDMS thin film. (ii) A focused ultrasonic pressure pulse is used for nondestructive testing (NDT) of the surface of a sample placed in the focal zone [79; 80]. Clear changes in the transient acoustic waveform are observed for the sample with cracks at the surface compared with a control sample. The approach is thus promising for nondestructive evaluation of defects. (iii) By directly irradiating the laser pulse on an absorber and analyzing the resultant photoacoustic wave, material properties can be evaluated [81]. The different states of metals can be induced using laser pulses with a wide range of intensities. By analyzing the shape and amplitude of the generated pressure, one can investigate phase transitions in metals. (iv) A fiber-optic LGUS transmitter is employed as ultrasonic tracking to accurately guide the placement of medical devices such as needles and catheters, which is critical in clinical procedures [82]. (v) The generation of controlled single microbubble is possible by relying on a highly-localized pressure by the LGUS approach [70].
# Performability Evaluation and Sensitivity Analysis of a Video Streaming on Demand Architecture
## 5 Numerical Analysis
### Models Comparison  
Figure 10 compares the availability (Figure 10a), downtime (Figure 10b), and MRT (Figure 10) of the four models. Across all three criteria, the behavior was consistent, with the Hot-Standby model outperforming the warm standby model, which outperformed the cold standby model, and finally, the basic model. In terms of availability, the base model had 2.8 \"9 s\", while the others had significantly better availability. The number of \"9 s\" for the cold standby model was 3.37, 3.44 for the warm standby model, and 4.0 for the Hot-Standby model. Therefore, there was a 1.2-9 difference between the base and Hot-Standby versions. However, this difference is more clearly seen when evaluating the downtime. The base model's downtime was 13.8 h per year, which is considered excessive for this type of service [8]. The values for downtime for the Cold-Standby, Warm-Standby, and Hot-Standby models were 3.7, 3.1, and 0.8, respectively. The downtime for the Hot-Standby model was less than one hour, which is a very acceptable value.  
Figure 10: _Cont._  
In order to compare the models based on MRT, the processing capacity of the Back-End was varied through the CB marking at P_ProcessCap2. In this example, we considered the processing capability to be the number of cores in a computer. We configured four different capacities for each model in this scenario, resulting in the basic, cold, warm, and hot versions each having 4, 6, 8, and 12 processor cores, respectively. The base model and Hot-standby model had MRT values of 217 ms and 158 ms, respectively. While this may seem like a small difference, it is significant when it comes to video streaming. Based on the simulations in this paper, it is therefore beneficial to invest in redundancy and equipment improvements. The decision on which type of redundancy and capacity machines to use is influenced by various factors, including the specific real-world context. The models can provide guidance on these questions.
# A Single-Launch Deployment Strategy for Lunar Constellations
## 4 Analysis of the Constellation Deployment  
The deployment strategy designed in Section 3 is here examined using numerical methods. Initial conditions equivalent to those of the Artemis-1 mission are considered [54], corresponding to \(a_{d}=202\),700 km, \(e_{d}=0.968\) and \(\theta_{d}=29\) deg. The satellites are assumed to be deployed in elliptical inclined lunar orbits, whose orbital elements are reported in Table 3, which are suitable for constellations that shall provide polar and global coverage of the Moon [30]. The analysis is performed using the General Mission Analysis Tool R2020a developed by NASA, in which the algorithm presented in Section 3 was implemented. The propagator includes the Sun and the Earth as point masses while the gravitational field of the Moon is described by the LP-165 model. The motion of the celestial bodies is modeled using the NASA JPL Development Ephemerides (DE405).
# Intrusion Detection in IoT Using Deep Residual Networks with Attention Mechanisms
## 6 Model Evaluation and Discussions  
### Dataset Description  
Generalization ability is an important metric to assess the performance of a model. A good model should have strong generalization ability, meaning it can perform well on new datasets. Therefore, in this study, the model's generalization performance is tested using different datasets.  
To evaluate the performance of the proposed model, we used ToN_IoT [13], a real dataset created from a large-scale IoT system developed by the Cyber IoT Laboratory at ADFA, New South Wales, Australia. This dataset contains normal category data and nine categories of attack data, including password, scan, ransomware, backdoor, denial of service, distributed denial of service, MITM, injection, and XSS attacks. The total number of data instances in this dataset is shown in Table 1. We trained our model using the ToN_IoT dataset to ensure it learns from a comprehensive set of attacks and normal behavior data.  
To test the generalization ability of our model, we then used the UNSW-NB15 [31] dataset. The raw network packets for the UNSW-NB15 dataset are created by the IXIA PerfectStorm tool at the Australian Cyber Security Centre's Cyber Scope Lab, which can be used to generate a mix of modern normal activity and synthetic contemporary attack behavior. The dataset contains a total of 49 features and 9 common attacks. The normal information accounts for 88% of the dataset size and the attack information accounts for 12%. We used this dataset to test the generalization ability of the model. The basic information of this dataset is shown in Table 2.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Record Type** & **Number of Records** \\ \hline Backdoor & 35,000 \\ DDoS & 25,000 \\ DoS & 20,000 \\ Injection & 35,000 \\ MITM & 1043 \\ Password & 35,000 \\ Ransomware & 16,030 \\ Scanning & 3973 \\ XSS & 6116 \\ Normal & 245,000 \\ \hline \hline \end{tabular}
\end{table}
Table 1: ToN_IoT dataset record description.  
