esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Results in Poland  
#### 3.1.1 Zachodnia Street  
In Zachodnia Street, a similar effect can be observed in similar points (Figure 4). On one hand, although all the vertical TCMs were not the same, similar values were obtained for the average speed (between 30.3 and 33.8 km/h) and for \(V_{85}\) (between 33 and 38). Moreover, maximum speeds when crossing them were always below 50 km/h, the speed limit of the street.  
On the other hand, approximate values were also registered in the middle points between the TCMS: a range from 37.3 to 40.6 km/h for \(V_{\rm m}\) and a range from 43 to 48 km/h for the \(V_{85}\). As seen,  
Figure 4: Speed values in the measurement points in Zachodnia Street, in Bialystok (Poland).  
the speed limit was also respected by more than 85% of the drivers and only very few exceeded the limit. The higher values in the middle points were measured in MP 4-5, where there is a longer distance between TCMs (195 m).
# An Overview of Distributed Firewalls and Controllers Intended for Mobile Cloud Computing
## 3 SDN, NFV, and CASB Technologies
### Integrated Approach  
Combining CASB with SDN and NFV offers a robust security framework specifically designed to address the distinct challenges arising from the growing reliance on mobile devices for cloud computing. CASB solutions provide essential functions like visibility, threat prevention, access management, and compliance enforcement. They help tackle issues such as data breaches, unauthorised access, and compliance violations. SDN and NFV provide the underlying infrastructure for implementing and enforcing security policies dynamically, allowing for efficient traffic monitoring and control across the network. By combining these technologies, organisations can effectively mitigate security challenges associated with MCC [54; 72; 73; 74].  
Integrating CASB with SDN and NFV allows for the creation of an organised, distributed firewall and controller system, providing robust security for mobile devices and their cloud interactions. CASB solutions serve as security enforcement points between cloud service users and providers, ensuring secure communication. SDN offers centralised control logic and separates control and data planes, streamlining the management and enforcement of security policies across distributed networks. NFV enables the deployment of network functions, like firewalls and intrusion detection systems, as virtual instances, enhancing flexibility and scalability in security enforcement [75].  
Additionally, by integrating CASB with SDN and NFV, organisations can employ advanced techniques and design concepts to provide an effective and reliable system for controlling and protecting data flow among mobile devices and cloud resources. CASB solutions offer fine-grained policy definitions, allowing organisations to control access at multiple levels, including file, application, individual, device, or organisation. SDN and NFV enable the dynamic configuration and control of routing and forwarding planes, facilitating efficient traffic management and ensuring data integrity and confidentiality.  
Furthermore, integrating CASB, SDN, and NFV into distributed firewall and controller systems greatly improves the security of mobile devices and cloud services. By utilising CASB's capabilities in threat protection, access control, and compliance enforcement, along with the flexibility and programmability offered by SDN and NFV, organisations can improve security practices, including authentication, data encryption, and threat detection. This integrated approach strengthens security across the entire MCC ecosystem, mitigating risks and ensuring data confidentiality, integrity, and availability [76].
# CoDA: Collaborative Data Aggregation in Emerging Sensor Networks Using Bio-Level Voronoi Diagrams
## 3 System Model
### The Model of Bio-Level Voronoi Diagram  
In the whole wireless sensor network, all the nodes are managed in the form of clusters, and the communication between the cluster head and its member nodes is carried out by single hop mode. The Voronoi diagram of the lower layer is composed of all the cluster heads, which reflects the adjacent distance and distribution of each cluster head. In addition, the upper Voronoi diagram is generated by the relay nodes, which constitute the backbone of the network. For each relay node, it owes a larger coverage range and can communication with various cluster heads, which attempt to transmit packets in multi-hop wireless routing. Therefore, the path optimization strategy should be designed for minimize the expense during the packets forwarding. Figure 1 show the bio-level Voronoi model presented in this paper. Especially, the division of the relay nodes in the upper Voronoi diagram is not absolute, and the cluster heads on the edge of the upper Voronoi diagram has the equal opportunity to select the appropriate relay node as the destination.
# Fault-Tolerant Control of Multi-Joint Robot Based on Fractional-Order Sliding Mode
## 3 Fractional-Order Sliding Mode Controller Design
### Design of Fractional-Order Sliding Mode Controller for Multi-Joint Robots  
When common faults occur in the actuators of multi-joint robots, such as constant gain and constant deviation faults, the actuators are not completely damaged at this time, so the fractional-order sliding mode control method can be used to design the controller to achieve fault-tolerant control.  
