esa-sceva/satcom-chunk-collection huggingface dataset:
# A Survey of Algorithms and Systems for Evacuating People in Confined Spaces
## 2 Evacuation Wayfinding Systems
### Human Experience Driven-Systems  
Due to limitations in processing power, early evacuation wayfinding systems were commonly computer-aided information reporting systems to assist emergency managers in making decisions [8]. Associated evacuation wayfinding algorithms at that time normally used human experience or purely mathematical models to simplify an evacuation process and seek optimal solutions. Before the 1990s, research in this area was very limited. The research in [9] considered the evacuation planning problem as a minimum cost network flow problem that converts the original building graph into a time-expanded network; by solving the time-expanded network via a linear programming algorithm, evacuees can obtain optimal routes and achieve the shortest evacuation time. The study in [10] designed a graph-processing software to represent an underground mine as a graph-based evacuation network in conformity with proper ventilation requirements; each edge was assigned with a weight in terms of its distance to the source of hazard and fresh air intake, and [PERSON]'s shortest path algorithm was utilised to find the safest paths for evacuees. The work in [11] proposed a traffic monitoring and analysis system to predict the possible traffic jams for emergency planners during urban-scale evacuations; real-time traffic data were collected at roadside traffic-counting stations and transmitted to the system via conventional telephone lines; an evacuation simulation model was used to provide the locations and timing of occurrence of potential traffic bottlenecks. The research in [12] presented a human experience-driven emergency alarm system to facilitate emergency authorities to evacuate residents before the landfall of a hurricane; a \"vertical evacuation\" methodology was proposed to lower the evacuation time, and the issuing of \"early evacuation orders\" was believed to be critical in reducing fatalities. To improve the disaster response ability in accidents at nuclear power plants, the study in [13] proposed an evacuation plan for residents within 20 miles of the plant to flee when a radiation leakage occurred. The work in [14] designed a real-time emergency monitoring and response system for a nuclear power plant; the response decisions were based on discussions between experts at off-site emergency response centres.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**System Type** & **Period** \\ \hline Human experience-driven systems & 1970s–1990s \\ Static WSN-based systems & 1995–present \\ Mixed WSN-based systems & 2006–present \\ WSN and cloud-based systems & 2007–present \\ Cloud-based systems and mobile phones & 2011–present \\ \hline \hline \end{tabular}
\end{table}
Table 1: Evacuation wayfinding systems.
# A Ka-Band Circular Polarized Waveguide Slot Antenna with a Cross Iris
## 2 Antenna Design  
Figure 1 shows the structure of the proposed CP waveguide slot antenna consisting of a rectangular waveguide, an air metal box with hexagonal cavity and matching slot, and a cross iris at the bottom of the rectangular waveguide. The proposed antenna has dimensions of \(10.1\) mm \(\times\) 23 mm \(\times\) 7.06 mm and is made of a copper with a conductivity of \(5.8001\times 10^{7}\) S/m. The detailed parameters of the proposed antenna are shown in Figure 1 and the dimensions are given in Table 1. The performances of the proposed antenna were simulated using the commercial electromagnetic simulator, High Frequency Structure Simulator (HFSS) [19]. The antenna is fed from the rectangular waveguide to excite the TE\({}_{10}\) mode for high power handling capability. The dimensions (\(a~{}=~{}7.11\) mm and \(b~{}=~{}3.56\) mm) of the feeding waveguide are determined by the cutoff frequency (21.077 GHz) using the following equation with dimensions shown in Figure 2[20]. As shown in Figure 2, the waveguide has its longest dimension along the x-axis (\(a\)).  
\[f_{cmn}~{}=~{}\frac{1}{2\pi\sqrt{\mu_{0}\varepsilon_{0}}}\sqrt{\left(\frac{m \pi}{a}\right)^{2}+\left(\frac{n\pi}{b}\right)^{2}}, \tag{1}\]  
When \(a>b\), the cutoff frequency of TE\({}_{10}\) (\(m=1\), \(n=0\)) mode becomes  
\[f_{c10}~{}=~{}\frac{1}{2a\sqrt{\mu_{0}\varepsilon_{0}}}, \tag{2}\]  
where \(\mu_{0}\) and \(\varepsilon_{0}\) are the permeability and permittivity of free space, respectively.  
