esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Virtual Inertia Boundary Evaluation Analysis  
In this section, a 114-node model is built on the MATLAB/Simulink platform, and the pre-designed model is subjected to pre-imagined disturbances to construct a sample library. The normalized input and output sample library is divided into training and testing sets. The training set sample data is input into the RBFNN model to train the RBFNN until the accuracy requirement is met.  
The pre-imagined active power increase disturbance is set, with the disturbance range of active power being 0.01 p.u.-1 p.u., the input range of the state of charge Q being between 100 Ah and 1000 Ah, the temperature range of the PV system being 15-40 \({}^{\circ}\)C, and the range of light intensity being 800-1200 W/m\({}^{2}\). An offline training sample library is constructed. The PV system input-output sample library contains a total of 4000 sets of sample data, and ESS input-output sample library contains a total of 4000 sets of sample data. Among them, 3200 sets of data are set as the training sample set for each system, and another 800 sets of data are set as the testing sample set.  
The fitting results are shown in Figures 5 and 6. The model fitting effect is good, and the MSE is gradually adjusted to the optimal value with the parameter Spread. The optimal  
Figure 4: 114 node example system.  
Spread value of the PV system virtual inertia MSE curve is 0.38, and the optimal Spread value of the ESS virtual inertia MSE curve is 0.19.  
The results of this model will be incorporated as constraint conditions into the virtual inertia optimal allocation model, serving as the boundaries of the virtual inertia that grid-forming PV and distributed ESS can provide.
## 4 Failure Mechanism of Granite under Triaxial Compression
### Failure Mechanism Analysis  
The analysis of the SEM results shows that the flake spalling fragments and vertical fracture surface are mainly caused by the tensile failure mechanism and there are shear failure characteristics in some parts. The micromorphology of the fracture is mainly transgranular and intergranular drawing and step patterns. The fracture is clean and has less powder and the micromorphology of the inclined fracture surface is due to the shear failure mechanism. The broken mineral particles and powders on the fracture surface can be seen everywhere. Combined with the deformation, failure, and strength characteristics of rock samples under different confining pressures, the following was seen:  
1. The triaxial compression test was carried out under the condition of low confining pressure. The granite sample with the maximum free water absorption predominantly experienced brittle failure. The microcracks extended from both opposite ends towards the middle where they finally connected. At this point, the rock sample instantly lost its bearing capacity, collapsed loudly, and produces mostly vertical fracture surfaces and flake spalling fragments. Combining this information with the SEM data, the failure mechanism is found to be comprehensive tensile and shear failure, with predominantly tensile failure.
2. Triaxial compression tests were performed under medium and high confining pressures. The granite sample with the maximum free water absorption experienced mainly plastic failure. After failure, it mainly produced an inclined fracture surface and sometimes a conjugate shear surface. The integrity of the sample after failure was good. Combined with the SEM results, the failure mechanism was shown to be tensile and shear comprehensive failure, with mainly shear failure and some localized tensile failure.
## 2 Wind Speed Modeling
### Probability Distribution Model
#### 2.1.2 Non-Parameter Distribution Model  
As we know, parameter distribution models assume that wind speed can be described by a probability density function. However, when the assumption does not coincide with the actual distribution, the calculation error is inevitably increased. In recent years, non-parameter distribution models of wind speed are proposed to solve the errors between the probability density function and actual wind distribution, and are suitable for analyzing the feature space of arbitrary structures.  
