esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Motivation and Background
### Threat and Network Model  
Threat model is based on the requirement that data gathered by sinks has to have _k_-anonymity property in which different sinks require different amounts of \(k\) value. This means individuals who are owner of sensed data do not want sinks to identify their records among other records of \(k\) individuals within a data collected in a specified time-frame through the quasi-identifier fields of records. For simplicity, it is assumed that one event record is generated for each individual within that time-frame. The required privacy levels of each sink differs so that, suppose there are \(n\) sinks, each \(i^{th}\) sink has a privacy level \(p_{i}\) where each level requires to share \(k_{i}\)-anonymous data with \(i^{th}\) sink and inequality of \(k_{1}<k_{2}...<k_{n}\) is valid.  
Sensors are clustered in separate sensor groups according to sensor localizations where each group has a group head sensor. In our network model which is shown in Figure 4, each sensor conveys its readings to group heads, they \(k\)-anonymizes data and send this _k_-anonymized output to each sink.  
If we compare network model of WSN with privacy preserved data collection model given in Figure 3, it can be stated that each group head sensor acts as a local trusted party and each sink is assumed an untrusted data collector.  
Threat model does not directly address confidentiality of data in transit. However, data which is sent from group head sensors to sinks have \(k_{n}\)-anonymity property. By eavesdropping on this data, attackers can obtain only \(k_{n}\)-anonymous data.  
Resistance to active attacks like malicious node attacks and false packet injection ones are out of the threat model scope.  
Figure 4: Network model.
# A Review of the Control Plane Scalability Approaches in Software Defined Networking
###### Abstract  
Recent advances in information and communications cloud-based services hold the potential to overcome the scalability and complex maintenance limitations of traditional networks. Software Defined Networking (SDN) surfaced as a promising paradigm to mitigate such limitations while offering flexible networks management. Particularly, SDN separates the control plane from the data plane to achieve abstraction of lower-level functionality, hence, allowing more efficient network management and utilization. However, SDN suffers from various performance and scalability problems leading to significant research efforts on maximizing the scalability of the control plane. This paper aims at reviewing different SDN controller scalability, topology-based and mechanism-based approaches, as well as discussing and analyzing how they attempt to solve the scalability challenge. Furthermore, this paper elaborates on the promising research trends and challenges. Our insights are also discussed to stimulate further research efforts addressing the control plane scalability in SDN.  
SDN; control plane; scalability; topological-based approaches; mechanisms-based approaches; parallelism optimization; routing-scheme optimization; machine learning optimization +
Footnote †: journal:  
_Future Internet_
## 5 Results and Robustness Analysis
### Robustness Analysis  
To assess the robustness of our findings, we conducted a contradiction test, as recommended by [PERSON] and [PERSON] [64]. This test involves applying the fsQCA algorithm to the negation of the outcome (i.e., a low propensity for failure) using the same solutions or causal configurations identified for the original outcome (a high propensity for failure). If a condition or configuration is sufficient for both the outcome and its negation, it indicates a contradictory or paradoxical relationship, potentially undermining the validity of the analysis. As shown in Table 7, the consistency and coverage values for the negation of the outcome are low, confirming the absence of contradictory relationships and supporting the robustness of our findings.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Antecedents** & **Configuration I** & **Configuration II** & **Configuration III** \\ \hline Solvency ratio (SOLL) & \(\bigotimes\) & & \\ \hline Operating revenue per employee (TPR) & \(\otimes\) & \(\otimes\) & \(\otimes\) \\ \hline Profit per employee (PPE) & \(\otimes\) & \(\otimes\) & \(\otimes\) \\ \hline GEAR (Indebtedness) & & & \\ \hline RTAS (Pre-tax profit) & & & \(\otimes\) \\ \hline Environmental impact (TRUCAM) & & & \\ \hline Net income (ROA) & & & \\ \hline Consistency & 0.867 & 0.734 & 0.890 \\ \hline Coverage & 0.181 & 0.085 & 0.022 \\ \hline Overall solution consistency & & 0.802 \\ \hline Overall solution coverage & & 0.223 \\ \hline \hline \end{tabular} Notes: Black circles (\(\bullet\)) indicate the presence of a condition, and circles with x (\(\otimes\)) indicate its absence. Large circles: core condition. Small circles: peripheral condition. Blank space: “don’t care” condition.  
\end{table}
Table 6: Sufficiency analysis of conditions for high propensity for failure (transformed solution).  
