esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Cost-Effectiveness Study  
Analyzing the cost-effectiveness of the technology, it can be stated that the energy consumption for the ozone generator used in this study was 20.2 kWh/kg O\({}_{3}\). Once the electricity consumption was calculated, and taking into account the recommended ozone dosage, 0.018 g O\({}_{3}\)/g TS\({}_{0}\), the electrical energy consumption per gram of TS\({}_{0}\) was 0.36 kWh/kg TS\({}_{0}\). Considering the average industrial electricity prices in Spain in 2023, which is 0.142 \(\upxi\)/kWh, the cost of ozone treatment would be approximately 0.052 \(\upxi\)/kg TS\({}_{0}\). Given the total solids concentration in the sludge treated in this study, about 43.6 kg/ton, the total cost amounts to 2.25 \(\upxi\)/ton of treated sludge. In addition to the ozone costs, it is necessary to take into account the nanoparticle treatment cost. The cost of the nanoparticles is 4 \(\upxi\)/kg. Taking into account that the recommended dosage, according to the recommendations of the supplier, is 2 g nanoparticles/kg SV, and the concentration of the sludge and vinaasses used in this study was 21.51 kg SV/ton, the nanoparticle dosage per ton was 43 g nanoparticles/ton. Consequently, the total cost for nanoparticle treatment amounts to approximately 0.17 \(\upxi\)/ton. Because of that, the total cost for the combined treatment, nanoparticles and ozone, would be about 2.42 \(\upxi\)/ton.  
The main income is derived from the methane generated. The methane yields obtained in the different experiments were the following:  
* Without pre-treatment: 0.160 m\({}^{3}\)/kg VS\({}_{0}\)\(<\)\(>\) 0.110 m\({}^{3}\)/kg TS\({}_{0}\)\(<\)\(>\) 2.36 m\({}^{3}\)/ton
* With ozone and nanoparticle pre-treatment: 0.195 m\({}^{3}\)/kg VS\({}_{0}\)\(<\)\(>\) 0.139 m\({}^{3}\)/kg TS\({}_{0}\)\(<\)\(>\) 3.00 m\({}^{3}\)/ton  
Taking into account the lower calorific value of methane, 10.55 kWh/m\({}^{3}\), and that the price of biomethane in September 2024 was about 0.11 \(\upxi\)/kWh, the total revenue from methane production would be:  
* Without pre-treatment: 2.36 m\({}^{3}\) CH\({}_{4}\)/ton\(\cdot\)10.55 kWh/m\({}^{3}\cdot\)0.11 \(\upxi\)/kWh = 2.74 \(\upxi\)/ton
* With ozone and nanoparticle pre-treatment: 3.00 m\({}^{3}\) CH\({}_{4}\)/ton\(\cdot\)10.55 kWh/m\({}^{3}\cdot\)0.11 \(\upxi\)/kWh = 3.48 \(\upxi\)/ton  
Balancing the incomes and the expenses, it can be concluded that the ozone and nanoparticle pre-treatments enhance methane production by about 0.65 m\({}^{3}\) CH\({}_{4}\)/ton. Nowadays, the current price of biomethane, 0.11 \(\upxi\)/m\({}^{3}\) CH\({}_{4}\), is not enough to recover the expenses, about 2.42 \(\upxi\)/ton. However, the associated advantages, such as better dewatering and VFA production, the higher quality of the biogas obtained, and the presented lower hydrogen sulfide concentration, could make the combined treatment convenient, mainly in a scenario of increasing prices of energy.
## 3 Results
### Effects of CJ on Hepatic and Renal MAPK Pathway Expression  
To further investigate whether the effects of CJ in the liver and kidney tissues of STZ-induced diabetic rats involve the MAPK pathway, the expression of markers related to the MAPK pathway was analyzed through Western blot. In the Veh group, the expression of p-p38, p-ERK, and p-JNK was significantly increased compared with the Normal group (Figure 6). However, CJ administration led to a dose-dependent decrease in the overexpression of p38 MAPK and JNK.  