We use several metrics to evaluate the performance of the proposed model, i.e., _accuracy_, _precision_, _recall_, and _F1-score_ because they are widely used to evaluate deep learning algorithms. The formula is as follows:  
\[Accuracy=\frac{TP+TN}{TP+TN+FP+FN} \tag{17}\]  
\[Precision=\frac{TP}{TP+FP} \tag{18}\]  
\[Recall=\frac{TP}{TP+FN} \tag{19}\]  
\[F1\text{-}score=\frac{2 TP}{2 TP+FP+FN} \tag{20}\]  
where \(TP\) is true positive, representing the number of positive samples correctly identified as positive, \(FP\) is false positive, representing the number of negative samples incorrectly identified as positive, \(TN\) is true negative, representing the number of negative samples correctly detected as negative, and \(FN\) is false negative, representing the number of positive samples incorrectly identified as negative.
# Validating a 5G-Enabled Neutral Host Framework in City-Wide Deployments
## 2 Background on Neutral Host Concept and Enabling Technologies
### Cloud/Edge Computing and Orchestration  
A Multi-Access Edge Computing (MEC) network architecture uses edge resources to enable cloud computing capabilities and IT service environments at the edge of the cellular network [23]. As a result, this environment is characterized by applications running close to the User Equipment (UE).  
The neutral host model leverages multiple MEC deployments to fully cope with 5G requirements in terms of bandwidth, coverage, and latency. Indeed, distributed edge resources allow neutral host frameworks to deploy end-to-end services across distributed pools of edge resources, enforcing ultra-low latency and high bandwidth, while real-time access to radio network information can be leveraged by applications.  
A MEC architecture poses some real challenges for the design of end-to-end services, mainly since the resources locally offered can be limited, thus highlighting the need for tightly centralized orchestration, which can dynamically operate the life-cycle of edge computing applications [17; 24].  
End-to-end network slicing, over the distributed cloud and edge resources for enhanced computing and processing capabilities, requires extensions or a new NFV MANO framework to support multitier orchestration, covering also different edges (e.g., ex tended edge). As such, multitier orchestration in network slicing is getting attention from industry and academia [23].  
Among well-known NFV MANO solutions only Open Source MANO (OSM) [25], an ETSI-compliant and hosted project, and SONATA [26] are capable of network slicing lifecycle management. As a result, our platform leverages ETSI OSM as the NFV MANO solution [27]. Moreover, to provide pervasive neutral host services our framework goes beyond the edge to the extended edge by deploying a multitier NFV Orchestrator (NFVO) module [28; 29] (cf. Section 3).
# An Iterative Guidance and Navigation Algorithm for Orbit Rendezvous of Cooperating CubeSats
## 3 Guidance and Navigation
### Navigation  
The navigation system consists of an algorithm that processes the available measurements \(\zeta\) to reconstruct the target estimated state \(\hat{\chi}\), defined as  
\[\hat{\chi}=\{\hat{r}_{T},\hat{\xi}_{T},\hat{\phi}_{T},\hat{\sigma}_{T_{T}},\hat {\sigma}_{t_{T}},\hat{\rho}_{k_{T}}\}. \tag{9}\]  
The model assumed for the motion of the target is that of Equation (1), where the external accelerations \(\vec{a}\) are considered to be null, i.e., the target is not maneuvering and no orbital perturbations are considered. The equations that describe the three measurements (one distance, and two angles) are the following:  
\[\left\{\begin{array}{l}\zeta_{1}=||(x_{2}-x_{1},\quad y_{2}-y_{1},\quad z_{ 2}-z_{1})||+\
u_{range}\\ \zeta_{2}=\arctan\frac{y_{2}-y_{1}}{x_{2}-x_{1}}+\
u_{beam1}\\ \zeta_{3}=\arctan\frac{z_{2}-z_{1}}{\sqrt{(x_{2}-x_{1})^{2}+(y_{2}-y_{1})^{2} }}+\
u_{beam2}\end{array}\right., \tag{10}\]  
where \(\
u_{range}\in\mathcal{N}(0,\sigma_{v_{range}})\), \(\
u_{beam1}\in\mathcal{N}(0,\sigma_{v_{beam}})\) and \(\
u_{beam2}\in\mathcal{N}(0,\sigma_{v_{beam}})\) are three random signals representing the noise on the measurements, whereas subscripts 1 and 2 correspond to the two satellites. Because of the optical nature of the sensor, the intensity of the noise signal on the range measurement depends on the distance between the two spacecraft. Therefore, it is assumed that the variance \(\sigma_{v_{range}}\) is equal to the 10% of the true range value, as proposed in [22].  
