esa-sceva/satcom-chunk-collection huggingface dataset:
# A MAC Protocol to Support Monitoring of Underwater Spaces +
## 3 Description of the UWSO-TDMA Protocol
### Initialization Phase
#### Clock Synchronization
Then, Node B sends a new _synch_ message with the time correction for Node C and its delay to Node B (Figure 2g). The SB receives this message too and sends a new _synch_ message acknowledging the presence of C (Figure 2h). Node A learns about Node C first by overhearing the exchange with Node B and after that, by the _synch_ message from the SB. Now, it is time for Node A to send a _synch_ message (Figure 2i). Node C listens to it and detects that A has less delay than B; therefore, it changes its synchronization node and answers with an _asynch_ message (Figure 2j). This message is received by Node B, which learns the change of C from B to A. This latter node recognizes it with a new _synch_ message indicating  
Figure 1: Synchronization protocol. the time correction and its delay to A (Figure 2k). This message is also received by the SB that updates its tree structure. It sends the last _synch_ message in which it indicates the time correction for each node and the delays to the SB (Figure 2l). After this, the slot selection process may proceed. The synchronization process is used by overhearing nodes in the network to determine the set of neighbor nodes that may help during the slot selection process. Even if nodes are not within transmission range, they may discover their presence by the following _synch_ messages. The synchronization process is similar to the leader election process [25]. For each sub-frame, only one node acts as the synchronization source. The set of synchronization nodes are saved in \(S=\{s_{i}\}\). The set of nodes synchronized by \(s_{i}\) is notated \(V_{i}=\{v_{i1},v_{i2},\ldots,v_{in}\}\); they are ordered by increasing transmission delay to \(s_{i}\). If any \(v_{ij}\) listens to more than one synchronization node, as shown in Figure 2, only one of them is selected, and the rest are saved as back up. In the _asynch_ message, the node indicates with which node it is synchronized, and this information is passed to the upper layers up to the SB. The set of shared nodes that may be synchronized by more than one node is notated \(Sh_{i}=\{v_{ij}\}\). The nodes with this condition indicate the presence of all possible synchronization sources to the upper layer. This information is used later in the slot selection process, as explained in Section 3.2.2. The information is confirmed in the following _synch_ messages. Once the synchronization nodes have been selected, the information flow from the SB to the leafs and from the leafs to the SB is fixed, and it will last until a new initialization process has begun.
## Results and Discussions
### Effect of Catechin on Cyclin Kinase Inhibitors p21 and p27 Expressions in A549 Cancer Cells  
Cell cycle progression is driven by cyclins which are associated with CDK [9]. As an inhibitor of cyclin dependent kinase, p27 was involved in the regulation of the cancer cell cycle [21].  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Concentration (\(\upmu\)mol-L\({}^{-1}\))** & **Inhibition Rate (\%)** \\ \hline
200 & 0.73 \(\pm\) 0.25 * \\
400 & 2.96 \(\pm\) 1.71 * \\
600 & 19.76 \(\pm\) 0.72 * \\ \hline \hline \end{tabular}
\end{table}
Table 2: Inhibition rates of catechin against A549 cells proliferation.  
Figure 1: Images of the A549 cells in control (**a**) and catechin (600 \(\upmu\)mol) treatment (**b**) groups after 24 h of incubation.  
Increasing oncology researches focused on p27 as the target therapy in breast cancer treatment [22]. Similar to p27, p21 has been reported to increase tumor cell susceptibility. A previous study showed that the expression of p21 resulted in the accumulation of cells in G0/G1, altering cell morphology and differentiation [23]. In addition, p21 has been reported to mediate the p53-induced G1 cell cycle arrest resulting from DNA damage [24]. Thus, the effect of catechin on cyclin kinase inhibitors p21 and p27's expression in A549 cancer cells were both evaluated in this study. Because anti-proliferation results showed the dose dependency, only the lowest and highest concentrations of catechin (200 and 600 \(\upmu\)mol\(\cdot\)L\({}^{-1}\)) treatments were selected to evaluate the expression of cyclin kinase inhibitors p21 and p27.  
