esa-sceva/satcom-chunk-collection huggingface dataset:
# Reliable Design for a Network of Networks with Inspiration from Brain Functional Networks
## 3 Network of Networks in Virtualized Networks  
### Interdependence of Virtualized Networks  
In this study, we assume an interdependent layered network where a single PN is virtualized by network slicing, and multiple VNs are constructed on the PN as shown in Figure 3. A PN comprises physical nodes (P-nodes) and physical links (P-links). Similarly, virtual nodes (V-nodes) and virtual links (V-links) form a VN. Because of the virtualization of the PN, each V-node corresponds to exactly one P-node, while a V-link comprises multiple P-nodes and P-links that realize the shortest path connecting the two endpoint V-nodes. We assume that the connectivity structure of the PN is given by the infrastructure provider, whereas each VN realizes its own connectivity based on the requests of the service provider. In Figure 3, the P-node and V-node in VN \(k\) with common index \(i\) are represented as \(r_{i}^{P}\) and \(r_{i}^{V_{k}}\), respectively. The P-interface \(j\) of P-node \(i\) and the V-interface \(j\) of V-node \(i\) in VN \(k\) are represented as \(i_{i,j}^{P}\) and \(i_{i,j}^{V_{k}}\), respectively. Refer to Table 2 for the description of the variables defined for the PV-NoN model and the evaluation in this paper. Figure 2: Expression of collective influence (CI). \(\text{CI}_{i}\) is depicted in Network 1. Ball\((i,l)\) is the area of influence of node \(i\), and \(\partial\text{Ball}(i,l)\) corresponds to the edge of Ball\((i,l)\). Networks 1 and 2 are interdependent via an internetwork link. Upon virtualization based on network slicing, the physical resources on the PN are shared among the VNs, thereby making the PN and VNs interdependent. Various services are being provided continuously over the VN, and environmental changes may occur at any instant because of traffic fluctuations. Consequently, our aim in this study is to model the VN availability under fluctuating traffic while considering the interdependence among the VNs caused by resource sharing. Although various physical resources on the P-nodes can be considered for virtualization (e.g., CPU, memory, storage, network I/O), we focus on the packet buffer memory (hereinafter referred to as the buffer) and network I/O because they are influenced directly by the traffic conditions. Consequently, we segment the P-nodes into physical interfaces (P-interfaces) and model the state of each P-interface taking the buffer and network I/O into consideration. The P-interface and the virtual interface (V-interface) are distinguished as shown in Figure 3.
## 3 Results
### MAS Performance  
No significant differences were reported for peak HR (F\({}_{(4,70)}\) = 1.7; \(p\) = 0.164) and CR-10 (\(\chi^{2}\)(4,N=20) = 4.2; \(p\) = 0.378) between protocols (RTT = 188 \(\pm\) 13 ppm and 8.9 \(\pm\) 0.5; VBT\({}_{60-10}\) + RTT = 188 \(\pm\) 14 ppm and 8.7 \(\pm\) 0.5; VBT\({}_{60-30}\) + RTT = 186 \(\pm\) 10 ppm and 8.8 \(\pm\) 0.6; VBT\({}_{80-10}\) + RTT = 190 \(\pm\) 10 ppm and 8.7 \(\pm\) 0.4; VBT\({}_{80-30}\) + RTT = 187 \(\pm\) 11 ppm and 8.9 \(\pm\) 0.6, respectively). A significant main effect of protocol (F\({}_{(4,72)}\) = 7.3; \(p\) < 0.001) and sex (F\({}_{(1,18)}\) = 8.3; \(p\) = 0.010) was observed for MAS performance. The protocol \(\times\) sex interaction did not reach statistical significance (F\({}_{(4,72)}\) = 1.0; \(p\) = 0.422). The main effects revealed that the MAS was significantly higher (i) for RTT than VBT\({}_{60-30}\) + RTT (\(p\) < 0.001; ES = 1.71; \(\Delta\) = 3.8%), VBT\({}_{60-10}\) + RTT (\(p\) = 0.006; ES = 0.93; \(\Delta\) = 2.8%), VBT\({}_{80-10}\) + RTT (\(p\) = 0.008; ES = 0.93; \(\Delta\) = 2.7%), and VBT\({}_{80-30}\) + RTT (\(p\) = 0.019; ES = 0.77; \(\Delta\) = 1.9%) (Figure 2), and (ii) for men than women (\(p\) = 0.010; ES = 1.34; \(\Delta\) = 13.0%).  