In the design of fractional-order sliding mode control law, consider \(q_{d}=[q_{d1},q_{d2}]^{T}\) as the reference of the double joints, \(e=[q_{d}-q]^{T}\) as the error signal matrix, and \(q=[q_{1},q_{2}]^{T}\) as the position angle, which is the system state variable.  
The sliding mode surface for SMC is set \(S_{1}=\dot{e}+e\), choosing the Lyapunov function \(V_{1}\) with the following format.  
\[V_{1}=\frac{1}{2}S_{1}^{T}S_{1} \tag{9}\]  
The SMC control law \(T_{1}\) could be deduced as follows.  
\[T_{1}=M\big{[}\ddot{q}_{d}+\dot{e}+sign(S_{1})\big{]}+B\cdot\dot{q}+G \tag{10}\]  
The fractional-order sliding mode surface is designed as follows.  
\[S_{2}=\dot{e}+Ce+{D_{t}}^{\alpha}e \tag{11}\]  
By choosing the constant matrix \(C=\begin{bmatrix}20&0\\ 0&20\end{bmatrix}\), derivation of the above formula can be obtained as  
\[\begin{array}{ll}\dot{S}_{2}&={D_{t}}^{\alpha-1}e+C\dot{e}+\ddot{e}\\ &={D_{t}}^{\alpha-1}(q_{d}-q)+C\dot{e}+\big{(}\ddot{q}_{d}-\ddot{q}\big{)}\end{array} \tag{12}\]  
In the design process of the sliding mode controller, the reaching law needs to be designed. The design of the reaching law is varied, and the symbol function \(-k\text{sgn}(s)\) is often used as the design criterion. Considering the chattering problem caused by the sign function, the chattering phenomenon exists when the state of the manipulator is switched near the sliding mode surface. In this paper, the hyperbolic tangent function is used instead of the \(sign\) function, and the designed reaching law \(T_{2}\) is as follows.  
\[\begin{array}{ll}T_{2}&=M\Big{(}q_{d}^{(\alpha-1)}+C\dot{e}+\ddot{q}_{d}\Big{)} +B\Big{(}q_{d}^{(\alpha)}+Ce+\dot{q}_{d}\Big{)}+K(q^{(\alpha+1)}+q)\\ &-\omega+\Gamma(e)\text{tanh}(S_{2})\end{array} \tag{13}\]  
where \(K\) is the converge matrix and its value was chosen as \(5*I_{2\times 2}\), where \(I_{2\times 2}\) is the identity matrix of dimension \(2\times 2\). \(\Gamma(\cdot)\) is the gamma function. In the algorithm implementation process, the torque has multiplied by a coefficient \(\rho,\ 0<\rho<1\), and then added with the offset value \(T_{off}\) to simulate actuator gain and deviation fault. In the simulation section, \(\rho\) switches between 20%, 40%, and 60%, while \(T_{off}\) gets its value among 50 N\(\cdot\)m, 100 N\(\cdot\)m, 200 N\(\cdot\)m and 300 N\(\cdot\)m.
## 5 Numerical Simulation Test  
In the previous section, we showed that the seismic traces recorded using the land-streamer setup is in very good agreement with those recorded using a conventional setup, not only in the time domain (Figure 3) but also in the frequency content (Figures 4 and 5). On top of that, the traveltime picks of both data sets are almost identical for most of the picking (Figure 6) with a maximum error of \(\pm\)4 ms, which is much smaller than the accepted error. However, this doesn't show the effect of the acquisition setup on the accuracy of the final subsurface velocity model.  
In this section, we will cover this point using a synthetic simulation. Here, we will show the effect of using conventional setup vs. land-streamer setup on the shallow and deep geological structure and local anomalies.  
Figure 7 shows the synthetic velocity model used in this simulation. The model is 450 m long by 85 m deep. It shows 4 subsurface layers; all having a flat boundary and homogenous velocities. The velocity and thickness values are shown in Table 1. In this model, we used horizontal layers with homogeneous velocities to focus only on the effect of local anomalies. We added four local anomalies at offsets 50 m, 150 m, 250 m, and 350 m with a velocity of 400 m/s. The sizes of these anomalies are gradually increasing in both spatial and vertical dimensions (Table 2), and all of them have the shape of half an oval. All four local anomalies are located within the first layer, which represents the weathering layer in this synthetic model.  
Figure 6: The histogram of the difference between first arrival traveltime picks of both conventional and land-streamer data sets. The differences between the traveltime picks of both data sets are smaller than \(\pm\)4 ms.  
