esa-sceva/satcom-chunk-collection huggingface dataset:
## 8 Conclusions and Further Work  
We have carried out an experimental study that provides insight into the consequences of using predetermined configurations of the ruleset in the context of SIDS that detect URI web attacks. Our results indicate that these configurations provide unsatisfactory performance in the SIDS and rulesets tested (Snort, ModSecurity and Nemesida Free), providing a detection capability lower than expected for known attacks even in the most sensitive configuration. Furthermore, the less sensitive configurations provided a meager detection rate (between 6 and 8%), while the most sensitive configurations provided an unacceptable precision and alarm rate. Although it still exhibited a low detection rate, only Nemesida offered an adequate balance in its default configuration. Running these SIDS simultaneously lets us have a cooperative decision that, in the best case, can either improve the detection rate or reduce _FP_ with respect to individual performance. Still, it is unclear whether the benefit exceeds the cost in terms of computational resources. Consequently, it is necessary to reflect upon the role of open-source untuned SIDS as core elements for protection in the context of web services as they can provide a false feeling of safety to the network admin. Finally, amid scarce guidance available, we have provided a cost-efficient method for selecting the rules to deactivate from a ruleset to improve precision and reduce false alarms in a target operational environment.  
In the future, we are planning to include new SIDS and datasets to improve the significance of the results of this study. We are also planning to add techniques from the field of machine learning to further improve SIDS performance.  
Conceptualization, J.D.-V., A.E.A. and R.E.A.; methodology, J.D.-V.; software, J.D.-V., J.M.-C. and G.M.; validation, R.E.A., A.E.A. and J.D.-V.; investigation, A.E.A.; resources, J.D.-V., J.M.-C. and G.M.; data curation, J.D.-V., and G.M.; writing--original draft preparation, A.E.A. and J.D.-V.; writing--review and editing, R.E.A., A.E.A., J.D.-V.; supervision, A.E.A.; project administration, R.E.A.; funding [PERSON], R.E.A. and J.D.-V. All authors have read and agreed to the published version of the manuscript.  
This work has been partly funded by the research grant PID2020-115199 RB-I00 provided by the Spanish ministry of Industry under the contract MICIN/AEI/10.13039/501100011033, and also by FEDER/Junta de Andalucia-Consejeria de Transformacion Economica, Industria, Conocimiento y Universidades under project PYC20-RE-087-USE.  
The datasets used in the study have been cited in the text.  
The authors declare no conflict of interest.
## 2 Samples and Methods
### Physical Simulation Workflow and Analysis  
The experiment was undertaken in two steps: preparation of the synthetic core, and geochemical reaction between the core and CO\({}_{2}\) fluids. During the first step, the selected mineral powder (particle size 0.25-0.5 mm) was blended with distilled water and placed in six columnar autoclaves (diameter 3.0 cm, length 11 cm, volume 77.7 cm\({}^{3}\)). The six core samples (# 1 to # 6) were used in the experiment over 5 days under P/T conditions equivalent to mesogenetic diagenesis (Figure 2). The syringe-pump injection system injected synthetic formation water saturated with CO\({}_{2}\) into the six synthetic core pores at 150 \({}^{\circ}\)C and 48.45 MPa, after which temperature and pressure were kept constant for 4 d (# 1), 7 d (# 3), 10 d (# 4), 13 d (# 5), and 16 d (# 6), while # 2 was used as a blank.  
During the experiment, the temperature and pressure of each autoclave were monitored automatically by the control system. After reaction, core and fluid samples were analyzed for ion contents, mineralogical changes, and porosity. The producing fluid was measured for its pH values using an Orion4 STAR acidity meter from Thermo within 6 h of each sampling. The ionic compositions of the water were analyzed after being spiked with 1 mol/L HCl in order to avoid carbonates precipitation, and measured using an OPTIMA 7300 DX ICP-OES (Inductively Coupled Plasma-Optical Emission Spectrometry) with an analytical precision of 10\({}^{-3}\)-10\({}^{-9}\). Mineralogical changes were examined using a JSM6700F scanning electron microscope from JEOL with EDS (Energy Dispersive Spectrometer) from INCA software (Oxford Company, Oxford, England). The porosity changes were analyzed using visual porosity estimation, which is an image analysis technique. Firstly, core samples were impregnated with blue epoxy and then polished and made into casting thin sections. Then, combined high-resolution images of these thin sections were taken under the optical petrographic microscope; the image analysis software can delineate different types of porosity and calculate the percentages of these porosities in the thin sections with an accuracy of up to 0.01%.  
