esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Identification Process in Mixed Scenarios  
The identification performance of FSA RFID systems in static scenarios was addressed by the authors in [4]. In this work we begin reviewing the Markov analysis of that scenario since it is the basis of the controlled-arrival scenario.  
Static scenarios are characterized by a block of tags (modeling a physical container like a pallet, a box, _etc._) that enter the checking area and remain there until all of them are successfully identified. Two related performance metrics are commonly considered:  
* The average identification time, defined as the mean number of time units (slots, frames, seconds, _etc._) until all tags are identified.  
* The system throughput or efficiency, defined as the inverse of the mean identification time, _i.e._, the ratio of identified tags per time unit.  
For mixed scenarios where the tags only remain in coverage for a certain sojourn time the goal must be different. In this case, the probability that some tag lost in a group must be minimized. As stated in the introduction, configuration must be established to ensure with a given Identification Confidence Level (ICL) that all the tags in a set have been identified before leaving the reading area. Given an ICL level the minimum required sojourn time can be derived from the cumulative distribution function of the reading time, which is computed in the next sections.
# Real-Time Communication Support for Underwater Acoustic Sensor Networks +
## 7 Simulations
### Validation of Simulations  
For validation, we used the example presented in Section 5. In that example there are seven nodes and 18 edges, including the multipath reflections. The minimum frame-allocation computed using the ILP model is given in Table 3. As it can be seen, 11 slots are enough to fit all transmissions and receptions in the nodes. The set of messages considered is the one described in that section. However, the release time of the messages was changed, in order to evaluate the impact of messages arriving to intermediary nodes. The idea is to validate the real-time behavior of the network by producing overload conditions in those nodes, and thus delaying the transmission of messages.  
We simulated the network during 30,000 slots, which is almost 300 frames, although for the sake of simplicity, Figure 5a,b only show results for the first 2500 slots. We have seen that the behavior of the whole simulation can be summarized by analyzing this initial period.  
In one side, Figure 5a shows the end-to-end delay of message \(m_{ga}\) when it is alone, i.e., the only message across the network is \(m_{ga}\). As it can be seen, the average time needed for the message delivery is 28 slots. On the other side, Figure 5b shows the end-to-end delay of message \(m_{db}\) when it is alone. Again, results are as expected, since the average time of 18 slots is coherent with the theoretical computations. At this point, it is important to clarify the trends that both figures present in their results. In fact, we can observe that there are a constant trend, and an oscillating trend. Specifically, there is a minimum constant delay of 23 slots for the path \(g\)--\(f\)--\(e\)--\(a\) of message \(m_{ga}\), and a minimum constant delay of 12 slots for the path \(d\)--\(c\)--\(b\) of message \(m_{db}\). Moreover, there is an oscillation of \(11\) slots due to the difference between the frame size (11 slots) and the message, which is every 100 slots for message \(m_{ga}\), and every 80 slots for message \(m_{db}\). In conclusion, we can ensure that simulator behaves as expected.
## 5 Discussion  
In the construction sector in Spain, digitalization has caused an increase in the demand for projects carried out with BIM both for public projects where its use is mandatory and private projects. Adopting BIM technology requires internal adjustment and corporate redesign, where improving employee productivity is a priority in the transformation from CAD to BIM [14].  