A further robustness check, as suggested by [PERSON] and [PERSON] [64], involves examining whether the negation of the identified causal configurations or solutions leads to the same outcome. If both a configuration and its negation are sufficient for the outcome, it indicates a potential contradiction or paradoxical relationship. Table 8 presents the results of this analysis, demonstrating that the negation of the configurations associated with a high propensity for failure does not lead to the same outcome. The low consistency and coverage values observed for the negated configurations further support the robustness of our findings and confirm the absence of contradictory relationships.
# A Novel Dynamic Approach for Risk Analysis and Simulation Using Multi-Agents Model
## 6 Generic Dynamic ABMS (GDABM) for Risk Analysis  
This section presents a proposed extension of ABM allowing the representation of the overall system behavior in normal and degraded modes in addition to the analysis of existing risks. This extension forms the new risk model called _Generic Dynamic Agent Based Model_ (GDABM) for risk analysis. Figure 4 shows the 4 components of GDABM:  
1. Behavioral Modes (BMs);
2. Failures Modes (FMs);
3. External Failure Agent Communication (EFAC);
4. Internal Failure Agent Communication (IFAC).  
Those components provide a standard pattern (or metamodel) for the system' agents. The first component is used to illustrate the system in its different operating modes. The second component allows risk situations to be identified with full details and components 3 and 4 are used to assess and represent the spread of risk from one element to another. GDABM represents the contribution of this work because these four components are the elements necessary and sufficient to model any agent behavior and also analyze and assess the risk according to the evolution of the system. The following sections discuss the components.
## 3 Results and Discussion
### Adsorption Studies
#### 3.2.5 Effect of Initial Concentration
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Biosorbent**} & \multicolumn{4}{c}{**Langmuir Parameters**} & \multicolumn{4}{c}{**Freundlich Parameters**} & \multicolumn{4}{c}{**Dubinin–Radushkevich**} \\  & \multicolumn{3}{c}{**Parameters**} & & & & & & & & & & \\  & \multicolumn{3}{c}{**C\({}_{\mathrm{H}}\) (m g\({}^{-1}\))**} & \multicolumn{3}{c}{**\(\mathrm{m}^{-1}\)**} & \multicolumn{3}{c}{**\(\mathrm{r}^{2}\)**} & \multicolumn{3}{c}{**\(\mathrm{x}^{2}\)**} & \multicolumn{3}{c}{**\(\mathrm{m}\)**} & \multicolumn{3}{c}{**\(\mathrm{Q_{e,cal}}\)**} & \multicolumn{3}{c}{**\(\mathrm{r}^{2}\)**} & \multicolumn{3}{c}{**\(\mathrm{x}^{2}\)**} \\  & \multicolumn{3}{c}{**C\({}_{\mathrm{H}}\) (m g\({}^{-1}\))**} & \multicolumn{3}{c}{**\(\mathrm{m}^{-1}\)**} & \multicolumn{3}{c}{**\(\mathrm{r}^{2}\)**} & \multicolumn{3}{c}{**\(\mathrm{x}^{2}\)**} & \multicolumn{3}{c}{**\(\mathrm{m}\)**} & \multicolumn{3}{c}{**\(\mathrm{m}\)**} & \multicolumn{3}{c}{**\(\mathrm{m}\)**} \\ \hline MO & 59.95 & 0.031 & 0.9709 & 0.0051 & 1.08 & 1.58 & 0.9525 & 0.3074 & 27.42 & 0.146 & 0.7970 & 0.0362 \\ M & 47.66 & 0.006 & 0.9352 & 0.0063 & 0.54 & 1.31 & 0.9855 & 0.0112 & 17.04 & 0.117 &The highest adsorption capacity for copper ions was observed for the MO material, likely due to its high number of adsorption sites or active functional groups. In contrast, the plant wastes of M and L exhibited 20-30% lower adsorption capacities than the original plant materials, indicating that essential oils contribute to the adsorption process through their functional groups. Despite this reduction, the waste materials still demonstrated good adsorption capacities, confirming their potential as effective biosorbents for copper ions. In this study, the maximum adsorption capacities for copper ions ranged from 28.08 to 59.95 mg g\({}^{-1}\). These values are comparable to those reported in the literature, confirming the suitability of all tested materials for copper removal from aqueous solutions. It should also be noted that the calculated adsorption energies for the four biomaterials ranged from 0.074 to 0.166 kJ/mol (Table 4). These results suggest that physisorption is probably the dominant mechanism for copper ion adsorption.
## 4 Results and Discussion
### Problem Structuring
#### 4.1.2 Physical Condition  
Used products may have degraded features with different degrees of damage. This indicator is typically calculated by fault features such as corrosions, cracks, wear, and so on. The damage level can be quantified and classified according to the degree of damage [32; 33; 42; 43].  