As shown in Figure 2, the expression of p21 significantly increased in the 200 and 600 \(\upmu\)mol\(\cdot\)L\({}^{-1}\) treatment groups by approximately 1.3 and 1.2 times, respectively (p \(<\) 0.05) compared to the control group. However, there was no significant difference between the two catechin treatment groups, indicating that the improvement of p21 might have reached a plateau when catechin concentration was at 200 \(\upmu\)mol\(\cdot\)L\({}^{-1}\). No significant incensement of p27 expression was observed in catechin treatments and control group, Figure 2. Thus, p21 might have a more sensitive response to catechin than p27 in A549 cells. A number of bioactive compounds have been evidenced to inhibit cancer cells' proliferation though enhancing the expression of p21 or p27, which further reduced the activity of cyclin E. For instance, p21 significantly increased in EGCG-treated A549 cells [25]. In addition, caffeic acid phenyl ester has been shown to arrest castration-resistant prostate cancer cells by upregulating p21 and p27 proteins [26].
## 7 Conclusions  
The integration of IoT technology in smart buildings presents a transformative solution for energy management, driving significant improvements in energy efficiency, operational cost reduction, and occupant comfort. The paper emphasizes the potential presented by IoT, which allows for intelligent data-driven decisions through real-time monitoring and advanced control systems. Indeed, by using energy, a 30% reduction is possible, and with operational costs, up to a 20% reduction is possible; however, several challenges remain. Some of the challenges relate to high initial investments, data security issues, and complexity in system integration. These surely require tremendous coordination among all the parties involved, robust security, and simplified integration procedures. Future research and innovation should direct focus on these areas so that such challenges can be addressed, the optimum optimization of IoT frameworks can be achieved, and its application scope in smart building environments can be further augmented. With proper strategies and collaborations, IoT can advance building systems --increasingly sustainable and more energy efficient--as this research points out. Improving energy efficiency via real-time data analytics and automated controls may result in substantial cost reductions and less environmental impact. The creation of sophisticated predictive algorithms may enhance energy consumption, hence increasing efficiency. The potential for IoT applications cantered on user-centric design is increasing, enabling buildings to adjust to individual preferences and consequently improving occupant comfort. Moreover, the integration of IoT systems with renewable energy sources might facilitate the development of self-sustaining buildings that enhance energy resilience. As smart city projects advance, IoT-enabled buildings may enhance connectivity with extensive urban infrastructure, hence enhancing overall living circumstances. By overcoming current obstacles and using new possibilities, IoT technologies may convert smart buildings into more efficient, safe, and responsive environments for their inhabitants. Conceptualization, M.P. and N.R. ; methodology, M.P. ; formal analysis, M.P. ; investigation, M.P. ; resources, B.M. ; data curation, M.S. ; writing--original draft preparation, M.P. ; writing--review and editing, B.M. ; visualization, N.R. ; supervision, B.M. ; project administration, Y.A. ; funding acquisition, Y.A. All authors have read and agreed to the published version of the manuscript. This research received no external funding. The authors declare no conflict of interest. This article uses the following abbreviation.
## 5 IETF ROLL Working Group
### Implementation and Evaluation
#### 6.2.2 CoAP Performance Evaluation  
Going beyond mere compatibility tests and the ability to implement the protocol on constrained devices, a few studies have been made in order to evaluate the protocol's behavior and suitability for certain IoT use cases. In particular the comparison between CoAP and HTTP has been studied a few times. The results of such comparisons are considered preliminary since the CoAP protocol itself is not fully standardized yet, and because the studies used the protocol at different stages of its development. However these results still provide a good indication of what to expect when the protocol is fully standardized and the used implementations are further optimized.  
For example, the work described in [107] provides an evaluation of CoAP compared to HTTP in terms of mote's energy consumption and response time in wireless sensor networks. The results for energy consumption, obtained by simulations for a fixed 10 second client request interval, show that while receiving and processing packets, the energy consumed when using CoAP is approximately half compared to the one consumed when using HTTP. While transmitting packets, the energy required by CoAP is 4 times lower than the energy required by HTTP. On the other hand, while being in idle mode, CoAP and HTTP result in similar values of energy consumption. When testing the response time on real sensor motes, the results show that CoAP/UDP introduces 9 to 10 fold lower response times than HTTP/TCP.  
