esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 State Variables, Compartments, Directed, Graphs, Pools and Species
For nonlinear systems, arrows and edges designate variable functions of constant parameters (e.g., \(A\), \(B\)) and state variables \(q_{i}\) (e.g., saturable functions, like \(A/(B+q_{i})\)[7]. A **pool** is the total amount of a substance in a system or subsystem [18], defined purposely loosely to suit the variety of ways different investigators were using it. We are a bit more precise here, defining **pool**--similarly, as a mass or volume size measure for a real or abstract space or material, emphasizing that it is not necessarily uniformly (homogeneously) distributed [19]. A compartment may be a pool, but not necessarily vice versa. On the other hand, we can speak of the \"pool of \(X\) in compartments \(i\) and \(j\)\" or the \"pool of \(X\) in the whole organism\" (or \"whole compartmental model or system\") because the term pool circumvents the homogeneity requirement on any of these entities. _Pool Models_ are particularly useful for steady-state _flux balance analysis_ (FBA) [6]. **Species** is a term commonly used in chemistry and molecular biology, usually defined as distinct molecular entities sharing the same chemical properties, and having the same chemical properties effectively means homogeneous. In a chemical or biological system kinetic (dynamical) model, distinct species are thus represented as distinct state variables--the classical definition of (homogeneous) compartment in a dynamic system model. Different definitions for some of these terms are embodied in the latest version (Level 3) of the _Systems Biology Markup Language SBML_, a \"_bridge_ language (_lingua franca_) for communication or exchange of mathematical models across programming tools\" (_shml.org_), with potential for confusion in modeling biological systems. In SBML, a \"compartment is a well-stirred container of finite size where species may be located\". And \"species is a pool of entities of the same kind located in a compartment and participating in reactions (processes)\". The main distinction of SBML from classical definitions (which we have adopted here) is that SBML \"compartments\" are _containers_ of species, whereas classical compartments are unique homogeneous entities represented as state variables. The different meanings of _species_, _compartment_ and _pool_--taken together--generate potential ambiguities for modeling. A multicompartmental (MC) model can have any number of compartments. But its observable (visible) dimensionality is limited by the locations of inputs and outputs and thus the particular (noise-free) data available. This means the granularity--or extent--of multicompartmentalization is experiment-dependent. The minimum dimension--the smallest number of possible compartments--is equal to the number of its distinct _modes_ visible in its output response for any particular specification of input and output locations.
## 4 Conclusions  
To replace conventional bulky transformers, a reduced-size 'isolated' SST was proposed in this research. Either the source was single phase, three phase or DC source. The proposed SST model could generate the single phase, three phase or DC voltage for any type of resistive or inductive load. When interfaced with RES, the symmetric configuration of SST made it working as a stand-alone system at the consumer end as well as the grid-tied inverter at the grid side. Simulation results were assessed for scenarios where SST acted as a variable frequency drive and as a power factor improvement device. In addition, when interfaced with RES, SST ensured bidirectional power flow as well. Simulation results validated the performance of the proposed SST for all the scenarios. Working of SST as the VFD and PFI device with a renewable interface makes it ideal for industrial applications where the proposed SST topology may eliminate the requirements of bulky conventional transformer especially for motor drive applications. This justifies the role of SST as VFD. The four-quadrant operation of controlled switches with the DC port within SST enables it to be integrated with the micro/super grids. The proposed SST design has the ability to reject grid side disturbances, and regulate VAR at the grid side and voltage levels for the consumer end. The presented topology of SST can easily support the wide range operation of the distribution system. Future work would be development of the hardware setup for the proposed SST topology, thus to check the validity of the proposed SST model on real time industrial applications. Apart from SST design, optimized integration of SST with hybrid-RES would be a design worth of importance.  
U.T., G.A., D.O.G., U.F., A.R. and M.U.A. suggested the idea, performed the simulations and managed the paper. [PERSON], [PERSON] and [PERSON] assisted in the idea development and paper writing.  
This research received no external funding.  
The authors declare no conflict of interest.  
Figure 31: (**a**) SST input current at the grid side. (**b**) THD analysis of the phase A input current.
