esa-sceva/satcom-chunk-collection huggingface dataset:
# ICN-Based Enhanced Content Delivery for CDN
## 2 Background and Related Work
### Related Work
The main features and discussions regarding the above hybrid approaches are compared and summarized in Table 1. To conclude, although the above hybrid studies differ in their approaches, they all clearly indicate that by integrating ICN into the CDN architecture, the content distribution efficiency of CDN can be significantly improved. When incorporating ICN into CDN, addressing the challenges of communication paradigm translation between the two is an important research area. The translation operations are typically executed by gateways or proxies that support both paradigms. The first is to convert HTTP semantics into a ICN naming convention. In a hierarchical ICN naming convention like NDN/CCN, the most common approach [36; 37; 38] is to construct a hierarchical name structure by extracting information from the HTTP URL and HTTP header. In [36], the sub-level of the HTTP domain name is split and reversely appended in the NDN name, followed by the version, segment, and other information. This approach improves the NDN routing capability with route aggregation. The strategy in [38] puts a default prefix at the beginning of the CCN name, HTTP protocol, request method, and request domain, and the request parameters are hierarchically appended.In a flat naming convention like MobilityFirst, the key to translation is generating a globally unique ID that identifies the content/host [39; 35]. In [35], the authors use a public key generated by the Elliptic Curve Cryptography (ECC) algorithm as the content ID. Secondly, regarding the different HTTP request methods, primarily GET and POST, the GET method can naturally correspond to the ICN pull-based interest packet, but the POST method is more complex. In [37; 38], the proposed method is for the gateway/proxy to save data upon receiving a POST, then sends a special interest to the ICN terminal. This prompts the ICN terminal to send an echo interest to the gateway and pull the data, thus achieving a push-like effect. In the integration methods discussed above, using overlay or network slice techniques can facilitate the fast deployment of ICN. However, they also introduce complexities in management and incur overhead associated with network abstraction. Inspired by the above research, we propose our ICN-enhanced CDN architecture, called IECDN. The ubiquitous caching capabilities of ICN can be a compelling incentive for ISP to embrace its deployment. In light of this, this paper envisions a scenario where ICN is employed as an infrastructure service, providing network services through dual-stack gateways. To ensure a cost-effective deployment of ICN, we propose the introduction of an IP-compatible ICN paradigm. This strategic combination of infrastructure deployment and an IP-compatible protocol stack aims to address the challenges posed by ICN deployment, presenting a balanced approach that capitalizes on the strengths of ICN while mitigating the associated complexities.
# A Review of Evaporite Beds Potential for Storage Caverns: Uncovering New Opportunities
## 1 Introduction
It is important to distinguish evaporite beds with dominant minerals of halite, potash, trona, and the present impurities because these minerals impose differences in the properties of the beds [40; 42]. Thus, the physical properties of each evaporite bed, including density, porosity, and permeability, are reviewed. Then, the mechanical properties of evaporite beds as crucial inputs for geomechanical studies are reviewed. Physical and mechanical properties are both affected by mineralogy and depositional environment and in turn influence salt cavern geomechanics. The key geomechanical issues in bedded caverns are then addressed. From a cavern life-cycle point of view, the geomechanical issues are divided into cavern design, solution mining, cyclic loading, and ultimately, \begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt} p{142.3 pt}} \hline
**Author(s) and Year** & **Title** & **Focus of Paper** \\ \hline \multirow{7}{*}{[PERSON] et al. (2021) [44]} & & Rock salt creep properties from the perspectives of macrocreep properties and microscopic creep deformation mechanisms; and rock salt constitutive models, including the empirical model, the component combination model, the fractional derivatives creep constitutive model, the nonlinear creep constitutive model, and the creep constitutive model considering the self-healing ability of damaged rock salt. \\  & & Cyclic loading and behavior of rocks under cyclic loading and fatigue tested under different loading conditions and various available experiments in addition to a review of typical results such as stress–strain curves, deformation evolution, dilatancy, and crack development and measurement. \\ \hline \end{tabular}
\end{table} 
Table 1: _Cont._-cavern abandonment. The design of caverns in bedded salts is highly affected by the depth, thickness, areal extent, and presence of impurities, which in turn affects the shape, size, number of caverns, and pillar size. In terms of solution mining, the two main issues encountered are wellbore integrity (mainly casing) and cavern stability (formations present including salt and interlayers) during solution mining based on different cavern designs such as single and double well caverns. Once the cavern is leached, gases are injected and withdrawn in a cyclic mode to overcome seasonal shortages and demands. In this case, during cyclic loading, pressure and temperature are two main parameters changing and impacting the geomechanical factors such as creep, which in turn affects cavern shrinkage and size reduction. This reveals the importance of monitoring, which is essential during the entire life of salt caverns from construction to abandonment. Referring to abandonment as the action after gases are no longer stored in the cavern, the method of cavern filling, the type of infilled fluid, and the plugging method can lead to geomechanical issues.