Similarly [108] evaluates the performance of HTTP/TCP and CoAP/UDP over a duty cycled radio layer. With a small modification to the duty cycling layer the authors achieved great improvement in performance at retained low power consumption. Furthermore they introduced an in-network caching mechanism that significantly improves the performance of software updates in incrementally deployed sensor networks.  
In a third evaluation [109] the author finds that CoAP/UDP perform better than HTTP/TCP for the intelligent cargo container use case he evaluated. In particular the author reports a 6 times lower message size and a 4 times lower _Round Trip Time_ (RTT). This is mainly due to CoAPs compressed header and the avoidance of the TCP handshaking mechanisms. A further study of the same use case [110] compares using CoAP/UDP to HTTP/UDP in addition to the typical HTTP/TCP. The study shows that generally UDP based protocols perform better for constrained networks due to using lower number of messages when retrieving resources. When comparing both protocols (CoAP and HTTP) when run over UDP, the study shows that CoAP performs better than HTTP. CoAP also has the added value of optional reliability since it has its own simple retransmission capability.
## 4 Results and Discussion
### Comparison of EEMD-SVM, EEMD-ELM, and EEMD-RNNs  
As shown in Figure 10, in terms of correlation (R\({}^{2}\)), there are no significant differences among the models. For standard RNN, SVM, and ELM, the values of R\({}^{2}\) are 0.994, 0.992, and 0.993, respectively, and the values of KGE are 0.987, 0.983, and 0.974, respectively. In terms of KGE, predictions with less bias and variability were achieved by the RNN-type network than SVM and ELM because recurrent networks provide higher nonlinearity. Moreover, landslide movements are essentially suspended during the dry season. Because static models can only learn current information and can only learn from a portion of historical data, static approaches, including ELM and SVM, provide unreasonable results. The suspended movement characteristics can be approximated well using dynamic RNN approaches through connections of adjacent hidden neurons and learning from a fully historical sequence.
## 2 GCI System Structure with Different Domain Controls  
Figure 1 illustrates the circuit topology and control structure of the three-phase GCI system. In Figure 1, \(L_{\text{f}}\) represents the filter inductor, and \(C_{\text{f}}\) denotes the filter capacitor; \(v_{\text{a}}\), \(v_{\text{b}}\), and \(v_{\text{c}}\) are the three-phase voltages at the grid connection point; \(i_{\text{a}}\), \(i_{\text{b}}\), and \(i_{\text{c}}\) are the currents through the filter inductor; \(R_{\text{g}}\) and \(L_{\text{g}}\) represent the equivalent resistance and inductance of the grid, respectively; \(C_{\text{dc}}\) is the equivalent capacitance; \(i_{\text{pv}}\) is the photovoltaic output current; \(v_{\text{ref}}\) is the reference value for the DC bus voltage; \(l_{\text{dref}}\) and \(l_{\text{dref}}\) are the reference values for the \(dq\)-domain grid-connected currents, respectively; \(H_{\text{v}}(s)\) is the voltage-loop PI controller function; \(K_{\text{d}}\) represents the decoupling coefficient; \(K_{\text{f}}\) denotes the voltage feedforward coefficient; \(m_{\text{a}}\), \(m_{\text{b}}\), and \(m_{\text{c}}\) are the three-phase modulation signals; \(S_{\text{a}}\), \(S_{\text{b}}\), and \(S_{\text{c}}\) are the three-phase switching signals.  