Figure 1: Schematic diagram of CO\({}_{2}\)-formation water-rock physical experiment.
## 3 Results and Analysis
### Evolution Law of Surface Subspace
#### 3.1.2 Numerical Simulation Results  
In order to facilitate the intuitive study of the evolution law of surface settlement during shield tunneling, the vertical displacement cloud map of the monitoring section is processed by view slicing, as shown in Figure 7. It can be seen from the diagram that when the shield is close to the monitoring section of \(-24\)\(-\)\(-\)\(12\) m, the maximum surface settlement is \(0.61\) mm; in the \(8\)th excavation step (\(-6\) m from the monitoring section), the ground settlement is about \(1.16\) mm, and the \(10\)th excavation step (\(0\) m from the monitoring section) reaches \(1.94\) mm. Analysis shows that shield approaching stage with the cutterhead gradually close to the monitoring section, the surface subsidence value increases gradually but the deformation value is relatively small, the cutterhead from the monitoring section \(-6\)\(-\)\(0\) m surface disturbance is more obvious. After the shield crosses the monitoring section, the ground settlement increases gradually and tends to be stable after reaching the maximum value of \(12.04\) mm in the \(16\)th excavation step (\(18\) m from the monitoring section).  
Figure 6: Transverse vertical displacement of the ground surface.  
Figure 7: \(Cont.\)Figure 7: The cloud atlas of vertical displacement during different tunneling stages. (**a**) \(-\)24 m. (**b**) \(-\)18 m. (**c**) \(-\)12 m. (**d**) \(-\)6 m. (**e**) 0 m. (**f**) 6 m. (**g**) 12 m. (**h**) 18 m. (**i**) 24 m. (**j**) 30 m.  
From the horizontal point of view, 10-15 m away from the tunnel vault is muddy gravel sand stratum, which has the characteristics of high natural water content, large void ratio, high compressibility, long consolidation time and high sensitivity, and is more susceptible to shield tunneling disturbance, showing more obvious settlement deformation. At the same time, there are significant peak differences and time asynchrony characteristics in this area. It is shown that the hole 1 # and hole 3 # reach the peak settlement of 2.21 mm and 1.72 mm respectively when the cutter head is 12 m away from the monitoring section, while the hole 2 # reaches the peak settlement of 4.41 mm when the cutter head is 18 m away from the monitoring section. The reason for this phenomenon is that the ground disturbance above the shield is more severe and lasts longer. In addition, 0.5 times the outer diameter of the shield (0-3 m) above the tunnel vault also produces large settlement deformation. The settlement peak is 2.42 mm, accounting for 21.1% of the total settlement, and is different from the hole 1 # and hole 3 #. The hole 2 # stratum shows the characteristics of large layered settlement amplitude and wide longitudinal influence range.  
Figure 8: Longitudinal displacement comparison diagram.  
Figure 9: _Cont._
## 4 Materials and Methods  
### Chemical and Reagents  
Ethyl gallate, 4-hydroxybenzaldehyde, vanillin, tyrosol, protocatechuic aldehyde, syringaldehyde, pyrogallol, 4-hydroxy-3-methoxycinnamaldehyde, 4-methylcatechol, tryptophan, ethyl 3,4-dihydroxycinnamate, 2,6-dimethoxyphenol, ethyl vanillate, homovanillyl alcohol, eugenol, 4-hydroxy-3-methoxyphenylacetone, salicaldehyde, coniferyl alcohol, epicatechin, catechin, scopoletin, \(p\)-coumaric acid, caffeic acid, resveratrolol, syristic acid, rutin, gallic acid, salicylic acid, protocatechuic acid, 2-methoxy-4-vinylphenol, 4-vinylphenol, 4-ethylguaiacol were purchased from Sigma-Aldrich (Steinheim, Germany). All chemicals were used in the highest purity available. Acetonitrile and methanol (gradient grade for liquid chromatography) were also purchased from Sigma-Aldrich. Deionized water was produced by Mili-Q\({}^{\circledR}\) Reference System (Merck KGaA, Darmstadt, Germany). Sodium chloride was purchased by Lach-Ner (Neratovice, Czech Republic). Standard solution of \(n\)-alkanes C8-C33 was purchased by CPAchem (Stara Zagora, Bulgaria). Technical gases, i.e., helium 4.6, hydrogen, and nitrogen, were purchased from Linde Gas (Prague, Czech Republic).