Figure 5: Effects of CJ on renal (**A**) NOX-4 and (**B**) p47 phox protein expression in STZ-induced rats. Normal, non-diabetic rats; Veh, STZ 30 mg/kg-treated rats; CJ 50, STZ 30 mg/kg + CJ 50 mg/kg-treated rats; CJ 100, STZ 30 mg/kg + CJ 100 mg/kg-treated rats; AG 100, STZ 30 mg/kg + aminoguanidine 100 mg/kg-treated rats. Values are expressed as the mean \(\pm\) SEM (\(n\) = 8). **_p_ \(<\) 0.01 compared with Normal. \({}^{+}\)_p_ \(<\) 0.01, and \({}^{++}\)_p_ \(<\) 0.01 compared with Veh.  
Figure 6: Effects of CJ on hepatic MAPK-related protein expression in STZ-induced rats. (**A**) p-p38/p38 expression; (**B**) p-ERK/ERK expression; (**C**) p-JNK/JNK expression. Normal, non-diabetic rats; Veh, STZ 30 mg/kg-treated rats; CJ 50, STZ 30 mg/kg + CJ 50 mg/kg-treated rats; CJ 100, STZ 30 mg/kg + aminoguanidine 100 mg/kg-treated rats. Values are expressed as the mean \(\pm\) SEM (\(n\) = 8). **_p_ \(<\) 0.01, ***_p_ \(<\) 0.001 compared with Normal. **_p_ \(<\) 0.01, and ***_p_ \(<\) 0.001 compared with Veh.  
The protein expressions of both p-p38 and p-ERK1/2 were significantly increased in the Veh group compared with the Normal group (Figure 7). However, the protein expressions in the CJ and AG groups were significantly decreased compared to the Veh group. Particularly noteworthy is the dose-dependent decrease observed in the CJ groups. The results of the CJ100 and AG100 groups showed similar outcomes, with levels of p-p38 comparable to the Normal group, while the AG100 group exhibited downregulated levels of both p-p38 and p-ERK1/2 compared to the Normal group. These results indicate that CJ inhibits the activation of the p38 MAPK and JNK pathway in diabetes induced by STZ treatment.
## 4 Results and Discussion
### Case 1: 250 W PEM Stack  
Table 3 clarifies the extracted optimal decision variables using the proposed algorithm, COA compared to existing literature, and other four competitive methods at 343.15 K and 1/1 bar. In our problem, it is anticipated to lessen the variance between the output voltages of the proposed optimization model with respect to the measured values at dissimilar current values. As noticed from Table 3, the ranking of SSE is COA followed by PSO [16], HGA [18], HADE [25], MVO [27], SSA [46], SCA [47], and MFO [48]. \begin{table}
\begin{tabular}{c c c} \hline \hline
**250 W Stack** & \multicolumn{2}{c}{**NedStack PS6**} \\ \hline \multicolumn{3}{c}{Technical data} \\ \hline Stack Parameter & Value & Value \\ Number of cells in a stack, \(n_{\text{cells}}\) & 24 & 65 \\ Active cell Area (cm\({}^{2}\)) & 27 & 240 \\ Nafion membrane thickness 1 & 127 \(\upmu\)m & 178 \(\upmu\)m \\ Maximum current density Jmax (A cm\({}^{-2}\)) & 0.86 & 1.2 \\ Rated power (W) & 250 & 6000 \\ \hline \multicolumn{3}{c}{Operating conditions} \\ \hline \(p_{a}\) (bar) & 1–3 & 1–3 \\ \(p_{c}\) (bar) & 1–5 & 1–5 \\ Stack temperature \(T\) (K) & 343.15–353.15 & 343.15–353.15 \\ Relative humidity in anode \(RH_{a}\) & 1 & 1 \\ Relative humidity in cathode \(RH_{c}\) & 1 & 1 \\ Voltage (V) & 15.09 & 60–32 \\ Maximum Current (A) & 22.1 & 225 \\ Reactants & H\({}_{2}\) and Air & H\({}_{2}\) and Air \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Technical data and operating conditions of PEM fuel cell stack. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**FC** & \multicolumn{4}{c}{**250 W FC**} & \multicolumn{4}{c}{**NedStack PS6**} \\ \hline \multirow{2}{*}{**Parameter**} & \multicolumn{2}{c}{**Bound \#1**} & \multicolumn{4}{c}{**Bound \#2**} & \multicolumn{2}{c}{**343.15 K, 1/1 Bar**} \\ \cline{2-7}  & **Lower** & **Upper** & **Lower** & **Upper** & **Lower** & **Upper** \\ \hline \(\xi_{1}\) & \(-0.952\) & \(-0.944\) & \(-1.1996\) & \(-0.8532\) & \(-1.1997\) & \(-0.8532\) \\ \(\xi_{2}\) & 0.001 & 0.005 & 0.001 & 0.005 & 0.001 & 0.005 \\ \(\xi_{3}\) & \(7.4  
At operating condition #2 (353.15 K and 3/5 bar), the comparison of the simulation results shows that COA has the ability to estimate an accurate