The synthetic first arrival traveltimes are calculated using an algorithm based on the finite-difference solution of the Eikonal equation [43].
## 2 Materials and Methods--Implementation, Integration, and Testing of the IDEASSat Flight Model
### Environmental and Functional Test
#### 2.5.1 Flatsat  
A flatsat refers to a complete tabletop integration test of spacecraft subsystems before the final flight model is assembled [25]. The purpose is to confirm that there will be no interference or abnormality between sub-systems in all operating modes. Since the correct ADCS information cannot be obtained, virtual ADCS information was provided in FSW test code when performing the ground test for operating mode transitions. The flatsat layout of IDEASSat as partly assembled is shown in Figure 9. During the flatsat stage, the main task was to confirm whether the subsystem functions are normal in various operations, and whether the corresponding subsystems are normally turned on. Additional functional testing also took place at this stage to ensure that the spacecraft EPS was capable of charging the batteries under sunlight, as well as regulating power distribution to the spacecraft subsystems. FSW mode switching, as well as subsystem and system level reset and power cycling capability, were also verified. The spacecraft deployment switches and mechanisms were also tested to verify that the spacecraft was capable of powering on following launch vehicle separation and deploying the UHF antenna and solar panels.  
Figure 8: The basic architecture of the NCU UHF ground station.
## 3 Results and Discussion
### Color Parameters of Pates  
The initial factors that consumers consider when selecting a product are its appearance and color [2]. The incorporation of yellow mealworm powder resulted in a notable alteration in the color characteristics of the pates, resulting in a more gray and darker color (Figure 3). Specifically, the L* parameter significantly decreased, indicating a darker color (Table 4). Additionally, the a* parameter demonstrated a significant reduction in redness, while the b* parameter displayed an increase in yellowness, as evidenced by the significantly higher values observed. The notable changes in the color of the pates with insect powder are due to the natural darker brown color of yellow mealworm powder compared to the natural color of wheat flour. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Time (Day)**} & \multicolumn{4}{c}{**P**\#**\# **Variant**} \\ \cline{2-5}  & **PC** & **P3\%** & **P6\%** & **P9\%** \\ \hline \hline \multicolumn{5}{c}{Shear Force (N)} \\ \hline
1 & 2.74 cx 1 \(\pm\) 0.13 & 2.06 bx \(\pm\) 0.06 & 1.82 ax \(\pm\) 0.34 & 1.64 ax \(\pm\) 0.14 \\
7 & 2.66 abx \(\pm\) 0.20 & 2.93 by \(\pm\) 0.58 & 2.45 ay \(\pm\) 0.22 & 2.77 aby \(\pm\) 0.21 \\ \hline \multicolumn{5}{c}{Spreadability (N-s)} \\ \hline
1 & 42.66 cx \(\pm\) 2.01 & 31.41 bx \(\pm\) 1.44 & 23.32 ax \(\pm\) 2.60 & 22.67 ax \(\pm\) 0.92 \\
7 & 45.25 ax \(\pm\) 4.97 & 42.51 ay \(\pm\) 6.13 & 35.93 ay \(\pm\) 6.53 & 37.34 ay \(\pm\) 4.95 \\ \hline \hline \end{tabular}  
* Different letters within rows (a–c: Tukey’s HSD, \(p<0.05\)) and columns (x–y; \(t\)-test, \(p<0.05\)) indicate the statistical difference.  
\end{table} 
Table 3: Changes in texture parameters of pátes with different yellow mealworm powder additions. After a week of storage, a contrasting trend was observed solely about the b* color parameter, whereby the values were significantly lower for the gates with insect powder. The storage time significantly impacted the color parameters of the gates with insect powder. Compared to the gates tested after one day, there was a significant increase in the L* values and a decrease in both the a* and b* values. Color changes can significantly affect consumer acceptance of such products, but consumers may favorably perceive darker colors. It may be associated with the lack of preservatives and the typical color of gates, especially those made from pork. For example, the color of gates prepared from duck and goose meat was greatly assessed by the sensory panel than the color of the gate made from chicken meat [47]. Similarly, in a study by [PERSON] et al. [48], the increasing amount of chicken breast as a replacement for pork loin in emulsion sausages resulted in a decrease in the color scores assigned by the sensory panel. The total color difference values indicated that as the quantity of yellow mealworm powder increased, the color differences became more pronounced (Table 4). The addition of 3% mealworm powder enabled the color differences to be discerned by an experienced
# Real-Time Wave Energy Converter Control Using Instantaneous Frequency
## 3 Comparison of Methods
### Application of Instantaneous Frequency Estimation to WEC Control  
The instantaneous frequency estimation methods described in this paper--namely the CHT and the PLL--are directly applied to optimize real-time control of WECs. In the context of WEC operation, effective energy absorption is highly dependent on the dynamic matching of the PTO system impedance to the time-varying incident wave conditions. Traditional methods relying on fixed or slowly updated parameters may fail to capture the rapid fluctuations present in realistic sea states. By employing real-time instantaneous frequency estimation, the PTO damping can be continuously adjusted based on the current sea state characteristics without requiring full knowledge of future wave  
Figure 8: Sensitivity of NRMSE to tuning parameters for CHT and PLL methods in Case 4. (**a**) NRMSE for CHT method as a function of median window fraction. (**b**) NRMSE for the PLL-based method as a function of cutoff frequency (Hz). Error bars represent one standard deviation computed over 50 random realizations.  
