esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 COGNIFOG Evaluation in an IoT Connectivity Testbed
### Test Description and Results
All metrics of interest from IoT devices #167590 and #167 DEC have been compiled in Table 2. Overall, the BW limitation for UDP traffic in Stage 3 has allowed MQTT traffic from real IoT devices to achieve performance in terms of latency, delay, and jitter similar to that observed during Stage 1 when no UDP traffic was present on the link. The reliability of MQTT packets also benefits from the BW limitation of UDP traffic, as the PDR for both IoT devices increases from 89.6% and 88.8% in Stage 2 to 100% in Stage 3. Lastly, and as expected, the performance of UDP-based background traffic (with \(R_{\text{UDP}}~{}=~{}8\) Mbps) was significantly degraded following the application of the BW limitation on the communication link between the IoT edge gateway and the edge server. Specifically, from the analysis of the obtained iPerf3 logs, the computed throughput for this type of traffic (\(S_{\text{UDP}}\)) decreased from \(S_{\text{UDP}}~{}=~{}2.79\) Mbps in Stage 2 (when no BW limitation was applied) to \(S^{\prime}_{\text{UDP}}~{}=~{}1.95\) Mbps in Stage 3 (when \(B^{\prime}_{\text{UDP}}~{}=~{}2\) Mbps). All in all, the tests conducted in the IoT testbed demonstrate how the BW management feature effectively enhances connectivity performance by ensuring the proper operation of high-priority services over low-priority ones on a shared network link. Although the feature was manually activated in these tests, it stands to benefit from the governance building blocks of the COGNIFOG framework in the future, potentially operating in an automated and intelligent manner. In fact, the same metrics used here to evaluate connectivity performance (i.e., latency, delay, and PDR) could be employed to assess whether the SLA policies defined by a potential COGNIFOG user are being met. As detailed in Section 4.2, the monitoring and the policy managers within the COGNIFOG framework would be responsible for collecting data on performance metrics and SLA compliance, respectively. These data would then be  
Figure 10: Timestamp comparison between the MQTT message generation in the IoT devices and the corresponding reception at the edge server. Figure 9: Computed \(D\) of the MQTT messages sent by the two IoT devices. delivered to the smart allocator, which, considering the status of infrastructure resources, would initiate a reconfiguration of service deployment if necessary. This could include, as demonstrated in this testbed, the activation of BW limitations for certain services.
# Fatigue Behavior of Prestressed Concrete Beam for Straddle-Type Monorail Tracks
## 4 Results and Discussions  
### Displacement and Stiffness  
As shown in Figure 12, the displacement of section C-C at midspan approximately linearly increases with the applied load in a post-fatigue static load test, indicating that the beam behaves within its linearly elastic stage under the overcrowding load (Ps). Figure 13 shows the development of displacement at section B-B, C-C, and D-D, when the maximum static load was applied. The values range from 13.5 mm to 13.8 mm at section C-C (midspan), and from 8.9 mm to 9.3 mm at section B-B and D-D (quarter-span), which have been deducted from bearing settlement. Displacement at each section rarely changes in the test, indicating the good stiffness performance [35]. Thus, when analyzing the mechanical property of the structure in a fatigue test by finite element model, the change of the stiffness can be ignored. The result in finite element analysis is shown as dashed lines in Figure 13, which match well with the experiment result with a difference of 2.18% in mid-span and 6.36% in L/4 section. The difference is supposed to be due to the simplification of the model, for example, no relative slippage between concrete and reinforcement was considered [36], and the material constitutive equations were simplified as well. However, the difference is really small, so the finite element analysis model is considered to have been validated.  
Figure 11: Schematic of symmetric constraints used in the midspan section.  
Figure 12: Static load-displacement curve at midspan.  
According to material mechanics, the diagrammatic multiplication method for calculating the deflection of a simple supported beam under the static load is as follows [37]:  
\[f=\int\frac{\overline{M}M_{P}\text{ds}}{B_{s}} \tag{5}\]  
where \(\overline{M}\) is the moment diagram of midspan when a unit force is applied; \(M_{P}\) is the moment diagram of midspan when the static load \(P_{\text{s}}\) is applied, and \(B_{\text{s}}\) is the bending stiffness of the beam.  