# A New Approach to Identifying Crash Hotspot Intersections (CHIs) Using Spatial Weights Matrices
## 2 Methodology
### Methods
#### 2.3.4 The Intersection Prediction Accuracy Index (IPAI)  
Evaluation of hotspot analysis prediction performance is an important issue relating to the suitability of this proposed approach. The Prediction Accuracy Index (PAI) [58], firstly proposed by [PERSON] et al., is the ratio of the hit rate to the fraction of area covered [59]. The PAI has been widely applied to measure hotspot analysis results [17; 59; 60; 61]. Previously, the Crash Prediction Accuracy Index (CPAI) was developed by using road length rather than area for evaluating traffic crash hotspot analysis performance [17; 61]. Based on the previous studies, the Intersection Prediction Accuracy Index (IPAI) was developed in this research with road network restraints for evaluating IHA prediction performance as follows:  
\[IPAI=\frac{\sum\limits_{j=1}^{m}x_{j},j\in HI/\sum\limits_{i=1}^{n}xi_{i},i\in I} {\sum\limits_{i=1,j=1}^{m}lsp(i,j)/\sum\limits_{i=1}^{r}li,i\in R} \tag{9}\]  
where \(HI\) is the set of hotspot intersections; \(I\) is the set of all intersections in the study region; \(R\) is the set of all roads in the study region; \(m\), \(n\), and \(r\) are the number of \(HI\), \(I\), and \(R\), respectively; \(x_{j}\) is the number of crashes of intersection \(j\) within \(HI\); \(x_{i}\) is the number of crashes at intersection \(i\) within \(I\); \(sp(i,j)\) is the shortest path set between intersections \(i\) and \(j\) within \(HI\); \(l_{sp(i,j)}\) is the total length of the shortest path set; and \(l_{i}\) is the length of road \(i\) within \(I\). Note that a road can be in any shortest path only once and cannot be duplicated. IPAI is an indicator to quantify the prediction performance of IHA. The total length of road and the total number of crashes in the study region are constants. That is, the higher the IPAI which means that the larger number of crashes in CHIs while the shorter lengths of shortest paths between CHIs, the better the prediction performance of the approach.
# HTSA-LSTM: Leveraging Driving Habits for Enhanced Long-Term Urban Traffic Trajectory Prediction
## 3 Experiments and Evaluation
### Training Setup and Evaluation Metrics  
To comprehensively account for the influence of driving style on vehicle trajectory, a driving style analysis module has been embedded within the HTSA-LSTM model. This module performs semantic parsing of the input data, identifying and segmenting fundamental driving behavior units, thereby enabling an unsupervised analysis of the driver's driving style. By incorporating the driving style analysis module, the model not only captures temporal and spatial dependencies through the spatiotemporal attention mechanism but also integrates the impact of driving style into the analysis.
# Suitability of Aerial Photogrammetry for Dump Documentation and Volume Determination in Large Areas
## 2 Materials and Methods
### Data Acquisition and Processing
#### 2.3.1 GNSS Measurement  
The GNSS measurement of selected heaps was carried out to compare the quality of measurements on the same day as TLS and AP data collection. RTK VRS method was used with a connection to a network of permanent reference stations, SKPOS(r) serving as a base for relative measurements in the Slovak Republic. Virtual reference station was generated at the site of measurement. Positional accuracy of the measurement is indicated by the coordinate standard deviation \(\sigma_{xy}<20\) mm and height accuracy \(\sigma_{\text{h}}<40\) mm. At heap No. 1, 205 points were measured, and at heap No. 2, a total of 534 points.
# A Country Risk Assessment from the Perspective of Cybersecurity in Local Entities
## 2 Materials and Methods
### Methods
#### 2.2.2 Obtaining Technical Metrics on Security  
The metrics selected for the security domain were chosen because of their ability to assess critical external perimeter vulnerabilities and their impact on the end user's perception of trust. These metrics are aligned with recognized security standards, such as certificate expiration and encryption quality indicators, which are essential to protect user communication and data. In addition, metrics were considered that assess the exposure of sensitive information and domain reputation in security databases, which is critical to minimizing cyber risks and improving the user experience. The selected metrics for modeling the security of the external perimeter were as follows:  
* S1: The remaining time to expiry of HTTPS/SSL/TLS certificates (in days);
* S2: The detection of obsolete cryptographic digests, such as SHA1, in encrypted communications;
* S3: The number of open ports in the main domain, in addition to those associated with web traffic;
* S4: The number of documents indexed in Google with information related to the internal perimeter;
* S5: Evaluation of the SSL/TLS digital certificate, where A is indicative of excellent quality and F is indicative of insecure quality [39; 40];
* S6: The presence of a Robots.txt file serves to prevent indexing by search engines and impede the reconnaissance phase of the cyber kill chain [41].  
