esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion  
### Cyclic Mechanical Testing  
The as-built EOS MS300 test coupons were strained to failure; however, only the data corresponding to the 28 th cycle are reported and have been used in this analysis. This was a deliberate choice, since unplanned interruptions of the tests at the 29 th cycle and the 516 th cycle for the vertically and the horizontally built test coupons, respectively, may have compromised both the evolution of the hysteresis (stress-strain) loops and the LCF life of the material. Nevertheless, the 28 th cycle data were sufficient to illustrate the cyclic elastoplastic characteristics of this material and to allow a comparison with the [PERSON] et al. [10] mid-life (32 nd cycle) results for the same strain range (\(\pm 0.9\%\)) or close to that (i.e., \(\pm 0.95\%\) for the vertical coupon). The obtained stress-strain hysteresis loops along with the aforementioned [PERSON] et al. [10] experimental data are illustrated in Figure 2.  
The hysteresis loops of the tested coupons (vertical, horizontal) appear to have symmetry, both in terms of their shape and in absolute stress values (approximately 900 and 1000 MPa, respectively). It can be observed that this is not the case for the [PERSON] et al. [10] data, as the hysteresis loop has a negative stress asymmetry of approximately 100 MPa. Of note is that the area enclosed by the hysteresis loops, representing the plastic strain energy dissipation, is considerably smaller for the horizontal and the [PERSON] et al. [10] vertical coupon compared to that of the vertical coupon. This is an indication of a higher LCF resistance of the vertical coupon; however, safe conclusions cannot be made unless further tests are performed for the estimation of the LCF life of as-built EOS MS300.
## 7 Results
### Traffic Demand Scale of 0.25  
The 25% traffic demand produced 34,449 vehicles during the first hour of the simulation. As expected, the C-V2X and IDEAL C-EEDR applications demonstrated the same behavior, as previous traffic demands in terms of the fuel consumption, travel time, and stopped delay, as shown in Figures 13, 14, and 15, respectively.  
With low and medium traffic demands (25%, 50%, and 75%) levels, the C-EEDR application exhibits the normal behavior where decreasing the system-wide fuel consumption leads to increasing the system-wide travel time and stopped delay. However, in a high traffic demand level (100% case), the C-EEDR application not only decreased the system-wide fuel consumption but also managed to decrease the system-wide travel time and stopped delay in almost all of the market penetration rates except for the 100% one.  
Figure 12: Average network-wide Stopped Delay of Energy-Efficient dynamic routing application using IDEAL and C-V2X communication on LA road network with traffic demand of scale factor 0.5.  
Figure 13: Average network-wide Fuel consumption of Energy-Efficient dynamic routing application using IDEAL and C-V2X communication on LA road network with traffic demand of scale factor 0.25.  
We ran the two-tailed \(t\)-test (\(p\)-values ranged between [0.18-0.96]) between the IDEAL-based C-EEDR and C-V2X-based C-EEDR applications and found that there was no statistically significant difference between the corresponding travel time, stopped delay, and the fuel consumption between the means of the 10 different seeds for each of the 9 market penetration rates at a significance level of 0.05.
## 9 A Novel Methodology for 5G-Enabled Broadband Connectivity Coverage of Smart Cities
This can be the case for quasistatic Rayleigh fading channels, where the channel characteristics remain constant (albeit still providing an NLOS scenario) for the transmission of a whole length of information. We choose to work with a \"stricter\" modulation scheme in the uplink channel, because in that stream we have the more sensitive biomedical data (digital baseband derived from the biophysical data); therefore, the requirements are higher in terms of robustness and reliability, also conforming to the URLLC specification. In our case, an Eb/No of 16 dB with a worst-case scenario of 10 dB SNR (from Section 9) can lead up to a rate of 500-600 Kbps for a 2 MHz bandwidth allocated per user within the smart city spectrum of a 5G-enabled network. This leads to roughly 500-600 bits of information length transmitted within the timeframe of URLLC-defined latency of 1 ms.