Figure 2: Comparison of the maximal aerobic speed between protocols. Data are depicted as means and standard deviation, whereas each point represents the individual data of each man (black circles) and woman (white circles). RTT, running track test; VBT\({}_{60-10}\) + RTT, velocity-based training (VBT) with 60% of one-repetition maximum (1 RM) and a velocity loss in the set of 10% followed by RTT; VBT\({}_{60-30}\) + RTT, VBT with 60% of 1 RM and a velocity loss in the set of 30% followed by RTT; VBT\({}_{80-10}\) + RTT, VBT with 80% of 1 RM and a velocity loss in the set of 10% followed by RTT; VBT\({}_{80-30}\) + RTT, VBT with 80% of 1 RM and a velocity loss in the set of 30% followed by RTT. *, significantly lower than RTT (\(p\) < 0.05; analysis of variance with Bonferroni correction).
## 3 Experimental Procedure  
### Absorption Test  
The experimental devices are depicted in Figure 4, including the bubble column, tubing pumps for gas-flow and liquid-flow, a mass flow controller, pH-meter, CO\({}_{2}\) meter, gas heating chamber, and cooler. To start with, the desired mixed amine concentration was prepared using distilled water. Next, the flow rate of carbon dioxide and nitrogen was adjusted using a mass flow controller into the proportion of 15% of CO\({}_{2}\), maintaining the gas inlet temperature at the bottom of the scrubber at 50 \({}^{\circ}\)C. The mixed amine was placed into the scrubber after the desired temperature and CO\({}_{2}\) concentration were achieved, and the experiment was started. During the experimentation, the pH of the solution, liquid temperature, gas inlet temperature, gas outlet temperature, pressure, and CO\({}_{2}\) concentration were recorded every 5 min. The liquid at the outlet was also withdrawn for titration to observe the concentration of carbonate in the scrubbing solution. At the end of steady-state operation, the liquid input was closed, and the solution was withdrawn to measure the volume of liquid (\(V_{L}\)) in the scrubber using a tubing pump. Using the measured data, including \(P\), \(y_{A1}\), \(y_{A2}\), T, and \(V_{L}\), all the indicators can be evaluated.
## Appendix A  
The transmitted frame is made of header and payload parts. The header part stores the information about the sent frame and consists of fixed PN preamble for the frame synchronization, frame sequence number, end frame flag, header, payload and padding lengths. The payload part is a combination of data and padding bits. Padding bits are used to fill the frame to a predefined bit length when the sum of header and data bits is smaller than the frame length. The structure and length of the transmitted frame in bits are shown in Table 11.  
The total length of a frame of 1013 bits was chosen to easily encode and decode it using a (1023, 1013) Hamming code.  
The script checks if the preamble is of even length and if not, add 0 to the end of it to make its length even. Frame sequence number stores the information about the frame's number which is the image column's number. The end frame flag shows if the transmitted frame is the last one. Header, payload and padding lengths store the information about the lengths of these parts of the frame.  
Data consists of image column pixels in binary form. To create the frame's data, the image is loaded and reshaped to a single column of data. Then it is converted to binary format from decimal format and after that reshaped into a row as seen in Figure 11. Conversion to binary format is carried out with most significant bit (MSB) on the left side.  
Generated frame in binary format is sent as an input to the Simulink FM-ACSK transmitter system and transmitted via PlutoTM SDR.