A typical non-parameter distribution model of wind speed based on non-parametric kernel density estimation is proposed in [26; 27]. Neither prior knowledge of wind speed data nor any hypothesis of probability distribution form is required. The model consists of the following steps:  
**Step-1.** Selecting the sampling sequences of wind speed;  
**Step-2.** Establishing the wind speed probability density function; and  
**Step-3.** Selecting the kernel function and smooth parameters to establish the wind speed model.  
Assuming the sampling sequence of wind speed is (\(v_{1}\), \(v_{2}\),..., \(v_{n}\)) and the probability density function of wind speed is \(f(v)\), the kernel density estimation can be expressed as:  
\[\hat{f}(v)=\frac{1}{nh}\sum_{i=1}^{n}K[(v_{1}-v_{i})/h] \tag{6}\]  
where \(n\) is the sampling size; \(h\) is the window width, which is also called smoothing parameter; and \(K(\cdot)\) is the kernel function. When the sampling size is large enough, the selection of kernel function has a limited influence on the result, and vice versa. Special attention must be paid to the selection of the optimal smoothing parameter \(h_{opt}\), since the smooth parameter has a great influence on the estimation accuracy of the kernel density.  
The asymptotic integrated mean squared error (AIIMSE) consists of bias and variance, and is a commonly accepted criterion to quantify the estimation error in a kernel density model [27]. The minimization of AIMSE means choosing an appropriate bandwidth that can achieve good balance between bias and variance, and ensuring that neither is too small or too large.
# A Secure IoT-Based Authentication System in Cloud Computing Environment
## 4 Security Analysis
### Resistance to De-Synchronization Attack  
An anti-synchronization attack means that an attacker interrupts and modifies the response message from the control server during the authentication phase, so that the authentication data between the client and the database of the control server are not synchronized [44]. Then, even if he/she is a legitimate user passing through the controlled server, all future authentication processes will fail.  
In our proposed scheme, only users' identities are stored in the control server's database. In addition, those identities will not be changed in any phases, i.e., the authentication and password change phases. For the user, data changes occurred in the authentication stage and the last step of the password change phase. However, password change only needs to be involved on the user side; thus, the attacker cannot interfere. In the last step of the authentication phase, the data in the user's smart card will be updated (\(C_{1}\), \(PID_{i}\)) to (\(C_{1}^{\prime}\), \(PID_{i}^{\prime}\) ) when authentication processes are successfully finished. If the update was interrupted, the user can still use the old data (\(C_{1}\), \(PID_{i}\)) to run a successful authentication process. It can be concluded that the scheme can resist synchronous attack.
## 2 Methods
### Economic Indicators and Calculations
#### 2.2.6 Present Value of Total Profit in System Service Life  
The above five indicators mainly focus on the present economic performance evaluation of ORC system. However, for an engineering technology solution, the evaluation of present economic performance is not enough. The economic performance of a solution throughout its service life should be evaluated.  
We usually use monetary units to evaluate engineering solutions, so in the economic analysis we mainly focused on the cash flow of the income and expenditure of the equipment (solution) throughout its service life. Can we add up the cash flows that occur throughout the service life directly? No, this is because the payment effect of money is not only related to the amount of money, but also to the time of occurrence. The value of $5 received this year is different from that of $5 that will be received next year. Even if inflation is not considered, its value is different because the initial earned money can be turned into funds for investment and generates new value. This is called the time value of money. The longer the time lasts, the greater value is added. Because the value of money varies with time, cash flows that occur at different times cannot be directly added and compared. In this way, the economic analysis and the comparison between different technological and economic solutions become more complicated. It is necessary to make a currency equivalent calculation with time factor, that is, calculate the cash flow at various times according to the bank interest formula. For a technical solution with a long payback period and complicated system, a dynamic analysis method considering the time value of funds can be adopted, and a fixed discount rate can be used to convert the future value in solution service life into present value to evaluate the economy performance of the technical solution.  
