esa-sceva/satcom-chunk-collection huggingface dataset:
# Effect of Corrosion Characteristics on Long-Term Aging of Austenitic 304 Steel
###### Abstract  
The objective of this study is to investigate the effect of long-term aging on electrochemical corrosion characteristics of austenitic AISI 304 steel. AISI 304 steel was subjected to aging treatment for an extended period at 700 \({}^{\circ}\)C up to a maximum of 10,000 h. The variation in the microstructure of aged specimens was observed with an optical microscope (OM) and a scanning electron microscope (SEM). The electrochemical polarization test was conducted to obtain the corrosion current density (Icorr) and corrosion potential (Ecorr). The metastable intermetallic M\({}_{23}\)C\({}_{6}\) carbides generated in the vicinity of \(\gamma\)/\(\gamma\) grain boundaries and coarsened with aging time. The \(\delta\)-ferrite island decomposed into \(\sigma\)-phase and M\({}_{23}\)C\({}_{6}\) carbide with an aging time increase. As the aging time increased, the current density increased, but the corrosion potential of the austenitized specimen exhibited a minimum value of 0.04 uA/cm\({}^{2}\). The highest pitting resistance exhibited at the austenitized specimen due to the absence of carbides. Consequently, the corrosion resistance of austenitic AISI 304 steel decreases with long-term aging time. The microstructural analyses well support this result.  
AISI 304; long-term aging; corrosion; sensitization; delta ferrite Article
# A Compact Circular Rectenna for RF-Energy Harvesting at ISM Band
## 4 Rectenna Calibration  
The measurement setup of the fabricated rectenna is considered as per reference [14], and the indoor environment is depicted in Figure 13. It consists of a traditional WLAN router considered as an RF source, which is placed 35 cm from the rectenna, and the voltmeter is connected to the output. The fabricated antenna is connected to the rectifier and it is placed in front of the NETGEAR (AC1200) WLAN router module, which has a built-in feature of changing RF signal power, thereby making the rectenna calibration simple. As we know, if we increase the distance between the RF source and the rectenna, there is deterioration in the performance of the rectified output so the separation between the RF source and the rectenna is optimized to obtain the maximum power conversion efficiency. The received power of the antenna from the RF source is calculated using Friis transmission formula, which is as follows:  
\[P_{Rx}=G_{Tx}G_{Rx}P_{Tx}\bigg{(}\frac{\lambda}{4\pi R}\bigg{)}^{2} \tag{5}\]  
where \(\lambda\) is the resonating wavelength, \(G_{Tx}\) and \(G_{Rx}\) are the transmitting and receiving gains of the antenna, \(P_{Tx}\) is the power transmitted, and \(R\) is the separation between the RF source and the receiver.  
The comparison of the measured and simulated conversion efficiency and output DC voltage and power of the rectenna is depicted in Figure 14. From Figure 14a, it can be observed that the efficiency of the proposed rectenna increases up to 0 dBm and then decreases gradually. The maximum efficiency of 52% with 2.17 V DC voltage and 0.48 mW output power is obtained at 0 dBm input power with a load resistance of 10 K\(\Omega\) at 2.45 GHz. The calibrated conversion efficiency is less than simulated due to the improper soldering of the surface-mounted-device components in the rectifier network and also their nonlinear behavior at high frequencies. The higher DC voltage of 4.2 V and 1.77 mW output power is observed at 20 dBm power input. The measured and simulated results are closely correlated to one another. The comparison of the rectenna design put forward in this work with the available related models in the literature is depicted in Table 2.  
Figure 13: Calibration setup for the fabricated rectenna.
## 3 Results and Discussion
### Stakeholder's Influence in Implementing CE Principles
#### 3.4.5 Contractor  
Contractors were ranked as the fifth most influential stakeholder in implementing CE principles, with a total of 14 points. Respondents who rated the contractor's influence as low shared a common rationale: they believe that contractors primarily follow the specifications and guidelines established by the owner and design team. In this view, contractors are seen as executors of the project rather than key decision-makers, especially when it comes to setting or driving CE objectives. However, one respondent (C1), who ranked contractors as having high influence, offered a different perspective. C1 argued that contractors can have a significant impact on the implementation of CE principles through their choices regarding construction methods, material procurement, and on-site practices, such as waste management. According to C1, contractors play a pivotal role in shaping the practical aspects of how circular economy strategies are realized during the construction phase.
