esa-sceva/satcom-chunk-collection huggingface dataset:
# VNF Placement Optimization at the Edge and Cloud +
## 7 Conclusions  
In this paper, we studied the problem of SC allocation for providing networking services to IoT applications, a problem called VNF Placement at the Edge and Cloud (PEC). The allocation scenario is based on a dynamic IoT multi-user model that creates dynamic traffic conditions according to the number of IoT devices associated with the IoT applications. The ultimate goal was to reconcile the  
Figure 15: Hybrid simulation/emulation approach.  
conflicting requirements of reduced deployment cost and overall delay. Furthermore, in contrast with existing works, a more detailed and complete delay model is considered, while integrating additional constraints that may be encountered in a real environment, such as location constraints, SCs with different topological characteristics, and compression/decompression of the processed traffic. Two algorithms were used: (i) an optimal solution formulated as MIP problem and (ii) a Tabu Search meta-heuristic. The superior performance of the proposed solution is validated through the comparison with two algorithms proposed in the literature. The results indicate the fact that a coordinated placement of servers and links can satisfy the conflicting goals of reduced cost and delay.  
Future work lies in the area of throughput performance and delay evaluation when deploying SCs both at the Edge and Cloud layers. Furthermore, we intend to assess the viability of our approach in a real-world environment and record the effects of a low-cost capacity planning solution on the overall delay. Finally, aspects of reduced reliability in the Edge will be studied and fault-tolerance models will be developed.  
Conceptualization, A.L. and M.I.; methodology, A.L. and I.L.; software, A.L.; validation, A.L., G.K. and I.L.; formal analysis, A.L., G.K. and I.L.; investigation, A.L., G.K.; resources, A.L., M.I.; data curation, A.L., G.K., M.I. and I.L.; writing--original draft preparation, A.L., G.K., M.I. and I.L.; visualization, A.L.  
This research received no external funding.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} NFV & Network Function Virtualization \\ VNF & Virtualized Network Function \\ IoT & Internet of Things \\ MEC & Mobile Edge Computing \\ European Telecommunication Standards Institute & ETSI \\ National Institute of Standards and Technology & NIST \\ Radio Access Network & RAN \\ Service Provider & SP \\ Service Level Agreement & SLA \\ VNF-PEC & VNF Placement at the Edge and Cloud \\ VM & Virtual Machine \\ SC & Service Chain \\ QoS & Quality of Service \\ SLA & Service Level Agreement \\ SR-IOV & Single Root Input/Output Virtualization \\ NUMA & Non Uniform Memory Access \\ MIP & Mixed Integer Programming \\ TS & Tabu Search \\ NP & Non-deterministic Polynomial-time \\ IC & Instance Consolidation \\ \end{tabular}
## 2 Materials and Methods
### Fabrication Process and Device Characterization  
The illustration of the fabrication process of MRR on SOI substrates is shown in Figure 2. MRR silicon waveguides were fabricated from the Silicon-on-Insulator (SOI) wafer layer using Elionix F125 Electron Beam Lithography (EBL) for pattern writing and the Hydrogen Silsesquioxane (HSQ) negative tone resist mask in the etching process for pattern transfer. The HSQ resist mask was diluted with the ratio of 1:1 in methyl isobutyl ketone (MIBK). The HSQ resist mask then spun at 3000 rpm for 60 s. The sample was then baked at 90 \({}^{\circ}\)C for 40 min. Next, the electron beam from the EBL system was exposed throughout the sample to write the MRR waveguides. The exposed patterns were then developed using tetramethylammonium hydroxide (TMAH) with a concentration of 25% in water for 30 s. The etching process was done using an Surface Technology System - Reactive Ion Etch (STS-RIE) machine with a combination of SF\({}_{6}\) and C\({}_{4}\)F\({}_{8}\) for 3 min. The residual of HSQ resist mask was cleaned from the sample surface using isopropyl alcohol (IPA). The fabricated MRR device consisted of a single ring waveguide and two straight waveguides, called the add-drop microring resonator system.  