If an ORC system is built for waste heat recovery, the total net profit earned from the commissioning to the service life of the system can be considered. Regardless of the initial investment, the annual net profit is net income minus operation and maintenance costs. In this way, the profits are the same every year. However, if interest rate is considered, the value of the same amount of money is definitely reduced when converting from 20 years later to today. Assuming that the service life of the ORC system is 20 years, the net profit from the first year to the twentieth year is converted into the profit at the beginning of commissioning according to the year. Then, the initial investment is subtracted after summing up. That is to say, the present value of total profit in system service life can be finally obtained.  
\[\text{C}_{\text{PV}}=\sum_{1}^{\text{n=20}}\frac{NE-COM}{\left(1+i\right)^{ \text{n}}}-C_{\text{tot}}, \tag{28}\]  
where \(C_{\text{PV}}\) is the present value of total profit in system service life, \(\left(1+i\right)^{-\text{n}}\) is the formula used to calculate the present value, n is the number of years, and \(i\) is annual interest rate.
## 5 Results and Discussion
### Comparison of Performance between NB-IoT and Other LPWAN or UNB Systems  
The outcomes discussed in the paper can be also directly related to those obtained for systems representing other IoT families, such as LPWAN or UNB (refer to the classification shown in Figure 1). In the authors' own previous investigations, documented in [29; 30; 31], the performances of IoT systems, such as LoRa, Weightless, and SigFox, were measured against interference and channel fading. Although results obtained therein relate the DSR to the carrier-to-noise and interference ratio (CNIR), this can be easily converted into the MCL by Equation (1) to compare it with NB-IoT performance. The calculated values of MCL were then gathered in Table 2, in which the last column indicates the MCL, allowing one to notice how the NB-IoT, with MCL = 164 dB, outperformed its LPWAN and UNB counterparts by at least 7 dB when compared with SigFox, and in further excess of this figure when compared with the other systems:  
\[\mathrm{MCL}_{[\mathrm{dB}]}=\mathrm{EIRP}_{[\mathrm{dBm}]}-\left(-174_{[ \mathrm{dBm}/\mathrm{Hz}_{2}]}+\mathrm{BW}_{[\mathrm{dBHz}]}+\mathrm{CNIR}_{[ \mathrm{dB}]}\right) \tag{1}\]
# Influence of CeO\({}_{2}\) Addition to Ni-Cu/HZSM-5 Catalysts on Hydrodeoxygenation of Bio-Oil
## 1 Introduction
The bio-oil hydrogenation process involves the polymerization and condensation of unsaturated components, and the macromolecule compounds generated will deposit on the surface and the pore of the catalyst, thus forming a coke deposit. Coke deposits can cover the active sites of catalysts and hinder the contact between the reactants and active sites of catalysts. The catalyst will then be deactivated. [PERSON] et al. [11] studied the coke deposition during the HDO process, and the results showed that coke deposits formed at high temperatures were more difficult to be desorbed. [PERSON] et al. [12] studied the effect of reaction temperature on deoxidation in the HDO process and found that deoxygenation of bio-oil was a function of time and temperature with the increase of temperature resulting in decreasing deoxygenation. Therefore, the reaction temperature of deoxyhydrogenation should be controlled within a suitable range. Rare earth metal oxides (e.g., CeO\({}_{2}\) and LaO\({}_{3}\)) can significantly improve the activity, stability, and anti-coking ability of nickel-based catalysts. The addition of a small amount of rare earth oxides in the catalyst as a promoter has received considerable attention from different researchers [13; 14]. CeO\({}_{2}\), as an effective redoxagent, can convert between Ce\({}^{4+}\) and Ce\({}^{3+}\) to achieve the conversion between oxygen and oxygen vacancies, resulting in a good oxygen storage capacity [15]. Thus, this work explores the effect of dispersion, particle size, and the reduction ability of the active component of Ni with the addition of CeO\({}_{2}\) based on Ni-Cu/HZSM-5 (Ni:Cu = 8:1).
## 2 Dataset and Methodology
### Methodology
#### 2.2.1 Data Preprocessing  
The raw data contained noise due to how it was collected, making this step mandatory before performing classification.  
Missing values, indicated with _NaN_, were found in all sensor features. They were handled by linear interpolation, which estimates values based on the surrounding data points [17].  