## 3 Results and Discussion
### Magnetic Field  
In the aspect of accuracy, the line graphs of Figure 4 indicate that the results of the CFNN and FFNN are very close; however, the CFNN still has better results. The CFNN is facing slight drops in accuracy by adding more hidden layers to the model after two layers, which makes it the best number for it. The same is observed for the FFNN; the two hidden layers have the lowest RMSE and highest R-squared which makes it the best option for it, but it is still not as accurate as the CFNN.  
In the aspect of response time, generally, it increases by adding more layers. That said, although the double-layer model has the second lowest one after the single-layer model by a great difference, by taking the accuracy of the models into account, the difference in time can be neglected.
# Vibration Signal Diagnostic Information of Reinforced Masony Elements Destruction
## 2 Materials and Methods
### Vibration Signal Correlation Function  
It is a functional estimator of the signal \(u\) = (_f_,\(\theta\)), which can be obtained by averaging the time domain and examining the interdependence between its two segments delayed by a time interval, \(T\). It has the following form [4; 27].  
\[\mathbf{R_{uu}}(\mathbf{\tau})=\frac{1}{T}{\int_{-T}^{T}\mathbf{u}(t,\theta)\mathbf{u}(t+\mathbf{ \tau},\theta)\mathbf{dt}} \tag{2}\]  
In practical applications, a sufficiently long observation time, \(T\), is chosen so that the properties of the correlation estimator are independent of the averaging time, \(T\). The necessary condition to obtain this property is the selection of \(T\) such that this time is much greater than the correlation time of process \(\tau k\). The defined correlation function can take values within the range of (\(-1\), \(1\)). A very important and convenient property of this estimate is its relation to the power spectral density (for operational modal analysis) by Fourier transforms.  
\[\begin{array}{l}\mathbf{R_{uu}}(\mathbf{\tau})=\int_{-\infty}^{\infty}\mathbf{G_{uu}}( \mathbf{f})\mathbf{exp}(2\pi\mathbf{if}\mathbf{\tau})\mathbf{df}\\ \mathbf{G_{uu}}(\mathbf{\tau})=\int_{-\infty}^{\infty}\mathbf{R_{uu}}(\mathbf{f})\mathbf{exp}(-2 \pi\mathbf{if}\mathbf{\tau})\mathbf{d\tau}\end{array} \tag{3}\]  
Thus, it can be seen that the correlation function and the power spectral density are the same estimators of the investigated process, but the domain of their determination is different. Therefore, certain properties of the process may be better captured in time delay domain \(\tau\) than in frequency domain \(f\).
# A Method for Building a Mixed-Reality Digital Twin of a Roadheader Monitoring System
## 6 Limitations and Future Research Directions  
### Limitations of This Study  
The present study is limited to a conceptual framework and initial validation using an experimental prototype of the roadheader, rather than a full-scale real-world system. While this approach effectively demonstrates the feasibility of the digital twin framework and bidirectional mapping capabilities, it does not fully address the complex conditions encountered in real mining operations, such as varying rock properties, uneven terrain, and dynamic environmental factors. Additionally, the system validation experiments were conducted under controlled conditions, excluding common underground challenges like dust, electromagnetic interference, and low visibility, which may significantly affect system performance. Although the mixed-reality interface shows potential to enhance the operator's visual perception, this study primarily focuses on technical aspects and does not quantitatively evaluate its usability or impact on decision-making processes.
# Deterministic Discrete Fracture Network Model and Its Application in Rock Mass Engineering
## 2 Acquisition of Structural Surface Parameters  
### Data Processing and Raster Model Generation  
After acquiring high-precision point cloud data using the scanner and its accompanying software, the data is imported into Cloud Compare for preprocessing. The preprocessing steps include cropping, subsampling, and patching. Subsequently, a raster model is generated using the point-by-point interpolation method to ensure that the model accurately reflects the spatial distribution characteristics of the original point cloud. The entire process is shown in part in \"Preliminary processing of point cloud data\" of Figure 1.