According to recent research to investigate the reasons for low BIM adoption among architectural firms in India [10], it was concluded that top management support and BIM ex  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{4}{c}{**H2(+): SMS \(\rightarrow\) TL \(\rightarrow\) BIME**} \\ \hline \(\beta\) (SMS \(\rightarrow\) TL) & Percentile 95\% & \(\beta\) (AT \(\rightarrow\) BIME) & 95\% confidence \\ \(p=0.0000\) & confidence interval & \(p=0.0000\) & interval \\ \hline
0.672 *** & (0.4533–0.6545) & 0.310 *** & (0.2399–0.5547) \\ \hline \multicolumn{4}{c}{H3(+): SMS \(\rightarrow\) CCU \(\rightarrow\) BIME} \\ \hline \(\beta\) (SMS \(\rightarrow\) BIME) & Percentile 95\% & \(\beta\) (CCU \(\rightarrow\) BIME) & 95\% confidence \\ \(p=0.0000\) & confidence interval & \(p=0.0178\) & interval \\ \hline
0.592 *** & (0.5280–0.7442) & 0.418 *** & (0.0208–0.5076) \\ \hline \multicolumn{4}{c}{VAF = (Indirect effect/Total effect) \(\times\) 100 = 68.15\%} \\ \multicolumn{4}{c}{Indirect effect = (a \(\times\) b) + (a \(\times\) b) + (a\({}^{\prime}\times\) b\({}^{\prime}\))} \\ \multicolumn{4}{c}{Total effect = direct effect + indirect effect = c\({}^{\prime}\) + (a \(\times\) b) + (a\({}^{\prime}\times\) b\({}^{\prime}\))} \\ \hline *** \(p<0.001\), *** \(p<0.01\), * \(p<0.05\), NS: not significant (based on t(4999), one-tailed test), t(0.05, 4999) = 1.645; t (0.01, 4999) = 2.327; t (0.001, 4999) = 3.092. \\ \hline \hline \end{tabular}
\end{table}
Table 5: Hypothesis testing and mediation analysis.  
pertise promote BIM adoption but do not inquire about which variables can influence BIM effectiveness from top management support. In order to do this, senior management support must promote adequate culture and values, enhancing technological learning [26,60] and a collaborative culture amongst members of a project team [65,66,68]. For an effective BIM implementation to exist, senior management support must make decisions to improve the work process, defining objectives and establishing the responsibilities of each person involved. Senior management support must establish leadership throughout the BIM implementation process, which allows monitoring and coordinating all activities of a project team, avoiding errors and misunderstandings [33,34].  
Finally, for BIM performance to be appropriate, it must have a well-defined hierarchical and collaborative structure, as well as fluid and determined work processes in addition to having adequate technological resources [26]. With all of the above, BIM effectiveness will be enhanced for competitive improvement and differentiation in the market.
## 2 Data Description and Network Partitioning  
### Data Description  
The study area is the urban network of Tianjin, as shown in Figure 1, and the data are obtained from about 400 cameras on the network within the study area. The cameras are installed at the stop lines in each direction of most intersections, covering more than 80% of the network, and the data can represent the overall traffic states. The cameras continuously take pictures of vehicles passing through the stop lines of the intersection, records the ID and GPS coordinates of the camera, the license plate number, photographing time, and driving direction.  
During the public holiday for Qingming Festival, there are three days with no work. The first two days are generally leisure time for residents to travel to the countryside for spring. And the last day is the Qingming Festival. On the day, residents usually sweep cemeteries and worship ancestors in the morning. Several major cemeteries in Tianjin are distributed around the city center, and the connecting roads with the center are all expressways or arterial roads, as shown in Figure 1. On the day of Qingming Festival, the department of traffic management in Tianjin opened special bus lines from the city center to cemeteries. The departure time was between 6:00 and 7:30, and the return time was around 9:00. The traffic characteristics will be very different from the ordinary weekends due to multiple factors, such as the tomb-sweeping activities, spring outings, etc. Further, the data of ordinary weekdays are also analyzed for comparisons to explore the differences in traffic characteristics between holidays and ordinary weekdays.
## 2 Materials and Methods
### Multi-Software Approach for Network Modeling
#### 2.3.2 Network Digitization and Model Development  
The digitization phase aimed to create a structured hydraulic model that accurately represents the Farsala water distribution network. The digitized network was georeferencedusing GIS-based spatial analysis, ensuring positional accuracy for pipelines, boreholes, storage facilities, and demand nodes.  
The network was divided into district metered areas (DMAs) to support pressure zoning and leakage assessments. Each DMA was assigned hydraulic attributes such as pipe roughness coefficients, demand patterns, and operational constraints.  
The refined ArcGIS model was then converted into a format compatible with WaterGEMS. The processed network data were imported into WaterGEMS, a specialized software in hydraulic modeling. Network elements such as pipes, junctions, reservoirs, pumps, and tanks were assigned hydraulic properties, including roughness coefficients, pump curves, and storage capacities. Boundary conditions were set by incorporating water sources, pressure constraints, and operational schedules based on observed SCADA data and field measurements. The Hazen-Williams equation was employed to estimate friction losses [34].  