## 6 Evaluation and Discussion
### Evaluation of the First Experiment  
Initial results of abuse in the dynamic groups experiment will be presented regarding configuration vulnerabilities as assessed using the CCSS framework. Table 2 contains all the factors and an explanation of why each value was chosen. Additionally, the results of the calculation of Exploitability, Impact, and Base are shown.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Factor** & **Value** & **Score** & **Explanation** \\ \hline Access Vector (AV) & Network (N) & \(1.0\) & Hackers could invite external users without accessing the internal network or executing any commands locally. \\ \hline Access Complexity (AC) & Medium (M) & 0.61 & For the hacker to invite an external user, he must have some permissions and an active user account. \\ \hline Authentication (Au) & Single (S) & 0.56 & A single login attempt is sufficient to exploit this vulnerability. \\ \hline Confidentiality Impact (C) & Partial (P) & 0.275 & As a result of the attack, the hacker was able to access all the virtual devices used in programming games, which may contain confidential data, but he could not access all the data stored in the company’s cloud. \\ \hline \hline \end{tabular}
\end{table}
Table 2: The first experiment’s evaluation values.  
Figure 13: An overview of how the attack is executed.  
In the first experiment, the exploitability factor was assessed at 6.9 points, indicating that exploiting this vulnerability is not difficult. This initial impression raises concerns about its potential danger. Although the impact factor was evaluated at 4.9, it is important to note that the actual impact may vary depending on the privileges assigned to dynamic groups. If higher privileges are granted, or if the scope of user access is narrower, the impact could be more significant. Conversely, lower privileges, or a more restricted user scope, could result in a lesser impact. It is crucial to recognize that the impact is directly correlated with the confidentiality, integrity, and availability of information and services. Taking all factors into account, the vulnerability was assessed with a Base Factor of 4.9 out of 10, placing it in the category of medium-risk vulnerabilities.
# Formal Analysis of Ratchet Protocols Based on Logic of Events
## Appendix A  
_Appendix A.1. Attack Scenario Demonstration: DH Key Substitution and Clean Predicate Violation_  
To illustrate how omission of LoET rules (such as _AxiomE_) can break authentication, we provide the following example:  
* Assume [PERSON] initiates a session and sends [PERSON] her DH public key: \(DH(A)=aG\).
* An attacker, [PERSON], intercepts the message and replaces \(aG\) with her forged DH key \(DH(M)=mG\).
* [PERSON], unaware of this manipulation and without enforcing _AxiomE_ (which links DH to subsequent ratchet operations), uses his private key \(b\) to compute \(K_{B}=H(mG\times b)\).
* [PERSON] proceeds to generate a new ratchet state using \(K_{B}\), encrypts a message using this state, and sends it back to [PERSON].
* [PERSON], expecting \(aG\) as the DH input, uses her private key \(a\) and [PERSON]'s original public key \(bG\) to compute \(K_{A}=H(bG\times a)\).
* Since \(K_{B}\
eq K_{A}\), [PERSON] is unable to decrypt the message. Worse, if [PERSON] had continued without verification, [PERSON] could use \(m\) to decrypt the message and impersonate [PERSON] by replying with forged messages under her control.  
This demonstrates how lack of causal enforcement (i.e., missing \(DH\to ratchet\) linkage) permits an attacker to inject invalid DH values and break authentication. In LoET, this violation is captured as a failure of the _Clean_ predicate: the ratchet update and encryption event are no longer causally traceable back to a valid DH event from [PERSON]. Thus, any decryption based on such a compromised key violates the LoET-defined trust chain.
## 3 Results and Discussion
### Antibiotic Activity Data  
Figure 5 shows the antimicrobial activity of AMO and its DPs against \(S\). _aureus_. Partial activity was developed at 5 mg/L AMO when the inhibition did not exceed 28% for the parent compound.  
Figure 5: Antibacterial activity of AMO and its DPs against \(S\). _aureus_. Groups with the same letter were not significantly different (Tukey post hoc, \(p<0.05\)).  
It was evident that 56% of DPs showed residual activity to _S. aureus_ and this was more pronounced for DP6, where activity was exclusively due to oxidation byproducts with 74% of antibiotic activity. DP1, DP8, and DP9 showed decreased antibiotic activity. Only DP13, DP14, and DP16 were revealed to have no antibiotic effects.  
Similar tests with _E. coli_ and _K. pneumoniae_ revealed that both bacteria were resistant at 5 mg/L AMO, with no significant antimicrobial activity (data not shown). The related DPs appeared to have no antibiotic and/or toxic effect against _E. coli_ and _K. pneumoniae_ (data not shown).  