## 6 Experimental Results
### Discussion and Analysis  
In order to evaluate the capability of VoteChain in supporting a national election, the performance testing results were analyzed against industry standards:  
1. **Time to Complete Transactions**: VoteChain maintained a transaction completion time well within acceptable limits, even as user numbers increased significantly. This consistency suggests that VoteChain's architecture and smart contract execution are optimized for rapid processing, a key requirement for ensuring voter confidence and system reliability during peak usage periods;
2. **System Uptime**: VoteChain demonstrated exceptional availability, with uptime consistently above 99.98%. This level of robustness is vital for the success of an election process, as it ensures the system remains operational without interruptions, even under heavy loads;
3. **Network Latency**: Latency remained within an acceptable range, with minimal delays observed even under maximum load. This low latency is essential for providing voters with real-time interactions with the system, contributing to a smooth and user-friendly voting process;
4. **Transactions Per Second (TPS)**: VoteChain has the capacity to handle a high number of transactions per second, scaling effectively with increased load. The TPS measurements were aligned with industry benchmarks, confirming that VoteChain can manage the transactional demands of a national election without performance degradation;
5. **Error Rates**: Error rates remained extremely low throughout the testing, with only isolated incidents of transaction failures, primarily due to network issues during extreme load conditions. These errors were swiftly resolved, and their impact on overall system performance was negligible.  
The performance testing shows that the VoteChain system is capable of supporting a national election with high voter turnout. The system demonstrated strong performance across all key metrics, including transaction speed, system uptime, and scalability. The low error rates and consistent transaction throughput suggest that VoteChain is well-prepared to handle the demands of a large-scale election, providing a reliable, secure, and efficient platform for electronic voting.  
Figure 26: Error rate.
## 4 Factors to Study in VANETs
### Broadcast Scheme  
Another important factor in Warning Message Dissemination in VANETs is the selected broadcast scheme [26]. In VANETs, intermediate vehicles act as relays to support end-to-end vehicular communications. For applications such as route planning, traffic congestion control, and traffic safety, flooding of broadcast messages commonly occurs. However, flooding results in many redundant rebroadcasts, heavy channel contention, and long-lasting message collisions (usually known as the broadcast storm problem).  
Over the years, several schemes have been proposed to address the broadcast storm problem in wireless networks. In [27] we can find some of the most interesting approaches, which are the following: (i) the counter-based scheme, which uses a counter to keep track of the number of times the broadcast message is received in order to decide whether to inhibit the rebroadcast; (ii) the distance-based scheme, in which the relative distance between vehicles is used to decide whether to rebroadcast or not; (iii) the location-based scheme, which is very similar to the distance-based scheme, though requiring more precise locations for the broadcasting vehicles to achieve an accurate geometrical estimation of the additional coverage of a rebroadcast; and (iv) the cluster-based scheme, where vehicles are grouped in clusters, and only one member of each cluster (the cluster head) can rebroadcast the warning messages. The _weighted p-persistence_, the _slotted 1-persistence_, and the _slotted p-persistence_ techniques presented in [28] are some of the few rebroadcast schemes proposed for VANETs. These three probabilistic and timer-based broadcast suppression techniques can mitigate the severity of the broadcast storms by allowing nodes with higher priority to access the channel as quickly as possible, but their ability to avoid storms is limited, since they are specifically designed for being used in highway scenarios. The Last One (TLO) scheme [29] tries to reduce the broadcast storm problem by finding the most distant vehicle from the warning message sender, so that this vehicle will be the only one allowed to retransmit the message. This scheme does not take into account the effect of obstacles (e.g., buildings) in urban radio signal propagation. More recently, we proposed a scheme called enhanced Message Dissemination based on Roadmaps (eMDR) [21], which uses location and roadmap information to facilitate an efficient dissemination of warning messages in 802.11p-based VANETs.  
It is easily noticeable that most existing solutions to the broadcast storm problem were only evaluated in obstacle-free environments, which are not comparable to real urban scenarios where plenty of obstacles can interfere with the signal, creating blind areas where vehicles will not receive the warning message unless intermediate forwarding nodes help to overpass the obstacle. In our experiments, we use both the location-based scheme and our eMDR scheme to assess the relevance of the broadcast scheme adopted.
# Mapping with Pleiades--End-to-End Workflow
## 2 Related Work
### Digital Surface Model Generation  
Accurate 3D reconstruction from stereo or multiple images is a well-known and well-researched topic in computer vision, photogrammetry and remote sensing. As depicted in Figure 1 the main steps are epipolar rectification, stereo matching, spatial point intersection, DSM resampling, DSM fusion and the related post-processing.
