esa-sceva/satcom-chunk-collection huggingface dataset:
# Reference Models for Digital Manufacturing Platforms
## 3 Reference Models Alignment
### Industrial Internet Consortium Reference Model
#### 3.4.3 Functional Viewpoint  
The functional viewpoint decomposes a typical IIoT System into functional parts, to describe the system structure and interrelations, interfaces, and interactions between its functional components, as well as with external systems. Thus, the functional viewpoint establishes five functional domains (industrial control, operations, information, application, and business), five system characteristics (safety, security, resilience, reliability, privacy, and scalability), and four crosscutting functions (connectivity, distributed data management, industrial analytics, and intelligent and resilient control), described as:  
* Functional Domains: Decomposition of the distinct functionalities of a distributed industrial control system into physical domains:
* Control Domain: Functions performed by industrial control systems, mainly reading sensor data (sense) and control through actuators (actuation).
* Operations Domain: Functions for management, monitoring and optimization of control domain functionalities (prognostics, optimization, monitoring and diagnostics, deployment, and management).
* Information Domain: System modelling, data collection, persistence, transformation, and analysis.
* Application Domain: Application of logic and rules to realize specific business functionalities, User Interfaces (UIs), and Application Programming Interfaces (APIs) to expose functionalities for humans and external applications.
* Business Domain: Functions to enable end-to-end operations of the IIoT system, including those supporting business processes, also integrating with traditional specific functions. Examples include Enterprise Resource Planning (ERP), Customer Relationship Management (CRM), or Manufacturing Execution System (MES).
* Crosscutting Functions: Functionalities that enable the Industrial Internet that need to be provided to enable the functional domains:
* Connectivity: Functionalities enabling information sharing and collaborative manufacturing.
* Distributed Data Management: Coordination of data management tasks across system components.
* Industrial Analytics: Application of analytics on the data collected from industrial assets.
* System Characteristics: System properties emerging from the interactions between system parts:
* Trustworthiness: Coordination and integration of different functions implemented in the different system components to guarantee overall system safety, security, resilience, reliability, and privacy.
* Scalability: Functions to enable or facilitate the efficient deployment of large-scale instances of the system.
The functional viewpoint is also concerned with the distribution of functional domains across distributed computational resources in different deployment patterns. Considerations on the use of technologies, such as cloud computing, containerization, Infrastructure-as-a-Service (IaaS), Platform-as-a-Service (PaaS), or Software-as-a-Services (SaaS) to distribute the different functional domains, are also part of the functional viewpoint.
# A Systematic Review of Cybersecurity Risks in Higher Education
## 7 Threat Agents in HE
### Cyber-Crime and Enterprise-Like Criminals  
According to FireEye, one of the most pressing threat agents facing HE is _Enterprise-like cybercriminals_ or data thieves seeking to steal and profit from sensitive personal and financial information from student, faculty and staff [3]. Organized criminals constitute actors who are motivated by their financial gain and profit. When they attack, the intention is to steal assets which can be monetized easily [55]. The different groupings of cybercriminals have very different abilities and frequency.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Threat Agents (Motivation)** & **2018** & **2019** \\ \hline Cyber Crime & 70 & 122 \\ Cyber Espionage & 3 & 5 \\ Hacktivists & 1 & 1 \\ \hline Total & 74 & 128 \\ \hline \hline \end{tabular}
\end{table}
Table 12: Threat agents from 2018 and 2019, reported by Hackmageddon.com (Visited 28 January 2021).  
The NCSC [10] is clear on the threat agents facing the UK universities--_Cyber-crime_ and _Nation States_ looking to steal personal data or intellectual property. _Organized cyber-crime_ is mentioned by [PERSON] [8] as another significant player, being behind both sophisticated and non-sophisticated social engineering attacks. [PERSON] points to a poorly secured university infrastructure being leveraged by criminals and opportunists in DDoS attacking others. FireEye describes this abuse as _Infrastructure hijackers_ aiming to tap into the university's vast resources, such as hosting and bandwidth [5]. [PERSON] [9] writes that accesses to the University-owned resources was the most attractive assets for the attackers within the time frame. Combined with frequent social engineering campaigns, he deduced that attacking the university's generic motive was financial, as the typical targets were usernames and passwords, financial data, and other resources. He points to _cyber-criminals_ using low-cost social engineering attacks as the most frequent threat agent.  