## 3 Results
### Role of SiTPS during S. indica-Colonization of Tomato Roots  
Even though _SiTPS_ was shown to express a functional viridiflorol synthase in _E. coli_ expression systems, no terpenoids have been detected previously in _S. indica_ cultures [48]. In addition, data showing _SiTPS_ upregulation in fungal tissues associated with tomato roots (Figure 3) suggested that SiTPS products might play a role during plant colonization. To investigate the role of SiTPS in root colonization, _S. indica_ mutants over-expressing _SiTPS_ were designed. According to the antiSMASH analysis performed on the _S. indica_ genome, SiTPS is a stand-alone gene and none of the genes in genomic proximity is related to terpene biosynthesis (Supplementary Figure S2). Based on that, we hypothesized that overexpressing _SiTPS_ would have an accumulative effect on the amount of compounds produced.  
After protoplast transformation, the regenerative clones were screened for having all four mating type genes (heterokaryotes) (Supplementary Figure S5A) and one _SiTPS_-overexpressing transformant (ovII) was selected and checked for _SiTPS_ expression (Supplementary Figure S5B). The ovII strain was used in time-course colonization experiments to evaluate its ability to colonize plant roots. An _S. indica_-transformant carrying the empty vector (control treatment-evV) and the wild type _S. indica_ were also used in this colonization study.  
The ratio of fungal DNA to plant DNA was used as a measurement of the colonization ability of each _S. indica_ strain. The colonization ratio was estimated in colonized roots at 2 and 11 dpi, but no difference was observed in the colonization ability between the over-expressing and the empty vector-carrying transformants at the selected time-points. No difference was observed in colonization ability between _S. indica_ transformants and the wild type either (Table 1).
# A Harmonized Information Security Taxonomy for Cyber Physical Systems
## 2 Background
### Standards' Lifcycles
#### 2.2.2 IEC62443 Suite: Security for Industrial Automation and Control Systems  
The IEC 62443 suite is under development by the International Society for Automation (ISA) workgroup for industrial cybersecurity. The suite is envisioned to eventually cover the full spectrum of industrial cybersecurity management systems. While being a widely applied standard for industrial systems, it is still incomplete. The IEC62443-1-4 module has been in development since 2013. Unlike the ISO27000 cycle, this standard presents three distinct lifecycles:  
* Security development lifecycle assurance, which focuses on the equipment manufacturers, as detailed in IEC62443-4-1 (it falls outside the scope of this paper and is therefore not discussed further here);
* The automation solution lifecycle;
* The cybersecurity lifecycle.  
Figure 1: The ISO27000 PDCA cycle for ISMS [13].  
Automation System Operational Lifecycle  
The intention of the automation solution and CPS lifecycle is to show the phases that are found in the design, implementation, and operation of a CPS (see Figure 2).  
