esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
Our study revealed the following important findings after comparing body composition, water-regulating hormones, body electrolytes, and recovery ability based on the intake of regular and electrolyte water before and after performing exhaustive exercise. First, body composition was significant in BFM (\(p\) = 0.035), ICW (\(p\) = 0.016), ECW (\(p\) = 0.008) when CON and 3 RINK were compared in the time interval between R and P0 (P0-R). In addition, the difference between P1 and P0 showed positive trends in BW, ICW, ECW, and urine levels. Second, the water-regulating hormones, such as ADH, aldosterone, and renin did not show interaction effects between the different periods of intake and groups. Third, the analysis of the difference in the amount of change between the time periods showed that the change between R and P0 (P0-R) after consuming 3 RINK compared to that after consuming CON was significant for the sodium (\(p\) = 0.028), hemoglobin (\(p\) = 0.001), and hematocrit (\(p\) = 0.016) levels. Fourth, the intake of electrolyte beverages improved according to the exercise time, ventilation per min, maximum oxygen intake, breathing exchange rate, and maximum heart rate. Finally, recovery availability was confirmed through lactic acid concentration. As a result of analyzing the difference in the amount of change by period, 3 RINK showed a significant difference in the change from R to P0 (P0-R) and from 5 to 10 min compared to after CON intake. Taken together, these results suggested that the intake of electrolyte water is more effective than the intake of regular water in preventing a decrease in BW and serum electrolytes. Moreover, it is more effective in improving exercise ability and recovery ability. In the future, it is necessary to further determine the effects of electrolyte beverages in terms of adjusting for electrolyte components, timing of intake, and participants' conditioning (i.e., athletes vs. the general public).  
Experiment design, performance, and analysis, D.-H.C.; experiment design and interpretation J.-Y.C., J.-H.K. and T.-K.K.; writing, D.-H.C. and T.-K.K. All authors acknowledged responsibility for the full content of the submitted manuscript and approved their submission. All authors have read and agreed to the published version of the manuscript.  
This research was funded by INTERFOREST Inc.  
The study was conducted according to the guidelines of the Declaration of Helsinki, and approved by the Institutional Review Board of Korea National Sports University (1263-202103-BR-004-01).  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
The authors declare no conflict of interest.
## 4 Simulation Results and Discussion  
In this section of the article, we compare and review the performance of the two KPIs for the public safety scenario illustrated in Figure 2, with/without ICIC techniques for the MCP and TCP distribution of GUEs, the different numbers of UABS deployment, and while considering a brute-force approach. We use Matlab-based computer simulation to define the brute force algorithm approach and objective functions. With extensive Monte Carlo simulation and design parameters set to the values given in Table 5, we calculate the KPIs of the wireless network, and for the obtained KPI values, we conduct a comparative study, and the following section discusses the same.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline \(\text{AR}_{\text{sim}}\) & 100 km\({}^{2}\) \\ \(\Lambda_{\text{neb}}\), \(\Lambda_{\text{gae}}\) & 4 and 100 per km\({}^{2}\) \\ \(\text{N}_{\text{uabs}}\) & 10, 20, 30 \\ \(\text{Pow}_{\text{Pow}_{\text{uabs}}}\) & 46 and 30 dBm \\ \(h_{\text{abs}}\), \(h_{\text{abs}}\) & 36 and 120 m \\ \(h_{\text{gae}}\) & 1.5 m \\ \(f^{\text{814}}\) & 763 MHz for downlink \\ \(a_{\text{abs}}\) & 0 to 1 \\ \(\beta_{\text{mabs}}\) & 0 to 10\(\%\) \\ \(\rho_{\text{mabs}}\) & 20 dB to 40 dB \\ \(\rho_{\text{uabs}}\) & \(-5\) dB to 5 dB \\ \(\tau_{\text{uabs}}\) & 0 dB to 12 dB \\ \hline \hline \end{tabular}
\end{table}
Table 5: System parameters and simulation values considered.  
Figure 7: A flowchart combining AG-HetNet system flow and the Brute Force algorithm used.