Cyber-crime is the most frequently mentioned threat agent in the reviewed literature for HE: Verizon points to _Ransonware_ as the top malware infection (80%), and _Financial gain_ as the primary motive behind incidents (92%) in HE in 2020 [64]. Table 12 shows cybercrime being attributed 95% of the reported incidents.
# Secure Proxy Re-Encryption Protocol for FANETs Resistant to Chosen-Ciphertext Attacks
## Efficiency Analysis
### Complexity Comparison with Other Methods  
Ref. [16] was the first paper to present unidirectional chosen-ciphertext secure proxy re-encryption published in 2008. Ref. [17], published by [PERSON] et al. in 2009, was the first study to consider not only CCA but also CR. Ref. [2] was the most efficient unidirectional proxy re-encryption published in 2010. Ref. [13] was a scheme announced in 2022 that is not unidirectional, but is secure for CCA and CR.  
We compared and analyzed the computational complexity of the four papers above to evaluate the proposed scheme. Here, L represents the number of bits in the modulus used in the scheme, \(k\) is the length of the generated key.  
As shown in Table 1, the paper focusing on efficiency published in 2010 had the best performance out of the papers published before our study was proposed. So, it is necessary to compare our paper with the paper published in 2010. The computational complexity of the 2010 paper was \(O(kL^{2})\), and our paper was \(O(L^{2}+kL)\). In complexity notation, \(O(kL^{2})\) and \(O(L^{2}+kL)\) can be said to have the same complexity.  
All schemes are based on the assumption that the 'Diffie Hellman problem is difficult', and papers did not use pairing functions, apart from (2008).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Schemes** & **2008** & **2009** & **2010** & **2022** & **Ours** \\ \hline Enc & \(O(kL^{3})\) & \(O(kL^{2}+kL)\) & \(O(kL)\) & \(O(kL^{2})\) & \(O(L^{2}+kL)\) \\ \hline ReKeyGen & \(O(kL^{3})\) & \(O(kL^{2}+kL)\) & \(O(kL^{2})\) & \(O(k^{2}L^{3})\) & \(O(L^{2}+kL)\) \\ \hline ReEnc: & \(O(kL^{6})\) & \(O(kL^{2}+kL)\) & \(O(kL^{2})\) & \(N/A\) & \(O(L^{2}+kL)\) \\ \hline Dec & \(O(kL^{6})\) & \(O(kL^{2}+kL)\) & \(O(kL)\) & \(O(kL^{2})\) & \(O(L^{2})\) \\ \hline \multirow{3}{*}{Security} & CCA, RCCA (Replayable CCA, CR) & CCA, CR & CCA, CR & CCA, CR & CCA, CR \\ \cline{1-1}  & 3-QDBDH & DDH & CDH & CDH & GDDH \\ \hline Pairing Free & X & O & O & O & O \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison with previous schemes.
# Development of A Loss Minimization Based Operation Strategy for Embedded BTB VSC HVDC
## 4 Case Study
### SCLM Control in Transient Condition
#### 4.4.1 Transformer Overload (100%) Case  
When a 1400-1800 2 CKT contingency occurs, the overload of the transformer will be 112.51%. At this time, the overload is solved by using SCLM control, and then the operating point for loss minimization is found. The change in the operating point of the HVDC after the SCLM control and the result of the system control are shown in Tables 7 and 8 and Figures 8-11. As in the previous STLM control, after SCLM control, there are no overload lines, and the maximum/minimum voltage of the system is 1.059/0.984 at the 154 kV level, and 1.038/0.933 at the 345 kV level, which also meets the voltage criterion in Table 2.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Losses**} & **With HVDC** & **With HVDC** & \multirow{2}{*}{**Without HVDC**} \\  & **(STLM Control)** & **(AC Flow)** & \\ \hline AC system losses & 1564.38 MW & 1578.10 MW & 1571.16 MW \\ DC system losses & 4.72 MW & 7.20 MW & - \\ Total losses & 1569.10 MW & 1585.30 MW & 1571.16 MW \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of system losses by operating method.  
