esa-sceva/satcom-chunk-collection huggingface dataset:
# MC-EVM: A Movement-Compensated EVM Algorithm with Face Detection for Remote Pulse Monitoring
## 4 Results and Discussion
### Results Discussion and Analysis  
MC-EVM showed promising results to improve the quality of pulse monitoring using the EVM process by including the face detection in order to track the detected face and reduce the noise originated by patient movements during the measurement. MC-EVM was validated in three different steps. First, we compared the estimated pulse values with and without face detection. Second, it was validated against ground truth pulse values of our own data that we collected and shared online. Third, it was validated against the results of similar research papers from the state-of-the-art.  
Figure 6 shows the face displacement over time for all the coordinates (top-left corner and bottom-right corner). The top figure shows the difference between every frame with its preceding frame. The bottom figure shows the difference between every frame and the normalized location using a sliding window of five frame sizes.  
We used the normalized location to try to normalize the detection error and the used formula to calculate the normalized location is shown in the following equation:  
\[nl_{i}=l_{i}-\frac{\left(\sum_{j=i-2}^{i+2}l_{j}\right)}{5} \tag{3}\]  
where:  
* _nl_: the normalized location;
* _l_: the correct location;
* _i_: the index of the frame starting from 2 to the number of frames \(-2\).  
It is important to mention that if we change the size of the sliding window, then we should change the starting and the ending values of the j index according to the new size, and we should then divide the result by the new size of the sliding window.  
Figure 7 shows the achieved results with and without face detection: the actual pulse measured using the heartrate device \"Amazfit Band 7\" compared to the calculated pulse using our proposed solution (left), and the validation of the results calculated as the percentage error between the two measurements where we reached a maximum difference of 4% more or less than the correct pulse values (right).
## 6 Field Data Inversion  
This study utilized field profile data obtained from a mining area in Chongqing city to validate the inversion performance. The experiment was conducted along a measurement line stretching 310 M with a point spacing of 10 M. A Terra TEM transient electromagnetic system manufactured in Australia was employed for this purpose. The transmitter current used was approximately 12 A, and the receiving coil was a small loop with a side length of 2.5 M and contained 20 turns.  
Five inversion attempts were carried out using various algorithms, and the average results were recorded. Figure 11 compares the attenuation voltage measured by the receiving coil at a distance of 0 m on the measurement line with the forward response obtained using different algorithms. The field data exhibited fluctuations due to external noise interference. In comparison to other algorithms, the inversion data from the ISSA are generally consistent with the field data and exhibit a higher degree of fitting. The average fitness values for the ISSA, SSA, JADE, and SAPSO are 0.0043, 0.0171, 0.0398, and 0.0394, respectively, serving as a criterion for assessing the algorithm's feasibility. By combining existing geological data and drilling records, we infer that the depth of 0-30 M consists of mudstone with a slightly higher resistivity. The low-resistivity anomaly body observed around 30-80 M corresponds to a strip distribution consistent with the spatial layout of the goat and is inferred to be the coal mining goat water accumulation zone. The region below 120 M comprises relatively intact and dense rock ore with minuscule pores, low groundwater content, and relatively high resistivity.  
Figure 12 displays the inversion results obtained using four algorithms: SAPSO, JADE, SSA, and ISSA. Upon comparison, it becomes evident that the inversion result achieved through the ISSA algorithm (Figure 12c) better reflects the geological characteristics of the Empty Ponding District. This indicates that the segmented regularization method based on the ISSA, as proposed in this study, is suitable for processing field data. The inversion results effectively portray underground anomalous geological bodies.  
Figure 11: One-dimensional forward response of three algorithms.
# Secure Proxy Re-Encryption Protocol for FANETs Resistant to Chosen-Ciphertext Attacks
## 4 Construction of UPF Model
### Proxy Re-Encryption  
4.4.1. RKeyGen(\(sk_{1},sk_{2}\)) Construction  
* Input: \(x_{H}\), \(x_{E}\).
* Output: \(f_{k_{C}}(S_{1},\ S_{2})\), \(f_{k_{E}}(S_{3},\ S_{4})\).  