Figure 1: \(Cont.\)  
Figure 1: Geometry of the proposed antenna: (**a**) 3-D view of the proposed antenna; (**b**) top view of the proposed antenna; (**c**) side view of the proposed antenna and cross iris.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Parameters** & \(C_{1}\) & \(C_{2}\) & \(S_{1}\) & \(S_{2}\) & \(L_{1}\) & \(L_{2}\) \\ \hline
**Value (mm)** & 6.5 & 4.4 & 4.3 & 8 & 6 & 5 \\ \hline
**Parameters** & \(H_{1}\) & \(H_{2}\) & \(H_{3}\) & \(H_{4}\) & \(B_{1}\) & \(B_{2}\) \\ \hline
**Value (mm)** & 7.06 & 2.5 & 0.5 & 1.2 & 5.9 & 3.28 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Dimensions of the proposed antenna.  
Figure 2: Geometry of the rectangular waveguide.
## 1 Introduction
### Frequency Characterization of the Plate and Modeling for Numerical Analysis  
The frequency characteristics of the plate should be identified before the design of the MRE-based ATDVA. Figure 1 shows the plate fabricated for that purpose. Figure 2 illustrates the experimental setup for measuring the natural frequencies of the plate. For the measurement of natural frequencies, an accelerometer (Endevco 65-100-X) was installed in the center, and an exciter (B&K shaker 4810) was placed in the center as well. A transfer function was obtained using the installed accelerometer and exciter and measured using an FFT analyzer (B&K Pulse 3560-B-040).  
The transfer function measurements revealed that the plate had natural frequencies of 10 Hz, 57 Hz and 122 Hz (Figure 3). A frequency range of 110-130 Hz near the 122 Hz natural frequency wherein the transfer function had a high magnitude was selected as the target range for vibration reduction.  
For a numerical analysis of the dynamic vibration absorber, the plate was modeled as a simple SDOF system for resonance to occur at the target frequency of 122 Hz for vibration reduction. The system was designed to have a mass of 12 kg similar to that of the plate and a stiffness of approximately 705,000 N/m.  
Figure 1: Target structure.  
The variable input can be defined by Equation (1):  
\[F(t)=\sin(2\pi\omega t) \tag{1}\]  
where \(\omega\) = _at_, \(a\) is a sweep rate which is defined as a frequency variation per unit time.  
The numerical analysis is conducted with \(a\) = 10. A fourth-order Runge-Kutta scheme was used in the numerical analysis.
## 4 Materials and Methods
### Untargeted Metabolomics Analyses  
Metabolomics analysis by ultra-high performance liquid chromatography-quadrupole time-of-flight mass spectrometry (UHPLC-Q-TOF-MS) was performed as previously described [66]. In brief, frozen liver tissue samples of approximately 12-60 mg were randomized to 8 batches for tissue homogenization and subsequent two-step metabolite extraction. QC samples were prepared by pooling equal volumes of extracts derived from 30 liver samples (QC pool) [99]. Dried aqueous metabolite extracts were reconstituted in water/acetonitrile (5:95, \(v/v\)) at a solvent/tissue ratio of 10 uL/mg and analyzed after HILIC separation in positive and negative ionization mode on a 1290 Infinity UHPLC System coupled to a 6550 iFunnel QTOF-MS (Agilent Technologies) equipped with a Dual Agilent Jet Stream electrospray source. Samples were measured in 6 analytical batches (3 positive and 3 negative ion mode) with a QC sample analyzed after every 5 th patient sample. Data was preprocessed by targeted extraction of annotated features [66]. Peak areas were normalized by median-normalization for each sample following LOESS correction based on QC samples for individual features (intra-batch correction). For inter-batch correction, the median of QC samples was calculated for each feature and batch. The values of the samples in each batch were subsequently divided by the corresponding value. Features with CVs >20% within individual batches and features with significant fold-changes between batches (max. fold change > 1.5 and Bonferroni-adjusted Kruskal-Wallis test \(p\) > 0.05) were excluded. Normalization and filtering were performed separately for both the positive and negative ion mode. Finally, structural assignment of features was performed as previously described [66] based on the accurate mass (\(\pm\)15 ppm) and/or fragmentationpatterns derived from MS/MS spectra. In summary, 98 metabolites were annotated and included in the statistical analyses.