# Ensuring Energy Efficiency of Air Quality Monitoring Systems Based on Internet of Things Technology
## 1 Introduction
### The Role of Internet of Things Technology in Air Quality Monitoring Systems  
The Internet of Things is actively integrated into various industries, and air quality monitoring is no exception. With the ability to connect physical objects and collect data in real time, IoT opens up new possibilities for air quality monitoring. Unlike traditional systems, which are limited to readings from fixed monitoring stations, the IoT allows sensors to be placed at many points within a city or region, creating a detailed picture of air pollution.  
One of the key advantages of IoT systems is the collection of data in real time. Sensors can capture various pollutants such as pollen, nitrogen dioxide, or fine particles at high frequency, which allows the system to quickly detect short-term peaks in pollution. This approach also contributes to the creation of more accurate air quality forecasting models, which allows for early warnings of potential environmental threats to the population. In addition, IoT systems are able to provide personalized information about air quality, taking into account the user's location and their individual needs. Data collected by IoT networks can be made available to a wide range of users through mobile applications and web portals, which contributes to informed decision-making about health and activity.  
The use of IoT in air quality monitoring covers a variety of scenarios. It can involve controlling pollution levels in urban areas, monitoring industrial emissions, or assessing air quality in offices, schools, and residential buildings. However, the implementation of such systems is accompanied by challenges. One of the main problems is the need to standardize sensors to ensure interoperability between the different systems. In addition, the issues of protecting data from unauthorized access and developing energy-efficient solutions for data transmission remain important.  
Despite these challenges, the potential for IoT to monitor air quality is significant. Further research and innovation will enable the creation of even more accurate, reliable, and affordable systems that will help improve people's quality of life and contribute to environmental conservation.  
The proposed integrated efficiency index (IEI) introduces a novel quantitative framework to evaluate sensors by balancing functionality, energy efficiency, and cost, addressing the lack of standardized metrics for IoT system design and supporting scalable, sustainable deployments in smart cities.
# The Effect of Chemical Activating Agent on the Properties of Activated Carbon from Sago Waste
## 3 Results
### Surface Groups  
Figure 5 illustrates the Fourier-transform infrared spectra of activated carbon from sago waste with various activating agents, and Table 4 shows the list of Fourier-transform infrared peaks and their assignments. There is a broad absorption around wavenumber 3650-2500 cm\({}^{-1}\) for ZnCl\({}_{2}\) and KOH activated carbon, indicating O-H bonds. This may be due to the occurrence of phenol and carboxylate functional groups [51]. The pattern was confirmed by the presence of C-O stretching vibration of phenol structure at 1072 cm\({}^{-1}\), and 1135 cm\({}^{-1}\) for ZnCl\({}_{2}\) and KOH activated carbon, respectively. However, due to the absence of O-H bonds in H\({}_{3}\)PO\({}_{4}\) and KMnO\({}_{4}\) activated carbon, the prominent band of the C-O functional group was interpreted as ether at wavenumber 1164 cm\({}^{-1}\) and 1163 cm\({}^{-1}\), respectively [52]. In addition, the samples exhibited a broad absorption band in the area 1555-1540 cm\({}^{-1}\), indicating a stretching vibration of the C=C aromatic ring structure [53]. In the fingerprint area, there were several absorption peaks assigned to C-H bending from the alkanes group at a wavenumber less than 1000 cm\({}^{-1}\). Furthermore, the presence of functional groups on the carbon of four different activating agents were also confirmed, this includes O-H, C=C, C-O, and C-H groups, indicating hydroxyl, aromatic, carboxyl, and alkane, respectively [52].  
Figure 4: (**a**) Thermogravimetric and (**b**) thermogravimetric derivative curves for activated carbons from sago waste prepared using various chemical activating agents.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Activating**} & \multicolumn{4}{c}{**Proximate Analysis (Weight \%)**} \\ \cline{2-5}  & **Moisture** & **Volatile Matter** & **Fixed Carbon** & **Ash** \\ \hline H\({}_{3}\)PO\({}_{4}\) & 15.20 & 52.64 & 2.56 & 29.60 \\ ZnCl\({}_{2}\) & 13.60 & 48.90 & 40.90 & 23.80 \\ KMnO\({}_{4}\) & 17.04 & 42.92 & 14.02 & 26.02 \\ KOH & 0.92 & 44.40 & 43.78 & 10.90 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Proximate analyses of activated carbons from sago waste prepared using various chemical activating agents.