According to [PERSON] [48], _E. coli_ and _K. pneumoniae_ revealed a resistance up to 25 mg/L AMO, even if the suggested ranges for Minimum Inhibitory Concentrations (MICs) were 0.25-128 mg/L for Enterobacteriaceae (i.e., _E. coli_ and _K. pneumoniae_) [49].
# A Reliable Publish-Subscribe Mechanism for Internet of Things-Enabled Smart Greenhouses
## 4 Performance Evaluation
### Evaluation Model
* Payload Reduction: According to Section 3.3, the payload of the proposed model is optimized to reduce the transmission of extra and irrelevant data by approximately 80 bytes compared to the traditional MQTT broker's payload framework. This significant reduction in payload size decreases network congestion and improves message processing efficiency. * Message Processing Time Optimization: Leveraging the optimized \(M/M/1\) queuing model, we focus on optimizing the message processing time (\(MPT\)), which has a direct impact on system capacity. The \(MPT\) is calculated by two factors: the number of events (\(E^{*}\)) and the arrival rate (\(\lambda\)). According to our assumption (four payloads are transmitted from greenhouse gateway to broker in one second), the \(MPT\) optimizes using the following formula: \[MPT=\lambda^{*}/E^{*}\] (5) By adjusting the arrival rate depending on the payload size and ensuring efficient processing per event, we enhance the broker's capability to handle higher loads. * Dynamic Adjustment of Arrival and Service Rates: The modified \(M/M/1\) model allows for dynamic adjustments of the message arrival rate (\(\lambda\)) and service rate (\(\mu\)) based on real-time data. This adaptability ensures the broker can maintain optimal performance under varying load conditions. * Efficient Message Handling: Through the organization of messages into specific topics and the reduction of payload size, we optimize the efficiency of the message-handling process. This scenario enhances the broker's processing efficiency and boostsits capacity to handle larger volumes of transactions, especially in situations with heavy traffic. Likewise, relying on the assumptions indicated in Section 4.1, we separate the findings of our comparison in Tables 1-3 for simpler and superior comprehension. Table 1 indicates the number of messages transmitted in both broker configurations as estimated by the modified Formula (4) with the two primary parameters of message arrival rate and message size.
# Influence of Random Modulated Power Converter on G3 Power Line Communication
## 5 Results and Discussion
### Effects of the Converter Switching Frequency
The worst Channel Capacity in the case of RPPM obtained is at switching frequencies around 60-63 kHz (498 kb/s) and results to be significantly higher than the minimum Channel Capacity obtained in [14] with the DC-DC converter driven by the RFM (390 kb/s at the spreading factor '\(\alpha\) = 30%' and 63 kHz switching frequency). These observations are also confirmed in terms of Channel Capacity Loss. Indeed, Figure 9 shows that the G3-PLC Channel Capacity Loss takes maximum value (18%) at 60 kHz, which results to be significantly lower than the maximum Channel Capacity Loss (48%) observed in [14] for RFM (with spreading factor '\(\alpha\) = 30%'). Figure 8: Channel Capacity of the G3-PLC with different switching frequencies applied to the RPPM modulated DC-DC converter. Figure 9: Channel Capacity Loss of the G3-PLC with different switching frequencies applied to the RPPM-modulated DC-DC converter.
# Performance of Cooperative Relay NOMA with Large Antenna Transmitters
## 1 Introduction
### Review of Existing Works and Motivation  
#### 1.1.1 5G Massive MIMO Channel Models  
To assess the performance of 5G wireless communication systems, two types of channel models are commonly used; correlation-based stochastic models (CBSMs) and geometry-based stochastic models (GBSMs) [17; 18]. The former has a lesser level of complexity and is mostly used to analyze the theoretical performance of MIMO systems [17; 19].  
However, the accuracy of a realistic MIMO system is restricted, and modeling wireless channels with nonstationary phenomena and spherical wave effects is challenging [17; 19; 20]. In contrast, GBSM has increased processing complexity, however, it can correctly represent true channel parameters and is better appropriate for mMIMO channel models [17]. GBSM  
Figure 1: NOMA system architecture for downlink communication.  
incorporates channel properties such as path-loss, delay profile, tilt angle, angle of arrivals, etc into the channel modeling for better performance evaluation [20; 21; 22].