## 3 Results and Analysis
### Analysis of Phenacetaldehyde Formation Mechanism in Maillard Reaction System  
#### 3.4.1 The Formation Mechanism of Major \(\alpha\)-DCs  
According to the analysis results in Section 3.3, the formation mechanism of MGO, BD and GO can be analysed. The 3-DG generated by the enolization of glucose under acidic conditions and G generated by oxidation retains the complete hexose skeleton. These long-chain \(\alpha\)-dicarbonyl substances are easily broken to form small-molecule \(\alpha\)-DCs through enolization and oxidation reactions.  
(1) MGO is mainly produced by the caramelization of glucose, and 3-DG is generated by the enolation of glucose through 1,2-enolation (Figure 4) [33]. The reverse aldol condensation of 3-DG at C3-C4 directly produces MGO [18; 34] (Figure 5). Under the condition of excessive glucose in CWC, this is the main pathway.  
Figure 4: Enolization of glucose to produce 3-DG.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Steamed Time**} & **Methylglyoxal** & **2,3-** & **1-oxo-2-** & **2,3-** & \multirow{2}{*}{**Cityoxal (GO)**} & **Total** \\  & **(MGO)** & **Butanedione** & **Phenylacetaldehyde** & **Pentanedione** & & \\  & **(BD)** & **(QP)** & **(PD)** & **(GO)** &  
(2) There are three GO formation mechanisms:  
1 G produced by glucose oxidation can form GO through C2-C3 cleavage or reverse aldol condensation [35; 36; 37] (Figure 6). Another product, 2,3,4-trihydroxybutyl, can undergo C2-C3 reverse aldol condensation and an oxidation reaction to produce GO (Figure 7).  
2 In Figure 5, the product of the reverse aldol condensation of 3-DG in C3-C4, 2,3-dihydroxypropanal can produce GO through the reverse aldol condensation and oxidation of C2-C3 (Figure 8).  
3 GO can be generated through reverse aldol condensation in C3-C4 and oxidation reactions of glucose (Figures 7 and 9). GO was produced only after 30 min of steaming, indicating that the glucose in CWC could not easily produce G, but was more likely to produce 3-DG, and the generated 3-DG mainly produced MGO.  
(3) There are two mechanisms of BD formation: under the condition of excessive glucose, the main method of BD formation is the oxidation and elimination of 2,3,4-trihydroxybutyl produced by the reverse aldol condensation of glucose [13]. In addition, glucose can be polymerized into oligosaccharides, which produce Amadori products and then undergo a \"peeling-off\" mechanism to generate 1,4-dideoxy-2,3-hexadiketologe, which is then subjected to reverse aldol condensation to generate BD (Figure 10) [38; 39].  
Figure 5: Reverse aldol condensation of 3-DG to produce MGO.  
Figure 6: Reverse aldol condensation of G to produce GO.  
Figure 7: 2,3, 4-trihydroxybutanal underwent reverse aldol condensation and oxidation reaction to produce GO.  
Figure 8: 2,3-dihydroxypropanal underwent reverse aldol condensation and oxidation reaction to produce GO.  
Figure 9: Reverse aldol condensation of glucose.
# The Challenges and Compatibility of Mobility Management Solutions for Future Networks
## 5 Recent MM Techniques and 5G Architecture
### Considerations for SDN-based MM Solutions
#### Advantages of radio access network as service (RANaaas)  
* The handoff support is attained at the access level of the network [83].
* The physical connection is managed centrally, hence, the granularity service at channel-based and per-flow-based is attained.
* The network resources are assigned on an on-demand basis at the RAN level [80; 81].
* On-demand handover execution is also possible [83].
* Supports permitting the devices to composite on more than one AP.
# IoT-Enabled Smart Cities: Evolution and Outlook
## 2 IoT-Enabled Smart Cities Landscape
### Cities Selection and Analysis Considerations  
So far, many city rankings have been published both in scientific literature [13; 14; 15], as well as by consultancy organizations [16; 17]. Thus, this paper is not trying to rank the cities chosen for review. The selection criteria and the features analysed for the cities in the next sections is meant to have a comprehensive yet non-exhaustive list of cities that, in the authors' opinion, sufficiently represent the evolution of the IoT-enabled smart city concept and, at the same time, exhibit some of the gaps that create the challenges that are still open and need to be addressed.  