# Evaluation of Mechanical Performance of a New Disc Spring-Cable Counter Pressure Shock Absorber
## 2 Design of the DSCCP Shock Absorber
### Key Parameter Calculation and Design Process
#### 2.2.4 Design Process  
Given the vertical bearing capacity \(P\) and the vertical deformation \(f\), the design process of DSCCP shock absorber can be summarized as follows:  
1. The outer and inner diameters \(D\) and \(d\) are selected and determined based on the space situation of the disc spring. Generally speaking, \(C=D/d=1.7\)\(\sim\)2.5. The reason is that poor space utilization may be caused if \(D\) is too large; and difficult installation of the internal cable may be caused if \(d\) is too small.
2. Designing the ratio between the free height \(h_{0}\) and thickness \(t\) based on the self-deformation characteristics of the disc spring. The ratio is suggested to be \(h_{0}/t<0.5\) if a linear relationship is required between bearing capacity and deformation, which is the common choice. The ratio is suggested to be \(0.5<h_{0}/t<\sqrt{2}\) if zero stiffness characteristic is required.
3. Choosing a stacking and pairing method to form the disc spring group, which is then used to determine the deformation and bearing capacity of each disc spring. The number \(i\) of paired disc spring group can be calculated according to the total vertical deformation.
4. Verifying the stress \(\sigma_{\text{OM}}\) of the disc spring at the OM point, as shown in Figure 3, which should be less than the ultimate stress of the material. Besides, the stresses at points II and III should also be checked to meet the design requirements.
# Policy Compression for Intelligent Continuous Control on Low-Power Edge Devices
## 6 Results and Discussion
### Compression Level  
We investigate the compression potential of our methods by repeating the experiments in Section 6.1 for a wide range of student network sizes, as listed in Table 3. In Figure 13, our loss based on the KL-divergence (Equation (8)) was used, while Figure 14 shows the results when using the Huber-based loss for both \(\mu\) and \(\sigma\). Using our KL-based loss, we can reach a compression of \(7.2\times\) (student 6) before any noticeable performance hit occurs. The average return stays relatively high at up to \(36.2\times\) compression (student 3), before dropping more significantly at even higher levels of compression. When going from student 3 to student 2, we also reduce the number of layers in the architecture from 3 to 2, which becomes insufficient to accurately model the policy for this task. The convergence rate noticeably decreases at each size step, with student 2 still improving even after 600 epochs.  
Figure 12: The exponential running mean of the average return during training for student and teacher-driven distillation on the Ant-v3 environment and a PPO teacher.  
The impact of the student size is much higher when using the Huber-based distillation loss. There is still a noticeable difference between the average return obtained by the largest (10) and second largest (9) student, even though this largest student is actually 2\(\times\) larger than their teacher for this environment. This makes this loss particularly unsuited for distillation, as it requires more capacity than the original SAC teacher algorithm to reach the highest potential average return. The largest student (10) here still performs slightly worse than the fourth-largest student (7) when trained based on the KL-divergence loss, but the performance gap does almost disappear for networks that approach the teacher in size. This means that our Huber-based distillation loss can still effectively transfer the teacher's knowledge to the student, but it requires considerably more capacity to learn two values (\(\mu\) and \(\sigma\)) independently, making it infeasible for compression purposes. The convergence rate of these students is also slower, making it more computationally expensive at training time.  
Therefore, we conclude that both proposed loss functions can be effective at distilling the stochastic continuous behavior of the teacher, but the efficiency in terms of required network size and number of samples is significantly higher for our loss based on the KL-divergence, to the extent that the Huber-based loss becomes impractical for compression.
## 4 Performance Metrics and Simulation Parameters  
The metrics considered in analyzing the performance of routing protocols are presented below.  
Packet delivery ratio (PDR): The value of the division total number of the data packet is delivered into the total number of data packets transmitted by all nodes [42]. This metric is given by the following formula:  
\[\mathrm{PDR}\ =\frac{\sum\mathrm{No\ of\ packets\ received}}{\sum\mathrm{No\ of\ packets\ sent}}\times 100. \tag{8}\]  
Overhead: The ratio of the RREQ packet number is calculated by dividing the number of RREQ packets with the sum of the number of packets sent and the number of RREQ packets [43] in the following equation:  
\[\mathrm{RREQ\ Overhead}\ =\frac{\mathrm{No\ of\ RREQs}}{\mathrm{No\ of\ RREQs}+\mathrm{No\ of\ packets\ sent}}\times 100. \tag{9}\]  
End-to-end delay (E2E): The data packet is based on the average time. The time of the transmission for the packet from the source is deduced based on the first delivered data packet to the destination [44], which is calculated as follows:  
\[\mathrm{E2E\ Delay}\ =\frac{\sum\mathrm{arrive\ time\ -\ send\ time}}{\sum\mathrm{No\ of\ packets}}. \tag{10}\]  
Energy Consumption: This metric calculates the energy level of each node at the end of the simulation [44]. The value of energy consumption can be calculated using the following equation:  
\[\mathrm{Energy\ Consumption}=\ \frac{\sum\mathrm{Energy\ consumed\ in\ each\ node}}{\mathrm{Initial\ energy}}\times 100. \tag{11}\]  
Simulation Parameters: The simulation performed using MATLAB 2019b and the environment has been used to simulate the network based on [23; 25]. Table 3 presents the simulation parameters of OPSO-LAR.