###### Abstract  
To be applicable to real-world networks, a realistic evaluation of network measurement tools requires the conditions of the test network to be as similar as possible to the networks in which they are later deployed. Thus, having an existing real-world network which can be utilized for testing, while also allowing the reconfiguration of certain characteristics and features, is of significant interest to researchers. Providing the researchers with corresponding management tools with which they can introduce artificial conditions and topology changes into the network would help in realizing more realistic testbed setups and scenarios. The purpose of this study is to develop a configurable test environment based on the Control and Management Framework (OMF) to support researchers in a realistic empirical evaluation of network measurement tools. The proposed OMF-enabled test environment has the merit of automatizing the deployments and executions of any measurement experiments, collecting their final estimates, and then transporting them back to a single experiment controller without requiring any physical access to the corresponding experimental nodes used. It is demonstrated how the test environment can be used for the evaluation of network measurement experiments based on four representative case studies. These include validating the accuracy of end-to-end round-trip time and available bandwidth estimation tools, measuring the TCP and UDP throughput, and analyzing the impact of packet loss and jitter on VoIP performance, all conducted over dynamically adjusted testbed configurations. These case studies highlight the versatility and effectiveness of the OMF-enabled test environment, positioning it as a valuable tool for advancing the realistic evaluation of network measurement tools in controlled yet adaptable settings.  
Keywords:realistic test environment; measurement tools; testbed; control and management framework +
Footnote †: journal: Applied sciences
###### Abstract  
Organosulfonic acid-functionalized mesoporous silica is a class of heterogeneous acid catalysts used in esterification processes due to its high surface area, shape-selective properties, and strongly acidic sites. Since water is generated as a by-product of esterification, the surface of mesostructured silica is modified to enhance hydrophobicity and catalytic performance. In this study, a series of propylsulfonic acid-functionalized nanocomposites based on natural rubber and hexagonal mesoporous silica (NRHMS-SO3H) with different acidities were prepared via an in situ solgel process using tetraethyl orthosilicate as the silica source, dodecylamine as the nonionic templating agent, and (3-mercaptoethanolpropyl)trimethoxysilane as the acid-functional group precursor. Compared with conventional propylsulfonic acid-functionalized hexagonal mesoporous silica (HMS-SO3H), NRHMS-SO3H provided higher hydrophobicity, while retaining mesoporousity and high surface area. The catalytic activity of synthesized solid acids was then evaluated via batch esterification of levulinate acid (LA) with alcohols (ethanol, \(n\)-propanol, and \(n\)-butanol) to produce alkyl levulinate esters. NRHMS-SO3H exhibited higher catalytic activity than HMS-SO3H and ultra-stable Y (HUSY) zeolite owing to the synergistic effect between the strongly acidic-functional group and surface hydrophobicity. The activation energy of the reaction over the NRHMS-SO3H surface was lower than that of HUSY and HMS-SO3H, suggesting that tuning the hydrophobicity and acidity on a nanocomposite surface is a compelling strategy for energy reduction to promote catalysis.  
nanocomposite; mesoporous silica; natural rubber; in situ sol-gel; solid acid catalyst; esterification +
Footnote †: journal:
## Appendix A Geodetic Methods Details  
The acquired data covered a period of approximately two months, \(\sim\)30 days prior to the occurrence of the main event (1 November 2020), extending 30 days after (3 January 2021). The raw GNSS data were processed using Bernese v5.2 GNSS software [57]. Data from several GNSS stations of the European Reference Frame (EUREF) were processed together with the daily 30-s RINEX GNSS data. The precise double-difference method was adopted for the analysis. The absolute antenna phase center corrections were used. Several auxiliary files were introduced on the processing procedure. The precise orbital solutions were obtained from the Center for Orbit Determination in Europe (CODE). For the troposphere modelling, the dry and wet Vienna mapping function (VMF) ([[http://ggosatm.hg.tuwien.ac.at](http://ggosatm.hg.tuwien.ac.at)]([http://ggosatm.hg.tuwien.ac.at](http://ggosatm.hg.tuwien.ac.at)), accessed on 1 June 2021) and the Neill mapping function were applied. The FES2004 model ([[http://holt.oso.chalmers.se/loading](http://holt.oso.chalmers.se/loading)]([http://holt.oso.chalmers.se/loading](http://holt.oso.chalmers.se/loading)), accessed on 1 June 2021) was used for the tide loading corrections. Ambiguities were solved using several strategies, depending on the baseline length between the stations, as is the wide- and narrow-lane, and the quasi-ionosphere-free (QIF) strategy. The final daily coordinates were evaluated for the repeatability error on a weekly basis and values were excluded in cases of large deviations from the weekly solution. The formed time series of the three coordinates of THIV station, on the global ITRF2014 reference frame, for the period 1 November 2020 to 3 January 2021, are presented in Figure A1. Following, we adduce information concerning the quality of our InSAR processing and solutions, including the inversion-related data (Table A1 and Figures A2 and A3) and the descending model (Figure A4). Figure A1: Time series of THIV continuous GNSS station for the north-south, east-west and vertical components. Red line marks the day of the earthquake. The reference frame is ITRF2014.