Component Completeness  
Completeness is defined by [PERSON] [44] as all the necessary parts being accounted for and included. It means that the incoming core should not be broken down into constituent parts and should be delivered entirely without missing parts. In case of any uncertainty as to the completeness of the incoming core, an extended inspection is carried out to verify the inner structure of the used product [25].  
Traceability of Identity  
Product identification and traceability are essential for the quality acceptance of used products. The core should have original equipment such as the manufacturer's identification number (e.g., manufacturer stickers) because of the wide variety of products,allowing the model, type, and parameters to be recognized [25]. The availability of a used product's identity, such as text, readable labels, and barcodes that are not missing or fading over the use phase of a product's life, allows easy recognition for quality sorting.  
Dimensional and Geometrical Tolerance  
The term dimensional tolerance is generally understood as the minimum and maximum values allowable for the dimension of the parts to work properly. Meanwhile, geometric tolerance is a significant assessment factor that depends on the consistency of the body shapes of used products. When classified, the sorted parts of used products will be reprocessed based on the quality loss degree according to an allowable tolerance. If the used products have the highest deformation from ideal dimension and shape (exceeding the acceptable tolerance), they cannot be reconditioned to their original performance by remanufacturing and they are then recycled [45].
# Research on Pupil Center Localization Detection Algorithm with Improved YOLOv8
## 2 YOLOv8 Model  
YOLOv8 is a new network released by Ultralytics in early 2023, proposed based on the YOLOv5 model. It comprises four models: YOLOv8l, YOLOv8m, YOLOv8s, and YOLOv8n [24]. Among them, the YOLOv8n network is the one with the lowest complexity in the YOLOv8 series. It maintains high detection accuracy while having faster inference speed and easier satisfaction of lightweight requirements. Hence, improvements have been made to the model based on YOLOv8n, with its structure shown in Figure 1.  
The YOLOv8 network structure [25; 26; 27] comprises four parts: input, backbone, neck (for feature enhancement), and prediction. Firstly, the backbone module continues to utilize the CSP concept for feature propagation, while introducing the C2f module (CSP bottleneck with two convolutions) module to replace the C3 (CSP bottleneck with three convolutions) module in the YOLOv5 model. Compared to the YOLOv5 model released in 2020, the C2f module employs a multi-branch flow design, providing the model with richer gradient information, enhancing its feature extraction ability, and improving the network learning efficiency. The SPPF module is retained, which can obtain rich gradient flow information while optimizing the network structure. Secondly, the neck module maintains the FPN + PAN structure, enabling multi-scale feature fusion capabilities. Finally, in the prediction section, the original coupled head is changed to a decoupled head, introducing the decoupled head to separate classification and regression into two separate structures, improving the model's convergence ability. A schematic of the YOLOv8 improvement module is shown in Figure 2. YOLOv8 utilizes an anchor-free network, which improves the imbalance issue between positive and negative samples through dynamic allocation of positive and negative samples.  
Figure 1: YOLOv8 network architecture.
# Software Engineering Techniques for Building Sustainable Cities with Electric Vehicles
## 4 Integrating Electric Vehicles into Smart Cities: Challenges and Opportunities
### Open-Source Software and Standards Opportunities
It enables efficient communication between devices and systems, and it is designed to be scalable and secure. * OpenADR: OpenADR (open automated demand response) is an open-source standard for demand response management. It can be used in smart city energy management systems, as well as in electric vehicle charging infrastructure, to manage energy demand and ensure grid stability. * Open charge point protocol (OCPP): OCPP is an open-source protocol for communication between electric vehicle charging stations and central management systems. It enables interoperability between different charging station manufacturers and management systems, which is essential for the growth of the electric vehicle charging infrastructure. These are just a few examples of the many open-source systems that are used in smart cities and electric vehicles. Open-source systems provide developers with a flexible and cost-effective way to build smart city applications, and they can help accelerate innovation and collaboration in the smart city ecosystem. OSS can help develop robust and secure software solutions that enable the seamless integration of EVs into smart cities, mitigating challenges through collaboration, standardization, interoperability, cybersecurity, and cost-effectiveness [80]. Open-source systems are an essential part of building smart cities, providing developers with a flexible and cost-effective way to accelerate innovation and collaboration in the smart city ecosystem.