Figure 3 shows the experimental setup for MRR based on SOI quality factor characterization. The surface morphology of the MRR device was characterized using Scanning Electron Microscopy (SEM) to observe several morphological parameters such as ring radius, gap, and waveguide width. The fabrication process successfully fabricated the MRR device with good repeatability. Good dimensional control of the MRR device was proven with morphological parameters. Subsequently, the value of the quality factor was characterized using an ANDO AQ4321D Tunable Laser Source (TLS) and Agilent 86142B Optical Spectrum Analyzer (OSA). TLS supplies the light to the MRR device, which covers wavelengths from 1525 to 1575 nm. The optical spectrum analyzer (OSA) was used to measure output power at the through-port of MRR device. The value of the quality factor is calculated by using Equation (1) [27];  
\[\mathrm{Q}=\frac{\lambda_{\mathrm{res}}}{\mathrm{FWHM}} \tag{1}\]  
where \(\lambda_{\mathrm{res}}\) is the resonance wavelength, and FWHM is value of half of maximum intensity between two extreme points of the resonance curve.
# Continuance Use of Cloud Computing in Higher Education Institutions: A Conceptual Model
## 3 Theoretical and Conceptual Background
### IS Discontinuance Model  
Additional factors that influence organization persistence, particularly in the context of CC use in HEIs, were identified in this study for an explanation of CC continuance use in the organization-level IS post-adoption context. This resulted in the inclusion of system investment as an organizational construct, as well as technical integration as a technological construct [16]. Each of these constructs relates to the commitment that an organization has to a subscription-based technology (e.g., CC) [14].  
Regarding technical integration, this study sought to examine whether the features of a technology impacted continuance decisions. Specifically, in view of the principal objective of CC, the study investigated the degree to which HEIs would benefit from the sophistication provided by the features of CC services. However, the positive impacts of the features of the technology are not assured in many cases. For example, certain organizations may not have the expertise needed to exploit the complexity of the technology [129; 130; 131], meaning that they cannot integrate it into the overall functioning of the firm. As a result of this, the organization would be compelled to discontinue their use of CC services [14; 78].  
In terms of the system investment variable, this--as a source of behavioral persistence--has often been referred to as a \"sunk cost\" in the literature [132]. Among managerial personnel, it is common to invest continually in an area despite reasonable evidence for not doing so. The notion of a sunk cost becomes relevant when the cost of acquisition can be regarded as a capital expenditure (CapX). System investment studies have assessed the role played by CapX in the formulation of computer software prices in the context of switching between software solutions [133], as well as the impact on succeeding decisions in terms of IS outsourcing [134]. In the case of CC, system investment is a noteworthy variable because of the technology's low barriers to entry and minimal overhead costs [135]. In view of this, it is not unreasonable to view CC services as any other utility that can be turned on and off at will [135; 136]. However, it is critical to recognize that many CC services are typically associated with significant implementation costs. A key implication of this is that system investment is fundamental in CC continuance in HEIs.  
Regarding competitive pressure, this refers to the pressure that an institution's leadership may feel regarding the performance-related abilities that its competitors are gaining through the exploitation of CC services (e.g., an increase in student assessment outcomes due to the use of CC platforms) [137; 72; 138].
## 7 Results and Analysis
### Sensitivity Analysis
#### 7.2.3 Bus Stopping Time  
The stopping time of the bus affected the speed of the bus. When the speed of the car was constant, the speed difference of the two vehicles could represent the stop time of the bus; however, the difference in speed led to differences in the vehicle delay results after the optimization of the model in this paper. Therefore, the influence of the speed difference of the two vehicles on the optimization results can be directly analyzed.  
A total of 178 sets of examples were classified according to the speed difference between the buses and cars on the arterial road, and the average values of per capita delay, per bus delay, and per car delay of each type of examples were calculated, respectively. Based on the average value of the first type of examples, the reduced value of per bus delay, the increased value of per car delay, and the difference between the two were calculated. The results are shown in Figure 19.  
Figure 18: Optimization results for buses with different passenger capacity.  