## 3 Numerical Simulation Modeling
### Grid Generation (Meshing)  
A fine resolution for a fluid flow model is required for convergence. With a wall function and no-slip condition, the flow over the solid surface for the standard \(k\)-\(\varepsilon\) model can deliver a sufficiently accurate result at a considerably lower computational cost. Typical grid meshing is achieved using size parameters for free tetrahedral with a fine mesh. Elements with maximum element size, minimum element size, maximum element growth rate, curvature factor and resolutions of narrow regions were 0.0135 m, 0.00169 m, 1.45, 0.5, and 0.6, respectively in this study.
## 3 Methodology and Algorithm Descriptions
### Research Design and Main Stages
#### 3.2.4 Stage 4: Experimentation and Parameter Variation  
Multiple experimental runs were conducted by varying the minimum support (0.10, 0.15, and 0.20) and confidence levels (ranging from 0.10 to 1.00 in increments of 0.05). For each configuration, outputs were recorded, including the rule count, execution time, memory usage, and statistical metrics. Experiments were repeated to ensure consistency and eliminate anomalies.
# Evolution Model of Coal Failure Using Energy Dissipation under Cyclic Loading/Unloading
## 4 Analysis and Discussion
### Energy Evolution Model of Coal Fracture
#### 4.4.2 Evolution Equation of Dissipated Energy  
By analyzing the relationship between the stress-strain, uniaxial compressive strength, loading rate, and energy dissipation rate [38; 39], the dissipated energy state is taken as an important index to judge coal failure. In this paper, the evolution law of dissipated energy was verified by analyzing the evolution characteristics of the energy consumption ratio in the process of coal fracture. The results show that the energy damage evolution equation of the dissipated energy parameter could better describe the energy state and stability of the coal under cyclic loading [40].  
Under the cyclic loading/unloading, the energy state and damage characteristics of coal are closely related to the stress level. Coal is composed of micro-structural units, and their state determines the stability of coal. Assuming that the micro-structure units and their strain state inside the coal follow the Weibull distribution, the energy damage parameter can be expressed as:  
\[D=1-k_{N}e^{[-(\frac{\zeta}{\pi})^{m}]} \tag{10}\]  
where \(k_{N}\) is a parameter related to the cycle times of coal at different stress levels; \(\alpha\) is a constant related to the nature of coal; and \(m\) is the shape coefficient of the Weibull distribution, which is related to the micro-structural state inside the coal.  
According to the previous analysis, the energy accumulation of coal under an axial load has a certain degree of self-inhibition, and its cumulative inhibition effect can be expressed as:  
\[U_{e}=vE_{0}\frac{1}{1+e^{-\varepsilon+\Phi}} \tag{11}\]  
where, \(U_{e}\) is the elastic strain energy accumulated inside the coal; and \(E_{0}\) is the initial elastic modulus of the coal.  
Then, the energy state evolution equation of the dissipated energy in the evolution process of the coal fracture can be deduced as follows:  
\[U_{d}=(1-v)E_{0}\frac{1}{1+e^{-(\varepsilon-\varepsilon_{c})^{2}}+\zeta} \tag{12}\]  
where \(\varepsilon_{c}\) is the peak strain variable of coal; and \(\zeta\) is a constant related to the nature of coal.  
Based on the data fitting verification, Equation (12) can fit the evolution law of coal dissipation energy well and describe its abrupt evolution characteristics.