To provide a more comprehensive assessment, additional metrics were incorporated into the _CIORank_ framework. These additional metrics are as follows:  
* SX_Safe_Browsing: The domain reputation databases are consulted to confirm if the entity's domain is listed as unsafe [42];
* SX_Shodan: A check is made to identify whether the entity's domain has any known vulnerabilities that have not yet been conveniently patched. The vulnerabilities are identified with the CVE code or Common Vulnerabilities and Exposures. The Shodan tool is used for this purpose, facilitating the cataloging and identification of each monitored entity [43].  
In the context of this domain, which is defined by eight technical metrics, an equal weight of 12.5% was set for each of them. This illustrates the contribution of each metric to the domain. A more detailed analysis of this design decision can be found in the Discussion and Proposals Section.
# A Survey on the Impact of Intelligent Surfaces in the Terahertz Communication Channel Models
## 5 Discussions and Research Directions
### Practical Application Examples  
To assess the practicality of IS-assisted THz communications research and address the challenges associated with erratic THz signal interference caused by mobile and human blockage, the authors in [129] conducted a case study using a 3D mapping environment. The study was developed in a multi-receiver propagation environment incorporating the implementation of multi-dimensional and distributed IS. The proposal promised to significantly increase the SNR, improve the overall success rate and energy efficiency, and maintain optimal quality of service (QoS) with adequate latency. These objectives were achieved by implementing a distributed IS framework algorithm designed to operate collaboratively. The authors considered practical factors such as complexity in IS system design, IS hardware constraints due to limited volume, high value of state-of-the-art THz hardware, accuracy of location information, and feasibility of deployment in a realistic environment. Very recently, driven by the advantages of the distributed IS scheme, the authors of [130] have proposed the double-layer true-time delay with sparse radio frequency chain antenna structures for IS-assisted communication in THz, aiming to improve the system performance.  
Some proof-of-concept prototypes of IS in wireless communication environments, specifically in the 5.8 GHz and sub-6 GHz bands, have been presented, where their potential benefits are evaluated under realistic conditions at these frequencies [153; 154]. The evaluations revealed significant SNR improvements and coverage in blocked or hidden areas, enhancing higher-quality communications. Although the authors focused on the 5.8 GHz and sub-6 GHz bands, they ensured the adaptability of the prototype, especially in the design of simplified unit cells, as reported in [153]. This adaptability would allow for the practical fabrication of ISs at mmWave and THz frequencies.  
Last but not least, in various high-speed and low-latency applications that are emerging as candidates for the future communication networks, such as UAVs, backscatter communication, mmWave utilization, and MIMO systems, ISs play a crucial role in boosting coverage, capacity, efficiency, and signal quality [155]. This is achieved by bypassing obstacles and reducing interference through skillful manipulation of radio signals. Additionally, in the context of non-orthogonal multiple access (NOMA) and simultaneous wireless information and power transfer (SWIPT), ISs contribute significantly by improving spectral efficiency, optimizing power transfer, and enhancing signal quality through their ability to manipulate radio signals [155].