# A Systematic Review of Cybersecurity Risks in Higher Education
## 8 Vulnerabilities in HE
### Administrative and Cultural Vulnerabilities in HE
#### 8.1.4 Missing Management Support, Resources, and Finance  
[PERSON] [8] states in his Policy Note that \"_Cyber risk cannot be delegated away from the governing body and the executive management needs to be held accountable for ensuring that informed and appropriate decisions are being made which meet or exceed the expectations of any organization's stakeholders - and the law_\". This is reflected in [PERSON] and [PERSON] [47], were one interview subject said: \"We can't do anything without their [management] authorization they have to support us in implementing information security in the organization.\" There is a tight connection between management support and financing and resources, and a lack of which can be a root cause of several vulnerabilities in HE. [PERSON] and [PERSON]'s case studies unveiled that colleges and universities in Malaysia had insufficient resources to adapt and implement sufficient security policies, caused by limited final budgets allocated to information security. FireEye [5] also cites financial challenges as present in western HEI, writing that \"_The central IT department's share of research grant money is often not enough to secure the data from that research. Despite this mismatch, central IT is still tasked with providing the right level of network security controls_\". FireEye points to that it is simply not enough funding to do the job and that most schools cannot afford to hire the experts they need to fill critical security roles. Resulting in an inability to detect, prevent, and respond to attacks. Additionally, CyberEdge [57] claimed that the educational industry suffers the biggest IT security skills shortage among the 19 surveyed different industries. Approximately 91.3% of the participants from HE experienced a shortage of qualified IT security talents, an increase of 4% from the 2018 report [56]. The UNIT report [6] also documented lacking human security resources and capacity, where 19 of the 21 interviewed institutions described insufficient investments in human infosec and privacy personnel to meet the sector demand. [PERSON] [42] also highlights the HE industry as under-funded within information security. He argues that limited budgets for information technology infrastructure cause a high vulnerability in HE, where security investments lose out to equipment needed for school and labs.
# Combining BioTRIZ and Multi-Factor Coupling for Bionic Mechatronic System Design
## 3 Engineering-to-Biology Mapping  
### Problem Description  
Vision, as a crucial avenue for humans and numerous vertebrates to gather environmental information, has garnered extensive research in biology [31; 32; 33; 34; 35]. In an effort to endow robots with comparable perceptual abilities, bionic active vision systems have become a significant focus of interest in recent years [36; 37].  
\"Eyes in the front, the animal hunts. Eyes on the side, the animal hides\" encapsulates the inherent trade-off between a wide vision field and precise visual localization in nature. Predators, with their powerful stereoscopic vision, can pinpoint the exact location of their prey, but may sacrifice a comprehensive awareness of their surroundings. Conversely, prey animals possess a broad vision field that allows them to detect potential predators in their environment, but this comes at the cost of developing binocular stereo vision, making precise localization nearly impossible.  
The majority of existing bionic active vision systems adopt a binocular structure, and are utilized for environmental monitoring, situational awareness, object detection, and tracking, among other applications [38; 39; 40; 41]. These systems typically emulate the visual system of a specific vertebrate [42; 43; 44], either by modeling binocular stereo vision for 3D measurements or by replicating a wide vision field for environmental monitoring and scene comprehension. Additionally, there are visual systems that enable switching between these two functions through mimicking eye movements, offering a compromise between the two, but they cannot simultaneously acquire both global vision and precise information [45].  
The application environment of machine vision is increasingly unstructured and complex. Our objective is to devise a bionic vision system that can simultaneously maintain a global vision field and acquire accurate local information. To achieve this, we incorporate a bio-inspired design paradigm that implements engineering-to-biology mapping and biology-to-engineering inversion during the design process.