The formula of the bending stiffness obtained from Equation (5) is shown in Equation (6):  
\[B_{s}=\frac{\int\overline{M}M_{P}\text{ds}}{f} \tag{6}\]
## 5 Security and Privacy Analysis
### Security
Let \(\tau\) be the average executor rotation period and \(\delta\) be the knowledge decay factor (where \(0<\delta<1\)). The attacker's effective knowledge at time \(t\) can be modeled as  
\[K(t)=\int_{0}^{t}\lambda e^{-\delta\left\lfloor\frac{\pi}{1}\right\rfloor}ds. \tag{27}\]  
As \(t\) increases, this knowledge reaches a steady state bounded by \(\frac{\lambda\tau}{1-e^{-\delta}}\). Consequently, the system compromise probability is bounded by  
\[P_{comp}(t)\leq 1-e^{-\frac{\lambda\tau}{1-e^{-\delta}}}\approx\frac{\lambda \tau}{1-e^{-\delta}}\text{ for small }\frac{\lambda\tau}{1-e^{-\delta}}. \tag{28}\]  
Additionally, the dynamic credibility updates continuously penalize compromised executors, further reducing the compromise probability over time. With proper parameter settings, this leads to \(P_{comp}(t)\leq e^{-at}\) for some \(\alpha>0\) as \(t\to\infty\). **Theorem 3** (Preemptive Security). : _The periodic scheduling mechanism with maximum runtime threshold \(T\) ensures that the maximum attack window for any executor set is bounded by \(T\), regardless of the attack detection capabilities of the system._  
Proof. : Consider an attacker who has successfully compromised an online executor set without being detected by the arbitration mechanism. In traditional architectures, such an attacker could maintain control indefinitely. In our architecture, let \(S_{i}(t)\) denote the cumulative runtime of online executor set \(\mathbf{P}_{i}\) at time \(t\), and let \(T\) be the maximum allowed runtime before mandatory rotation. By design, we enforce  
\[\forall i,t:S_{i}(t)\leq T. \tag{29}\]  
When \(S_{i}(t)=T\), the executor set \(\mathbf{P}_{i}\) is selected for rotation with probability 1, regardless of its apparent behavior. This creates a strict upper bound on the duration of any successful attack against the entire executor set. Additionally, the random selection of \(k\) executor sets for rotation further complicates the attacker's planning, as even executor sets with \(S_{i}(t)<T\) have non-zero probability of being rotated. Specifically, for an executor set \(\mathbf{P}_{i}\) with runtime \(S_{i}(t)\), its rotation probability at time \(t\) is at least \[P_{rotate}(S_{i}(t))= \begin{cases}\frac{rankp(S_{i}(t))}{m},&\text{if }S_{i}(t)<T\\ 1,&\text{if }S_{i}(t)\geq T^{{}^{\prime}}\end{cases}  \tag{30}\]  
where \(rank_{\mathbf{P}}(S_{i}(t))\) is the position of executor set \(\mathbf{P}_{i}\) when all executor sets are sorted by their cumulative runtime in descending order, and \(m\) is the total number of available executor sets. This preemptive rotation policy ensures that no attack can persist beyond the time window \(T\), providing a fundamental security guarantee independent of detection mechanisms.
## 2 Proposed EPP-Based Beam Scanning Approaches
### Test case (3): \(R=0.7778\)
#### 2.1.2 Comparison between the Five Test Cases of Compression Ratio  
The results of the aforementioned five test cases of the compression ratio R are summarized in Table 6. It is clear that for test cases 1, 2, and 3, as the cosine wave amplitude increases, the scanning angle increases, and the HPBW decreases. However, test case 2 at \(R=0.875\), provides the largest decrease in the HPBW. On the other hand, for test cases 4 and 5, as the cosine wave amplitude increases, the scanning angle increases giving rise to a larger scanning range than in the previous test cases, and the HPBW increases. But, the null between the first side lobe and main beam is relatively high, resulting in stronger  
Figure 21: Polar plot of the synthesized radiation pattern using the EPP-BSA approach for 8-element uniform LAA due to compression ratio equal to 0.5 for \(A_{c}=2\,\lambda\) and changing from negative to positive.  