Changes to or incidents on the system should trigger the responsible party to initiate a security review for the system. The recommended triggers for re-evaluation are well defined in IEC62443-2-4, but in summary, they include the following common occurrences:  
* Changes in system communication design or methodology;
* Process operational changes that impact the control or safety systems;
* Any modification of safety parameters or changes to the safety system;
* Expansion of or changes to the system hardware.  
The CPS Security Lifecycle  
The security lifecycle consists of three main phases:  
* Risk assessment;
* Design and implementation of countermeasures;
* Monitoring and maintenance.  
These phases are shown in Figure 3.  
The security lifecycle is applied during the first three phases of the automation solution lifecycle and then on a periodic or triggered basis during the maintenance phase.
## 3 Results and Discussion
### Structure Elucidation of the Cu Complex by NMR
The results obtained from the NMR, ATR, Raman, and elemental analyses suggested the possible composition of the Cu complex (Table 2) and its tentative structure (Figure 3). In addition, it is possible that Cu has a coordination number 6 or 4. The proton solid-state NMR spectrum showed a broad peak at 7.6 ppm, which probably indicated the presence of H-5 and H-6 protons in the coordinated uracil and 2-thioouracil (Figure 3). There were no signals observed for NH protons in the proton solid-state NMR spectrum, confirming that the coordinated 2-thioouracil and uracil contain deprotonated nitrogen atoms. The broad peak at 2.9 ppm could indicate that DMSO-h\({}_{6}\) and H\({}_{2}\)O participate in the Cu complex. The solid-state NMR spectra also confirmed the absence of 2,4-DIt in the Cu complex. The signal at 172.8 ppm probably corresponded to the carbon, C-2, in the coordinated 2-Tu. The signals at 150.8 ppm (C-2) and 167.7 ppm (C-4) could indicate the carbons in the coordinated uracil. The signals for the carbons, C-6 and C-5, in the coordinated uracil can be found at 157.4 ppm (C-6) and 120.3 ppm (C-5), whereas for the coordinated 2-thioouracil, they are found at 157.4 ppm and 116.1 ppm. The solid-state CP MAS (Figure 4A) and CPPI MAS (Figure 4B) showed a signal at 40.5 ppm, confirming the presence of DMSO in the Cu complex. The \({}^{1}\)H NMR solution spectrum showed a signal at 2.54 ppm, which is also an indication of the presence of DMSO-h6 in the Cu complex. In addition, there was one HSQC correlation (2.54 ppm-40.06 ppm). Actually, the possible structure of the Cu complex proposed in the present study was similar to that reported by [PERSON] et al. for Cu(II), Mn(II), Fe(II), Co(II), and Ni(II) complexes with uracil [43]. So, we suggested bidentate coordination through the S- and O-atoms of 2-Tu, as well as through both O-atoms of U. Another similarity with the proposed structure in the above cited article is the possible presence of H\({}_{2}\)O as a ligand in the inner coordination sphere of the Cu complex. In addition, the ligands were deprotonated at a nitrogen atom in the third position [43], whereas in our case, the deprotonation was in both the first and third positions. Various metal complexes (Cu(II), Cd(II), Hg(II), Pd(II), Pt(II), Pt(IV), Rh(III), and Ir(III)) exhibit a bidentate coordination mode with 2-thioouracil derivatives [12; 19; 27; 28]. DFT calculations previously suggested a number of stable conformations of Cu(II) complexes with U or 2-Tu participating in monodentate or bidentate coordination with the  
Figure 4: \({}^{13}\)C CP MAS NMR spectrum of Cu complex (**A**); \({}^{13}\)C CPPI MAS NMR spectrum of Cu complex (**B**); \({}^{13}\)C CP MAS NMR spectrum of the 2,4-DIt (**C**). metal [13]. In contrast, no DFT data exist in the literature for the stability of a Cu complex that contains both U and 2-Tu as bidentate ligands. Therefore, similar calculations should also be performed for such chelate Cu complexes with more than one ligand (e.g., U and/or 2-Tu) in order to assess their relative stability.