# A Failure Risk-Aware Multi-Hop Routing Protocol in LPWANs Using Deep Q-Network
## 5 Detailed Description of the Proposed Protocol
### DQN-Based Routing Decision Mechanism
#### 5.2.3 Network Training and Routing Decision
```
0:\(\varepsilon_{start},\ \varepsilon_{end},\ \tau,\ \gamma,\ \alpha\), experience replay update frequency \(C_{\text{exp}}\), target update frequency \(C_{t}\), minibatch size \(B\), maximum training episodes \(N_{\text{eps}}^{\max}\), maximum iterations \(N_{iter}^{\max}\)
0: replay memory \(\mathbf{M}\), experience counter \(N_{\text{exp}}=0\), evaluation network with random weights \(\mathbf{\omega}\), target network with weights \(\mathbf{\omega}^{-}=\mathbf{\omega}\) Offline Learning
1:for\(N_{\text{eps}}\)\(=1:N_{\text{eps}}^{\max}\)do
2:for\(N_{iter}\)\(=1:N_{iter}^{\max}\)do
3: Event-related data packet generated
4:whileGW is out of one-hop range do
5: Determine the candidate relay set \(\mathbb{N}^{cf}\)and calculate RFRV for each \(EN\in\mathbb{N}^{cf}\) by Equation (4)
6:if\(\mathbb{N}^{cf}=\varnothing\)then
7: Go to line 16
8:endif
9: Formulate state vector \(\mathbf{s}\) by Equation (16)
10: Get Q-values for \(\mathbf{s}\) in the evaluation network
11: Select action \(a\) via annealing \(\varepsilon\)-greedy strategy
12: Forward data packet according to the action \(a\)
13: Perform lines 16-25
14:endwhile
15: Send the data packet to the GW
16: Compute the reward \(r\) by Equation (17)
17: State transitions to \(\mathbf{s}^{\prime}\)
18: Store \((\overline{s},a,r,\overline{s}^{\prime})\) into \(\mathbf{M}\) and set \(N_{\text{exp}}=N_{\text{exp}}+1\)
19:if\(|\mathbf{M}|\geq B\) and \(\text{mod}\left(N_{\text{exp}},C_{\text{exp}}\right)=0\)then
20: Sample a random minibatch \(\left(\mathbf{s},a,r,\mathbf{s}^{\prime}\right)\) from \(\mathbf{M}\) Update target Q - values of all samples with \(y(\mathbf{s}^{\prime},r)=21\): \(\left\{ \begin{aligned} & r+\gamma\underset{a^{\prime}\in A}{ \text{max}}Q(\mathbf{s}^{\prime},a^{\prime};\omega^{-}),& C_{3}\\ & r,& otherwise\end{aligned} \right.\)
```  
**Algorithm 3** DQN-Based Routing Decision Mechanism
# Thangka Image--Text Matching Based on Adaptive Pooling Layer and Improved Transformer
## 3 Results
### Model Comparison Experiment
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Mark**} & \multirow{2}{*}{**Model**} & \multicolumn{3}{c}{**Image-Text**} & \multicolumn{3}{c}{**Text-Image**} & **OIPS** \\ \cline{3-8}  & & **R@1** & **R@5** & **R@10** & **R@1** & **R@5** & **R@10** & \\ \hline VSE & SCAN & 23.2 & 53.5 & 72.1 & 16.3 & 55.6 & 68.9 & 48.7 \\ VSE & CAMP & 36.1 & 68.2 & 82.7 & 21.2 & 59.1 & 79.7 & 46.5 \\ VSE & CAAN & 37.5 & 69.6 & 85.4 & 22.6 & 60.2 & 78.5 & 47.5 \\ VSE & GSMN & 41.8 & 72.6 & 88.3 & 23.7 & 63.1 & 79.7 & 42.5 \\ VSE & IMRAM & 42.9 & 71.2 & 87.6 & 25.7 & 62.5 & 79.1 & 44.8 \\ Pre-trained & ViLBERT & 39.0 & 70.5 & 84.1 & 23.5 & 62.7 & 78.8 & 16.1 \\ Pre-trained & OSCAR & 45.8 & 78.5 & 92.8 & 27.5 & 63.8 & 84.2 & 4.3 \\ Pre-trained & CLIP & 48.5 & 82.8 & 93.1 & 29.8 & 64.1 & 86.8 & 6.7 \\ Pre-trained & MVP & 49.8 & 84.4 & 93.8 & 30.4 & 66.3 & 85.5 & 5.2 \\ VSE & **Our** & **54.7** & **85.4** & **94.3** & **33.6** & **70.3** & **86.4** & **32.7** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of the results of different methods on the Thangka dataset. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Mark**} & \multirow{2}{*}{**Model**} & \multicolumn{3}{c}{**Image-Text**} & \multicolumn{3}{c}{**Text-Image**} & **OIPS** \\ \cline{3-8}  & & **R@1** & **R@5** & **R@10** & **R@1** & **R@5** & **R@10** & \\ \hline VSE & SCAN & 61.8 & 87.5 & 93.7 & 45.8 & 74.4 & 83.0 & 48.3 \\ VSE & CAMP & 68.1 & 89.7 & 95.2 & 51.5 & 77.1 & 85.3 & 45.9 \\ VSE & CAAN & 70.1 & 91.6 & 97.2 & 52.8 & 79.0 & 87.9 & 48.1 \\ VSE & GSMN & 72.6 & 93.4 & 96.8 & 53.9 & 80.2 & 87.1 & 43.5 \\ VSE & IMRAM & 74.1 & 93.0 & 96.6 & 53.9 & 79.4 & 87.2 & 45.8 \\ Pre-trained & ViLBERT & 71.5 & 91.0 & 96.9 & 52.9 & 78.5 & 86.9 & 28.6 \\ Pre-trained & OSCAR & 76.4 & 94.3 & 98.2 & 59.4 & 84.4 & 91.6 & 5.4 \\ Pre-trained & CLIP & 78.2 & 95.6 & 98.2 & 67.0 & 87.7 & 93.4 & 3.1 \\ Pre-trained & MVP & **78.9** & 95.8 & 97.9 & **69.2** & **88.1** & **94.2** & 2.3 \\ VSE & **Our** & **95.9** & **98.3** & 57.3 & 85.7 & 92.3 & **40.5** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of results of different methods on public datasets. models in terms of OIPS, indicating that it is more efficient and faster in execution. By employing our method, we can precompute and cache visual and textual features, which only requires similarity calculations and sorting during the retrieval process. In conclusion, these results demonstrate that our model achieves a favorable balance between performance and efficiency.
# Low-Light Image Enhancement Method Based on Retinex Theory by Improving Illumination Map
## 3 The Proposed Algorithm
### Illumination Map Enhancement Based on Gaussian Laplacian Pyramid Fusion  
The initial illumination component and reflection component were obtained in Section 3.1. This section mainly improves the illumination component in terms of both brightness and contrast. For the problem of image brightness enhancement, the existing image enhancement algorithms have limited ability to enhance the darker regions. These include methods such as the bio-inspired multi-exposure fusion framework (BIMEF) [45], MF, structure-revealing low-light image enhancement via robust retinex model (SR) [46], and low-light image enhancement with semi-decoupled decomposition (SDD) [47], with results shown in Figure 4. The brighter areas are over-enhanced, resulting in blurred details with some methods, such as BIMEF, low-light image enhancement via illumination map estimation (LIME) [48], low-light image enhancement using the camera response model (LECARM) [49], and SDD, with results shown in Figure 5.  
Figure 4: Experimental results of several methods in the darker areas of the image. (**a**) Original image; (**b**) BIMEF; (**c**) MF; (**d**) SR; (**e**) SDD.  
To solve the above problems, we designed the BEF function in two directions, as detailed in Section 3.2.1. The adaptive contrast enhancement (ACE) method improves the contrast by combining the high-frequency information of the image, but the high-frequency component is often doped with noise. Therefore, we devised a form of piecewise function to improve the image contrast pixel by pixel, as detailed in Section 3.2.2. The design of the fusion weight and Gaussian Laplacian pyramid are explained in Sections 3.2.3 and 3.2.4, respectively.