Figure 20: Polar plots of the synthesized radiation patterns using the EPP-BSA approach for 8-element uniform LAA at compression ratio equal to 0.5 at: (**a**) \(A_{c}=0\), (**b**) \(A_{c}=0.25\,\lambda\), (**c**) \(A_{c}=0.5\,\lambda\), (**d**) \(A_{c}=0.6\,\lambda\), (**e**) \(A_{c}=0.65\,\lambda\), (**f**) \(A_{c}=0.75\,\lambda\), (**g**) \(A_{c}=\lambda\), (**h**) \(A_{c}=1.25\,\lambda\), (**i**) \(A_{c}=1.5\,\lambda\), (**j**) \(A_{c}=1.75\,\lambda\), and (**k**) \(A_{c}=2\,\lambda\) and changing from positive to negative.  
interference. As a consequence, we can conclude that test case 2 with \(R=0.875\) yields the best results in terms of HPBW and scanning range.
## 2 Data and Methodology
### Data Analysis from Sampling and Response Designs  
The data analysis was divided into three parts. In the first one, the CCDC's ability to correctly detect disturbance in 2019 was evaluated per VI and ensemble VIs, considering also the performance of the metrics as a function of the type of disturbance (savanna clearing or fire). In the second part, the ability of the algorithm for precise detection of the time of disturbance occurrence was analyzed. Finally, the last part looked for the possible existence of temporal patterns of clearing and fire in 2019. The strategy used for accuracy assessment is described below.
# Evaluation of the Effectiveness of Surface Defect Removal by Slide Burnishing
## 3 Results and Discussion
### Surface Topography  
Following grinding, which was used as a pre-treatment before slide burnishing, a unidirectional arrangement of micro-irregularities was obtained on the surface (Figure 7), which results from the action of the grinding wheel. Visible on the surface are the peaks of elevations that dominate the total height of the micro-irregularities. The Sp parameter is approximately 60% of the Sz parameter value. An analysis of the arrangement of the resulting micro-irregularities reveals that groove formation and micro-cutting dominate during grinding. Some discontinuities and pinchers are visible on the surface, which confirms the occurrence of these mechanisms during grinding.  
The surface topography of the samples changes after slide burnishing. The peaks of the micro-irregularities become flattened (Table 4). High-strain areas are visible on the surface. These areas are characterized by large irregularities, which indicates the occurrence of friction and adhesive interaction between the tool tip and the burnished surface, which is particularly noticeable for the feed f = 0.03 mm/rev. The use of a feed of f = 0.03 mm/rev during slide burnishing causes the micro-irregularities to become almost completely \"crushed\" after grinding, whereas at f = 0.08 mm/rev, they are still visible on the surface. The use of the force F = 150 N during slide burnishing with f = 0.03 mm/rev translates into a reduction in the height parameters compared to the surface burnished with F = 100 N and f = 0.03 mm/rev. Interestingly, after slide burnishing conducted with F = 150 N and f = 0.03 mm/rev, the height of the peaks and the depth of the depressions have similar values. A comparison of the applied feeds during slide burnishing demonstrates that an increase in feed causes an increase in 3D surface roughness parameters, whatever the applied force F.  
The greatest changes in relation to the ground surface were obtained for the Sz and Sp parameters. An interesting result is a slight increase in the 3D height parameters for the surface burnished with F = 150 N and f = 0.08 mm/rev compared to the sample that was slide burnished with F = 100 N and f = 0.08 mm/rev. This can be explained by the most likely occurrence of shear damage under the sample surface. The minimum values of 3D height parameters were obtained for the sample slide burnished with F = 150 N and f = 0.03 mm/rev. The Sa parameter for the surface subjected to burnishing with F = 150 N and f = 0.03 mm/rev is about 20% higher than that obtained after slide burnishing of X6 CrNiTi18 steel [19]. Compared to the results of previous studies, the values of the 3D surface roughness parameters obtained in this work are similar to those reported after slide burnishing of 317 Ti steel [8].  
Figure 7: Surface topography of a thin-walled C45 steel sample before slide burnishing.
## 2 Methodology for Parameter Estimation from SAR Data
### Introduction to Bayesian Statistics and Numerical Methods  
In Bayesian statistics, all unknown quantities are handled as random variables and are described by probability distributions, which in turn describe a measure of the state of knowledge of the parameter's value. In Bayesian analysis, the probability function of a parameter (prior) is updated by incorporating new information, _i.e._, data, to derive a posterior probability function.  