Figure 7: Sensitivity of NRMSE to tuning parameters for instantaneous frequency estimation across all cases: (**a**) CHT median window fraction; (**b**) PLL cutoff frequency.  
profiles. Specifically, the HT approach leverages past wave elevation data within a moving time window to estimate the predominant oscillation frequency, enabling the dynamic calculation of optimal PTO parameters. The PLL method, alternatively, provides a causal and computationally efficient estimation of frequency by locking onto the phase of the incoming wave signal, offering improved tracking performance under rapidly changing conditions. Through these methods, the WEC system is capable of adapting its control strategy on a wave-by-wave basis, resulting in enhanced CWR and improved overall energy harvesting efficiency compared to conventional fixed parameter designs. The proposed framework, therefore, represents a significant step toward practical, real-world WEC deployments where computational efficiency, robustness to non-stationary ocean conditions, and real-time adaptability are critical for maximizing energy capture.
## 2 Experimental Section
### Instrumentation and Synthesis
#### 2.2.4 1-Fluoro-3-(Nitroprop-1-Enyl)Benzene **11**  
This compound was prepared [23] by reacting 3-fluorobenzaldehyde (1 g, 8.05 mmol) with ammonium acetate (0.19 g, 2.4 mmol) in nitroethane (4.98 g, 66.4 mmol), under reflux overnight (approximately 17 h) in an oil bath at 125 \({}^{\circ}\)C. The compound was identified by thin layer chromatography (TLC). The mixture was then concentrated under high vacuum to remove the excess nitroethane and then the yellow mixture was dissolved in chloroform (20 mL), washed with water (3 \(\times\) 20 mL) and with sodium chloride solution (25%, 20 mL). The mixture was dried over (MgSO\({}_{4}\)) and concentrated under high vacuum. A yellow liquid was obtained (0.75 g) this being 52% of theoretical yield. \({}^{1}\)H NMR (300 MHz, CDCl\({}_{3}\)): \(\delta_{\rm H}\) (ppm): 8.04 (s, 1H, C=C-H), 7.46-7.39 (m, 1H, Ar-H), 7.46-7.39 (m, 1H, Ar-H), 7.22-7.19 (d, \(J\) = 7.80 Hz, 1H, Ar-H), 7.13-7.09 (d, \(J\) = 9.30 Hz, 1H, Ar-H coupled with F), 2.42 (s, 3H, CH\({}_{3}\)). \({}^{13}\)C NMR (300 MHz, CDCl\({}_{3}\)) \(\delta_{\rm C}\) (ppm): 164.3 (d, \(J\) = 246.79 Hz, 1C, C-F), 148.7 (C=C, \(\beta\) carbon), 134.6 (Ar), 132.0 (Ar), 130.6 (C=C, \(\alpha\) carbon), 130.5 (Ar), 125.9 (Ar), 125.7 (Ar), 13.9 (CH\({}_{3}\)). GC/MS \(m\)/\(z\) (M\({}^{+}\)) 181.
## 3 Results and Discussion  
### Fresh Performance  
#### 3.1.1 Slump-Flow Test  
The slump flows of the SCC mixes are plotted in Figure 7a, and the \(t_{500}\) times in Figure 7b. These parameters define the flowability and flow speed of SCC under free flow, respectively. In slump-flow terms, the \(RW\) mix was of SF2 slump-flow class, while the \(W1.5\) and \(W3.0\) mixes were of SF1 class [61]. All the mixes were of VS2 class.  