Redundant features of time stamp, 6g acceleration, and orientation were removed. The 6g acceleration and orientation were established as invalid by the authors. Activity code 0 represents a break between activities and was dropped from the dataset. Subject 9 only performed 5 of the 18 activities; therefore, this subject's data were also dropped.
## 3 A Brief Focus on Endocannabinoid System  
The endocannabinoid system (ES) plays a key role in the inflammatory processes, and includes CB1 and CB2 cannabinoid receptors. The first endogenous cannabinoid ligands (eCBs), intensively investigated, are arachidoylethanolamine (anandamide or AEA) and 2-arachidonovylglycerol (2-AG) [21]. Furthermore, ES comprises different enzymes involved in the synthesis, reuptake, and degradation of cannabinoids. Anandamide is synthesized from \(N\)-acyl-phosphatidylylethanolamine (NAPE) by the enzyme NAPE-specific  
Figure 2: Cannabinoid type derived from an Olivetoid backbone. R = H (neutral cannabinoids); R = COOH (acidic cannabinoids).  
phospholipase D (NAPE-PLD), while 2-AG is synthesized from diacylglycerolcerol (DAG) by DAG lipase (DAGL). The endocannabinoid system could be a target for different pathologies, in particular for cancer, with different levels of dysregulation that can involve cannabinoid receptors or the enzymes [34]. The most important enzyme involved in eCBs degradation are fatty acid amide hydrolase (FAAH) for anandamide and monoacylglycerolglycerol lipase (MAGL) for 2-arachidonovylglycerol. Beyond CB1 and CB2 cannabinoid receptors, other targets are suggested, such as GPR55 [35], transient receptor potential vanilloid 1 (TRPV1) ion channel [36], and peroxisome proliferator-activated receptor (PPAR) \(\alpha\) and \(\gamma\) localized in the nucleus [37]. The CB1 and CB2 receptors are coupled to G protein, whose activation leads to an inhibition of adenyl cyclase, decreased production of cAMP, and variation of ion channel activity. THC is the most abundant cannabinoid and the first psychoactive constituent that was isolated in 1964 [38], which binds CB1 and CB2 receptors. THC, with its metabolite THC 11-oic, could explain analgesic, antiemetic, and antiglaucoma effects or anesthetic action [39]. Unlike THC, CBD acts as an antagonist of CB1 and CB2 receptors and its activity on the endocannabinoid system could be explained by its inhibition of FAAH enzyme for increase of endocannabinoids, such as anandamide, that normally have a short shell life. Beyond the action on the endocannabinoid system, there are different targets of CBD such as 5-HT1A, TRPV1A, D2, A1, MOR, PPAR \(\gamma\), sodium, and calcium channels [40]. These different endogenous targets underline the CBD actions on anxiety, depression, pain, memory, and metabolism (Figure 3). The activation of CB1/2 receptors and transient receptor potential (TRP), maybe vanilloid 1, could inhibit some cancer cell invasion and metastasis, acting on different pathways involved in the angiogenesis, tumor vascularization, and tumor cells ability to destroy matrix membranes. Nuclear receptors PPAR, in particular PPAR\(\alpha\) and PPAR\(\gamma\), and the receptor GPR55 can be considered targets of cannabinoids also in cancer. Moreover, cyclooxygenase 2 (COX-2) can play a critical role on the behavior of endocannabinoids on cancer [41].  
Figure 3: Multiple molecular targets for THC and CBD; agonist \(\rightarrow\); antagonist —I; negative allosteric modulator —I; inhibitor —I; partial agonist —\(>\).