model of PEM 250 w stack with lower SSE (0.6691) compared to the others, Table 4. The value of SSE with COA is less than HABC by 91.64%, HADE by 91.62%, and 81.33% compared to MOV. Moreover, it is noticed from Figure 3a that the convergence of the objective function occurred quickly smoothly after about 27 iterations. The polarization (I-V) curve is shown in Figure 3b with the effect of different losses. The activation loss, as noticed, has a large value compared to the concentration loss which has been recorded as the lower value. Figure 4 shows the polarization curve (V-I curve) of the 250 W PEM fuel cell stack at different operating conditions and two boundaries of decision variables (bound #1 and bound#2). The figure shows the best agreement of the model curve with measured values. The power of the stack model is plotted and compared to the measured value, as presented in Figure 5.
# DOSS Protocol for Data Caching and Sharing Security in a 5G Network
## 7 Security Analysis
### Protocol Security Analysis  
In our threat model, a Dolev Yao model [55] is used as the adversary which controls the network communication channel. It is capable of reading, intercepting, modifying, and sending messages. Eavesdropping, manipulation, interception, impersonation, and message injection are some of the attacks it can initiate. Hashing, encryption, and sign-on values that are known to the attacker can also be used by the attacker. The analysis is based on a symbolic model, which assumes perfect cryptography, but the computational strengths of the primitives are ignored. The protocol, however, must meet specific security properties and the analysis of the protocol is based on security requirements sets 1 and 2 [48] and 2 [49], respectively.
# A Novel Security Architecture for WSN-Based Applications in Smart Grid
## 3 Clustering Protocols in WSNs
### Discussion  
Table 1 shows many clustering algorithms based on different methodologies. We notice that the selection of CHs using an optimized-based approach is better in terms of fault tolerance, energy efficiency, and robustness as in [25]. To develop a secure clustering algorithm a trust metric must be considered, it may rely on many parameters, such as the packet delivery ratio [23], the received signal strength of the sensor nodes [27], and the successfully received packets rate [21]. In addition, considering the energy level is important to enhance the network lifetime. Table 1 shows that most of the studies consider the energy level in the selection of the CHs as in [19, 23, 25, 26]. As shown in Table 1, in many clustering algorithms, inter and intra-clustering communications are single hop. Moreover, we remark that all the discussed protocols create variably sized clusters. Additionally, almost all clustering protocols are centralized approaches as in [22, 25, 26, 27]. However, a variable size clustering leads to a non-balanced load on the CHs. In addition, the centralized approaches do not scale in the case of WSNs deployed in a SG because of the high number of sensors and the large deployment area. To overcome the aforementioned shortcoming of clustering in WSNs, we propose a new distributed clustering algorithm where the sensor nodes cooperate to create the clusters and elect the adequate CH based on a hybrid approach where different parameters are considered. The clustering process elects 1-hop clusters with a fixed size to balance the load on the CHs.
# Dynamic Controllability of Processes without Surprises
## 5 Sudden Termination Constellations and Patterns
### Sudden Termination Constellation and Pattern STP-2  
We define STP constellation STP-2 (see Figure 5) as follows: a contingent activity \(C\) and a semi-contingent activity \(S\) with constraints \(ubc(C.e,S.e,w)\) and \(lbc(C.s,S.e,v)\) as above are in an STP, if there is no way to schedule the start of \(S\) and the start of \(C\), such that the value for the end of \(S\) can be fixed, when \(S\) starts.  
