esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Critical Cluster for Traffic Loading
### Influence of Bridge Length  
The calculated influence lines for the FRB are adjusted to provide estimates of similar influence lines for bridges with different spans. The FRB is 1813 m long with a main span of 1006 m. For other spans, it is assumed that the relative dimensions of the towers/spans and the slopes of the suspension cables will be similar. It follows that the influence line ordinates, cable force due to unit applied point load, will be of similar magnitude to the FRB. The total lengths of the notional bridges are scaled to keep the same ratio of main- to side-span length. Based on these assumptions, Table 2 shows the total and main-span lengths for five notional bridges.  
An hour of data is generated as a very long train of vehicles that travels across the bridge. The load effect is recalculated as the train moves in increments of 10 m until the last vehicle in the train reaches the bridge. For the critical MTHF cluster, the maximum load effect per hour is identified for each of the 940 h of data (see Table 1). The process is repeated to find the maximum load effect per day for each of the 127 days of data. Figure 9 shows the probability paper plots for maximum-per-hour and maximum-per-day load effects on Gumbel probability paper. It is a matter of Engineering judgement as to which is the more effective way to estimate extreme values; it is essentially a trade-off between many points of less reliability or fewer points of greater reliability. As would be expected, the load effects increase with increasing bridge span, but the trends are consistent. The intensities of truck loading associated with these load effects have been calculated, i.e., the total combined length of truck as a percentage of total lane length. There is a significant trend of decreasing intensity as the span increases [52].  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Bridge No.** & **Main Span (m)** & **Total Length (m)** \\ \hline
**1** & **800** & 1441 \\ \hline
2 (FRB) & 1006 & 1813 \\ \hline
3 & 1200 & 2174 \\ \hline
4 & 1400 & 2524 \\ \hline
5 & 1600 & 2897 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Notional bridges lengths.  
Figure 9: Probability paper plots of cable force for medium trucks/high flow (MTHF) for five bridge lengths: (**a**) maximum-per-hour; (**b**) maximum-per-day.  
Both the maximum-per-hour and maximum-per-day plots of Figure 9 are reasonably linear for all spans, confirming good fits to the Gumbel distribution. The maximum-per-day data is extrapolated to find characteristic maximum values. As data was collected 7 days per week, 1000-year and 75-year return periods correspond to probabilities of non-exceedance of \(1-1/(365\times 1000)=0.999997\) (Eurocode) and \(1-1/(365\times 75)=0.99996\) (AASHTO) respectively. Figure 10 shows the resulting characteristic maximum axial forces in the main cable values for both the 75- and 1000-year return periods for each bridge length. As expected, there is an increase in the characteristic values with span for both return periods. For example, as the span increases by 100%, the 75-year cable force increases by 65%.
## 3 Results and Discussion
### Parametric Effect on Heat Transfer  
#### 3.2.1 Effect of Heat Flux  
Figures 7 and 8a show the variations in inner-wall temperature with bulk fluid enthalpy at different heat fluxes with the pressure of 7.5 MPa and 8.0 MPa (\(Re_{\text{in}}\) = 5100-5300), respectively.  
Figure 6: Variation of inner-wall temperatures and mixed convection (\(\overline{Gr_{b}}/Re_{b}^{2.7}\)) at different heat fluxes for pressure of 7.5 MPa and mass flux of 200 kg/m\({}^{2}\)s. (**a**) Relationship between temperature and tube length (x/D); (**b**) Relationship between \(\overline{Gr_{b}}/Re_{b}^{2.7}\) and enthalpy.  
As seen in Figures 7 and 8a, when the bulk enthalpy was less than 300 kJ/kg, the inner-wall temperature slowly increased, as the bulk fluid temperature was still lower than the pseudocritical temperature. This might be because the bulk fluid with large specific-heat capacity can take away the added heat. However, when the enthalpy was higher than 300 kJ/kg, the inner-wall temperature quickly increased with the increase in heat flux.  
