esa-sceva/satcom-chunk-collection huggingface dataset:
# Chloride Resistance of Assembled Bridge Piers Reinforced with Epoxy-Coated Steel Bars
## 4 Service Life Prediction  
### Determination of \(D_{\text{NSSD}}\)  
#### 4.1.1 \(D_{\text{nssd}}\) and \(D_{\text{nssm}}\)  
Assuming that the bridge pier joints are in a saturated state due to prolonged immersion in seawater, the transport process of chloride ions can be described using [PERSON]'s second law:  
\[\tfrac{\partial C(x,t)}{\partial t}=D_{\text{nssd}}\tfrac{\partial^{2}C(x,t)}{ \partial x^{2}} \tag{2}\]  
where \(C(x,t)\) represents the volumetric concentration of chloride ions (in mol/m\({}^{3}\) or kg/m\({}^{3}\)) at a distance \(x\) from the erosion surface after a duration of \(t\); The non-steady-state diffusion coefficient for chloride ions, \(D_{\text{nssd}}\), can be determined by measuring the chloride ion concentrations at various depths within the concrete through natural diffusion experiments and obtained by regression analysis using the analytical solution, Equation (3), derived from Equation (2):  
\[C(x,t)=C_{0}+(C_{\text{s}}-C_{0})\Big{[}1-\text{erf}\Big{(}\tfrac{x}{2\sqrt{D_ {\text{nssd}}}t}\Big{)}\Big{]} \tag{3}\]  
where, \(C_{0}=C(x,0)\); \(C_{\text{s}}=C(0,t)\); \(\text{erf}\) is the error function, \(\text{erf}(z)=\tfrac{2}{\pi}\int_{0}^{z}\exp\big{(}-z^{2}\big{)}dz\).  
Due to the naturally slow diffusion process of chloride ions in concrete, the duration required for natural diffusion experiments is excessively long. In engineering practice, the method involving the application of an external electric field, as specified in NT BUILD 492 [28], is commonly used to accelerate the transport process of chloride ions in concrete. This method is employed to determine the non-steady state migration coefficient (\(D_{\text{nssm}}\)) of chloride ions in concrete, thereby assessing the concrete's resistance to chloride ion permeability. Relevant study [29] indicates that there is a relationship between \(D_{\text{nssd}}\) and \(D_{\text{nssm}}\) as described in Equation (4):  
\[\ln D_{\text{nssd}}=B\ln D_{\text{nssm}}+\ln A+\varepsilon \tag{4}\]  
By fitting a substantial amount of actual engineering data [29], the values obtained are as follows: \(B=1.0\), \(A=0.5\), and \(\varepsilon\sim\text{N}\big{(}0,0.3^{2}\big{)}\). Substituting \(B=1.0\), \(A=0.5\), and \(\varepsilon=0\) into Equation (4) results in Equation (5):  
\[D_{\text{nssd}}=\tfrac{D_{\text{nssm}}}{2} \tag{5}\]
# A Multi-Model Proposal for Classification and Detection of DDoS Attacks on SCADA Systems
## 4 Materials and Method
### Attack Scenarios for the Testbed  
This section elaborates on the baseline situation of the testbed and the attacks against the testbed. DDoS attacks, one of the most common attacks on SCADA systems, were discussed and attacks against an RTU selected by the attacker were performed. Different types of DDoS attack scenarios were implemented and aimed to affect the operation of the system. These scenarios were:  
1. Baseline (normal or no-attack) situation;
2. TCP flooding attack scenario;
3. UDP flooding attack scenario;
4. SYN flooding attack scenario;
5. Spoofing IP flooding attack scenario;
6. ICMP flooding attack scenario.  
In the baseline situation scenario (when the SCADA system was not under attack), the obtained network traffic was listened to and recorded. In this scenario, water circulated continuously between the water tanks and the necessary operations were performed automatically according to the change in the water level. The pinginging method was used to establish communication between RTU and MTU.  