Definition 8 (Sudden Termination Pattern 2): Let \(P\) be a dynamically controllable process. Let \(S\) and \(C\) be activities in \(P\), with the duration constraints \(d(S,S.d_{min},S.d_{max},sc)\) and \(d(C,C.d_{min},C.d_{max},c)\). Let \(ubc(C.e,S.e,w)\) and \(lbc(C.s,S.e,v)\) be constraints in \(P\).  
\(S\) and \(C\) are in a sudden termination pattern STP-2 iff \(\forall S.s,C.s\)\(\forall S.d_{min}\leq S.d\leq S.d_{max}\)\(\exists C.d_{min}\leq C.d\leq C.d_{max}:\ S.s+S.d>C.s+C.d+w\), or \(S.s+S.d<C.s+v\).  
In the following we derive conditions to check whether an STP-2 might occur for a given pair of activities \(S\) and \(C\).  
Theorem 3: _Let \(P\) be a dynamically controllable process. Let \(S\) be a semi-contingent activity, and \(C\) be a contingent activity in \(P\), with duration constraints \(d(S,S.d_{min},S.d_{max},sc)\) and \(d(C,C.d_{min},C.d_{max},c)\). Let \(ubc(C.e,S.e,w)\) and \(lbc(C.s,S.e,v)\) be constraints in \(P\)._  
_A sudden termination of \(S\) cannot occur, iff \(v<S.e-C.s\leq C.d_{min}+wholds\) in \(P\)._  
Proof: Please refer to Appendix A.1.  
Figure 5: Sudden Termination Pattern 2 (STP-2).  
**Theorem 4**: _Let \(P\) be a process. Let \(S\) be a semi-contingent activity, and \(C\) be a contingent activity in \(P\) with duration constraints \(d(S,S.d_{min},S.d_{max},sc)\) and \(d(C,C.d_{min},C.d_{max},c)\). Let \(ubc(C.e,S.e,w)\) and \(lbc(C.s,S.e,v)\) be a constraint in \(P\)._  
_A sudden termination of \(S\) cannot occur, iff both the constraints \(lbc(C.s,S.e,v)\) and \(ubc(C.s,S.e,C.d_{min}+w)\) hold in \(P\)._  
Proof.: The constraints express exactly the condition described in Theorem 3. Hence in a process model \(P\) including these constraints \(S\) and \(C\) cannot be in an STP-2.
## 2 E-Health
### Challenges of E-Health  
Thanks to rapid technological breakthroughs, new e-health solutions have been continually growing to fulfill the needs of contemporary practice. Appropriate technology infrastructure, systems integration, standards, and social, ethical, and economic challenges are all major roadblocks to widespread e-health adoption and the delivery of higher-quality, more productive healthcare. In the era of modern communication networks, e-health systems are expected to meet the following demands [13; 14; 15].  
Timeliness  
Healthcare systems and organizations should be structured in such a way that patients can receive care quickly in real-time. Misdiagnosis or inability to diagnose a medical problem, illness, or injury in a timely manner, while occasionally harmless, can lead to the condition worsening and worsening results. Thus, many medical services are announced as ultra-reliable low latency (URLL) services.  
Security and Privacy  
Privacy, or the capacity to keep an individual's medical information confidential from unauthorized parties. To achieve this goal, specific activities must be taken, such as correct patient identification, improved communication, adequate care, and reduced risk of unfavorable outcomes.  
Due to the critical importance of medical data, high-security algorithms should be deployed to secure medical data. Moreover, due to the constraints on medical devices' energy and the size of data packets communicated over the network, novel energy-aware quantum security schemes are recommended to be introduced for e-health systems.  
Cost efficient  
Healthcare services should be reasonably priced due to their high importance. Thus, cost-efficient devices and solutions should be introduced to e-health systems to provide medical services at the expected cost.  