In the range of 5 m/s to 10 m/s, with the increase in speed difference, there was no evident change rule in the per capita delay and the average car delay. The average delay of buses first decreased and then increased. When the speed difference was 7-8 m/s, the delay per bus was the smallest, and the difference between the reduction value of the delay per bus and the added value of the delay per car and the delay per bus was the largest, which meant that the model of this paper had the best effect of reducing the average delay of the bus at that time.
# Application of Nanofluids in CO\({}_{2}\) Absorption: A Review
## 2 Nanofluid
### The Mechanisms of Enhancement in Nanofluids
#### 2.3.2 Bubble Breaking Effect  
[PERSON] et al., have concluded that the velocity disturbance field is the reason for the enhancement of mass transfer, which is formed due to the movements of nanoparticles [41]. In the bubble absorption process, the collision of nanoparticles and nanoparticles with bubbles occurs. When the bubbles move toward the interface and form a dynamic movement, the nanoparticles strike the interface of gas-liquid and this results in breaking the bubbles [78]. This phenomenon increases the diffusion area. Considering that this enhancement in the specific interfacial area occurred due to the particles, it can be concluded that they can increase the overall mass transfer coefficient [79].
# Security of IoT Application Layer Protocols: Challenges and Findings
## 5 Service Discovery Protocols
### Mitigations  
As already pointed out, the lack of built-in security services exposes SSDP-enabled nodes to threats and attacks. Hence, proper countermeasures must be sought. In particular, it is important to take account of the peculiarities of SSDP. In fact, this protocol is typically deployed on a local network and relies on UDP transport protocol on port 1900. Therefore, as a mitigation measure towards conventional DDoS attacks, it might be necessary to block this type of incoming traffic. In fact, it is known that open SSDP is already a vulnerability. Of course, these measures are not effective to mitigate DDoS attacks that leverage on SSDP nodes using random source ports.  
At the level of individual nodes, SSDP services should be disabled whenever not needed, since they are often enabled by default on most devices. In addition, unicast M-SEARCH request messages should be treated carefully and possibly blocked because of the abnormal usage of this type of messages.  
It is also worth mentioning that encryption mechanisms able to ensure _authenticity_ and _confidentiality_ of the exchanges and avoid possible abuse of their content, must be implemented at the level of the services built on top of the SSDP protocol, rather than at the level of the protocol itself.  
Various solutions for securing smart home IoT appliances based on SSDP have been proposed in the literature (see, e.g., [74; 75]) In particular, [PERSON] et al. [74] highlight that security and privacy of these appliances can be easily compromised and propose a solution based on access restrictions at the network level. In [75] it has been shown that a flow-based monitoring solution is effective for detecting security threats.
## 1 Introduction
Generally speaking, the monitoring data can supervise the safe construction of the deep foundation pit to some extent, but they cannot effectively predict the deformation pattern of the deep foundation-pit enclosure structure and the surrounding environment. Therefore, a reasonable monitoring data-analysis method can effectively reveal the development law of deformation. Artificial intelligence predictive monitoring deformation data technology is a research field of great interest, and the method uses existing monitoring data as learning objects to predict the development trend of deformation to effectively control the safety risk and guide the engineering practice in the actual project. Currently, the main machine-learning methods commonly used are random forests, decision trees, gradient growth, multilayer perceptual machines, etc. [PERSON] [11] classified surface-settlement levels in urban areas using a random forest with data-driven feature selection, and [PERSON] [12] investigated different machine-learning methods with 5-fold cross validation (CV) to estimate the maximum surface settlement of the ground caused by the deep foundation-pit excavation, [PERSON] [13] predicts deep foundation-pit surface settlement from sensor data via an LSTM model with a fully integrated empirical pattern decomposition method with adaptive noise. [PERSON] [14] uses an SVM algorithm based on four kernel functions, linear, polynomial, sigmoid, and radial basis function (RBF), to predict the significance of maximum ground surface settlement (MGS) for safe operations. In addition, some studies demonstrate the growing interest in applying machine-learning techniques to geotechnical engineering, with a focus on prediction, classification, optimization, and risk assessment in various geotechnical applications [15]. [PERSON] [16; 17] uses transfer learning to overcome dataset size limitations in geotechnical analysis and combines data-value analysis with machine learning to enhance the decision-making process in observational method projects. [PERSON] [16] used the open-source AutoML framework to construct different ML models for predicting the maximum ground settlement during the construction of soil pressure balance shield tunnels. Most of the mainstream research directions of artificial intelligence for predicting the deformation of deep foundation-pit enclosure structures directly study the surface settlement generated during the construction [18; 19; 20; 21]. However, many other factors, such as geological environment, stress, and settlement of structures, were not too much involved in the previous deformation prediction study. Incompleteness of the impact factors may lead to poor model accuracy. Therefore, to solve this problem, this paper intends to analyze and summarize the data that was monitored during the construction of the deep-foundation pit to obtain the significant characteristic factors that cause the deformation of the enclosure structure and, based on this, to establish a method of deformation prediction modeling from the mainstream machine-learning methods.