## 5 Methodology
### Federated Learning Process
#### 5.2.1 Training Workflow  
Each training round includes the following steps:  
1. Model Broadcast: A central aggregator sends the current global model \(\mathbf{w}_{t}\) to participating edge nodes.
2. Local Update: Each client \(k\) performs training on local data, as follows: \[\boldsymbol{w}_{t+1}^{(k)}=\mathbf{w}_{t}-\eta\
abla\mathcal{L}_{\parallel}( \mathbf{w}_{t})\] where \(\eta\) is the learning rate and \(\mathcal{L}_{\parallel}\) is the local loss.
3. Secure Aggregation: The server collects and aggregates updates, as follows: \[\boldsymbol{w}_{t+1}=\sum_{k=1}^{K}\frac{n_{k}}{n}\mathbf{w}_{t+1}^{(k)}\] where \(n_{k}\) is the number of samples at client \(k\), and \(n=\sum_{k}n_{k}\).
## 4 Conclusions  
This study presented the optimization process of tailoring an SOE for a silicone-on-glass Fresnel lens in a CPV unit based on single-wavelength simulation. The goal of the optimization was to achieve high values of optical efficiency and acceptance angles. The irradiance uniformity on the solar cell was also evaluated for the CPV unit with each type of SOE. Firstly, three types of SOE, KOD, KFTS and SP, were considered, and the optical characteristics of each type of SOE were evaluated by ray-tracing simulation under single wavelength of 550 nm. For each type of SOE, preliminary design and optimum design were determined and the resulting optical performances were simulated. Finally, a best KOD II-type SOE was achieved, which exhibits a highest acceptance angle of 1.7\({}^{\circ}\), as well as a good optical efficiency. Nevertheless, the SP II-type SOE exhibits a best irradiance distribution on the solar cell.  
Moreover, the simulation results of the CPV unit with the best KOD II-type SOE under broadband solar spectrum were also performed and the results were compared with those from single wavelength simulation. The acceptance angles were the same (1.7\({}^{\circ}\)), and the optical efficiency under normal incidence from broadband simulation (87%) was higher than that from single wavelength simulation (85%). Meanwhile, the irradiance uniformity under broadband solar spectrum simulation was not as good as that under single wavelength simulation. Therefore, the irradiance uniformity on the solar cell under broadband simulation can be considered along with optical efficiency and acceptance angle in the future optimization process of SOE.
# Threat Modelling and Beyond-Novel Approaches to Cyber Secure the Smart Energy System
## 7 Risk Analysis Using Formal Methods
### UC1: Smart Home and HVAC Hijacking  
#### 7.2.1 Probabilistic Model Generation  
Figure 7 shows the configuration analyzed in UC1. The system is modeled in the PRISM model checker as MDP (Markov Decision Process) because of the non-deterministic nature of cyber-attacks.  
An attack in the modeled system usually leaves traces in a form of unusual values. The response of the system is defined through service policies:  
* _A1 policy_: If controller HEMS receives a low temperature reading from sensor ST, it sends a command to actuator A1 to start the heating process;
* _A2 policy_: If controller HEMS receives a high temperature reading from sensor ST, it sends a command to actuator A2 to start the cooling process;
* _ST policy_: ST sends regularly temperature readings to HEMS.  
In the modeling process it is assumed that an attacker actively attacks the system, and has skills and means to perform certain attacks by exploiting existing vulnerabilities. An attacker's skills are measured through the maximum number of vulnerabilities that he can try to exploit in one attack scenario--the _cost_ value. For example, the attacker's skill level in range one to five (\(cost=(1:5)\)) means that a less skilled attacker is able to exploit only one vulnerability in one attack scenario, while a more skilled attacker can exploit up to five vulnerabilities at the same time in one attack scenario.  
The threat modeling and attack modeling processes identified the minimum set of vulnerabilities that can be exploited in order to perform a HVAC hijack attack. Table 8 presents the list of vulnerabilities, which are linked to the threats from Table 2. It is assumed that the attacker is aware of these vulnerabilities, and that the exploitation of at least one vulnerability is necessary in order to successfully carry out an attack. The model also implies that in one iteration the choice of vulnerabilities that are going to be exploited is random. All listed vulnerabilities have defined exploitation probabilities, described in more detail in Section 6. The exploitation probability is the likelihood that an attempt to exploit that particular vulnerability is going to be performed and successful, under previously given assumptions.  
The next step, after the system modeling is completed, is the definition of the attack properties. These properties are the formal definition and precondition of the successful attempt of an attack within the modeled system. The properties are defined by using  
Figure 7: Smart home and HVAC hijacking use-case architecture.  
the Probabilistic Computation Tree Logic (PCTL) [81], embedded in the PRISM model checker. The formal verification of the defined attack properties results in the maximum likelihoods of successful attack attempt-risk exposure scores. Table 9 describes the modeled attack properties.