Effective care  
E-health services should be supported by scientific proof, e.g., laboratory tests, clinical research, and epidemiological studies. Application servers of medical networks should have interfaces and links to medical facilities and be monitored by the operators in these facilities.  
Equity  
It implies giving everyone the same level of care, regardless of gender, color, education, geography, or socioeconomic situation.  
Patient-centered healthcare  
This includes tailoring healthcare services to the individual patient's requirements, values, and preferences.
## 3 Results
### Basic Example: Simple Harmonic Motion  
Simple harmonic motion can be modeled as a spring-mass mechanism. In this model, the acceleration of the mass is proportional to its position. The proportion depends on the value of the mass and the stiffness of the spring:  
\[m\frac{\mathrm{d}^{2}x}{\mathrm{d}t}=-kx(t) \tag{11}\]  
here, \(m\) is the mass, and \(k\) is the spring constant. Since we will consistently be relating rates of change over time, we will drop the time variable \(t\) when speaking of these dynamical models. We will use the conventional 'dot' notation to denote the time derivative.  
Equation (11) is a second order differential equation, but we may render it a first order system in the usual way:  
\[\begin{array}{c}\dot{x}=y_{v}\\ \dot{y}_{v}=-\frac{k}{m}x\end{array} \tag{12}\]  
In this formulation the introduced variable \(y_{v}\) is the velocity of the system. By scaling this variable by the resonant angular frequency, we arrive at a more symmetrical form in which both \(x\) and the variable \(y\) are now in the same units (position):  
\[\dot{x}=\omega y \tag{13}\]  
\[\dot{y}=-\omega x \tag{14}\]  
The frequency of this system is \(f=\omega/2\pi\) Hz. We note that Equations (13) and (14) is an equivalent system to the the one given in Equation (11) when \(\omega=\sqrt{k/m}\). Differentiating Equation (13) yields \(\ddot{x}=\omega\dot{y}\) and substituting Equation (14) for \(\dot{y}\) yields \(\ddot{x}=-\omega^{2}x\). The outputs of this system (which we are interested in hearing) are the changing values \(x\) and \(y\), which we get by integrating (in this case with RK4) \(\dot{x}\) and \(\dot{y}\).  
The amplitude and initial phase of the system are given by initial conditions. The amplitude is simply \(A=\sqrt{x_{0}^{2}+y_{0}^{2}}\) and the phase is \(\phi=\tan{{}^{-1}(y/x)}\).  
This implementation of simple harmonic motion is not new in digital audio. [PERSON] and [PERSON] used oscillators in a similar form to design high-Q bandpass filters [22]; and, this oscillator is analyzed quite thoroughly in Chapter 3 of [21]. In that work it is presented as a basic building block for lumped networks.  
By way of contrast, a more frequent implementation of a harmonic oscillator is the lookup oscillator (also called a wavetable oscillator). This consists of a phase signal that references values in a pre-computed table containing one period of a sinusoidal waveform. Oscillators of this variety are covered in most any introductory text to digital audio, for example Chapter 2 in [23]. The lookup oscillator can be generalized to lookup any arbitrary waveform, but we restrict ourselves to the sinusoidal case:  
\[x(n)=\sin(\omega\phi(n)) \tag{15}\]  
where \(\phi(n)\) is a phase signal and (as usual) \(\omega=2\pi f\).
## 2 Related Works  
### IEEE 802.15.4  
In the IEEE 802.15.4 beacon enable mode, the devices communicate using the superframe structure. The superframe consists of three parts: the beacon frame, active period, and inactive period, as shown in Figure 1. Nodes send and receive packets during only the active period. The active period is divided into CAP (Contention Access Period) and CFP (Contention Free Period). The devices communicate using CSMA/CA in the CAP. The proposed method assumes to use only CAP.  
Figure 2 shows the flow chart of IEEE 802.15.4 Clear Channel Access (CCA). MacC-SMABackoffs is the number of retransmissions. Contention windows (CW) are used to communicate fairly. Each node checks whether the channel is idle before sending packets.  