Figure 7: Simulation proceeds for SCLM control.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Control Mode** & **P (1410 \(\rightarrow\) 1411)** & **AC Voltage (Rectifier)** & **AC Voltage (Inverter)** \\ \hline Normal operation & \multirow{2}{*}{131 \(\times\) 2 MW} & 1.048 kV & 1.039 kV \\ (STLM control) & & (Supply 95.31 MVAR) & (Supply 3.80 MVAR) \\ \hline SCLM control & \multirow{2}{*}{17 \(\times\) 2 MW} & 1.019 kV & 1.048 kV \\  & & (Supply 82.83 MVAR) & (Supply 72.70 MVAR) \\ \hline \hline \end{tabular}
\end{table}
Table 7: HVDC setpoint change using the SCLM control for the transformer overload.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Normal Operation** & **Conventional Control** & **RAS Control** & **SCLM Control** \\ \hline AC system losses & 1564.38 MW & 1583.92 MW & 1587.21 MW & 1585.80 MW \\ \hline DC system losses & 4.72 MW & 4.72 MW & 0.86 MW & 0.61 MW \\ \hline Total losses & 1569.10 MW & 1587.64 MW & 1588.07 MW & 1586.41 MW \\ \hline \multirow{2}{*}{\% Loading} & \multirow{2}{*}{66.02\%} & 110.44\% & \multirow{2}{*}{98.08\%} & \multirow{2}{*}{96.41\%} \\  & & (Security violated) & & \\ \hline \hline \end{tabular}
\end{table}
Table 8: Comparison of system losses by operating change (transformer overload case).  
Figure 8: Active power operation change in the transformer overload case.  
Figure 9: Reactive power operation change in the transformer overload case at the rectifier end.
# Analysis of Urban Residents' Travelling Characteristics and Hotspots Based on Taxi Trajectory Data
## 4 Related Work
predicted residents' activities by analyzing the number of passengers in urban hotspot areas. They also proposed an enhanced prediction method based on the autoregressive integrated moving average (ARIMA) model to forecast spatiotemporal changes in passenger volumes within hotspot areas. This information was used to provide taxi drivers with recommendations for passenger hotspot areas [37]. [PERSON] et al. simulated the dynamic customer-seeking behavior of taxi drivers under uncertain conditions. They associated dynamic demand planning with the cumulative distribution function of user waiting time, leading to the development of a dynamic taxi demand prediction model [38]. [PERSON] et al. introduced a generalized spatiotemporal autoregressive model and employed a LASSO-type penalty method to address the model's high dimensionality. This approach improved the prediction efficiency of the model to a certain extent [39]. [PERSON] et al. proposed an end-to-end deep learning model, incorporating a deep belief network to analyze nonlinear dependencies, to address the travel demand prediction challenge [40]. [PERSON] et al. conducted statistical analysis and data mining on taxi trajectory data in a California study area. They identified characteristic travel time patterns of residents in the region and predicted the upcoming peak tourist season [41]. This type of research leverages trajectory data to capture and analyze trends in time series data, aiding in the prediction of spatiotemporal changes in passenger volumes. However, it is important to note that prediction models heavily rely on a large volume of experimental data, and their accuracy can be affected by natural weather conditions and man-made policies.
## 1 Introduction
A biomaterial barrier that blocks the infiltration of cells but enables the passage of cytokines and GFs may be useful to avoid adhesion formation, without altering the normal healing process. Recently, [PERSON] et al. [32] developed a bilayer membrane combining a nanofibrous poly (\(\epsilon\)-caprolactone) (PCL) electrospun mesh with a layer of self-assembling peptide hydrogel (SAPH) laden with type-B synovioocytes that showed good biomechanical behavior, preventing post-operative tendon adhesion. [PERSON] et al. [33] also developed a multifunctional nano fibro membrane that acts as a physical barrier to prevent fibroblast penetration. Hyaluronic acid reduced fibroblast attachment and helped lubrification and tendon gliding, with ibuprofen adjunct acting as an anti-inflammatory drug. Moreover, [PERSON] et al. [34] recently compared a bioresorbable membrane composed of sodium hyaluronate and carboxymethyl cellulose and an auto-crossed linked gel derivative from hyaluronic acid in preventing tendon adhesion after crush injuries, showing that the gel was effective only after tendon fiber repair in a rat model. A multicenter clinical trial recently investigated an autocross linked hyaluron-based gel (Hyaloglide(r)), revealing positive results in preventing adhesion recurrence following tenolysis of flexor tendons in zone II of the hand [3]. This non-stick mechanism, albeit clinically effective, has been scarcely examined in animal models. In this context, we herein assess, for the first time, the effectiveness of Hyaloglide(r), a resorbable gel-formulated adhesion barrier, on rabbit models affected by tenolysis, showing positive results in the prevention of peritendinous adhesions. The study particularly concentrated on the histological and electron microscopy analyses. Owing to its mechanical characteristics, Hyaloglide(r) may enhance regeneration of the tendon synovial sheath, ensuring tendon elasticity and avoiding peritendinous adhesion formation, thus enabling favorable recovery of tendon function. The aim of the present study was to assess the efficacy of Hyaloglide(r) application in tendon adhesion prevention through the evaluation of primary endpoints, such as the histological and histomorphometrical score, and secondary endpoints, such as gene expression analysis and biomechanical evaluation. We hypothesized that in the treated group, the evaluated scores would be statistically better compared to the control group.