## 4 Discussion  
The obtained results verify that higher order PMGs are more sensitive to SRI variations compared to lower order PMGs. In addition, results obtained are consistent with reported results for GI-MMF [6] (in case of the response to temperature) and FMF [4] (in case of the response to SRI) in the literature. Consequently, it can be shown that by applying the single sensor element and PMG multiplexing, the simultaneous measurement of RI and temperature is feasible. However, the applied interrogation system which is based on equilibrium mode excitation and the applied Bragg wavelength peak detection (\"center of mass\" determination) is not sufficient to prove the simultaneous measurement of both parameters at the moment as the envelope of the measured Bragg reflection peaks of the etched GI-MMF with BG varies for different SRI values (see Figure 3a). The change of the envelope for every SRI value can be explained by the different sensitivities to SRI of the modes (the amount of the evanescent field spread in the surrounding varies depending on the mode) of each PMG and thus the corresponding Bragg wavelength change of each mode varies differently with different RI values. This causes a broadening of the individual Bragg reflection peaks. Another reason for the change of the envelope of the measured spectrum for different refractive indices might be the handling of the sensor element and immersion into the different RI solutions. Since the fiber was moved and held in another position for each RI solution, the power of the different modes might vary slightly (through change in the equilibrium mode distribution) and thus the envelope of the reflected FBG spectrum might fluctuate.  
Since the ultimate goal of achieving a mode-multiplexed sensor system requires the excitation of individual modes, the interrogation system shown in Figure 1 is currently being modified by adding a SLM to the optical path. Therefore, in the next configuration, the new interrogation system will allow the individual excitation of certain modes of each PMG (modes that propagate mostly at the core/cladding interface or only within the core of the sensor element) and thus the uncertainty of the peak detection due to envelope changes will be eliminated. Compared to other BG based fiber optic RI sensors, which had reported, i.e., a sensitivity of 71.2 nm/RIU [10], the obtained RI sensitivity of the proposed sensor system is relatively low. Therefore, in future research different techniques to increase the RI sensitivity will also be explored, such as coating the fiber optic sensor with thin polymer overlays [11].
## 4 Conclusions  
In this paper, we have focused on energy-efficient strategies for MECS in a backhaul capacity-limited cellular network for minimizing the power consumption while satisfying a computation delay cost constraint. For the motivation, taking the backhaul capacity, contents popularities and the number of users into account, a constraint expression that can trade off the system energy consumption and average delay has been derived, and it can illustrate the impacts of different MECS network parameters. The numerical results indicate that our method can reduce the energy consumption by about 63% with the trade-off in delay efficiency (increasing the minimum delay by around 20%), perform very close to the optimal solution, and much better than the worst-case scenario, i.e., the approximation bound.  
**Acknowledgments:** The work presented in this paper was partially supported by National High Technology Research and Development Program(Grant number 2015 AA01A707), 2014 National Natural Science Foundation of China (Grant number 61371081), 2015 National Natural Young Science Foundation of China (Grant number 61401381).  
**Author Contributions:** All the authors contributed to the conception, design and performance of the experiment, the analysis of the data and the writing of the paper. [PERSON] and [PERSON] initiated and discussed the research problem; [PERSON] conceived and designed the scheme; [PERSON], [PERSON] and [PERSON] performed the experiments and made the figures. [PERSON] and [PERSON] and [PERSON] analyzed the data; [PERSON], [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] wrote the paper.  
**Conflicts of Interest:** The authors declare no conflict of interest. The founding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results.  
Figure 5: (**a**) Delay performance; (**b**) Energy performance.
## 4 Use Case Study: [PERSON]'s Healthcare Monitoring  
This section illustrates the operation of the Semantic IoT Middleware (SIM) through a practical scenario involving [PERSON], an elderly lady with a history of high blood pressure and previous cardiac events. [PERSON] utilises an array of IoT healthcare devices, including a heart rate monitor, blood pressure monitor, and temperature sensor to monitor her health conditions continuously. These devices are crucial for tracking her health and alerting her and her healthcare providers to potential risks or emergencies. The scenario also includes [PERSON], the provider of a health monitoring app; [PERSON], part of an emergency response team; and [PERSON], a healthcare data analyst.