# Augmented Reality: Mapping Methods and Tools for Enhancing the Human Role in Healthcare HMI
## 3 Results
### Intraoperative Solution  
While AR has just begun to be accredited to simulators for preoperative planning and surgical training, it is a more encouraging technology for achieving solutions within the intraoperative phase. AR can support the surgeon in understanding and visualizing the anatomical regions of interest and the topographical relationships between surgical instruments, orthopedic implants, and anatomical landmarks. In addition, through AR, virtual images can be generated and projected onto the real environment, offering guidance to perform minimally invasive surgery.  
Studies concerning AR-based intraoperative solutions represent the vast majority of the reviewed papers. Considering the wide variety of surgical interventions, and thus the different needs of orthopedic surgeons in performing these procedures, the intraoperative solutions were further subdivided according to the anatomical part treated and the type of surgical procedure to which AR was applied. Section 3.3.1 describes arthroplasty procedures involving hip (n = 7), knee (n = 5), elbow (n = 1), and shoulder (n = 4), and fixation procedures on femur and tibia (n = 2). Section 3.3.2 describes AR-based solutions applied to spine surgery procedures, and details pedicle screw placement (n = 17), percutaneous surgery (n = 3), lumbar facet joint injections (n = 1), rod bending implant (n = 1), and Sacral Alar Iliac (SAI) screw implantation (n = 1). Section 3.3.3 discusses AR-based applications for treating tumor pathologies (n = 4).
## 2 Islanding Detection Methods
### Remote Methods
#### 2.4.5 Impedance Insertion Method  
This method involves inserting capacitor bank or low-value impedance into the utility grid [12]. As a result of the reactive power being out of control, the balance between generation and consumption is altered. At the distribution line, the voltage and frequency are adjusted, and on the utility side, the frequency change is measured using frequency relays. This approach works well and can identify islanding with a short response time. Although this method is pricey, it does not meet some requirements because it takes too long to install the capacitor bank once the main grid connection is severed.
# A Topology Optimization Method for Reducing Communication Overhead in the Kalman Consensus Filter
## 2 System Model and Problem Formulation
### Network Model  
This work considers a WSN with topology \(\mathcal{G}=(V,E)\), where \(\mathcal{G}\) is undirected and connected, \(V\) is the set of sensor nodes, \(V=\{1,2,\cdots,I\}\) and \(I\) is the number of sensor nodes in the network, \(E\) is the set of communication links, and \(|E|=K\) denotes the number of communication links in the network. The neighboring nodes set of node \(i\) is denoted as  
\[\mathcal{N}_{i}=\{j\in\mathcal{V}:\mid\!\!/(i,j)\in\mathcal{E}\}\]  
Adjacency matrix \(\mathbf{N}\) denotes the neighborhood relationship of a network. If node \(i\) and node \(j\) are adjacent, the corresponding entry in \(\mathbf{N}\) is \(n_{ij}=1\); otherwise, \(n_{ij}=0\). Let \(d_{i}\) be the degree of node \(i\), and \(d_{i}=\sum\limits_{j=1}^{I}n_{ij}\). Degree matrix \(\mathbf{D}\) is a diagonal matrix, where the \(i\)-th entry is \(d_{i}\). Laplacian matrix \(\mathbf{L}\) is the symmetric and positive semi-definite matrix defined by \(\mathbf{L}=\mathbf{D}-\mathbf{N}\). The eigenvalues of \(\mathbf{L}\) can be arranged as  
\[0=\lambda_{1}(\mathcal{G})<\lambda_{2}(\mathcal{G})\leq\ldots\leq\lambda_{I}( \mathcal{G})\]  
where \(\lambda_{2}(\mathcal{G})\) is also called the algebraic connectivity of graph.