Beacon Interval (BI) means the time interval between two superframes. The length of BI is determined by the parameter BO (Beacon Order), which is expressed by  
\[BI=aBaseSuperframeDuration\times 2^{BO}(ms), \tag{1}\]  
where \(aBaseSuperframeDuration\) is the basic length of the superframe and consists of 16 time slots, and one time slot length is 0.96 ms. So, the \(aBaseSuperframeDuration\) is 15.36 ms. Superframe Duration (SD) means the length of the active period. The length of SD is determined by SO (Superframe Order), which is expressed by  
\[SD=aBaseSuperframeDuration\times 2^{SO}(ms). \tag{2}\]  
From Figure 1, BO and SO satisfy SO \(\leq\) BO. The ratio of the length of the active period to that of the superframe is called DC. DC is expressed by  
\[DC=\frac{2^{SO}}{2^{BO}}. \tag{3}\]  
Figure 1: The superframe structure of IEEE 802.15.4.
## 7 Results and Analysis
### Security Metrics
First, the integration of ML-based threat detection could be explored to improve the system's ability to adapt to evolving attack strategies. Additionally, implementing more advanced anomaly detection techniques, such as deep reinforcement learning, may further improve attack detection rates, especially in dynamic and highly variable environments. Moreover, the scalability of our system in larger, so more distributed networks will need to be tested. Ensuring that the framework can maintain high performance and security levels as the network grows is crucial for its real-world deployment. Figure 14: Poisoning resilience comparison: Our ZTA framework maintains higher accuracy compared to the baseline ([PERSON] et al. [11]) even as the percentage of malicious participants increases. Figure 13: Reduction in successful attacks: Our Zero Trust Architecture (ZTA) reduces attack success rates to 11%, compared to 87% for conventional RBAC ([PERSON] et al., 2022) [15], and 100% with no defense.
## 4 Applied Methods
### Support Vector Regression  
Support vector machine (SVM) was introduced to solve both classification and regression problems. Among many methods of machine learning, the SVM is considered as a relatively new technique which is based on the structural risk minimization [93]. SVM can be referred to both regression and classification methods. Among them, support vector regression (SVR) is only able to solve extrapolative problems by building a predictive model. The SVR works based on regression estimation and function approximation considering an alternative \"loss function.\" The SVR is defined as a common technique which is based on the Vapnik-Chervonenkis (VC) theory [94,95]. If the VC dimension is low, the expected probability of error is also low, which means good generalization [96].  
The concept of the \(\varepsilon\)-insensitive loss function is graphically shown in Figure 6. Only the samples out of the \(\pm\)\(\varepsilon\) margin (known as \(\varepsilon\)-insensitive tube) will have a nonzero slack variable. Normally, if the predicted value is inside the region, the loss will be zero, while if the predicted point is outside the tube, the loss is the magnitude of the difference between the predicted value and the radius \(\varepsilon\) of the tube.  
Mentioning about statistical equations behind SVM and SVR is out of scope of this study. In order to have a detailed view of SVM and SVR, studies conducted by [PERSON] et al. [97]  
Figure 5: Scatter plot of the predicted vs. actual iron ore price through the use of classification and regression tree (CART) model.  
Figure 6: The concept of the \(\varepsilon\)-insensitive loss function.  
and [PERSON] et al. [98] are suggested. Generally, to construct a SVR model, three factors are very important to determine. These factors are the Gaussian kernel parameter \(\gamma\), the loss function parameter \(\varepsilon\), and the penalty term C [98]. A series models of SVR were built defining a fitness function in three loops for each combination of C, \(\varepsilon\), and \(\gamma\). Then, the optimum C, \(\varepsilon\), and \(\gamma\) in each run was selected. Table 3 presents the obtained results of the developed SVR models in terms of performance indices i.e., R\({}^{2}\), root mean square error (RMSE) and mean absolute error (Ea). In addition, different combinations of C, \(\varepsilon\), and \(\gamma\) can be seen in Table 3. As shown in Table 3, model number 9 (R\({}^{2}\) = 0.98, RMSE = 6.63, Ea = 3.84 and) outperforms the others. The results demonstrated a high level of accuracy of SVR in estimating iron ore price. Correlation between predicted and measured values of iron ore price using SVR predictive model is displayed in Figure 7.