# Improved ISR for IEEE 802.11 ah Nonlinearity Compensation via Adjustable Constellation Borders
## 3 Improved ISR
### Decision Radius Optimization  
To optimize the performance of the proposed scheme, we used Monte Carlo simulation for 802.11 ah examples of \(\mathcal{M}=16\), 64, 256 with 3, 4, 5 dB input back-off (IBO), respectively. Note that although \(\mathcal{M}=2\), 4 are also defined in the 802.11 ah modulation and coding scheme (MCS) table, both modulations are not considered in this study, because their improvement should be evaluated in low IBO (e.g., 0 dB), whose performance of power spectral density (PSD) exceeds the 802.11 ah spectral mask [19]. The converged number of iterations (\(i_{c}\)) and converged BER (\(BER_{i_{c}}\)) are evaluated when \(\alpha=0.01\):0.01:1 with an appropriate convergence threshold \(\delta\) (the iteration process stops when \(BER_{i_{c}}-BER_{i_{c}+1}\leq\delta\)) and differently normalized SNRs (\(E_{b}/N_{0}=30\), 40, 50 dB).  
Figure 3a-c demonstrate the overall performance evaluation of the converged BER and number of iterations for \(\mathcal{M}=16\), 64, 256, respectively. With a better understanding of the time domain, the converged BER can be optimized when \(E_{b}/N_{0}\) increases. For \(\mathcal{M}=16\), there are three candidates of sub-optimal \(BER_{i_{c}}\) (\(\alpha=0.47\), 0.58, 0.73) for \(E_{b}/N_{0}=50\) dB, and we chose \(\alpha=0.73\), as it consumes the lowest number of iterations. The chosen \(\alpha\) can achieve optimized \(BER_{i_{c}}\) and \(i_{c}\) for all SNRs, although the optimization of \(BER_{i_{c}}\) is not obvious when the SNR is low. For higher order modulations (i.e., \(\mathcal{M}=64\) and 256), the improvement decreases due to the shrinking of the decision region. Nevertheless, the proposed scheme does not degrade the performance of ISR in any SNR and can reduce  
Figure 2: Decision region of data subcarriers for ISR. (**a**) Conventional constellation borders. (**b**) Adjustable constellation borders.  
the BER without additional computational complexity, as the alpha can be pre-computed while being independent of the data to transmit.
# Self-Organizing Control of Mega Constellations for Continuous Earth Observation
## 2 Materials and Methods
### Continuous Coverage Constraint of Mega Constellation
#### 2.1.3 Intersatellite Motion Constraints
\begin{split} A_{2}&=-2\text{sin}^{2}\Big{(}\frac{2\pi}{S} \Big{)}\text{cos}^{2}\Big{(}\frac{2\pi}{S}\Big{)}\text{cos}^{2}\Big{(}\frac{2 \pi}{P}\Big{)}-\text{cos}^{2}\Big{(}\frac{2\pi}{S}\Big{)}\text{cos}^{2}2\alpha_ {\text{max}}+\text{cos}^{2}\Big{(}\frac{2\pi}{P}\Big{)}\text{cos}^{2}2\alpha_{ \text{max}}\\ B_{2}&=-\text{sin}^{2}\Big{(}\frac{2\pi}{S}\Big{)} \Big{[}\text{cos}^{2}\Big{(}\frac{2\pi}{S}\Big{)}\text{cos}^{2}\Big{(}\frac{2 \pi}{P}\Big{)}-\text{cos}^{2}2\alpha_{\text{max}}\Big{]}\end{split}  \tag{17}\]  
The detailed derivation steps and the proofs of real roots are given in Appendix A. We denote \(\Omega_{t}\) as the target RAAN under \(J_{2}\) perturbation of the designed orbital plane. Then the drift range of \(\Omega_{i}\) of all coplanar satellites away from \(\Omega_{t}\) can be written as  
\[ \begin{split}\delta\Omega_{i}&=\Omega_{i}-\Omega _{t}\geq-\frac{1}{2}\arcsin\Big{(}\sqrt{\frac{B_{2}}{A_{2}}}\Big{)}=\delta \Omega_{\text{min}}\\ \delta\Omega_{i}&=\Omega_{i}-\Omega_{t}\leq\frac{1 }{2}\arcsin\Big{(}\sqrt{\frac{B_{2}}{A_{2}}}\Big{)}=\delta\Omega_{\text{max}} \end{split}  \tag{18}\]  