At low heat fluxes, the effect of buoyancy is still small due to its slow variation in thermo-physical properties, whereas the temperature differences between the inner-wall and the bulk fluid gradually enlarged along the tube. With the increase in heat flux, the heat transfer coefficients (HTCs) first increased due to the intense variations in fluid thermo-physical properties, as seen in Figure 1. However, when the high-temperature \"vapor-like\" fluid in the region was close to the inner wall, the added heat that was transferred from the near-wall region to the central region reduced. Due to this reason, the corresponding heat transfer coefficient decreased. Besides, in the region of 200-230 kJ/kg (corresponding to x/D < 40), a typically enhanced HTC was observed, which was affected by the development of \"thermal entrance effect\".
# Head-Related Transfer Functions for Dynamic Listeners in Virtual Reality
## 5 Experiment II: Dynamic 6-DoF Virtual Reality
### Procedure  
The scenes _Above_ and _Below_ were evaluated on a separate day in a follow-up experiment by a subset of 8 participants. The rest of the scenes were evaluated in one main experiment, one after the other by all of the 12 participants. The presentation order was fixed to start with localization, followed by timbre, and finishing with overall quality. The participant was presented with a multiple stimuli quality scaling task in VR, where the localization accuracy, timbral fidelity, and overall audio quality were to be rated on a 100-point continuous scale, depending on the task. There were verbal labels marking the regions of the scale as bad, poor, fair, good, and excellent in 20-point intervals. The five HRTF conditions were presented side-by-side in the VR user interface, and the participant was allowed to switch between them. The HRTF conditions and interface buttons were randomized for each scene. The interface is displayed together with the sphere denoting audio object location in Figure 4.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Scene** & **Task** & **Content** \\ \hline In Scene & Localization accuracy & One audio object: Pink noise \\ Handheld & Localization accuracy & One audio object: Pink noise \\ Above & Localization accuracy & One audio object: Pink noise \\ Below & Localization accuracy & One audio object: Pink noise \\ Cello & Timbral fidelity & One audio object: Anechoic cello \\ Castanets & Timbral fidelity & One audio object: Anechoic castanets \\ Park & Overall audio quality & Two audio objects: Ducks and tree clipping \\ Fountain & Overall audio quality & Two audio objects: Piano and fountain \\ \hline \hline \end{tabular}
\end{table}
Table 2: Scenes, task in the scene, and audio content.  
Figure 4: User interface in VR for the localization task. The white sphere denotes visually the location of the audio object.  
For the localization and overall quality tasks, the participants were instructed to freely move and teleport around in the 6-DoF scene and focus on either the localizability or general audio aspects of the experience. In these scenes, there was no audio reference to compare against; instead, the participants were assumed to possess an internal reference obtained from the day-to-day experience of listening in the real world. The internal reference should inform the participant how self-movement in relation to visual cues should be reflected in the audio reproduction. In the timbre scenes, the participant was instructed to stand on a marker 1 m away from the audio object and face towards the sound. In these scenes, the _No HRTF_ condition was defined as the timbre reference, and all the ratings are relative to this condition. The _No HRTF_ was included in the set of conditions as a hidden reference.
# Password Cracking with Brute Force Algorithm and Dictionary Attack Using Parallel Programming
## 4 Empirical Studies
### Experimental Setup
#### 4.2.1 Brute Force Setup  
For brute force, the experiment was carried out using Python with the use of the \"ProcessPoolExecutor\" class available in the \"concurrent.futures\" module. The program first prompts the user to enter the targeted password and the number of CPU cores to use. Then, it creates a list of all possible combinations of letters and numbers of increasing length up to the length of the target password. Each combination is passed to the \"attempt_match\" function to check if it matches the target. The process of checking each combination is performed parallelly with the help of the \"ProcessPoolExecutor\" from the \"concurrent.futures\" library, which allows for parallel execution of tasks by distributing them across the specified number of CPU cores. The function returns the first combination that matches the target password, and the total time taken to complete the process is recorded and displayed at the end. The parameters used are as follows:  
* \"length\": This is a parameter for the \"attempt_match\" function that specifies the length of the password combinations being generated and checked.