Specific coding was made by the attacker for each attack type and 5 different DDoS attacks were performed against the target RTU. Each of TCP, UDP, SYN, Spoofing IP and ICMP flooding attacks were carried out at different times and separately. Each of these attack scenarios were executed for approximately 2 min. During the attacks, the target RTU system processes were interrupted for a short period of time. Processes such as water recirculation and alarm generation were disrupted. These adverse conditions also affected the other RTU system and the operation of the entire testbed system was interrupted for short periods of time. Abnormal situations such as incorrect measurement of the tank water level or buzzer alarming at the wrong time were observed. When the execution of the attack scenarios ended, the system operation slowly recovered and, after a while, the system returned to its former state. If the time taken to restore the system operation is too long to be tolerated, irreversible major problems may occur for SCADA systems. For this reason, it is important to attack SCADA systems and monitor and analyze the attack responses. In this study, this issue is emphasized.
## 3 Results and Discussion
To assess the ascorbate efficiency as a co-catalyst, its amount was reduced, employing a substrate-to-ascorbate ratio of 1:5.5. This adjustment yielded comparable results to those obtained with equimolar ascorbate relative to the substrate. The reaction achieved a 26% SA yield with a selectivity of 57% after 24 h of reaction time, a 45% SA yield with the same selectivity after 48 h, and a 61% SA yield with 67% selectivity after 72 h (Figure 3). These results demonstrated that Cu\({}_{2}\)O/ZnO-PDA, when combined with optimized ascorbate amount, achieves catalytic performance better than the benchmark CuO/TiO\({}_{2}\) along 72 h of reaction time. Analyzing the data reported in Figure 4, an initial production of FA from MA was observed, accompanied by a simultaneous reduction to SA, while the selectivity for the desired product remains comparable throughout the reaction time, suggesting that the initial reaction step involves the isomerization of MA to FA (step not catalyzed), followed by subsequent catalyzed reduction to SA [47] (Figure 5). Figure 3: (**a**) Conversion of MA during the time in photoreduction reaction catalyzed by Cu\({}_{2}\)O/ZnO-PDA in the presence of sodium ascorbate as co-catalyst or added in equimolar ratio to MA. (**b**) Selectivity to SA during the time in MA photoreduction reaction catalyzed by Cu\({}_{2}\)O/ZnO-PDA in the presence of sodium ascorbate as co-catalyst or added in equimolar ratio to MA. The effectiveness of the newly prepared catalyst was supported by conducting control reactions using its individual components (ZnO, Cu\({}_{2}\)O, or CuSO\({}_{4}\)) as photocatalysts under identical reaction conditions. These tests yielded no SA and only resulted in a conversion to FA (32% after 12 h). The tuned system was able to overcome the copper oxide photodegradation problem thanks to the presence of a PDA shell around the photocatalyst, and it can boast high selectivity in the reduction reaction of MA to SA thanks to the presence of ascorbate in the co-catalytic amount. Extending the lifespan of the catalyst exposed to UV light, and thereby the reaction times, can result in higher yields while aligning with the principles of green chemistry.
# A Crypto-Steganography Approach for Hiding Ransomware within HEVC Streams in Android IoT Devices
## 4 Results and Comparisons
### Objective Quality Assessment Analysis  
To further ensure the superiority of the proposed crypto-steganography approach, we compared its accomplishment with recent related steganography algorithms that are introduced in [38; 39; 58]. These introduced algorithms are based on only one stage of LSB steganography without employing further security stages like encryption or random embedding strategy as investigated in our proposed work in this paper. Table 7 shows the objective outcomes of the comparative analysis between the proposed approach compared to the related algorithms [38; 39; 58] in terms of all quality assessment metrics that were presented in Table 3.  