We denote the position vectors of two adjacent coplanar satellites in the geocentric inertial frame as \(\mathbf{r}_{i}\) and \(\mathbf{r}_{i+1}\). The constraint of \(\theta\) can be transformed to the relative distance constraint \(d_{i,i+1}\) by means of cosine theorem. \[ \begin{split} d_{i,i+1}&\geq\sqrt{\|\mathbf{r}_{i} \|^{2}+\|\mathbf{r}_{i+1}\|^{2}-2\|\mathbf{r}_{i}\|\|\mathbf{r}_{i+1}\|\cos \theta_{\text{min}}}\\ d_{i,i+1}&\leq\sqrt{\|\mathbf{r}_{i}\|^{2}+\| \mathbf{r}_{i+1}\|^{2}-2\|\mathbf{r}_{i}\|\|\mathbf{r}_{i+1}\|\cos\theta_{ \text{max}}}\end{split}  \tag{19}\]  
Considering that mega constellations are nearly circular orbits, the position vectors in the geocentric inertial frame are substituted by semi-major axis \(a\), given in Equation (20). \[ \begin{split} d_{i,i+1}&\geq\sqrt{2a^{2}(1-\cos \theta_{\text{min}})}=d_{\text{min}}\\ d_{i,i+1}&\leq\sqrt{2a^{2}(1-\cos\theta_{\text{max}} )}=d_{\text{max}}\end{split}  \tag{20}\]  
To sum up, the intersatellite motion constraints that every two adjacent coplanar satellites should obey during their lifetime are presented in Equations (18) and (20).
# Noise-Free Security Assessment of Eviction Set Construction Algorithms with Randomized Caches
## 5 Analysis of Eviction Set Construction Algorithms
### Evaluation Framework
#### 5.1.2 Simulation Example  
To simulate the behavior of an application in our cache framework, we first built a memory hierarchy by specifying the configuration of each cache level. We needed to specify the addressing used for each cache level, their cache sizes, the size of the cache lines, the number of ways, the replacement policy, etc. To understand how this worked, let us demonstrate the example in Listing 1, which shows the implementation of the single address elimination method to find an eviction set for the _target_ address in the candidate set. At each iteration, the single address elimination function called the test eviction function (line 12) to verify that the candidate set still evicted the target address.  
To interact with the cache framework, the single address elimination implementation used memory access instructions, such as the load and the write instructions. Both instructions could be used to determine the cache status (hit or miss) of the accessed address. For example, in line four, the instruction _load_state_ was used to return the cache status of the specified address (hit or miss).  
```
1 deftest_eviction(self,target:int,eviction,set:list)->bool:
2#First,-thetargetadoreshouldbeachedtoverifyifthegiveneviction_set
3#arraycanevictintornot.
4 self_cache_simulator.load(target)
5
6 self.cache_simulator.load(eviction_set)
7
8#Thetargetadoresisreaccessed,andthesimulatorreturnsits
9#cachestate(TrueifitsasHit,otherwiseitreturnsFalse).
10 hit=self.cache_simulator.load_state(target)
11
12 returnnothit
13
14 defsingle_address_elimination(self,target_address:int,candidate_set:list)->list:
15 whilelen(eviction_set)<self.warg:
16 removeanaddressfromthecandidate_setarray
17 candidate_address=candidate_set.pop()
18
19#checkifthecandidatesetstillevictingthetargetedaddressx\"\"\"
20 miss=self.test_eviction(target_address,candidate_set+eviction_set)
21
22#Ifthetestfails,itmeansthatthe\"candidate_address\"iscongrenent
23#dotheargetaddressandshouldbeaddedtotheevictionsetarray
24 ifnotmiss:
25
26
27 returneviction_set
```  
Listing 1: The implementation of the single address elimination algorithm with our cache simulation framework.