To configure demand allocation, the customer meter method was implemented at each network node, ensuring precise assignment of consumption rates at network junctions based on historical usage records. Appropriate patterns for different activities such as residential use, commercial demand, and firefighting were created to determine daily variations and were assigned based on telemetry data. As part of the digitization process, available data on valve positions and conditions were integrated into the model, ensuring more precise pressure management and emergency response planning.
## 3 Kriging-Based Adaptive Surrogate Modeling for Limit Capacity of the Tower
### Example Study
Taking 42 validation points into account, the averages of \(R_{\mu}^{i,j}\) and \(R_{\sigma}^{i,j}\) (with \(j=1,\,2,\,3\,\dots\,42\)) and the maximums of \(R_{\mu}^{i,j}\) and \(R_{\sigma}^{i,j}\) in each loop were derived to evaluate the performance of the kriging surrogate. Figure 8 shows the trends of the averages and maximums of change ratios with the scale of DoE increasing. As can be seen in Figure 8a, the average change ratio of the means of 42 validation points decreased with the increasing scale of DoE, and when the scale reached 2000, this measure fluctuated slightly around 0.2%. Similarly, the average change ratio of the standard deviations of 42 validation points (Figure 8c) reduced and kept fluctuating around 5% with the scale getting to 2000. Comparatively, fluctuations of the maximum of change ratios are more apparent (Figure 8b,d). According to the above validation results (Figures 6-8), the presented adaptive kriging modeling for capacity surface \(\theta_{L}\) of transmission towers is feasible and effective. Figure 9a shows the surrogate result for capacity surface \(\theta_{L}\) of tower \(ZY\) using the proposed adaptive modeling framework. Correspondingly, the approximated capacity surface \(\theta_{H}\) is shown in Figure 9b according to the wind loading relations (i.e., Equations (4)-(6)). Figure 8: Averages and maximums of change ratios. (**a**) the average change ratio of the mean; (**b**) the max change ratio of the mean; (**c**) the average change ratio of the standard deviation; (**d**) the max change ratio of the standard deviation. In Figure (a)a, most of the support points are in the two regions enclosed by green curves, which indicates that the limit capacity of the tower is more sensitive to the structural uncertainty in these two regions. Comparing Figure (b)b with Figure (b)b, it is shown that the trends of capacity surface \(\theta_{H}\) are similar. With the load component \(H_{w}\) increasing, the surfaces exhibit a more significant nonlinear trend.
## 2 Materials and Methods
### Remote Monitoring--Satellite Reading  
Throughout the observation period, the maps of the spectrum bands available from two satellites (Sentinel A and B) were downloaded from the ESA website (European Space  
Figure 2: Mobile system to measure the electrical potential difference.  
Agency, www.esa.int, accessed on 1 September 2022.). In particular, the bands utilized were the B3 (560 nm), the B4 (675 nm), the B8 (842 nm) and B11 (1610 nm). These bands were necessary to obtain the _MCARI2_ (Modified Chlorophyll Absorption in Reflectance Index 2) and _NDWI_ (Normalized difference water index) spectral indices, described in the following formulas:  
\[MCARI2=\frac{1.5[2.5(R_{842\text{ nm}}-R_{675\text{ nm}})-1.3(R_{842\text{ nm}}-R_{560\text{ nm}})]}{\sqrt{\left[2R_{842\text{ nm}}+1\right]^{2}-\left[6(R_{842\text{ nm}}-5\sqrt{R_{675\text{ nm}}}-0.5)\right]}} \tag{1}\]  
\[NDWI=\frac{R_{560\text{ nm}}-R_{842\text{ nm}}}{R_{560\text{ nm}}+R_{842\text{ nm}}} \tag{2}\]  
The _MCARI2_ spectral index was chosen because it was considered suitable for the predictions of some biophysical parameters of plants [28; 29; 30; 31], while the _NDWI_ index was chosen because it is more correlated to the presence of water in the crop and uncultivated soil [32; 33], but also because its correlation with soil fertility characteristics had been recently described and evaluated [34].