## 4 Discussion
[136] used the novel hybrid model Bagging-based Naive Bayes Trees (BAGNBT) at Mu Cang Chai district, located in northern Viet Nam, and the result showed that BAGNBT is a promising and better alternative method for landslide susceptibility modeling and mapping. [PERSON] et al. [137] applied artificial neural network (ANN) optimized with particle swarm optimization (PSO) for the problem of landslide susceptibility mapping (LSM) prediction, and the result showed that PSO-ANN model showed higher reliability in estimating the LSM compared to the ANN. [PERSON] et al. [90] developed a new ensemble model which is a combination of a functional algorithm, stochastic gradient descent (SGD) and an AdaBoost (AB) Meta classifier namely ABSGD model to predict the landslides in the Sarkhoon watershed, located within the Zagros Mountains, Iran, and the result showed that the combined use of a functional algorithm and a Meta classifier prevents over-fitting, reduces noise, and enhances the power prediction of the individual SGD algorithm for the spatial prediction of landslides. To summarize, there is no algorithm that works perfectly for all optimization problems, and new algorithms must be applied and verified to determine the one that is most efficient [113]. It is also recommended to use some soft-computing benchmark algorithms and conventional methods to assess the validity of the proposed models. In order to manage and decrease the cost and destructive effects of landslide disasters, policy makers, governments, planners, and managers need to develop more reliable and precise landslide susceptibility maps.
## 4 Performance Evaluation and Obtained Results
### Evaluation Set-Up  
The performance evaluation of the proposed N-MANC approach versus the N-AFHSS, the N-LEACH and the N-MANNC is performed under the network considerations as summarized in Table 1. It is important to note that a simulation is conducted for each of the compared approaches with the objective to elucidate the actual contribution of a specific addition or modification to an approach in terms of network scalability, energy efficiency, network reliability and network lifetime. The proposed approach as well as the approaches to which it is compared are all simulated under the same simulation conditions to ensure fair performance comparison.  
It is also important to note that eNodeBs of the LTE network are often designed to handle QPSK, 16 QAM and 64 QAM modulations schemes for the Downlink (DL) communications. However, due to the limited computational ability of the NB-IoT node, often micro-controller based in the majority of IoT applications such as smart water, electricity metering etc.), the modulation scheme of choice is the QPSK as specified in Table 1[58].  
\begin{table}
\begin{tabular}{c c} \hline Transmitting device \((h_{n})\) & 7.8 m \\ average height & \\ \hline Effective Height & 27.81 m \\ of the BS \((h_{BS})\) & \\ \hline Antenna Gain \((G_{a})\) & 6 dBi \\ \hline System bandwidth & 200 kHz \\ \hline Carrier frequency & 900 MHz \\ \hline Residual Frequency Error & \(\pm\)50 Hz \\ \hline Average NB-IoT cell radius range & 10 km (urban) \\ \hline Number of Nodes per cell \((T)\) & [1 k, 10 k, 50 k, 100 k, 150 k] \\ \hline Communication period \((P)\) & 1 h \\ \hline Maximum payload length & 1600 bytes \\ \hline Localization Technique & \(RSSI\)-based \\ \hline Channel estimation for NPDCCH & Sequential \\  & Channel Estimation in the \\  & Presence of Random Phase Noise [57] \\ \hline Interference Rejection Combiner & MRC \\ \hline Number of Tx antennas & 1 \\ \hline Number of Rx antennas & 2 \\ \hline Duplex mode & Half-Duplex Frequency \\  & division Duplex \\  & (HD-FDD) \\ \hline Initial NB-IoT node & \(3\times AA\) Batteries \\ energy (Power capacity) & Nominal voltage = \\  & \(3\times 1.5=4.5\) V \\  & Each AA has 1500 mAh \\  & Initial Energy = 6.75 J \\ \hline BS power & 43 dBm \\ \hline Receiving Sensitivity level & \(-\)90 dBm \\ \hline Time offset period & 2.5 μs \\ \hline Channel Model & Time-invariant slow fading \\  & with random phase noise \\  & following Additive White \\  & Gaussian Noise (AWGN) \\  & distribution \\ \hline Slow or & Static nodes \\ almost no nodes mobility & No fading channel \\ considered & consideration \\ \hline LTE Modulation Scheme & Quadrature Phase Shift \\  & Keying (QPSK) \\ \hline \end{tabular}
\end{table}
Table 1: Key simulation parameters.