* \"target\": This is a parameter for the attempt_match function that specifies the target password to be cracked. It can either be passed as an argument or obtained by user input.
* \"Numb_of_cores\": This is a parameter obtained by user input that specifies the number of CPU cores to be used for the concurrent execution of the password-checking task. It is passed to the ProcessPoolExecutor class as the max_workers argument.
* \"start_time\": This is a variable that stores the current time when the password-cracking process starts.
* \"end_time\": This is a variable that stores the current time when the password-cracking process ends.
* \"total_time\": This is a variable that stores the time elapsed between start_time and end_time and represents the total time taken to complete the password-cracking process.
## 4 Inverse Problems
### Iterative Inverse Methods  
An alternative method for the solution of inverse problems is the iterative approach. This consists of optimization algorithms that iteratively solve the forward problem while varying the input parameters \(\left(B\right)\) until an error function defined as the difference between target experimental data \(\left(e\right)\) and the forward problem output \(\left(E\right)\) is minimized [101]. The advantages of this method are its easy generalization to any kind of inverse problem, its capability to operate on top of any existing solver for the forward problem, the existence of methods to reduce solution multiplicity, and its inherent capacity to handle scattered and noisy experimental data (Figure 6) [102]. All these advantages come with a detrimental increase in computational resource requirements, given by the repetitive solution of the forward problem. To reduce computational expense, some formulations have proposed the use of surrogate simpler models for the forward problem at the initial stages of the optimization process [103, 104]. Some statistical tools based on Bayesian data analysis and inference have been implemented to improve the performance of iterative inverse methods for cases where the error distribution of the measured target is known or can be safely assumed [105].  
The core concept of iterative inverse methods is the solution of an optimization problem that drives the solution into reproducing the target data with the smallest possible error. The definition of an optimization problem requires the selection of an appropriate target function to minimize, an optimization algorithm suited for the particular characteristics of the forward problem and optimization parameters, and the implementation of parameter constraints to point and restrict the convergence of the algorithm into desirable outputs. In this section, we briefly discuss the most common optimization target functions, algorithms, and constraints used on inverse problems in biomechanics.
# Hydrodynamic Investigation on a Land-Fixed OWC Wave Energy Device under Irregular Waves
## 3 Validation  
### Irregular Wave Generation Validation  
The irregular wave analyses in the numerical model is based on the JONSWAP spectrum with \(H_{\text{s}}\) = 0.06 m. The number of wave components, \(M\) is 200 with random phases uniformly distributed between 0 and 2\(\pi\) rad, the frequency range (\(f_{L}\), \(f_{H}\)) of the input spectrum is set to (0.5\(f_{p}\), 2.5\(f_{p}\)), with a frequency increment \(\Delta f=f_{p}/100\). Thus, the repeat interval of the signal is 100 \(T_{p}\).  
In order to validate irregular wave generation in the numerical tank, the wave generation test has been conducted on irregular wave conditions with static water depth \(h\) = 0.80 m without the OWC-WEC. The length of the NWT is set to seven times the peak wavelength (7.0 \(\lambda_{p}\)), of which 3.0 \(\lambda_{p}\) is used as a damping layer for reflected wave at the left- and right-hand ends of the flume (each one is 1.5 \(\lambda_{p}\)).  
The simulation duration is selected as 100 \(T_{p}\). This is determined to be sufficient to verify that all wave components are simulated and the correct spectrum distribution can be obtained at any location in the tank. Figure 2 presents the comparison of the theoretical input spectra and frequency spectra for two peak wave periods obtained by fast-Fourier transformation (FFT) performed on the free surface signals at three locations along the numerical wave tank. It is shown that there is a satisfactory agreement between the theoretical input spectrum and observed numerical wave spectrum.