If a population parameter (e.g., the case study transponder ERCS) is called \(\theta\), then it can be described by a probability distribution function \(p(\theta)\). Even before a measurement is done, a certain, subjective, (prior) probability density can be attributed to the parameter. In case of the transponder's ERCS, one might claim that the parameter needs to be positive and that it certainly will not be above \(100\,\mathrm{dBm^{2}}\). If now some new data \(y\) (e.g., from the case study RADARSAT-2 campaign) becomes available, one wants to update one's believe on \(\theta\) given data \(y\). This can be written as \(p(\theta|y)\) where \(p(\cdot|\cdot)\) describes a conditional probability. The notation follows [12]. Now, [PERSON] rule allows to compute the posterior probability \(p(\theta|y)\) from a prior probability \(p(\theta)\) and a likelihood function \(p(y|\theta)\)[12]:  
\[p(\theta|y)=\frac{p(\theta)p(y|\theta)}{p(y)} \tag{1}\]  
The likelihood function \(p(y|\theta)\) describes how likely the data is, given the parameter \(\theta\).  
Describing parameters by probability distributions is a natural fit when not only the best estimate of a parameter needs to be stated, but also a confidence interval needs to be quantified, as is the case for calibration. Deriving a confidence interval from a distribution is straight forward for any kind of distribution, _i.e._, the analysis is not limited to Gaussian distributions.  
Deriving posterior distributions is in practice mostly achieved through numerical methods, which allow to consider more complex problems and arbitrary distributions. The simulation method used in the case study is the Markov chain Monte Carlo (MCMC) approach [12]. The posterior distribution is approximated by sequentially drawing samples from approximate iterative intermediate distributions until the simulation converges to the target distribution. Several software packages exist which simplify computations; here the PyMC library is used [17].
## 6 Experimental Results and Analysis
### Comparison with the Previous Works  
Figure 17 presents a benchmark comparison of the thermal performance index (TPI) for the continuous V-rib twisted tapes (CVRTs) investigated in this study against various twisted tape configurations reported in the literature. The comparison includes twisted trapezoidal and trapezoidal tapes at a twist ratio of \(y/w=4.0\), using air as the working fluid and a thread height ratio of \(H/w=1/6\), as reported in [9]. Also considered are twisted tapes with dimples at \(y/w=3.0\), using an Al\({}_{2}\)O\({}_{3}\)-water nanofluid as the working fluid with a depth-to-diameter ratio of \(\delta/Dp=0.25\) as detailed in [10]. Additionally, the study includes saw-tooth twisted tapes at \(y/w=3.0\) with air as the working fluid and a saw-tooth angle of \(\alpha=30^{\circ}\)[32], twisted wavy tapes at \(y/w=4.0\), also with air, having a wave amplitude of \(D/4\) and a wave number of 3 [33], and punched twisted tapes using water as the working fluid and a circular cut diameter of 5 mm [34]. The results indicate that the CVRT with an attack angle of \(\beta=15^{\circ}\), arranged in a backward orientation, exhibits superior TPI performance compared to all other geometries studied, with the exception of the saw-tooth twisted tape at a Reynolds number of 6000. This suggests that the CVRT design offers a strong balance between heat transfer enhancement and pressure drop.  
Importantly, the CVRT structure demonstrates not only excellent thermal performance but also practical advantages in terms of fabrication. The design requires only a pair of meshing gears and a twisting device. This simplicity translates to enhanced feasibility for industrial-scale heat exchange applications, where both performance and manufacturability are critical considerations. As such, the CVRT configuration represents a highly promising solution for future thermal system designs.
# Research on Misalignment Fault Isolation of Wind Turbines Based on the Mixed-Domain Features
## 3 Fault Isolation of Transmission System Based on PSO-SVM
### Particle Swarm Optimization  
PSO is a kind of swarm intelligence optimization algorithm in addition to the ant colony algorithm and the fish swarm algorithm, and it was first proposed by [PERSON] and [PERSON] in 1995 [PERSON] and [PERSON] (1995). The principle can be briefly described as follows.  