The decreasing slump flow of SCC when adding RCWTB (variations of \(-14.2\%\) and \(-21.3\%\) for the \(W1.5\) and \(W3.0\) mixes, respectively) were because this waste increased the specific surface area of the SCC components. The GFRP fibers and micro-fibers had a high surface area [38], as can be noted in Figure 4, while the balsa-wood particles exhibited a high surface porosity [70]. This phenomena in turn resulted in a reduction of the free water in the SCC, which increased the internal friction among all their components [14], thus reducing the ability of the cement paste to drag all of them uniformly. However, it should be noted that the increased water/cement ratio partially compensated such performance,so this negative trend could have been more pronounced if no water compensation had been conducted [39].  
The \(t_{500}\) time increased with the addition of RCWTB, although the variation was not as linear with the RCWTB content as in the case of slump flow. The increase in time between the \(RW\) and \(W1.5\) mixes was only about 0.3 s, while the increase between the reference SCC mix and \(W3.0\) mix was 2.2 s (31.3% increase). In view of this behavior, it appears that the negative effect of the addition of 1.5% RCWTB occurred primarily when SCC reached a high slump flow, since it did not affect the time that SCC took to reach a reduced slump flow of 500 mm. This behavior has also been found for other alternative raw materials [19; 26] and can be explained by the ability of the cement paste to maintain adequate cohesion between all SCC components at low slump-flow levels [6]. This was also reflected in the low sieve-segregation values achieved (EN 12350-11 [71]), less than 4% in all the SCC mixtures regardless of the RCWTB content.  
As a consequence of the slightly different trends shown by both slump flow and \(t_{500}\) time, the two properties did not have a very close linear dependence, as shown in Figure 8. This indicates that the free flow of SCC containing RCWTB does not occur at a uniform rate over time, especially when this waste is used at a content of 1.5%.  
Figure 8: Relationship (linear simple regression) between the results of the slump-flow test.  
Figure 7: Experimental results of the slump-flow test: (**a**) slump flow; (**b**) \(t_{500}\) time.
## 4 Discussion
### MCDA as Decision-Making Options for Computational Models
#### 4.2.2 MCDA Applicability for Discrete Event Simulation Models  
Discrete Event Simulation models generally represent decisions from an aggregate level where mechanisms for defining entity movement or routing are specified. The entities moving through the system have no control over the decision itself; instead, progression-based decisions are made for the entities at the system level using percentages or logical determinations, such as entity type, percent chance, or shortest queue lengths [24; 25; 26; 102; 103]. Starting at the conceptual model building phase, focusing on DES decision elements aids in the identification of relevant criterion, fuels learning and collaboration, and contributes to assessing model validation [25; 104; 105]. While the static structure of DES simulations generally dictates the paths that one can follow, many simulation platforms allow for the incorporation of logic within entities to allow greater depth in path selection [26; 106].  
MCDA can be utilized to determine the weights of the transition options coming out of decision nodes, for representing path-selection logic within entities, and for determining or altering resource schedules. Decision attributes that have clear separations of importance may be better represented using ratio assignment techniques. Attributes with assumed equal weightings or weightings based on sampling from the uniform distribution may be more suitable when using approximate techniques. The number of attributes present in the relevant decision-making processes and the number of entity types, as well as the level of agreement within the simulation's conceptual model, its simulation building team, or its stakeholders, should be evaluated within the context of the problem being modeled to select the appropriate technique within the corresponding MCDA technique category. Within the domain of healthcare, MCDA techniques can provide alternative means for modeling staff scheduling, patient admission, patient routing, and resource allocation.  
A survey of simulation application priorities emphasizes the relevance of human performance modeling, modeling complex behavior, and human decision-making towards the health care and service industries [107]. Within the scope of quantitative methods, DES models can incorporate resources and constraints, include soft variables from surveys and expert opinions, and cope with the high levels of variabilities existing between and within variables [108]. Data such as patient arrival times, discharge times, bed types, and time to bed within an emergency department are common variables when examining system performance and exploring improvements [24]. This type of criteria could be utilized for constructing ratio assignments or approximate techniques within a simulation to drive simulated decisions.
## 2 Materials and Methods
### Statistical Analysis  
The recorded dynamic biospeckle movies were analyzed with a custom-made MATLAB program using the vision and image analysis toolbox of MATLAB (Math work, USA R2016a). All data in this study represent the mean of nine identical experiments performed in triplicate and are presented as the mean \(\pm\) standard deviation. Significant differences between the treatments and control were determined with the Student's \(t\)-test. For comparing different concentrations of heavy metals against different time points, two-way ANOVA was used. For either statistical test, the significance threshold was set at \(p<0.05\).