## 3 Methodology
### Classifier Selection and Model Evaluation  
Multiple machine learning models were employed in this research, including SVM, NB, RBFN, RF, and MLP. These models were identified as the best-performing ones in accuracy, training time (time taken to build the model), F1 score, and area under the Receiver Operating Characteristic (ROC) curve.
## 4 Assumption and Threat Model  
**Assumptions:** The network is assumed to be deployed in a situation where all the security principles, like privacy, confidentiality, integrity and availability must be preserved (e.g. military zones). The network is vulnerable to DoS, replay, wormhole and battery-drain attacks. Nodes are assumed to be computationally constraints and are not tamper-proof devices. A node is therefore assumed to be able to perform a limited number of public cryptography operations. A base station (BS) with powerful computational capabilities (e.g., a laptop) with no energy consumption limitation is assumed and a private/public key pair is associated with this BS. All the sensor nodes are then preconfigured with a public key of a BS before the deployment of the network. Deluge is assumed as the underlying over-the-air programming (OTAP) protocol.  
**Threat Model:** An attacker has a powerful set of devices, both in terms of computation and communication medium. As node communications are based on a wireless medium, it is assumed that there is an untrusted and insecure channel in which an attacker can eavesdrop, inject, delay, modify or remove any packet transmitted. The base station cannot be compromised by attackers; therefore, the public/private key pair is always safe. Jamming attacks are possible on the network, and since a jamming attack is out of the scope of this study, it is assumed that the adversary cannot perform a permanent jamming without being detected and removed. The attacker might try to disseminate malicious code updates into a network or also replay previously sent packets (replay attack). The attacker also might perform denial of service (DoS) attack on the network.
# Definition and Determination of Fin Substitution Factors Accelerating Thermal Simulations
## 2 Materials and Methods  
In principle, the approach presented under Section 3 is suitable for different fin geometries and arrays as well as thermal and fluid dynamic boundary conditions. However, the approach is only demonstrated in a simplified special case.  
Concerning the geometry of the fins and their orientation, only rectangular fins of a vertical finned array are considered because this is one of the best-studied cases.  
For the same reason, air is chosen as the surrounding fluid.  
The heat dissipation in finned arrays results from natural convection and thermal radiation. In the temperature range considered in this work, heat dissipation by natural convection dominates. However, it should be emphasized that the greater the temperature difference between the heat-emitting surface and the environment, the greater the proportion of heat that is dissipated by radiation [13]. In the following only natural convection is considered. There are two reasons for this: firstly, it is easier if the approach is only demonstrated using one heat transfer mechanism, and secondly, neglecting radiation leads to conservative results. However, thermal radiation can also be considered with the presented approach. This is possible, for example, by linearizing the thermal radiation and forming an effective heat transfer coefficient [13].  
The space between two fins can be viewed as a semi-enclosed channel. Concerning the thermal boundary condition, when determining the Nusselt numbers in such channels, a distinction is made between a constant temperature and a constant heat flow at the fin base. Only isothermal cases were considered.  
In summary, the heat transfer of vertical rectangular finned arrays via natural convection with air as surrounding fluid and a constant fin base temperature was chosen as the demonstration case.  
The approach requires analytical equations and empiric correlations as well as flow simulations (CFD) and solid-state simulations (FEM). Already existing equations and correlations for the selected demonstration case were used. The simulation models were also designed for the demonstration case. These and the material properties of the used air are described below.
## 4 Resource Implications  
The eLearning messaging system envisioned here has fixed and variable costs. In the absence of significant population drift, the development phase illustrated in Figure 1 would be undertaken only once. Its costs include a database manager and an analyst familiar with predictive modeling, along with associated hardware and software. Another fixed cost is the design and development of the dashboard. (Of course, subsequent redesigns may be required and careful forethought would help minimize the need for redesign and redevelopment.) The variable costs include those needed to implement the signaling system for each new course offering: primarily, management of the current database (Figure 3).  