# A New Dynamic Game-Based Pricing Model for Cloud Environment
## 4 The Proposed Game-Based Pricing Method
### Constructing the Game Tree
#### 4.2.2 Step Two: Constructing the Cloud Provider's Sub-Games  
At this step, the proposed method constructs the second depth of the game tree. The resource broker is in charge of constructing and solving the game. It calculates the provider's payoff for selecting various prices at each decision point according to Equation (1). As an increase in the price leads to a lower demand for the resource, choosing higher prices does not necessarily mean more profit for the cloud provider.  
According to Equation (1), in order to compute the provider's benefit for each action, it is necessary to calculate the corresponding penalty for each action as well. The proposed approach considers this penalty for cloud providers due to the probability of unsuccessful execution of the user's task and SLA violation. Considering this probability leads to better resource allocation since the provider tends to improve its profit. Execution of users' tasks may fail due to various reasons, such as resource breakdown and system overload [34]. In the event of an error or a malfunction, the provider should be obliged to compensate the user for violating the SLA. The proposed approach employs a failure probability model presented in [35] in order to minimize the penalty paid by the provider.  
This model helps to predict the failure probability of a task execution on a physical machine with a high accuracy (up to 90%).  
At this stage, we estimate the energy cost required to complete the user's task on each virtual machine, as stated in Equation (7). Because large-scale computing systems such as clouds have increased energy consumption rates, a significant portion of their operating costs are related to paying electricity bills [36; 37]. Given that high energy consumption imposes high expenses on the cloud providers, the proposed model considers energy consumption an influential factor in resource allocation and pricing mechanisms.
# Recent Progress on Irradiation-Induced Defect Engineering of Two-Dimensional 2H-MoS2 Few Layers
## 2 Charged Particle Irradiation
### Swift-Heavy Ion Irradiation
#### 2.1.7 Silver Ion Irradiation  
[PERSON] et al. [115] irradiated magnetron-sputtered amorphous MoS\({}_{2}\) films of thicknesses 50-750 nm with a \(5\times 10^{15}\) cm\({}^{-2}\) dose of 2 MeV Ag ions. The sliding life of Ag ion-irradiated films increased ten-fold to thousand-fold compared to as-sputtered films. The improvement in wear life was correlated with a significant improvement in adhesion of the films with the substrates and a small increase in the density of the ion-irradiated films.
# A Set of Active Disturbance Rejection Controllers Based on Integrator Plus Dead-Time Models +
## 3 Extended State Observer
### ADRC3 Controller Design  
For the system  
\[\dot{\mathbf{x}}=\left[\begin{array}{cccc}-p_{1}&1&0&0\\ -p_{2}&0&1&0\\ -p_{3}&-1/T_{2}^{2}&-2/T_{2}&K_{s}/T_{2}^{2}\\ -p_{4}&0&0&0\end{array}\right]\mathbf{x}+\] \[+\left[\begin{array}{c}0\\ 0\\ K_{s}/T_{2}^{2}\\ 0\end{array}\right]u(t-\overline{T}_{d})+\left[\begin{array}{c}p_{1}\\ p_{2}\\ p_{3}\\ p_{4}\end{array}\right]y; \tag{37}\] \[\left[\begin{array}{c}\overline{y}\\ \overline{y}\\ \overline{d}\end{array}\right]\approx\left[\begin{array}{cccc}1&0&0&0\\ 0&1&0&0\\ 0&0&1&0\\ 0&0&0&1\end{array}\right]\mathbf{x}\]  
and selection of four poles at \(s=-1/T_{c}\) and equally delayed outputs we get the observer parameters  
\[p_{1}=\frac{4}{T_{c}}-\frac{2}{T_{2}};\ p_{2}=\frac{6}{T_{c}^{2}}-\frac{2p_{1} T_{2}+1}{T_{2}^{2}} \tag{38}\] \[p_{3}=\frac{4}{T_{c}^{3}}-\frac{p_{1}+2p_{2}T_{2}}{T_{2}^{2}};\ p _{4}=\frac{\overline{T_{2}^{2}}}{K_{s}T_{c}^{4}}\]  
Here, the ESO gives the variables \(\underline{y}\), \(\underline{y}\) and \(\overline{d}\). The are used both in the PDA controller (18) and for input disturbances rejection. With respect to the speed of transients with the closed loop time constant (23), one may expect as a reasonable choice \(T_{c}\approx T_{d}/3\).  