Supposing in a \(D\)-dimensional search space, there is a population \(X=(X_{1},X_{2},...,X_{n})\), consisting of \(n\) particles. The \(i\)-th particle is represented as a \(D\)-dimensional vector of \(X_{i}=[x_{i1},x_{i2},...,x_{iD}]\), which represents the position of the \(i\)-th particle in the \(D\)-dimensional search space, and it also represents a potential solution to the problem. According to the objective function, the fitness value of each particle's position \(X_{i}\) can be calculated. If the velocity of the \(i\)-th particle is \(V_{i}=[V_{i1},V_{i2},...,V_{iD}]\), the individual extremum is \(P_{i}=[P_{i1},P_{i2},...,P_{iD}]\), the population extremum is \(P_{g}=[P_{g1},P_{g2},...,P_{gD}]\), and then the velocity and position of the particle are updated according to the following equations:  
\[V_{id}^{k+1}=V_{id}^{k}+c_{1}r_{1}(P_{id}^{k}-X_{id}^{k})+c_{2}r_{2}(P_{gd}^{ k}-X_{id}^{k}), \tag{21}\]  
\[X_{id}^{k+1}=X_{id}^{k}+V_{id}^{k+1}, \tag{22}\]  
where \(d=1,2,...,D\); \(i=1,2,...,n\); \(k\) is the number of iterations; and \(c_{2}\) are learning factors, which are non negative constants, making the particles learn from their own or other better particles to achieve the purpose of being close to the better position of itself or the whole group; \(r_{1}\) and \(r_{2}\) are random numbers distributed in [0, 1] to maintain the diversity of group. In order to prevent the blind search of particles, it is generally recommended to limit the position and velocity to a certain range of \([-X_{\text{max}},X_{\text{max}}]\) and \([-V_{\text{max}},V_{\text{max}}]\).  
The realization of the fault isolation based on PSO-SVM ( in the model, PSO is used to select the parameters of SVM classifier) is shown in Figure 2.
# A Class of Perfectly Secret Autonomous Low-Bit-Rate Voice Communication Systems
## 1 Introduction
Therefore, we introduce a new synthesizer on the Alice and Bob side for this phase, whose synthesis filters are set based on secret randomly chosen LSP parameters [18; 20]. These values are available to both Alice and Bob since they are formed from previously distilled secret keys. In this way, we obtain all the necessary conditions for the design of a perfectly secret autonomous low-bit-rate voice communication system.
# Recovery of Polyphenols from Agri-Food By-Products: The Olive Oil and Winery Industries Cases
## 4 Polyphenol Analysis: From Quantification to Antioxidant Capacity
### Spectrophotometric Methods
#### 4.2.5 Oxygen Radical Absorbance Capacity (ORAC)  
The ORAC method is a HAT assay used to determine antioxidant activity. It is based on the quenching of the fluorescence of fluorescein by peroxyl radicals. In the presence of antioxidants, which react with peroxyl radical, the decrease in the fluorescence signal is slowed down. The fluorescence decay over time is recorded, and the area under the curve is related to the antioxidant capacity. Trolox is habitually used as the standard in calibration, and results are reported as Trolox equivalent concentration [72; 73; 77]. [PERSON] et al. [58] determined the antioxidant capacity by ORAC of grape pomace extracts of cv. Malbec, and reported 2756 umol TEAC g\({}^{-1}\). [PERSON] et al. [78] analyzed the antioxidant activity of Arbequina olive residues. The antioxidant capacity of fresh samples was 66.81, 101.75, and 283.5 umol TEAC g\({}^{-1}\) dw, for DPPH, FRAP, and ORAC, respectively.  
It is not evident what is the equivalence of the results provided by the different assays. It is not just a question of the units used to express the results; quite often, there is not a clear correlation between the results from different assays. However, as a general trend, whencomparing results from different samples measured by several assays, samples having high results in one assay usually show high values in the others (See Figure 6).  
Finally, it should be mentioned that, although less applied than spectrophotometric assays, electrochemical assays can also be used to determine the antioxidant capacity of samples, with cyclic voltammetry, differential pulse voltammetry, and square wave voltammetry being the most widely used techniques [79]. In this sense, electrochemical detection combined with molecularly imprinted polymers (MIPs) is an innovative approach for the quick detection of antioxidants [80].