The list of cities that are reviewed is as follows: Santander (Spain, Section 2.2), Busan (South Korea, Section 2.3), Singapore (Section 2.4), Shenzen (China, Section 2.5), Atlanta (USA, Section 2.6), Amsterdam (Netherlands, Section 2.7), Sunshine Coast (Australia, Section 2.8) and Rio de Janeiro (Brazil, Section 2.9).  
As it has been previously mentioned, the intentionally high-level and non-exhaustive review of IoT-enabled smart cities that is presented in the following sections is meant to showcase the fragmented vision and approaches that are provided in cities around the world. In some cases, this fragmentation is present even in the same city. In this sense, the analysis carried out for each city focuses on the idea that, nowadays, commonalities are basically reduced to the offered services, but not on the solutions adopted to provide them neither on the strategies to provide them (not only between one city and another, but also within the same city). Moreover, the selection tries to provide a worldwide perspective, which adds cultural and geo-political variety to the mix, in order to allow generalization of the discussion and the derived conclusions. The objective is to stress the heterogeneity that currently exists in the IoT-enabled smart cities landscape while, at the same time, it is evident that priorities and needs of cities all over the world are significantly similar.  
Finally, it is important to mention that, in many cases, the information about actual smart city services provided at field trial or production level (not small-scale or lab-based proof-of-concept implementation), which were the target of the review, is not available through scientific or technical publications but through high-level brochures and/or press releases that hinders stricter methodological analysis.  
In summary, the main value of the review and discussion that can be found in the following sections lies on the insights that can be derived in view of the similarity of smart city services that cities of different sizes from four continents have rolled out and the significant differences in the strategy and solutions that they have adopted. The open challenges that are presented and discussed in Section 4 emanate from these insights.
## 3 Results and Discussion
### Post-Classification Stage  
In this stage, the pre-classification into 3 groups scheme was applied over the same training set in the same way as in phase 2, but using this time a reduced multi-class classification scheme with the feature sets \(c_{3}\) and \(c_{4}\).  
Figure 10 presents the results of this experiment, where once more time, the best results are obtained using the feature set \(c_{3}\) and the MLP algorithm, obtaining accuracies of 95.30%, 91.77% and 90.02% for the groups 1, 2 and 3, respectively.  
Once the chromosomes were classified into 3 wide classes, the next step was to use this classification as the input of a multi-class classifier. The highest accuracies were obtained by the MLP classifier, being 95%, 91% and 90%, for the groups 1, 2 and 3, respectively.  
Finally, the performance of the whole classification scheme was tested by a complete classification round, creating a dataset containing every testing sample. When the same sample was assigned to two different classes during the phase 1, its final class was reassigned according to the highest membership percentage. Once the classification into three wide classes was obtained, the Phase 2 was carried out, using the multi-class algorithms over 24 classes, where 76.44% of the whole set of chromosomes was correctly classified.  
Figure 9: Comparison of minimum and maximum accuracies for both, binary and multi-class schemes.  
Figure 10: Experiment 3. Post-classification using the \(q_{4}\) architecture. (**a**) Multi-class group 1, conformed by chromosomes 1 to 5. (**b**) Multi-class group 2, conformed by chromosomes 7 to 15. (**c**) Multi-class group 3, conformed by chromosomes 16 to 24.