# PriSign, A Privacy-Preserving Single Sign-On System for Cloud Environments
## 4 System and Security Model
### System Model  
As shown in Figure 1, the architecture of a PriSign scheme consists of five types of parties: a central authority (CA), a ticket issuer (I), policymakers (P), users (U), and ticket verifiers (V). The specific role of each party is described below.  
* CA is a trusted global party responsible for setting up the system (step 1), providing certification service for the ticket issuer (step 3) and all users (step 8) as well as issuing keys for a group of policymakers (step 4). Moreover, CA can trace the identities of malicious users (step 10).
* I is a ticket issuer and should register with CA (step 2 and step 3). I is tasked to verify any user's credentials according to attribute disclosure policies generated by itself and to issue tickets for users (step 9).
* P\({}_{i}\) is an independent policymaker which receives keys from CA (step 4) and is responsible for issuing a share of the policy key for each verifier and binding this share to the verifier's identity (step 5). The PriSign scheme is set up with \(n\) policymakers, of which \(t\) policymakers can cooperate to issue complete policy keys for verifiers. If a verifier is offline, \(t\) policymakers can cooperate to issue a new key with the same policy as the offline verifier that is bound to the identity of a new proxy verifier. This proxy-verifier can replace the offline verifier in order to complete the verification of tokens that are designated to be verified by the offline verifier.
* U with with a set of attributes should apply for a credential from CA (steps 7 and 8). To obtain a ticket, U needs to present a credential to I anonymously and disclose a subset of attributes to prove that the attribute disclosure policy enforced by I is satisfied (step 9). To access a designated service with a ticket, U computes an access token of the ticket using an attribute vector that matches the designated verifier's policy (step 1).
* V should request that each of \(t\) online policymakers receive a share of the policy key bound to its identity (step 5) and aggregate them into a complete policy key (step 6). V with a complete policy key that matches the user's attribute vector then provides token verification services for those users who designate it as the verifier, and can detect any double-spending tickets (step 12).  
In practical applications, in order to prevent users from accessing services unrestricted, tickets applied by users from issuers can only be used once, such as movie tickets and traffic tickets. However, electronic tickets are easily copied and shared, meaning that malicious users must be prevented from double-spending. In this paper, we use the pre-generated double-spending identity _dsid_ as the unique identity of the ticket; if the verifier detects that the double-spending identity is repeated, it suspends access.  
Figure 1: Architecture of PriSign.
# Genetic Multi-Objective Optimization of Sensor Placement for SHM of Composite Structures
## 2 Tested Structure and Experiments
### Numerical Experiments  
Data for the intact plate were collected from the laboratory experiment. Additionally, numerical experiments have been performed to collect data from various conditions of structural damage. In order to develop a system for recognizing other health conditions (the presence of delamination in this case), it was necessary to supplement the set of learning examples with data coming from damaged conditions. For this purpose, it was decided to supplement these data using an appropriately tuned numerical model. The model was tuned based on data for the intact condition, and then, after an appropriate level of compliance between the model and the experimental data was reached, the condition with the presence of delamination was simulated. The model developed in this way made it possible to obtain a set of learning examples covering damage conditions. The detailed description of the definition of the numerical model and performing numerical simulations as well as further processing of data was presented in the previous work [7].  
The parameters and constraints of the model were assigned to be consistent with the physical tested plate. Each layer of the composite plate is represented by a single element throughout the thickness. Numerical modal analysis was used to determine the first seven dominated eigenvalues and eigenvectors. To obtain the strain values, the absolute values of the transverse displacements were normalized in descending order. The strain values were calculated according to Formula (1). To obtain a denser mesh, the strain fields were smoothed using regression analysis. An example comparison of experimental and numerical transverse displacements shapes and adequate strain shapes is shown in Figure 2.  
Figure 2: Transverse displacement shapes and strain fields in X and Y directions for 7 th mode: experimental (**a**–**c**) and numerical (**d**–**f**).