The expressions of the PR and PI controllers used in the current inner loop are as follows:  
\[\left\{\begin{array}{l}H_{\text{PR}}(s)=K_{\text{p1}}+\frac{2K_{\text{c}}s} {s^{2}+\omega_{1}^{2}}\\ H_{\text{PI}}(s)=K_{\text{p2}}+\frac{K_{\text{f}}}{s}\end{array}\right. \tag{1}\]  
In the formula, \(\omega_{1}\) is the fundamental angular frequency, \(K_{\text{p1}}\) and \(K_{\text{r}}\) are the proportional coefficient and resonance coefficient of the PR controller, respectively, and \(K_{\text{p2}}\) and \(K_{\text{i}}\) are the proportional coefficient and resonance coefficient of the PI controller, respectively.  
Figure 1: _Cont._  
According to the literature [26], there is an equivalent relationship between the PR controller adopted in the phase domain and the PI controller in the \(dq\) domain, that is,  
\[H_{\text{PR}}(s)=\frac{1}{2}[H_{\text{PI}}(s+j\omega_{1})+H_{\text{PI}}(s-j \omega_{1})] \tag{2}\]  
Therefore, this paper analyzes the parameter alignment between the PR and PI controllers as follows: \(K_{\text{p1}}=K_{\text{p2}}\), \(K_{\text{r}}=K_{\text{i}}/2\).
# IoT-Enabled Smart Cities: A Review of Concepts, Frameworks and Key Technologies
## 3 Review of IoT-Enabled Smart City Components and Solutions
### Smart Environment  
The smart environment domain includes environmental data collection, monitoring and analysis for pollution reduction, water quality and supply monitoring and weather and climate events management [67]. In this regard, air quality monitoring is a crucial factor for tracking levels of air pollutants (e.g., NO\({}_{\rm x}\), O\({}_{3}\), CO\({}_{2}\), N\({}_{2}\)O, PM10, PM2.5, etc.), which represent a serious issue for human health (caused by transportation, heating and industrial emissions). Smart waste management is also included in this area since it has numerous impacts on the environment. Control policies for waste production are handled with smart waste bins that are installed with sensors and are capable of providing the real-time analysis of the capacity that is currently available [92].  
As for smart water, sensing devices that are devoted to the assessment of water quantity and quality typically measure parameters such as pH, conductivity, turbidity, total dissolved solids, etc. [99]. Electromagnetic and ultrasonic sensors are employed to measure the pressure for water consumption rate analysis [100]. The application of WSNs for water quantity and quality monitoring systems has opened up a new generation of smart water monitoring systems, providing a more advanced context awareness and near real-time interaction [101].  
Smart environment applications and services are typically based on ambient and chemical sensors, which are used to measure physical quantities expressing environmental parameters and conditions, such as temperature, humidity, pressure [7] and different kinds of pollutants. Smart sensing and visualization technologies (satellites, LiDAR) are applied to greenhouse gas emissions (GHG) and land usage [99]. Location-based services and GIS data are also employed.  
In Singapore, solid waste is managed by the Integrated Waste Management Facility (IWMF), which includes innovative IoT technology that allows for an increase in the process efficiency and a reduction in GHG emissions [102]. In Amsterdam, the Green City Watch is a geo-spatial AI platform that monitors urban green infrastructures in near real time using AI algorithms and satellite images [103]. In Stockholm, solar-powered smart waste bins are installed, which automatically report when they are fully charged and also perform waste packaging [66]. The Busan smart city, South Korea, employs smart water management systems in the whole urban water cycle [3]. In the context of the European TRAFAIR project, six European cities (Florence, Pisa, Livorno and Modena in Italy and Santiago de Compostela and Zaragoza in Spain) have adopted the Snap4 City platform for monitoring urban air quality (using sensors that collect data in the six cities) and providing urban air quality predictions using simulation models [104].
## 2 State of the Art
### Cloud and Edge Computing  
While cloud computing has opened the doors to unlimited resources, it suffers from intrinsic problems related to the remoteness of resources. In this context, edge computing presents itself as a solution that moves resources closer to the user. Recent studies and frameworks have highlighted the effectiveness of edge computing in improving performance in many contexts [7; 8]. Among these, we include VR/AR systems due to the interaction of real-time components and the high bandwidth of the transferred data, which represents a significant challenge in this context. On the other hand, the edge has limited resources [9], which leads to poor scalability, which translates to a rapid drop in performance in terms of computation when the number of simultaneously connected users increases. For this reason, hybrid cloud edge architectures combining the strengths of cloud and edge computing allow the distribution of computational tasks between cloud servers and edge devices based on their computational needs and latency requirements. The development of hybrid architectures is supported by the continuous increase in IoT devices and the advances in network technologies such as 5G.