## 3 Offline Optimization and Adaptation Under Nonlinear Task Constraints
### Constraints Definitions in Joint Space and Cartesian Space
#### 3.2.2 Waypoints  
Waypoints are Cartesian positions \(\overline{\mathbf{X}}_{t}\) on the robot's end effector or any other interest points that we expect the robot to reach at timestep \(t\) or a temporal interval \(\mathcal{T}\subset[0,T]\). Waypoints are defined in the Cartesian space with the FK function \(T\) to associate with some DOFs of the joint positions. Based on the distance square between the expected points and the actual points, we denote the waypoint constraint function as follows:  
\[c_{k,t}=|T(\mathbf{q}_{t}(\mathbf{\mu}_{t},\mathbf{\sigma}_{t}))-\overline{\mathbf{X}}_{t}|^{2} \tag{21}\]  
And express the one-side constraint with:  
\[P_{\mathbf{\mu}_{t},\mathbf{\sigma}_{t}}\left(|T(\mathbf{q}_{t}(\mathbf{\mu}_{t},\mathbf{\sigma}_{ t}))-\overline{\mathbf{X}}_{t}|^{2}\leqslant d_{k,t}^{2}\right)\geqslant\alpha_{k,t} \tag{22}\]  
where \(d_{k,t}\) is the distance threshold and \(\alpha_{k,t}\) is the confidence level. Due to the nonlinear nature of the constraint function, we can not derive the exact distribution of \(c_{k,t}\) with Bayesian theory. To tackle the problem, the Unscented Transform [40] method is adopted in [21]. However, the initial factor deeply influences the estimation result--when the mean and standard deviation change, the factor can not reflect the variance of the constraint function precisely. Alternatively, in this paper, we utilize moment matching to estimate the \(c_{k,t}\) distribution:  
\[\begin{split} S=\{c_{k,t}(\mathbf{q}_{1},\mathbf{q}_{2},\ldots,\mathbf{q}_{D }),\mathbf{q}_{k}\in\{\mathbf{\mu}_{k}-2\mathbf{\sigma}_{k},\mathbf{\mu}_{k}+2\mathbf{\sigma}_{k} \}\}\\ \mathrm{E}(c_{k,t})\approx&\mathrm{Mean}[S]\\ \mathrm{V}(c_{k,t})\approx&\mathrm{Var}[S]\end{split} \tag{23}\]  
where \(D\) is the DOF number required to express the constraint function. We utilize 2x the standard deviation to constitute the set \(S\) and compute the mean and variance based on moment-matching. Given that the distribution of the random variable \(c_{k,t}\) is a generalized-\(\chi^{2}\), we approximate it with a simpler Gamma distibution to represent the CDF in an analytical way. The shape \(\alpha\) and rate \(\beta\) of the Gamma distribution are derived as follows:  
\[c_{k,t}\sim\Gamma(\alpha,\beta) \tag{24}\] \[\alpha=\mathrm{E}(c_{k,t})^{2}/\mathrm{V}(c_{k,t}),\beta=\mathrm{ E}(c_{k,t})/\mathrm{V}(c_{k,t})\]  
The CDF of the Gamma distributions are then computed using \(F_{\alpha_{k,t}}=F(d_{k,t};\Gamma(\alpha,\beta))\) to represent the waypoints constraint probability via Equation (22).
# Design and Implementation of Virtual Security Function Based on Multiple Enclaves
## 4 System Design
### Secure Boot  
The Manager is the core of the system and is responsible for the secure startup of the system, including configuration file parsing, enclave creation and destruction, and dynamic configuration modification. The Manager is mainly composed of Manager_Main (untrusted) and Manager_Enclave (trusted). The Manager_Main is responsible for the static initialization of Click and the parsing of command line parameters and the configuration file. The Manager_Enclave is used to protect the lexer (lexer is used to parse Click configuration files) creation, routing generation, elements connection, etc. In order to ensure the security of the configuration file, we use the SGX seal and unseal functions to seal it. Hence, Manager_Main will call into Manager_Enclave to perform configuration file parsing, lexer, element and port connections.  
Figure 4 is a flow chart of the secure boot. When the system starts executing, the Manager_Main first initializes the Click static route, which is mainly responsible for the initialization of parameter types and routing parameters, and the initialization of the global Handler. Then the Manager will read the command line parameters and the configuration file encrypted by SGX seal to see if other functions are needed for this operation, such as whether hot swapping is supported. Next, the Manager will enter Manager_Enclave via an ecall. Manager_Enclave first unseals the configuration file, then reads the configuration and parses the configuration file. Manager_Enclave creates a lexer and converts the configuration file to a String type and passes it as a parameter to the lexer. The lexer creates a route, then extends the route based on the passed String parameters, sorts and adds elements and port connections. At this point, Manager_Enclave is executed internally, and the system will return to Manager_Main to continue execution. Next, the system traverses the entire route according to the order and configures each element. The configuration of each element creates an associated enclave, and multiple enclaves need to be locally authenticated to ensure that they each run on the same platform. This includes local attestation between enclaves and local attestation between them and Manager_Enclave. After the local attestation is successful, the Manager_Enclave will communicate with the enclave running the functions in turn, and perform the configuration and initialization of the functions in each enclave in this order. In addition, the functions running inside each enclave are composed of multiple elements of Click. Therefore, when the function in the enclave is initialized, the enclave internally generates routes, adds elements and port connections, and executes the elements in turn.