# Performance Study and Efficiency Improvement of Ice Slurry Production by Scraped-Surface Method
## 3 Results and Discussion
### Influence of Solution Flow Rate  
Ice slurry formation characteristics of 4 wt% sodium chloride solution were experimentally measured at flow rates of 1.0, 1.1, 1.2, 1.3, 1.4, and 1.5 m\({}^{3}\)/h while maintaining the fixed rotary scraping speed (n = 29 rpm). The measurement results are shown in Figure 9. The ice slurry formation temperature was basically the same under different flow rates, which indicated that the ice slurry formation temperature mainly depended on the sodium chloride concentration of the solution. When the flow rate was 1.3 m\({}^{3}\)/h, the ice crystals were generated slightly earlier than with other flow rates.  
The relationship between IPF and ice-making time at different flow rates is shown in Figure 10. At the same ice-making time, as the flow rate of the solution increased, the IPF of the ice slurry first increased and then decreased. When the flow rate was 1.3 m\({}^{3}\)/h, the IPF of the ice slurry reaches the highest value. It was also found that when the flow rate reached 1.3 m\({}^{3}\)/h, the COP of the system reached the maximum value, as shown in Figure 11. The ice-making solution flowed out of the annular shower tube and flowed from the top to the bottom along the inner wall surface. When the flow rate was low, the amount of cooling obtained per unit of time was small, which resulted in a low IPF. When the flow rate was increased from 1.3 to 1.4 m\({}^{3}\)/h, the ice slurry generation time was extended from 1080 s to 1160 s, an increase of 7.41%. During the experiment, we observed that when the flow rate increased to 1.4 m\({}^{3}\)/h, the ice-making solution flowed out of the annular shower tube with a  
Figure 8: Effects of initial NaCl concentration on coefficient of performance (COP).  
Figure 7: Relationship between IPF and power consumption (3% wt NaCl solution).  
serious splash and part of the solution did not flow down the inner wall surface. Therefore, the solution actually participating in the effective heat exchange through the inner wall surface was reduced and the amount of cold obtained per unit time of the ice-making solution was also decreased, resulting in the increase of ice slurry generation time and the decrease of the system COP. The results shown in Figures 9-11 indicated that the optimal flow rate of solution for this ice-making system was 1.3 m\({}^{3}\)/h.
# Study of Motion Control and a Virtual Reality System for Autonomous Underwater Vehicles
## 5 Data Communication between the Virtual Reality System and MATLAB  
The virtual reality system proposed in this paper can directly use Visual C++ programming to implement the AUV motion control algorithm in the simulation system. Alternatively, it can first use MATLAB to design the AUV motion control algorithm and then use the MATLAB engine to implement the data communication between MATLAB and the virtual reality system under the Visual C++ environment. The algorithm is shown in Figure 10. During the simulation phase, the program calls MATLAB modules that run as background processes and updates the member variables stored as the numerical \"current\" time in the program.  
The MATLAB computing platform and the member variables of the stored data of the virtual reality system perform bidirectional data transmissions. Let the current time be the (k\(-\)1) the time of the simulation. At this time, the Visual C++ software will initiate data transmission to the engine and perform the computation in the MATLAB system. The computation results are then outputted through the Engine library function, reassigning new values to the corresponding member variables at time k.  
The motion of the AUV 3D model in the virtual scene is controlled by continuously assigning values to the position, pose angle, velocity and angular velocity member variables of the AUV. The MFC class library of the Visual C++ platform is used to define and control the motion functions of the AUV in virtual scenes.  
These functions manage the member variables that represent the position, pose, velocity and angular velocity of the AUV, and use the Move function to control the motion states of the AUV 3D model. This continuous transmission of the member variables between the control algorithm and the AUV motion synchronizes the two systems. The system obtains the input value from the virtual reality system based on the Visual C++ platform, and uses MATLAB as the computing background to realize the AUV motion control algorithm, and then outputs the computing results to the virtual reality system.  
The system demands that the pose changes of the AUV 3D model in the Vega scene should be consistent with the results obtained from the computing background. Likewise,  
Figure 10: 3D underwater virtual scene in OpenGL.  
the functions in the OnDraw class also rely on these member variables to complete the drawing and update the simulation curve. At the kth time, during the simulation, the functions in the OnDraw class will determine the coordinate range, draw the curve, and change the coordinate annotations according to the numerical values stored in the member variables at that time. The motion status of the AUV can be transferred to the function within the OnDraw class synchronously.