# Energy-Efficient and QoS-Aware Computation Offloading in GEO/LEO Hybrid Satellite Networks
## 3 Results  
### Experiment Setup  
For simplicity without loss of generality, the GEO/LEO hybrid satellite networks considered in this paper consist of five LEO satellites and three GEO satellites. It is assumed that each LEO satellite has one or more missions to be processed and the data size of each mission is randomly distributed between 0.5 and 2.5 Gb. The processing density is randomly selected from the set \(\{10,15,20\}\) and the QoS constraints are randomly generated in the \([650,500]\) ms. We use containers to process the missions and each offloaded mission can be assigned a maximum processing capacity of \(6\times 10^{10}\) cycles/s on GEO satellites and \(2\times 10^{9}\) cycles/s on LEO satellites. Table 2 shows the parameters set in this paper [1; 17; 18]. All the experiments were conducted on the same computer with an Intel Xeon Silver 4110 CPU with 32 GB of RAM, using Python 3.8.15 with source code.  
In addition, we compare the proposed SSPCO with four benchmark methods to evaluate the effectiveness of SSPCO.  
1. GEO offloading computing (GOC): all missions are offloaded to GEO satellites for processing;
2. LEO offloading computing (LOC): all missions are processed on LEO satellites;
3. Genetic algorithm-based computation offloading (GACO) method [31]. GACO is an optimization method inspired by biological evolution mechanisms. It simulates processes such as biological inheritance, mutation, and selection to perform iterative searches for the optimal solution. In GACO, the fitness of individuals is evaluatedin each iteration according to a fitness function (identical to our objective function), with superior individuals having a higher chance of being selected to generate the next generation.
4. Particle swarm optimization-based computation offloading (PSOCO) method [32]. PSOCO is a swarm intelligence optimization algorithm that simulates the foraging behavior of bird flocks. In PSOCO, each solution in the search space is considered a 'particle', and all particles move iteratively in the search space to find the optimal solution. The velocity and direction of each particle's movement are influenced by its individual best experience and the overall best experience of the swarm. Like GACO, the fitness function of PSOCO is identical to our objective function.  
The experimental results first show the energy consumption and processing delay consumed by each mission of the five different computation offloading methods, as shown in Figures 5 and 6. Then, the effects of the number of missions obtained by different methods are analyzed, as shown in Figures 7 and 8. Finally, Figures 9 and 10 plot the effects of QoS constraints.  
Figure 8: Impact of the different number of missions on the average energy consumption.  
Figure 6: Comparison of processing delay of different computation offloading methods.  
Figure 7: Impact of the different number of missions on the total energy consumption.
## 2 Influence of Passing (Low) Beam Intensity on Detection Distances
### Test Subjects
#### 2.2.1 Results  
The mean measured detection distance for the full low beam was 62.8 m and was 52.6 m for the dimmed low beam. If the reaction time were taken out of this equation, assuming a reaction time of 500 ms Bullough [24], both mean detection distances would be 7 m longer. Both data sets, as seen in Figure 2, follow a normal distribution.  
Both headlight conditions represented in Figure 2 illustrate similar variations around the mean, which could be caused by differences in the immediate background around the test dummies, resulting in different contrasts and therefore in different detection distances. Nonetheless, there is little overall difference between the full and dimmed low beam conditions. A two-sample Kolmogorov-Smirnov test shows that the distances for each headlight condition were not significantly different, with \(p=0.08\) being the probability of rejecting the null hypothesis in error. However, a slight trend could be assumed from this data, given the expected longer detection distance found with the full-output low beam.
# Automatic Definition of Traffic Analysis Zones Based on Big Data
## 5 Process Flow
### Filtering the Records by the Number of Measurements  
The following module filters out sectors where the number of measurements is less than 20 due to data reliability concerns. This means that only one or two measurements occurred throughout the year for the given types of day in those sectors, making it impossible to reliably establish a time series. Of course, this value is freely adjustable and does not affect the further operation of the program.