As elaborated in Table 3, some of these objective quality metrics must have lower values, and others must have higher values to achieve the recommended and appreciated performance for the proposed crypto-steganography approach. In this perspective, it can be concluded from Table 7 that our proposed crypto-steganography approach outperforms almost all of the  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**HEVC Stream** & **Cover Frame** & **Stego Frame** \\ \hline Balloon (Frame 5) (Ransomware 5) & & \\  &  
objective quality assessment metrics in comparison with the related works. This is obvious by obtaining lower values of the MSE, NAE, AD, LMSE, MD, SC, and \(\Delta\)E metrics and higher values of PSNR, NK, SSIM, FSIM, MS-SSIM, UQI, SNR, NQM, WSNR, and VIFP metrics for the proposed approach compared to the related steganography approaches.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Metric** & **Approach** & **Balloon** & **Kinono** & **Shark** & **Microworld** & **GTfly** \\ \hline MSE & This work & 0.1765 & 0.0972 & 0.0955 & 0.0795 & 0.0473 \\  & [58] & 0.4657 & 0.0984 & 0  
Therefore, the proposed crypto-steganography approach is highly recommended for ransomware hiding applications due to: (a) ensuring ransomware data integrity, (b) achieving high imperceptibility, (c) adding an extra level of security by applying AES encryption incorporated with steganography algorithm, (d) the capability of randomness in ransomware embedding process (e), accurate ransomware extraction, and (f) the capability of embedding different sizes of secret data within various video streams with different features.
# A Rapid Verification System for Automatic Emergency Braking Control Algorithm of Passenger Car
## 2 Scheme of Verification System  
### Working Mechanism of AEB  
The passenger car AEB system can obtain the relative position information between vehicles. If the driver cannot make a braking judgment in time, it will urge the vehicle to brake automatically to avoid collision and ensure the safety of passengers. The working mechanism of the AEB system is shown in Figure 1, where VT refers to the vehicle target and VUT refers to the vehicle under test.  
The AEB system can be divided into three functional layers, namely the perception layer, the decision-making layer, and the execution layer. The perceive layer is mainly composed of millimeter wave radar, a displacement sensor installed on the brake pedal axis, and a steering wheel angle sensor. They are connected to perceive environmental information and driver status, and send the braking command to activate the actuator on the execution layer. The electronic pressure regulating valves as the actuator receive the signal from the controller and then generate corresponding braking pressure. Finally, accurate braking torque will be formed and applied to the corresponding wheels of the passenger car.
## 2 Materials and Methods
### Solid-Phase Microextraction (SPME) and GC-MS Analysis  
HS-SPME analysis was performed on Varian CP-3800/Saturn 2000 apparatus (Varian, Walnut Creek, CA, USA) equipped with a Zebron ZB-5 MSI (30 m \(\times\) 0.25 mm \(\times\) 0.25 \(\upmu\)m) column (Phenomenex, Shim-Pol, Warszawa, Poland). Analysis was performed using a 2 cm DVB/CAR/PDMS fiber (Supelco, Bellefonte, PA, USA), which was exposed over the sample for 20 min and then desorbed at 220 \({}^{\circ}\)C for 3 min. Approximately 150 \(\upmu\)L of extract was transferred to a headspace vial and maintained in a laboratory water bath at 60 \({}^{\circ}\)C. To each sample, 10 \(\upmu\)L of a homogeneous suspension of 2-undecanone in water at a concentration of 1 mg/mL was added as an internal standard.  
The GC-MS analysis was performed according to the following settings: the GC oven was programmed from 40 \({}^{\circ}\)C for 3 min, to 110 \({}^{\circ}\)C at rate 5 \({}^{\circ}\)C/min and then to 270 \({}^{\circ}\)C at rate 20 \({}^{\circ}\)C/min. Scanning was performed from 40 to 550 m/z in electron impact (EI) mode at 70 eV. The carrier gas in the analysis was helium with a flow of 1.0 mL\(\cdot\)min\({}^{-1}\). Samples were injected at a 1:5 split ratio. All analyses were run in triplicate.  
Identification of the analyzed compounds using HS-SPME was based on three methods:  
1. retention indices calculated using the retention index calculator were compared to values in the NIST17 database,
2. spectra were compared to the NIST17 database,
3. retention times were compared to retention times of authentic standards.
## 2 State of the Art
### Hybrid Protocols  
Hybrid protocols combine the advantages of TDMA and CSMA/CA. Z-MAC [7] is an hybrid protocol that optimizes the bandwidth utilization by selecting CSMA/CA under low contention and TDMA under heavy contention. Z-MAC is based on DRAND [8] which assigns slots to nodes in such a way that any node has a slot different from those assigned to its one-hop and two-hop neighbors. The advantage of Z-MAC is that it does not depend on the number of network nodes but on the cost of an asymptotic convergence.  