## 4 Materials and Test Methods
### Test Methods  
This study employed various tests to evaluate the properties of the fresh and hardened UHPC mixtures. Details regarding the specific tests conducted are presented in Table 6.  
* The flowability of the fresh concrete was measured using a standard flow table (ASTM C230 [60]) in accordance with the ASTM C1437 [61] standard.
* The setting time was determined using the Vicat needle test (ASTM C191 [62]). The test procedure involved pouring the concrete mix into a mold and smoothing the surface. The Vicat needle was then introduced into the sample at predetermined intervals (starting at 30 min after mixing) to measure the depth of penetration. The initial setting time was recorded when the penetration depth fell below 25 mm, and the test continued until the needle penetration reached zero, indicating the final setting.
* The squeezing test, adapted from the work of [PERSON] et al. [63], was employed to characterize the basic rheological properties of the fresh concrete. This test utilized a universal testing machine equipped with two grooved steel disks (diameter: 6 cm) positioned 21.5 cm apart within the clamps. An excess amount of concrete mix was placed between the disks, ensuring a uniform distribution. Excess material around the disks was removed with a spatula. Subsequently, the concrete was subjected to constant loading rates of 30 mm/min and 3000 mm/min, and the resulting load-displacement curve was recorded. A visual representation of the squeezing test setup is presented in Figure 4.
* Dry unit weight and water absorption were determined in accordance with ASTM C642 [64] on three specimens from each mixture.
* Electrical resistivity was measured at 28 days using the AASHTO TP119 [65] procedure on three samples per mix.
* Compressive strength was assessed at 7 and 28 days on 5 cm \(\times\) 5 cm \(\times\) 5 cm cube specimens following ASTM C109 [66].
* Flexural strength was determined at 28 days on 4 cm \(\times\) 4 cm \(\times\) 16 cm prism specimens, as per ASTM C348 [67].  
\begin{table}
\begin{tabular}{c c c c c} \hline \multicolumn{2}{c}{**Hardened Test**} & \multicolumn{3}{c}{**Fresh Test**} \\ \hline
**Test** & **Standard** & **Curing** & **Test** & **Standard** \\ \hline Compressive strength & ASTM C109 [66] & 7, 28 & Flow & ASTM C1437 [61] \\ \hline Electrical Resistivity & AASHTO TP119 [65] & 28 & Vicat (Setting Time) & ASTM C191 [62] \\ \hline Dry Unit Weight & ASTM C642 [64] & 28 & Squeezing & [PERSON] et al. [63] \\ \hline Water Absorption & ASTM C642 [64] & 28 & Cube & Prism \\ \hline Flexural strength & ASTM C348 [67] & 28 & 5 & 16 \\ \hline \end{tabular}
\end{table}
Table 6: Details of tests.
## 2 Methodology
### Steps of the Proposed Method  
1. Set up the proposed SPS-DS system described in Section 2.1 and use a heating gun to heat up the area of the WTB surface where there is known defect at the subsurface.
2. Record a sequence of the speckle patterns at the heated area. Decompose the recorded video into frame-by-frame images.
3. From the initial frame at time \(t_{0}\) to time \(t_{x}\), generate carrier masks at four phase values in different pixels and form a large mask to modulate the whole image, as expressed in Equation (2).
4. Transform the modulated speckle patterns from time \(t_{0}\) to time \(t_{x}\) into 2D in the frequency domain and select the central speckle rings in each speckle pattern as described above with \(\mathcal{R}\) denoting the lower frequency in Equation (4). Transform the derived frequency domain patterns into the original domain using inverse 2D Fourier transform to form the complex terms from \(\mathcal{R}_{0}\) to \(\mathcal{R}_{x}\).
5. Multiply the derived \(\mathcal{R}_{x}\) at time \(t_{x}\) with conjugates of that at time \(t_{0}\), as described in Equation (7), and calculate the phase map \(\Delta\phi_{t_{x}}\) at time \(t_{x}\), as in Equation (8).
6. In each of the initial phase maps derived at \(t_{x}\), adopt the WFF algorithm in their complex domain and obtain a new filtered phase \(\Delta\dot{\phi}_{t_{x}}\).
7. Form the sequence from time \(t_{0}\) to time \(t_{x}\), as described in Equation (9), produce the dynamic phase map series, and develop the defect variation along with the temperature change due to the dynamic thermal loading as a video sequence.  
A flow chart of the proposed method is shown in Figure 3.