## 3 Results and Discussion
### Discussion on Sensitivity Analysis of Food Waste Ratio  
From Figure 8, it could be seen that as the ratio of food waste increased, CO\({}_{2}\) emissions decreased in all patterns, but annual costs increased. The trade-off relationship between CO\({}_{2}\) emissions and annual expenses due to changes in the food waste ratio was evident, and Case 4 had an advantage in most conditions. However, comparing Cases 2 and 4, it could be seen that when the food waste ratio was close to 50%, Case 2 had an advantage in annual expenses.  
In Case 4, when the food waste ratio was 10%, the heat sales expenses amounted to 2.28 billion yen/Y, while when the food waste ratio was 50%, the heat sales expenses amounted to 1.18 billion yen/Y. In addition, electricity sales expenses at 10% amounted to 197 million yen/Y, while the expenses amounted to 676 million yen/Y at 50%. As a result, in Case 4, the annual cost when the food waste ratio was 1.07 billion yen/Y at 10%, whereas when the food waste ratio was 50%, it was 1.94 billion yen/Y; this was the largest increase in annual expenses. As the food waste ratio increased, the calorific value of waste decreased, and the amount of heat obtained during waste incineration decreased. Hence the heat sales expenses due to heat utilization decreased. As the food waste ratio increased, the amount of biogas power generation increased, but the amount of decrease in heat sales expenses was larger than the amount of increase in electricity sales expenses due to methane fermentation; hence, the annual cost increased.  
Case 4 used heat, and the annual cost changed significantly considering the change in food waste ratio. This suggests that if the waste quality is unstable, the annual cost may also be unstable. In addition, since it is necessary to supply stable steam to supply heat to the Industrial Zone, if the waste quality is unstable, it may greatly impact the supply chain. Accounting for changes in waste quality due to population decline is necessary when implementing a plan like Case 4.  
The results of this evaluation indicated that the annual cost tended to increase as the food waste ratio increased. This tendency reflected the result calculated by utilizing the estimation formula of construction expenses considering land and personnel costs when introduced in Japan or electricity sales and price of heat sales in Japan as shown in Tables 4 and 5; similar trends are not always seen in Asian countries. However, when heat is mainly used for peripheral facilities such as industrial facilities, the lack of heat in waste incineration due to the high food waste ratio can be an issue.
# Link Budget Analysis with Laser Energy for Time Transfer Using the Ajisai Satellite
## 3 Simulation Results
### Results  
To analyze the effects of geometric configuration on the laser link budget and to find the optimal laser energy at the transmitting station for the TLTT application, we simulated  
\begin{table}
\begin{tabular}{c c c c c c} \hline  & **Parameters** & **Sejong** & **Geochang** & **Beijing** & **Koganei** \\ \hline \multirow{4}{*}{Laser} & \(\lambda\) & Wavelength & 532 nm & 532 nm & 532 nm & 532 nm \\  & \(E_{t}\) & Pulse energy & 2.5 mJ & 15 mJ & 1 mJ & 50 mJ \\  & Pulse width & 50 ps & 9.2 ps & 200 ps & 35 ps \\  & Repetition rate & 1 KHz & 60 Hz & 1 KHz & 20 Hz \\ \hline \multirow{4}{*}{Telescope} & \(a_{t}\) & Tx Primary mirror & 0.1 m & 1.0 m & 0.16 m & 1.5 m \\  & \(b_{t}\) & Tx Secondary mirror & - & 0.25 m & - & \\  & \(\eta_{t}\) & Transmit optic efficiency & 92.3\% & 75\% & 70\% & 30\% \\  & \(\theta_{d}\) & Beam divergence angle & 5–200 arcsec & 8 arcsec & \textless{103 arcsec} & 5 arcsec \\  & \(\theta_{p}\) & Beam pointing error & \textless{5 arcsec} & \textless{4 arcsec} & \textless{5 arcsec} & \textless{5 arcsec} \\  & \(a_{r}\) & Rx Primary mirror & 0.4 m & 1.0 m & 0.60 m & 1.5 m \\  & \(b_{r}\) & Rx Secondary mirror & 0.1 m & 0.25 m & - & - \\  & \(\eta_{r}\) & Receiver optic efficiency & 64.9\% & 35\% & 70\% & 10\% \\ \hline Detector & \(\eta_{q}\) & Quantum efficiency & 20\% & 20\% & 20\% & 15\% \\ \hline \multirow{3}{*}{Position} & \multicolumn{2}{c}{Longitude} & 127.3029E & 127.9201E & 115.8920E & 139.489E \\  & \multicolumn{2}{c}{Latitude} & 36.5210N & 35.5902N & 39.6069N & 35.710N \\ \cline{1-1}  & \(h_{t}\) & Altitude & 176.415 m & 934.063 m & 82.300 m & 121.820 m \\ \hline \end{tabular}
\end{table}
Table 2: Parameters of 4 SLR stations in the target network [18].  