There are several differences between Z-MAC and our solution combining TDMA/CA and SERENA:  
* We can notice that Z-MAC does not allow an immediate acknowledgement of unicast messages, while this acknowledgement is needed in wireless communications to confirm quickly the correct reception of the frame. Z-MAC uses the channel noise level to estimate contention: nodes refrain from sending when the channel noise level is too high. TDMA/CA uses both CCA (Clear Channel Assessment) and acknowledgements.
* Z-MAC allows nodes to transmit a frame during any time-slot, but prioritizes the owners of a slot by setting different backoff ranges for owners and for non-owners. On the other hand, TDMA/CA forbids nodes to send outside of their time-slot, which allows nodes to transmit without any backoff.
* In case of data gathering applications, where all traffic is done according to a tree rooted at the sink, the end-to-end delays can be high with Z-MAC, where slot allocation is totally arbitrary with regard to the parent-child relation. With the combination of TDMA/CA and SERENA, in an upstream cycle, a child always transmits before its parent, allowing it to aggregate data collected from all its children before transmitting them to its own parent. Thus, all collected data can reach the sink in a single cycle. In a downstream cycle, the propagation of information originated at the root to all network nodes is done in a single cycle. Moreover, the frequency of upstream/downstream cycles is determined according to the application constraints.
* From the energy point of view, Z-MAC reduces the activity period duration in the polling cycle enforced by the application. It does not allow nodes to sleep during this activity period because nodes are allowed to send during any time slot. SERENA, on the other hand (see Section 3) aims at maximizing network lifetime by scheduling node activity. SERENA increases the sleep period of nodes without reducing the amount of data transferred.  
A performance comparison is done on an example in Section 3.7.
# Effect of Laser Parameters on Optical Stealth Transmission System Performance
## 4 System Simulations and Discussion  
### Covertness  
During encoding, to avoid temporal fluctuation, a complementary encoding scheme is used in the optical stealth transmission system. The detected waveform comparison between single coding and complementary coding are shown in Figure 6. The bit rate is 2.5 Gbps and the bandwidth of oscilloscope is 10 GHz. The encoded signal with OOK modulation reveals 0 bits due to power discontinuity in time domain. The power of encoded signal with complementary modulation is continuous in time domain. The eye diagram of encoded signal with complementary modulation is the same as the encoded signal with OOK modulation.  
The bias current is the most important factors affecting communication performance of the stealth channel. Therefore, when studying the effect of parameters on covertness, we mainly focus on the bias current. With different bias currents for stealth signal, the optical spectra of public channel are shown in Figure 7a,b. The stealth signal is hidden  
Figure 5: The schematic diagram of optical stealth transmission system based on gain-switched optical pulses. (**a**) The system setup; (**b**) the complementary encoder in stealth transmitter; (**c**) the stealth receiver. Tx, transmitter; OC, optical coupler; SMF, single mode fiber; EDFA, Erbium-doped fiber amplifier; DCF, dispersion compensation fiber; IM, intensity modulator; OF, optical filter; PD, photonic detector.  
Figure 6: The (**a**) waveform and (**b**) eye diagram of the stealth signal with single and complementary encoding.  
in the spectral domain when bias current is set to 0.04 A. At 0.16 A, the power of stealth signal power increases, the peak power in the spectral domain is high due to the optical frequency comb characteristics. This high power can easily reveal the presence of the stealth signal. The resolution of the optical spectral analyzer is larger than the symbol rate; the spectral lines cannot be detected in spectral domain. The resolution of the commercial high-precision optical spectral analyzer can reach about 100 MHz. Therefore, for the stealth channel, parameters of the signal generator should be optimized to avoid forming optical frequency combs. Figure 7c,d show the waveform of the stealth signal with different bias currents. After complementary encoding, the stealth signal obtains noiselike temporal characteristics. This feature has not been changed by adjusting bias current. Eye diagrams of the public signal with stealth channel under different currents are shown in Figure 7e,f. When the bias current is 0.16 A, the eye diagram deteriorates due to the increase of signal power. This deterioration can be suppressed by power control in the stealth channel transmitter.