# SatEC: A 5G Satellite Edge Computing Framework Based on Microservice Architecture
## 2 Related Work
### Satellite On-Board Computing  
With increasing satellite communication performance requirements, the satellite on-board data will depend more and more on the capability of the edge computing platform. Reconfigurable on-board processing using Field-Programmable Gate Array (FPGA) has been used in a flexible satellite communication system [27]. Additionally, some researchers have begun to study the application of advanced design concepts being used in on-board systems. OBC-NG is a reconfigurable on-board computing architecture for spacecraft [28]. It is based on a distributed heterogeneous network which incorporates Commercial Off-The-Shelf (COTS)-based processors and FPGAs. However, the above-mentioned system only considers the on-board application of devices or platforms. There is no consideration of how create the hardware and software resources of 5G satellite nodes in order to provide services to users. Therefore, it is necessary to design a framework for 5G Satellite edge computing and provide various kinds of specific services based on on-board computing resources and storage resources.
## 2 Materials and Methods
### Data Analysis  
Open Gaze and Mouse Analyzer (OGAMA) software (version 5.1) was used to convert raw ET data into appropriate quantitative data. These data were then processed by calculating the difference between focus on weight-related and non-weight-related body parts. A positive result indicated a greater focus on weight-related parts, whereas a negative result suggested a focus on non-weight-related parts. A result near zero indicated a balanced attention between the two.  
For the post-treatment and follow-up assessments, reliable changes were calculated only for measures with available clinical and community means and standard deviations, specifically for EDI-DT, EDI-BD, PASTAS, BAS, and BCQ [37; 41; 42; 44]. The analysis followed the Jacobson and Truax [50] criteria, using the Leeds Reliable Change indicator calculator in Excel [51] for single case assessments. The Reliable Change Index (RCI), which compares baseline and follow-up measurements, takes into account individual variability in scores over time and the standard error of measurement (SEM). The SEM helps quantify the expected variability in scores due to measurement errors alone. The RCI is calculated by dividing the difference between baseline and follow-up scores by the SEM. If the RCI exceeds 1.96, for a 95% confidence interval, it indicates that the change was statistically significant and not merely due to measurement error.
## 1 Introduction
Additionally, issues related to pathogen/insect survival and microbial safety can arise [25]. The improvement in heating uniformity as well as the maximization of the energy coupling to the food are still research challenges since they mainly depend on the electromagnetic field distribution within the microwave treatment chamber and the dielectric properties of materials. To overcome this problem, several solutions have been proposed, such as using the resonance phenomenon and using various microwave frequencies; however, adding a stirrer in the reactor is generally the most effective and has been employed widely in microwave continuous-flow reactors. In fact, the stirring systems improve the uniformity of heating [26, 27]. To this end, the finite element method and numerical simulation can be useful tools for the optimization of food heating strategies [28, 29, 30, 31]. The only scientific research in which microwaves were used to disinfest almonds is reported in [10]. In this study, the authors asserted that the MW thermal treatment of unshelled almonds could represent an alternative to chemical fumigation, balancing the complete killing of insects with minimal thermal impact on product quality. However, in this research, a domestic microwave oven with discontinuous flow was used and heating uniformity was not investigated. The primary aim in this study is the development of a semi-industrial microwave (MW) prototype with continuous mass flow for almond processing, capable of handling a feed rate of at least 2 kg min\({}^{-1}\), maintaining a minimal residence time of at least 90 s, and achieving almond temperatures of up to 60 \({}^{\circ}\)C with homogeneous temperature distribution inside the treatment chamber. Moreover, numerical simulations were carried out with the main aim to identify the best design solution of the MW prototype (number of MW sources along the treatment chamber), ensuring an optimal heating uniformity during the whole process.