Figure 2: Target ground network for TLTT implementation with three combination cases between laser transmitting station (Sejong) and receiving stations on the google maps.  
the link budget and the detection probability for 30 days from 29 Mar. 2021, based on the ephemeris dataset of the Ajisai satellite.  
The parameters related to the geometric configuration, including slant range, zenith angle and phase angle, were calculated, along with the position of the Ajisai satellite estimated from the SGP4 orbit propagator, the Two Line orbital Elements (TLE) of Ajisai and the Earth Orientation Parameter (EOP) [19; 20; 21]. Considering a clear sky, we adopted the atmospheric attenuation coefficient of \(\sigma\)(\(\lambda\), V, 0) = 0.25 for 1.5 km of visibility scale height, the visibility scale height of \(h_{scale}\) = 1.2 km and cirrus cloud thickness of \(t_{cc}\) = 1341 m in Equations (2) and (3). In total, 102 night passes have the common view for all three geometric combinations consisting of one transmitting (Sejong) station, three receiving stations and the Ajisai satellite, for 20 degrees of elevation cut off angle.
# SLMAS: A Secure and Light Weight Mutual Authentication Scheme for the Smart Wheelchair
## 4 Security Evaluation of the Proposed Methodology
### Informal Analysis
#### 4.1.6 Smart Node Capture Attack  
Assume \(\mathcal{A}\) has successfully seized a smart node and has obtained its saved and additional data: \(\{PR_{ID_{n}},SN_{ID_{n}},SKEY_{TA,n}\}\). The proper master key \(MSK\) and cover-up key \(X_{n}\) cannot be calculated by \(\mathcal{A}\), irrespective of whether it receives information, because the master key \(MSK\) and mask key \(X_{n}\) are encoded to be resilient to collisions with the one-way hash function. \(\mathcal{A}\) cannot construct a further communication session, as the session key is required for further sessions, and the session key is made up of random numbers and pseudonyms. As a result, the proposed approach can withstand attempts to capture smart nodes.
# Effect of Environmental Parameters on Structural Health Status Assessment Using OMA Techniques
## 2 Dynamic Monitoring  
Dynamic monitoring of a structure is the process of assessing structural characteristics through the study of induced vibrations [1]. It is based on the hypothesis that a change in the mechanical characteristics of the structure due to damage or deterioration causes a more or less appreciable change in the structural dynamic behavior [1]. An extensive description and classification on modal analysis methods is given in [1] and [7]. Modal parameters, such as natural frequencies, mode shapes and their variants, and damping are commonly used in SHM. However, the use of natural frequencies alone as damage indices has some limitations; they are quite insensitive to local damage and the number of available frequencies is limited--generally less than 10 [8].  
For multi-degree-of-freedom (MDDF) systems, the dynamic behavior can be described by a system of differential equations in the time domain, shown in expression (1).  
\[[M]\{\ddot{y}(t)\}+[C]\{\dot{y}(t)\}+[K]\{y(t)\}=\{f(t)\} \tag{1}\]  
where: \(\{\ddot{y}(t)\}\), \(\{\dot{y}(t)\}\) and \(\{y(t)\}\) are the vectors of acceleration, velocity and displacement, respectively; \([M]\), \([C]\) and \([K]\) are the mass, damping and stiffness matrices, respectively; and \(\{f(t)\}\) is the forcing vector. Expression (1) is valid for a linear, invariant and \([K]\) are constant), observable system with viscous damping. It describes the dynamic behavior of a system with N degrees of freedom. The equation of motion, which is coupled in this formulation, can be decoupled under the assumption of viscous proportional damping using the principle of orthogonality between the modes of vibration and solving an eigenvalue problem. This leads to a system of algebraic equations and the modal model [1; 2; 7; 9; 10].  
The dynamic model at the basis of the most used OMA methods, which is proposed in the following, is represented by the model in phase space. This is obtained through a series of mathematical manipulations of the spatial model shown in (1) [1]. In the phase-space model, the dynamic behavior of the structure is described through three fundamental matrices: the state matrix [A], the controllability matrix [C] and the observability matrix [O].  
A fundamental role for the application of methods based on operational modal analysis is played by the theories of signal analysis [1; 2]. The roles played by statistical operators, especially second-order operators (correlation functions), and by transformations from time to frequency domain, i.e., the Fourier transform, are fundamental.