## 4 Experimental Results
### Experimental Setup  
To confirm the performance of the proposed NMP algorithm, we use the Python platform to implement our algorithm. We compare our proposed NMP with the state-of-the-art algorithms, STAMP and STOMP. Table 1 shows the hardware and software used to implement the proposed algorithm.  
Data setsThe findings are based on two datasets that are multivariate and multi-sample. The first dataset is unbalanced and consists of time series with anomalies [15] that involve 11 features with 509 k samples. The dataset is downloaded from the link: [[https://www.kaggle.com/drscarlat/time-series](https://www.kaggle.com/drscarlat/time-series)]([https://www.kaggle.com/drscarlat/time-series](https://www.kaggle.com/drscarlat/time-series)), (accessed on 20 November 2021). Anomalies make up about 0.09% of the data. Normal and abnormal data are separated, and anomaly-tagged events occur every nine time steps. The data set is segmented into 126 timestep segments, evenly separating the test batch's aberrant signals. Samples were randomly combined and supplemented to increase the feature dimension. The feature axis was cloned five times, randomly mixed, then concatenated across the dataset to increase the number of features from 11 to 55. The second data set depicts the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) pandemic, which was made public by Johns Hopkins University [34]. It describes three factors over the course of 10 months for 188 nations, ranging from the beginning day of the epidemic to the day it ended after four months.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Description** \\ \hline Personal computer & x64 \\ Operating system & Windows 7 \\ Processor & Intel Core i7-2670 QM \\ RAM & 6 GB \\ CPU GHz & 2.20 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Development environment.  
Time series with anomalies:Time and Accuracy; SARS-CoV-2:Recovered, Infected, and Deaths.
## 2 Materials and Methods  
### Study Area  
The Jingdian Electric Power Irrigation Project in Gansu Province is a significant irrigation initiative located in northwest China. It is characterized by high lift, large flow, and numerous stages. The project supports agricultural irrigation in Jingtai County, Gulang County, and Minqin County in Gansu Province, as well as parts of Alashan Left Banner in Inner Mongolia. The Jingdian Irrigation District spans Jingtai County and Gulang County in central Gansu Province (see Figure 1), bordered by the Tengger Desert to the north, Changing Mountain to the south, and the Yellow River to the east. This large-scale irrigation area was established by the state to address issues of drought, water scarcity, and land desertification, along with the southward movement of deserts in Jingtai County and Gulang County. The study area, located in the eastern part of the Jingdian Irrigation District, covers 351.3 km\({}^{2}\), with an irrigated area of 235.4 km\({}^{2}\). The surface elevation ranges from 1300 to 1710 m above sea level, featuring a terrain that is higher in the west and lower in the east. The region consists of numerous mountains and hills, forming several basins, including the Caowotan Basin, Si'ertan Basin, Luyang Basin, and Xingquan Basin. The flat terrain in these basins is conducive to farming and irrigation. The primary food crops in the irrigation area are potatoes, spring wheat, and corn, while the main cash crops include forest fruits, goji berries, flax, and melons.  