## 5 Conclusions
**Nomenclature**  
**D**: Inventory diameter [m]  
**L**: Inventory length [m]  
**\(\Delta\)s**: Insulation thickness [m]  
**V**: Volume [m\({}^{3}\)]  
**m**: Mass [kg]  
**av**: Specific surface [m\({}^{2}\)/m\({}^{3}\)]  
**\(\varepsilon\)**: Void fraction  
**w**: Velocity [m/s]  
**\(\alpha\)**: Heat transport coefficient [W/m\({}^{2}\)K]  
**k**: Total heat transfer coefficient [W/m\({}^{2}\)K]  
**t**: Time [s]  
z**: Axial dimension [m]  
**r**: Radial dimension [m]  
\(\rho\)**: Density [kg/m\({}^{3}\)]  
**cp**: Specific heat capacity [J/kgK]  
**\(\lambda\)**: Heat conduction [W/mK]  
**a**: Thermal diffusivity [m\({}^{2}\)/s]  
**T**: Temperature [K]  
**Q**: Thermal energy [J]  
\(Q\)**: Thermal power [W]  
\(m\)**: Mass flow rate [kg/s]  
**\(\tau\)**: Duration [s]  
**\(\eta\)store**: Thermal utilization coefficient  
**P**: Pressure loss [Pa]  
**C\({}_{\rm{r}}\)**: Radiation parameter [W/m\({}^{2}\)K\({}^{4}\)]  
**Subscripts**: S**: Solid  
**F**: Fluid  
**Ins**: Thermal insulation  
**j**: Inner (j = 1) and outer (j = 2) thermal insulation  
**U**: Environment  
**W**: Surface area  
**c**: Active cooling fluid  
**mix**: Mixing temperature  
**PtH**: Maximum solid media storage temperature  
**loss**: Heat losses  
**tot**: Solid medium and thermal insulation  
**SM**: Solid medium
# Ext-LOUDS: A Space Efficient Extended LOUDS Index for Superset Query
## 4 Discussions
### Comparisons
#### 4.2.2 Experiments on Synthetic Datasets  
Database parameters, such as the size of the dataset \(|\text{D}|\) and the number of distinct elements \(|\text{U}|\) in the dataset, also have a significant impact on the space and time overheads. We did a comparative experiment by fixing \(|\text{D}|\) and \(|\text{U}|\), respectively.  
The Effects of \(|\text{D}|\)  
We carry out experiments on \(|\text{U}|\) fixed to 10,000 and \(|\text{D}|\) varied from 50,000 to 500,000. The space and time overheads are shown in Figure 8a,b, respectively. From these two figures, we can see that the space and time overheads increase with the increase of \(|\text{D}|\) for all the 3 indexes. Compared with the other 2 competitors, Ext-LOUDS has the best space overhead and has a query time overhead close to the best, which shows it has a good scalability and a good overall performance.  
Figure 6: Query times for the three algorithms: (**a**) MSWEB; (**b**) DBLP.  
Figure 7: Space overheads for different duplication ratios: (**a**) MSWEB; (**b**) DBLP.  
The Effects of \(|\)U\(|\)  
We carry out experiments on \(|\)D\(|\) fixed to 100,000 and \(|\)U\(|\) varied from 1000 to 50,000. The space and time overheads are shown in Figure 9a,b, respectively. From these two figures, we can see that the space overheads for trie-based algorithms increase with the increase of \(|\)U\(|\), whereas it has little impact on inverted indexes. The reason lies in that the smaller the \(|\)U\(|\) is, the more compact the trie will be. For an inverted index, the number of elements in all the inverted lists always equal to the total number of elements in the corresponding dataset, so its space overhead keeps stable. For the time overheads, the query time of all the 3 algorithms decrease with the increase of \(|\)U\(|\). This is because a larger \(|\)U\(|\) means a shorter inverted list in inverted index or a lesser occurrence in trie that a query element may encounter, thus accelerating the query speed. Ext-LOUDS still has the best space overhead and has a query time overhead close to the best when compared with the other two indexes, which shows it has a good overall performance.
## 3 Results and Discussion
### Calculation of Average Number of Gold Atoms per Nanoparticle  
The average number of gold atoms per nanoparticle may be calculated from high-resolution TEM analysis [40]. The HRTEM images of the synthesized gold nanoparticles shows the average particle size of about 31 \(\pm\) 1.6 nm (\(D\), nm). Assuming a spherical shape and a uniform FCC structure [41], the average number of gold atoms (\(N\)) for each type of nanosphere was calculated by the following equation, where \(\rho\) is the density for FCC gold (19.3 g/cm\({}^{3}\)) [42; 43] and M stands for atomic weight of gold (197 g/mol):  
\[N=\frac{\pi}{6}\frac{\rho D^{3}}{\text{M}}=30.89602D^{3}, \tag{1}\]  
On applying the above equation, the average number of gold atoms per synthesized nanoparticle was found to be in the range from 885,251.797 to 1,070,422.767 atoms.