## 1 Introduction
### Reference Parameters and Datasets
Cloud corrections were made for most of the LST maps, and heavily clouded areas were erased and replaced using other daily data maps from Landsat 8-9 OLI TRIS with similar conditions to obtain LST\({}_{Corrected}\). To correct these maps, we used the following condition, in which the clouds and land were represented as values of 0 and 1, respectively:  
\[\text{Con(\"LST\"}<=MaxCloud,0,1)\]  
where LST is the raster name of the previously calculated raster (Equation (8)), and _MaxCloud_ is the maximum value that represents the clouds in the LST raster. We then multiplied the condition by the original LST raster to obtain the values of clouds as zeros. After the clouds were 0, we copied the raster and assigned 0 as a NoData value. To obtain a complete map with the cloud correction, we searched for maps of the same year without clouds in the desired area. We estimated LST with additional datasets from another day within the year and evaluated to create a full raster by merging the corrected dataset with another dataset from a different day. Finally, GPP was computed as follows:  
\[\text{GPP}=\text{MOD17A2}^{*}\;0.0001 \tag{9}\]  
where MOD17A2 represents the values obtained from MODIS products for GPP estimations. The 0.0001 is a scale factor provided by the LP DAAC for GPP estimations [24].
# Research on Safe Following Distance on an Expressway Based on Braking Process Analysis
## 2 Related Work
reclassified the braking process and established the stopping sight distance model under different road conditions [17]. [PERSON] corrected the stopping sight distance by running speed and braking speed reduction [18]. [PERSON] established the parking sight distance test model and used the simulation platform to test and evaluate the sight distance on the highway in mountainous areas [19]. [PERSON] et al. revised the parking sight distance model from three aspects and put forward suggestions to ensure the parking sight distance from the angle of the height of the target [20]. According to the characteristics of the safe driving of vehicles on an expressway, this paper analyzes the braking process of vehicles with regard to the kinematic principle and establishes three different safe following distance calculation methods based on braking deceleration under different slope conditions. We used Matlab software for simulation analysis. The results of the simulation can be a specific and clear reference for the limited speeds used on expressways. This study has certain practical guiding significance for improving the vehicle density and transportation efficiency of expressways and reducing the incidence of rear-end traffic accidents.
## 2 Numerical Model
### Equivalent Blast Loading  
When an explosive detonates within a rock mass, a shock wave is generated in the surrounding rock causing the crushing and fracturing of the rock mass. Due to the dissipation of energy, the shock wave propagates and eventually decays into an elastic seismic wave at a long distance [33]. The rock mass can be divided into three zones around the perimeter of the blast hole. These zones are the crushed zone, the fractured zone, and the elastic zone based on the physical condition of the rock mass [34].  
A unified constitutive relation based on continuum mechanics was employed to simulate blasting vibration. This approach considered the crushed and fractured zone as a part of the blasting vibration source. The blasting load was then applied to the corresponding elastic boundary, which was referred to as the equivalent elastic boundary. To simulate blasting waveforms, it is essential to have a time history load in the form of pressure or velocity. According to [PERSON] et al. [35], the results were found to be the same whether a pressure function or an equivalent velocity time series was used. [PERSON] et al. [13] and [PERSON] [26] previously conducted numerical simulations, utilizing velocity time histories as input loads. In this study, the velocity time history recorded on the site's surface, consisting of biotite gneiss, with a frequency of 43 Hz, was used as the input blasting load to the equivalent elastic boundary in the normal direction, as shown in Figure 5. The blast source was modeled as a cylindrical charge with a length of 1 m and a radius of 0.5 m.  
It is important to note that the vibration's frequency characteristics undergo significant changes during the rock fracturing process. The primary frequency of the waves propagated through the elastic medium after fracturing has been shown to fall between 1 and 300 Hz [36]. A selection of vibration velocities, varying in terms of PPVs and frequencies, were modified from the test blast to account for different loading conditions. As the rock fracture was not simulated in this study, the alteration in the frequency during this process could not be modeled. Instead  
Figure 5: Velocity time history as input blasting load.  
of modeling such a process, we used modified time histories. Elongated velocity time histories were created using the velocity functions derived from recorded time history. We adjusted each motion's time interval (\(\Delta t\)) according to the desired frequencies, increasing (\(\Delta t\)) by a factor of 2 and 4.3 for 21.5 and 10 Hz and decreasing by 2 and 2.34 for 86 and 100 Hz, respectively. Additionally, the velocities were amplified by an amplification factor ranging from 1 to 8 to investigate the tunnel damage caused by various PPVs. It is important to mention that separate free field analyses were also performed to predict the PPVs at which damage would occur.