The proposed system places value on accuracy of the information provided to students. That means, for example, that the numerical values displayed in Figures 4 or 5 for one student may be quite different from those for another student even if both students have identical grades on the assignments. That's because the prediction model incorporates more than just grades--it considers additional information about each student's unique circumstances. The success of this microtargeting means balancing accuracy with costs. For any one situation, we cannot know how best to address those tradeoffs until we get more experience with building and implementing these kinds of systems. Even so, a start-simple approach seems advisable. Exactly what that means must depend on things like whether the course is for-credit, the course content, the degree to which non-completion is problematic, and available resources.  
Those resources must be approved and committed by someone: usually an educational administrator or program director. Funding requests will more likely meet with success once administrators come to understand that the system has value to more than just students and instructors [38]. This system, for example, might be used to address things such as outcomes assessment (assurance of learning) oftenrequired by accrediting bodies. Equally important, the system provides documentation of steps taken to mitigate dropout rates or mediocre student performance. With this system, we might also get a sense of the characteristics of students likely to succeed in a course (or program, if instituted program-wide). Such information could help to determine which students should be accepted into programs and which might need remedial intervention. Put another way, the information produced by the messaging system might have widespread use to many constituents, all of whom could benefit in various ways.
## 1 Introduction
### Project Overview  
The Central Yunnan Water Diversion Project spans a total of 663.9 km and passes through six cities, namely Lijiang, Dali, Chuxiong, Kunming, Yuxi, and Honghe in Yunnan Province. The Kunming segment of the water transmission trunk canal, which covers a distance of 116.758 km, comprises six tunnels, four inverted siphons, an aqueduct, and a hidden culvert. The most significant inverted siphon within the Kunming section is the Longuan inverted siphon, which is situated primarily in the Panlong District of Kunming City and measures 5.07 km in length. It includes two working wells and a shield tunnel, with the originating well spanning 59 m and the receiving well comprising a circular structure with a radius of 10 m. The length of the diversion tunnel is approximately 5 km. As illustrated in Figure 3, the shield receiving well is situated within the green belt on the west side of the intersection between Kunqu Expressway and Fengyuan Road.  
Illustrated in Figure 4, the enclosure structure is characterized by a circular underground diaphragm wall that is 1.5 m in thickness, complemented by an inner lining wall that is 1 m in thickness. The excavation depth of the foundation pit is recorded at 79.5 m. The main structure of the enclosure was erected using the open-cut and reverse construction approach.  
Figure 1: Schematic diagram of overall instability and destruction.  
Figure 2: Schematic diagram of local instability and destruction.  
The receiving well enclosure, depicted in Figure 5, consisted of a circular structure with a radius of 10 m, a 1.5 m thickness, and a 96.6 m depth, comprising 14 underground diaphragm walls. Specifically, there were seven first open-shaped wall sections and seven closed sections. Phase I and II each had seven slot sections, with the P1, P2, and P3 sections representing phase I wall sections, and S sections indicating the phase II wall section. The two phases were staggered using milled joints.  
The underground continuous walls were constructed in strata primarily consisting of soft soil, which included silty clay and peat soil. The soft soil was characterized by high natural water content, low permeability, and low shear strength. It was classified as a highly sensitive soil layer, which tended to exhibit significantly reduced strength when disturbed. Therefore, treatment of the base soft soil and weak soil layers was required. The silty soil layer was susceptible to flow and sand flow, and the stability of the soil layer after excavation was weak. Table 1 presents the physical and mechanical properties of the geotechnical parameters involved.  
Figure 4: Longitudinal section of receiving well structure (unit: m).  
Figure 3: Satellite map of receiving well location.