# MAC Protocols for mmWave Communication: A Comparative Survey +
## 5 Distributed Protocols
### Md-Mac  
For outdoor mesh networks in the 60 GHz mmWave bands, memory-guided directional MAC (MD-MAC) was proposed in [69]. Approaches similar to MD-MAC does not need any resource allocation or extensive cooperation among different nodes in the communication system. In this protocol, all the devices maintain their own allocation properties. They do not share their uplink and downlink state information with other nodes in the network. These properties and data are stored in an allocated slot, and the slot is kept updated after or before every single time frame. The state of a previously idle slot switches from idle to transmit following a successful transmission. Meanwhile, it also collects and saves its neighbor's information. Jain's fairness index (JFI) is frequently used to measure the fairness performance of the MD-MAC protocol. For node counts of 10, 20, and 30, the JFIs of the MD-MAC protocol are 0.90, 0.81, and 0.78, respectively.
* Advantages: The MD-MAC has the ability to quickly adapt and maintain link-level fairness. This memory-guided protocol is an excellent choice for outdoor mesh networks. Most of the mmWave MAC protocols do not take into account the deafness problem; however, the MD-MAC protocol uses predictability and learning to deal with this issue while demonstrating a small control overhead.
* Disadvantages: Based on the JFI, the MD-MAC protocol falls slightly behind some other commonly used MAC protocols because of the interferences. This could an explanation for why the MD-MAC is not suitable for indoor mmWave WPANs.
## 2 Results and Discussion
### Kinetic Studies  
Kinetic adsorption experiments were carried out in order to obtain information about the mechanism of adsorption at the solid-liquid interface. In this study, the [PERSON] [40] pseudo-first-order kinetic model (PFO) (Equation (2)) and the pseudo-second-order Ho and McKay (PSO) [41; 42; 43; 44; 45; 46] kinetic model (Equation (3)) were used to test the experimental data.  
The PFO model is based on the assumption that the rate of adsorbate uptake with time is directly proportional to the difference between the equilibrium concentration of the solution and the amount of adsorbate on the solid adsorbent with time, an assumption that is generally true in the initial stages of the adsorption process. It is commonly observed that the kinetics follow the PFO model when the adsorption process is diffusion-controlled.  
\[ln(q_{e}-q)=lnq_{e}-k_{1}t \tag{2}\]  
where \(q\) and \(q_{e}\) represent the adsorption capacity at time \(t\) and at equilibrium, respectively, in mM/g, and \(k_{1}\) is the adsorption rate constant, in min\({}^{-1}\).  
The PSO kinetic model is based on the assumption that the rate-limiting step is chemisorption and predicts the behavior over the whole range of adsorption. If this assumption is true, the Ho-McKay model presumes that the adsorption rate is dependent on the adsorption capacity, and not on the concentration of adsorbate in the solution.  
\[\frac{t}{q}=\frac{1}{k_{2}q_{e}^{2}}+\frac{1}{q_{e}}t \tag{3}\]  
where \(k_{2}\) is the PSO rate constant, g mg\({}^{-1}\) min\({}^{-1}\).  
Typical graphic representations of the two kinetic models are presented in Figure 9 for the Lagergren model and in Figure 10 for the Ho-McKay model, for the adsorption on the adsorbent PAB3. The slopes and intercepts of the plots of \(ln(q\)e-\(q)\) vs. \(t\) were used to determine the PFO rate constants, \(k_{1}\). The PSO rate constants, \(k_{2}\), were estimated from the intercept and slope of \(t\)/\(q\) vs. \(t\). The values of the rate constants and the determination coefficients (\(R^{2}\)) obtained in all the cases are shown in Table S1.  
Figure 9: Plots of Lagergren pseudo-first-order kinetic equation for the adsorption of sulfamethoxazole and tetracycline, respectively, on PAB3 polymeric adsorbent.  
The data presented in Table S1 show that the determination coefficients found for the PFO kinetic model were much lower than those obtained for the PSO model. This proves that the kinetics of sulfamethoxazole and tetracycline removal by adsorption on the studied polymeric adsorbents are best described by a PSO kinetic model.