Given the high non-linearity of the model in Equation (1) and of the measurements in Equation (10), the algorithm considered for the state estimation is the UF in its standard version [28]. The process noise covariance matrix \(Q\) for this problem is defined as  
\[Q=\begin{pmatrix}q_{0}\frac{\Delta t_{S}^{3}}{3}I(3)&q_{0}\frac{\Delta t_{S}^ {2}}{2}I(3)\\ q_{0}\frac{\Delta t_{S}^{2}}{2}I(3)&q_{0}\Delta t_{S}I(3)\end{pmatrix}, \tag{11}\]  
where \(q_{0}\) is a tuning parameter of the filter, \(\Delta t_{S}\) is the sampling time, and \(I(n)\) is the identity matrix of dimensions \(n\times n\).
# Aptamer-Based Biosensors for the Analytical Determination of Bisphenol A in Foodstuffs
## Abbreviations  
ACET: Alternating Current Electro-Thermal Effect; Au-PtNPs, Golden-Platinum NanoParticles; AgCNFs, Silver nanoparticles/Carbon NanoFibres composite; AgNPs, Silver Nano-Particles; ACF, ACriFlavine; Apt, Aptamer; AuNPs, Gold Nano Particles; BDD, Boron-Doped Diamond; BPA, BisPhenol A; BPB, BisPhenol B; BPS, BPF, BisPhenol F; BisPhenol S; CdTe QDs, Cadmium Tellured Quantum Dots; CNTs-COOH, Carbon Nanotubes-COOH; Cy3, Cyanine dye 3; dsDNA, double-stranded DNA; cDNA, capture DNA; hDNA, help DNA; mDNA, message DNA; rDNA, report DNA; DNAzyme, deoxyribozymes; EDC, Endocrine-Disrupting Chemical; EFSA, European Food Safety Authority; Exo III, Exonuclease III; ECL, ElectroChemiLuminescence; FCM, Food Contact Material; GCE, Glassy Carbon Electrode; GO, Graphene Oxide; HCR, Hybridization Chain Reaction; HQ, HydroQuinone; HRP, HorseRadish Peroxidase; IFE, Inner Filter Effect; ITO, Indium-Tin-Oxide; LC, Liquid Crystal; f-MWCNTs, functionalized Multi-Wall Carbon NanoTubes; PDDA, Poly(DiallyDimethylAmmonium chloride); PEC, Photo-ElectroChemical; PPY, PoliPYrrole; RT-qPCR, Real-Time quantitative Polymerase Chain Reaction; SERS, Surface-Enhanced Raman Scattering; SML, Specific Migration Limit; SPGE, Screen-Printed Gold Electrode; TDI, Tolerable Daily Intake; TdT, Terminal deoxynucleotidyl Transferase; dT, 2'-deoxyTimine; TMR, TetraMethylI Rhodamine; UCNPs, Upconversion NanoParticles; WE, Working Electrode.
## 5 Weathering Processes
### Natural Weathering Processes
#### 5.1.2 Mechanical Weathering Processes  
In the two cases studied, two different but inter-related mechanisms were observed:  
1. Thermal weathering due to the effect of changes in temperature suffered by rocks, both seasonally and in night-day cycles. This temperature variation may lead to the development of a thermal gradient between the surface and the interior of the rock, given its low thermal conductivity, the effect being their final breaking [17; 43; 44; 45; 46]. This process is clearly seen in the case of the Penas de Cabrera shelters, where the metamorphic rock grains, with dark tones, can become relatively abundant (10%) in some parts of the shelters. Given their colouring and their textural characteristics, these components have higher specific heat and expansion coefficients than quartz and feldspar (majority components in the host rock). This leads to differential expansion processes and, therefore, variations in greater magnitude through dilatation-retraction phenomena, which in the long term cause their separation. As a consequence, on many occasions, lines of holes were observed in the rock. These alignments, which originally corresponded to laminations enriched in slaty grains, produce incipient alveolar surfaces (Figure 6B,C). In the studied cases, the heating agent is the sunshine, for which reason the term 'insolation weathering' can be used [47]. Effects of thermal expansion due to fire [48] have not been recognised. The effects of lighting fires have only been translated into the formation of blackish patinas with abundant sooty particles;
2. Frost weathering as a consequence of successive freeze-thaw cycles. The water present in the rock, whether through the infiltration of meteoric water or through the nocturnal condensation phenomena, is introduced in the rock through weakness planes (fractures, joints, and bedding planes) and may freeze in the winter periods. In many instances, these weakness planes constitute the contact between the superficial alteration finds and the fresh rock. In this case, the effectiveness of the weathering phenomena is increased by its simultaneous action with chemical and biological weathering processes. The final result is the detaching of large superficial rock flakes (Figure 6D) and the granular disintegration of the rock. Structural (fractures, joints) and textural (laminations, abundance of phyllosilicates) features of the host rock have an effect on its frost resistance and its response to other weathering processes such as salt weathering [49; 50]. In the two studied rock groups it was possible to check that the phenomena of formation and shedding of flakes are very common and intense. Nevertheless, it must be stressed that in the Tajo de las Figuras site a greater effectiveness was appreciated as a consequence of the greater thermal oscillation.