## 5 Performance Evaluation and Simulation
### Result of Simulation
On the other hand, offline mode usually causes the battery to enter the inadequate mode, resulting in considerable backup power expenses. Figure 12a shows that increasing the capacity of the edge server has no meaningful effect on the system's average cost. Since more emphasis is placed on energy control during the transmission phase in the DO2 QIEO network, the edge side's processing power will not significantly impact the system's average cost. In various scenarios of computation capacity, the suggested algorithm has a lower system-cost than ODOC and OSGO. The simulations are done in circumstances where the channel bandwidth provided in the network extends from 14 to 24 MHz, as shown in Figure 12b. As the system's channel bandwidth allocation rises, the relative advantages of offloading to the edge diminish. The DO2 QIEO method, on the other hand, outperforms ODOC and OSGO. Figure 11: The system run-time costs in both online and offline modes. (**a**) Average cost for online mode. (**b**) Average cost for offline mode. Figure 10: Results of online experiments for local and edge computation (**a**) processing time for given IoT data file size (**b**) battery consumption for given IoT data file size. Due to the advantages of scalability, privacy, and security of blockchain, there are several benefits of incorporating blockchain into edge computing. Blockchain is essential for secure data transmission in edge and wireless networks [58]. Within the enormous, distributed edge nodes, blockchain enables dependable connections and management of computing, communications, and storage resources. Blockchain-based service offloading is thought to ensure security when offloading services across numerous edge nodes. Figure 13 compares the results of system latency under various blockchain nodes. The number of blockchain nodes is configured to 2, 4, 6, 8, 10, 12, and 14. Because the number of nodes affects consensus execution time, overall system latency is also influenced. As seen in Figure 13, the DO2 QIEO method has the lowest system latency for various blockchain node settings since it produces superior offloading strategies than other methods. BSCO needed the greatest system latency, almost 0.8-0.9 ms more than EEDTO. The rationale for this outcome is that the BSCO has a heavy computational and consensus load. In terms of system latency, the DO2 QIEO method has the lowest latency compared to other schemes, implying that our offloading strategy technique may significantly reduce the expected time, particularly in latency-sensitive workloads and multiple node consensus settings.
## 3 Material and Methods
### HS-SPME/GC-MS Analysis  
In order to favor thermodynamic equilibrium, samples were pre-incubated at 37 \({}^{\circ}\)C, for 30 min. VOCs extraction was conducted at 37 \({}^{\circ}\)C for 10 min, using a 65 \(\upmu\)m polydimethylsiloxane (PDMS)/divinylbenzene (DVB) fiber (Supelco, Bellefonte, PA, USA). Then, the loaded SPME fiber was desorbed into GC inlet port for 2 min. All GC-MS experiments were done in triplicate. Carrier gas (helium 6.0) flow rate was set as 2.2 mL min\({}^{-1}\), and inlet temperature was set at 250 \({}^{\circ}\)C. The oven temperature program was as follows: the initial temperature of 40 \({}^{\circ}\)C was kept for 4 min, then ramped at 100, 220 (held for 7 min) and 250 \({}^{\circ}\)C, at respective rates of 10, 15 and 20 \({}^{\circ}\)C min \({}^{-1}\). The oven was maintained at this last temperature for 5 min. Spectra acquisition was performed within the range of m/z 25-300, using electron ionization (EI) at 70 eV. Both ion source and the transfer line temperatures were set as 220 \({}^{\circ}\)C, while the quadrupole analyzer was kept at 150 \({}^{\circ}\)C.  
The GC-MS analyses were carried out by using an Agilent 6890A gas chromatograph coupled to an Agilent 5975 Inert XL MSD mass spectrometer (both from Agilent Technologies, Santa Clara, CA, USA), associated with an autosampler MPS2 (Gerstel, Sursee, Switzerland). The system was equipped with a DB-624 U1 60 m \(\times\) 0.25 mm \(\times\) 1.40 \(\upmu\)m column (Agilent, Palo Alto, CA, USA). Compound identification was processed by searching the obtained mass spectrum in the NIST11 mass spectral library. The criterion for peak detection was a signal-to-noise of at least 3, and peak integration was done manually. Spectrum search encompassed baseline subtraction and averaging over a peak. Forward and reverse match quality of at least 750/1000 was considered as the lower match threshold. Peaks detected in the corresponding blanks were deleted from the total dataset, for the obtainment of signals attributed solely to bacterial activity.