## 6 Sixth-Generation (6G) Testbeds and Platforms
### Testbeds for 6G Technologies
#### 6.2.1 mmWave Testbeds  
Constructing practical mmWave massive Multiple Input-Multiple Output (MIMO) testbeds is imperative. Organizations such as AT&T, HUAWEI, NTT DoCoMo, New York University, and Intel/Fraunhofer have extensively measured channel characteristics in the 30 GHz to 100 GHz range during the 5G research era. Noteworthy developments include the 28 GHz mmWave MIMO Prototypes by Samsung [238] and Qualcomm [239]. The University of Leuven introduced the Flexible Organization and Reconfiguration of Millimeter-Wave Antenna Tiles (FORMAT) [240], a reconfigurable millimeter-wave tile-based antenna array platform. FORMAT demonstrated a 4.8 Gbps downlink speed with 64 QAM modulation at 28.5 GHz. Lund University's real-time mmWave massive MIMO testbed, LuMaMi28, showcased performance in various scenarios [241]. Addressing mmWave's limited coverage due to atmospheric attenuation, researchers presented a real-time photonics-assisted millimeter-wave communication technology in [242]. This technology, operating at 26.5-29.5 GHz, demonstrated 1.5 Gbps real-time bi-directional uncompressed high-definition video transmissions.
## 3 Results and Discussion
### Microstructure and Phase Identification
In particular, cell parameters a and c were derived from the equation resulted by combining Bragg law and the plane-spacing equation for hexagonal systems [45]:  
\[sin^{2}\theta=\frac{\lambda^{2}}{4}\left[\frac{4}{3}\cdot\frac{h^{2}+hk+k^{2}} {a^{2}}+\frac{\beta}{c^{2}}\right] \tag{1}\]  
where \(a\) and \(c\) are the cell parameters, \(h\), \(k\) and \(l\) are the Miller indices related to the referring peak and \(\lambda\) is the wavelength of the radiation used during the test and equals 1.5406 A for Cu K\({}_{\alpha}\) radiation. To avoid texturing influence on the result, the \"intensity averaging\" method was used [45]. Once a and c were obtained, the ratio c/a and the cell volume of each sample were calculated. The results of this analysis are provided in Figure 4, in which the similarity between both the c/a ratio and cell volume of the SE-CB and SE-HB specimens is evident. In contrast, both these parameters are significantly greater for ArS samples. Previous studies [46; 47] proved that the increase in cell parameters and volume might be related to a higher oxygen concentration in the sample, accordingly to the environment used for their building. It is in fact very likely that the exclusive use of shielding gas as only mean of protection during the deposition might not be enough to prevent an increase in the concentration of the interstitial elements (O, N, H). To confirm this, the evaluation of the interstitial content was carried out.
# Study on Gearbox Fault Warning Based on the Improved M-IALO-GRU Model
## 2 Wind Turbine Parameter Description and Feature Selection
### Selection of Feature Parameters
#### 2.2.1 Multi-Input Model  
In the practical application of a wind turbine gearbox, there are two input models: single-input model and multi-input model. Given that wind turbines usually require long operation, their maintenance and failure prediction rely on accurate temperature predictions. The oil temperature of the wind turbine is affected by many factors, such as load, rotation speed, ambient temperature, etc. It is difficult for a single input model to fully capture these changes. In contrast, multi-input models are able to capture the complex interactions between these factors, thereby improving prediction accuracy. Therefore, using the multi-input model is an effective strategy to improve the accuracy of oil temperature prediction and ensure the efficient and stable operation of wind turbines. The MISO model [24; 25; 26] uses historical data of other m-related parameters as the input and temperature data as the output. The structure of the MISO model is shown in Equations (1) and (2).  
\[X_{t}=\left[x_{t}^{1},x_{t}^{2},x_{t}^{3},.....,x_{t}^{m}\right] \tag{1}\]  
\[\overline{y}_{t+1}=f(X_{t}) \tag{2}\]  
where \(x_{t}^{m}\) is the observed value of the input parameter at the m-th correlation parameter at time \(t\), and \(X_{t}\) is the input parameter.  
Figure 3: Pre-processed completed data.