## 4 Discussion  
The results of this study highlight the potential of reducing the cost of soil electrical conductivity detection and reducing the effect of temperature on the accuracy of soil electrical conductivity measurements, thereby increasing its potential to be widely used. In this paper, the feasibility of a in situ online soil electrical conductivity detection prototype is proposed and validated, based on the AD5941 for the limitations of the classical four\(-\)terminal method soil electrical conductivity detection principle with a constant current source, and the detection accuracy is improved by temperature compensation. The results of the comparative study with the commercial sensor (2265 FS portable soil electrical conductivity meter) show that the measurement accuracy of both are similar, and the linear fit, absolute error range, and relative error range are similar. This indicates that the performance of the prototype has been comparable to that of the commercialized sensor, but the price is much lower than that of the commercialized sensor.  
In the structural design, the focus in this study was on solving the problem of inconvenience and easy damage of traditional multi\(-\)pin sensors, and a cone\(-\)type probe was used to make the prototype more stable. In the hardware design, the low\(-\)power analogue front\(-\)end chip AD5941 was selected to achieve the absolute impedance measurement of the four\(-\)terminal method, so as to achieve the purpose of measuring soil electrical conductivity and effectively reduce the cost of the prototype.  
Although in this study the performance test of the original soil conductivity detection machine was carried out with a clay loam soil from the teaching experimental forestry farm of Beijing Forestry University in Sujiatuo Township, northwestern Haidian District, Beijing, as a sample and a series of research results were obtained, it is necessary to add more types of soils as the research samples in order to further advance the depth of this study, for example, sandy soils, clay soils, and so on. We can build a soil electrical conductivity calibration model based on soil type criteria to better facilitate usage.
# Optimization of Airflow Organization in Bidirectional Air Supply Data Centers in China
## 4 Airflow Organization Optimization  
### Airflow Optimization Strategies  
An analysis of the numerical simulation results in Section 3 reveals that the original data center exhibits non-uniform air supply, leading to severe thermal recirculation and cold air bypassing within server racks and cold aisles. These issues result in uneven temperature distribution at the rack inlets and the formation of numerous hotspots throughout the facility.  
From a practical application perspective, this study proposes the airflow optimization strategies presented in Table 4. In this data center, servers vary in type, specification, and physical dimensions. To standardize server arrangement, operational personnel position all servers close to the intake-side rails during installation, creating varying distances between server rear panels and exhaust-side rack doors. Consequently, when implementing the blanking panel strategy for unoccupied rack spaces, only the intake-side openings are sealed. All boundary conditions, including the air supply parameters of CRAC units and heat source configurations, remain consistent with the original model to ensure comparability.
## 3 VRDeepSafety Simulation Environment
### Mathematical Model of VRDeepSafety
#### 3.5.2 V2X Communication Model  
V2X communication enhances AD by providing information unavailable from a single vehicle's onboard sensors alone. This bidirectional information exchange occurs through V2V and V2I channels. To mathematically model these exchanges, we use matrices.  
The V2V communication matrix \(\mathbf{C_{V2V}}(t)\), represents the data exchanged between \(n\) vehicles at time \(t\):  
\[\mathbf{C_{V2V}}(t)=\begin{bmatrix}c_{11}(t)&\cdots&c_{1m}(t)\\ \vdots&\ddots&\vdots\\ c_{m1}(t)&\cdots&c_{mm}(t)\end{bmatrix} \tag{4}\]  
where \(c_{ij}(t)\) represents the data transmitted from vehicle \(i\) to vehicle \(j\). These data can include positional data, velocity vectors, intended paths, and environmental hazard warnings. This matrix is often sparse due to the localized nature of vehicle-to-vehicle communication.  
Similarly, the V2I communication matrix, denoted as \(C_{V2I}(t)\) represents the data flow between \(m\) infrastructure units and vehicles. It is defined as:  
\[\mathbf{C_{V2I}}(t)=\begin{bmatrix}i_{11}(t)&\cdots&i_{1m}(t)\\ \vdots&\ddots&\vdots\\ i_{m1}(t)&\cdots&i_{mm}(t)\end{bmatrix} \tag{5}\]  
where \(i_{ij}(t)\) represents the information flow from infrastructure unit \(i\) (such as traffic lights, road signs, or connected intersections) to vehicle \(j\). These matrices are typically sparse due to the localized nature of the communication. To optimize bandwidth and computational resources, data prioritization techniques are used. For instance, critical messages such as collision warnings or sudden braking events are given higher priority than less critical updates.  
Figure 2: Vehicle kinematics with Ackermann steering.