Figure 1: Location map of the study area.  
The study area belongs to the temperate continental arid climate, characterized by drought and little rain, large temperature differences between day and night, sufficient sunshine, high evaporation intensity, and windy sand. According to the observation data of Jingtai County Meteorological Observation Station from 1960 to 2020, the precipitation in the irrigation area is relatively small, with annual precipitation of 104-298 mm, annual average precipitation of 191 mm, and an annual average temperature of 8.8 \({}^{\circ}\)C. It can be seen in Figure 2 that the average monthly precipitation in the study area is extremely uneven, mainly concentrated in June to September, accounting for 73% of the annual precipitation. The monthly average temperature shows a single peak distribution, with the highest in July and the lowest in January. According to the monitoring results of the evaporating dish, the average annual evaporation of the water surface is 2290 mm, the maximum annual evaporation of the water surface is 2752 mm, the ratio of evaporation to precipitation is 1:18, and the drought index is up to 3.0, belonging to the extremely arid area. The average number of sunshine hours for many years in the irrigation area is 2726 h, and the average wind speed is 1.8 m/s. The frequency of dust storms in the region is relatively high, mostly in late spring and early summer.
## 4 Applications of AI Techniques in Power Systems
### Power System Operation  
The matching of total generation with load demand and related system losses is necessary to operate automatic generation control (AGC) efficiently in interconnected power systems. A power system's operational point may eventually experience variations from the nominal system frequency and planned power exchanges to other places, which could have a negative impact [75]. Older techniques relied on linearized linear models or approximations of nonlinear processes, neither of which could produce accurate results. A higher level of precision was achieved with the use of AI technology. Approximately in the early 1990s, ANN applications began, and they are still in use today. The flight controller (FC) is primarily utilized in the design of regulators. Genetic algorithms (GAs) also optimize complex nonlinear AGC problems. In load frequency controllers, fuzzy membership functions are also tuned using GAs. [PERSON] et al. [75] mentioned that the solution to some AGC issues exhibits FL and ANN hybridization. There is a discussion of contemporary approaches to AGC issues [76]. In order to combine the robustness of GA with the simplicity of linear matrix inequalities (LMIs), a GA and LMI are utilized in the study [77]. The rapid development of AI has extensively developed AGC research by using different soft computing such as improved stochastic fractal search (ISFS), hybrid bacteria foraging (HBF), grasshopper optimization algorithm [78], pattern search (PS) [79], differential evolution (DE) [80], whale optimizer (WO) [81] and bacterial foraging algorithm (BFA) [82]. Additionally, there are some hybrid approaches such as PSO-BFA [20], hybrid crow search (hCA) combined with PSO [21], and others [22; 23; 24].
# Non-Thermal and Thermal Physical Procedures--Optimistic Solutions in the Winemaking Industry
## 3 The Influence of Thermal Technologies in Wine Quality
### Microwave Application
Contrary to these results, [PERSON] et al. [59] showed a minor decrease of reductive sugar, density, and total acidity, but also of the total phenolic index in Cabernet Sauvignon wines, when microwave maceration was applied compared to skin maceration for 3 days at 20 \({}^{\circ}\)C. According to [PERSON] et al. [60], microwave maceration induces significant changes in the volatile profile of Tamaioasa romaneasca wines. Thus, an increased level of hotrienol and trans-geraniol was observed, while lower proportions were registered for linalool compared to the classical maceration procedure. According to [PERSON] et al. [61], microwave exposure presents a prolonged influence on wine color and phenolic compound evolution during the maturation phase. The authors postulated that utilizing microwave-assisted maceration technology could potentially reach the same results regarding wine color changes as barrel maturation. Microwaves promoted the breakdown of crushed grape cell walls, greatly increasing the concentration of polysaccharides rich in arabinose and galactose in mucus. Microwave-treated samples had higher total polyphenol indexes. Microwaves generate the release of some favorable chemical components regarding wine color but, as a main disadvantage, this technique also causes sensory alteration.