**Remark 1**.: _In the controllers it is possible to use either the plant (\(y\)), or the observer's output (\(\overline{y}\)), whereby the second possibility significantly decreases impact of the measurement noise [20]._
## 3 Results and Discussion
### Determining the Pressure During Pressing  
The pressure within the pressing chamber affects the degree of cell rupture in the fruit, thereby impacting the juice yield and extraction efficiency [26]. The data on the pressure inside the pressing chamber provide crucial insights into the mechanical dynamics of the sea buckthorn juice extraction process.  
The springs with higher maximum forces--Spring 2 (800 N) and Spring 1 (750 N)--consistently generate higher internal pressures across all rotation speeds. Spring 2 produces the highest pressures, reaching up to 570 Pa at 200 rpm, which correlates with its higher maximum force compared to Spring 1. Spring 3, with the lowest maximum force (47.5 N), results in significantly lower internal pressures, highlighting its limited effectiveness in creating a sufficient mechanical force for optimal juice extraction. Spring 4 (630 N), while having a lower maximum force than Spring 1, still achieves moderate internal pressures, suggesting that its design and material properties contribute to its performance (Figure 9).  
As the screw rotation speed increases from 140 rpm to 200 rpm, the internal pressure within the pressing chamber rises for all spring types (\(p<0.05\)). This increase is more pronounced with springs that have higher maximum forces. The positive correlation between the rotation speed and internal pressure is attributed to the enhanced mechanical action and shear forces exerted on the fruit mass at higher speeds, intensifying the compaction and pressure within the chamber.  
Figure 8: Change in compression density during sea buckthorn pressing depending on the design of the pressure control spring and rotation speed (different letters above the bars indicate significant differences with increasing the rotation speed, \(p<0.05\)).
## 1 Introduction
Traditional enzymatic dissociation methods using proteases (i.e., Trypsin) are the most common cell harvesting methods. However, they only result in a 60-70% cell recovery, depending on the MCs properties. The enzymatic process may also cause reduced cell viability and high apoptotic activity, limiting the transplanted cells' therapeutic efficacy. Therefore, biodegradable MCs are advantageous, as cell harvest or use of new scaffolds is not necessary for cell transfer [24]. In this manner, high cell viability and potency can be maintained. This feasibility study's product is a porous poly(0,l-lactide-co-glycolide) (PLGA) based MC (called _BR44_), manufactured as a microsphere with physically embedded porcine gelatin, a natural polymer already approved for clinical use [25]. Due to the adverse effects of FBS in clinical applications [11, 26, 27], the MC was used with proprietary defined xeno- and serum-free cell culture medium (_UrSuppe_) suitable for the dynamic cultivation of hASCs in stirred single-use bioreactors. We think that this approach of developing a biodegradable MC combined with a serum-free medium to grow hASCs is very appropriate;since it has recently been shown that compatible serum-free media is cell-specific and requires optimization for MC-based dynamic cell culture systems [28]. To our knowledge, only three biodegradable MCs are commercially and readily available: Fujifilm's Cellnest Macroporous Microspheres [29] and the classical Cultisphere G [30] and Cultiphere S [31], while a few examples of resorbable MCs, produced using various techniques and starting materials, including their properties and optimal cultivation conditions, can be found in the scientific literature [7; 8; 32; 33; 34; 35] (see also Table S1 in the Supplementary Material). Hence, we realized that studies combining human stem cells from adipose tissue, biodegradable MCs, a defined xeno- and serum-free medium, and dynamic cell culture conditions are scarce. This study aimed to verify whether the biodegradable MC _BR44_, together with the defined xeno- and serum-free medium _UrSuppe_, are appropriate AMs to cultivate adult stem cells. The ASC52 telo cell line was used for this feasibility study to expedite and facilitate experimentation. This cell line was generated using hTERT-immortalized cells extracted from human subcutaneous fat and shown to carefully reproduce most of the typical characteristics of primary hASC [36], presenting an ideal cell line for platform validation. In addition to the characterization and comparison of cell growth behavior, special emphasis was also placed on critical cell quality attributes (CQAs). We, therefore, believe that this study can contribute to the development of new protocols for the large-scale manufacture of hASCs and lead to a bioreactor-based manufacturing system for the preparation of cell-based therapeutics.