This algorithm is for the setting of re-encryption keys. CC generates random numbers \(\alpha,\ \gamma\) and then computes \(S_{1}=x_{H}\alpha,\ S_{2}=x_{E}\alpha\ \gamma,\ S_{3}=x_{E}\alpha,\ S_{4}=x_{E}\gamma\). We put \(rk_{1\to 2}^{1}=(S_{1},\ S_{2})\) as re-encryption key and \(rk_{1\to 2}^{2}=(S_{3},\ S_{4})\) as decryption key for the re-encrypted message. With this, CC encrypts \(S_{1},\ S_{2}\) with the secret key \(k_{C5}\), and \(S_{3},\ S_{4}\) with \(k_{E}\). Then, CC transfers the generated re-encryption/decryption keys to CS and E.
## 3 System Model
### System Architecture
As shown in Figure 1, our system consists of three subsystems:  
(a) Traffic flow analysis system: The acquired traffic flow is multi-dimensional data that contain many variables, i.e., traffic flow data, vehicle spacing, average speed, map data (latitude and longitude), road information, travel cost and so on. It is difficult to distinguish which variables are needed in prediction. Therefore, this paper proposes a method to decrease unnecessary variables in most traffic flow data. Considering the correlations among the factors, the subsystem selects the optimal variables as the input for traffic flow data prediction heuristically. Figure 1: Overall working flow chart. (b) Offline system: In order to reduce the prediction computing cost, the system involves an offline system to analyze the MVHS-LSTM parameters. According to the historical traffic flow data, the offline system can find the parameters suitable by using the MVHS-LSTM model. We fine-tune multiple hyperparameters for the LSTM model, such as the window length, number of iterations, learning rate and so on. These adjustments are made to ensure optimal values in the offline system. The MVHS-LSTM model combines with these parameters and achieves a balance between the error and the time of the traffic flow prediction. In practice, the system determines the distribution of various dynamics based on users' demand for prediction accuracy and prediction cost. (c) Online system: The online system imports the calculated parameters from the offline system and outputs the data that are predicted once. The system continuously circulates and consistently outputs predicted data throughout the process. The obtained traffic flow data will be filtered through the method in this paper to screen out the variables that are beneficial to the prediction results. The historical data are put into the offline system for analysis, and then, the real-time prediction is carried out through the online system. The proposed architecture takes account of both prediction accuracy and computing time via the cooperation of the offline system and the online system.
# Anti-Jamming Low-Latency Channel Hopping Protocol for Cognitive Radio Networks
## 2 Related Work
\[p_{a,i}=\frac{(1-O_{a,i})}{\sum_{j=1}^{N_{e}}(1-O_{a,j})} \tag{1}\]  
An SU constructs its channel hopping matrix based on its ID. The channel hopping matrix of an SU is constructed with \(L+1\) columns, where each column comprises multiple sub-columns, given an SU ID length of \(L\). In OLAA_T, each column of the channel hopping matrix \(M_{a}\) is composed of \(k_{a}\) sub-columns, and each sub-column contains \(2(n_{e}+1)\) frames,with each frame containing \(4N_{c}\) slots. The value of \(k_{a}\) is equal to the number of available channels for SU \(a\). Each \(T\) frame includes \(2N_{c}\) default slots and \(2N_{c}\) adjustment slots. The \(2N_{c}\) default slots are divided into two groups, each of which has \(N_{c}\) consecutive slots. A total of \(N_{c}\) channels are assigned to each group. If a channel is unavailable, a randomly selected available channel is substituted. During each adjustment slot, a channel is chosen based on its usage probability, which is determined using Equation (1). In each \(R\) frame, all slots are assigned the same channel. In the same sub-column, the \(R\) frames are assigned the same channel. However, the \(R\) frames in different sub-columns are assigned different channels, requiring \(k_{a}\) sub-columns to allocate all available channels for SU \(a\). In OLAA_R, there are \(k_{a}\) default sub-columns and \(k_{a}\) adjustment sub-columns in each column of the channel hopping matrix \(M_{a}\). Each sub-column contains \(2(n_{e}+1)\) frames, and each frame has \(2N_{c}\) slots. In each \(T\) frame, \(2N_{c}\) default slots are divided into two groups, with each group having \(N_{c}\) consecutive slots. The channel assignment in the \(T\) frames, as well as the \(R\) frames in the default sub-column, remain the same as in OLAA_T. However, for the \(R\) frames in the adjustment sub-columns, SU \(a\) selects channel \(i\) with a probability obtained using Equation (1).
# Monocular Depth Estimation Using Res-UNet with an Attention Model
## 4 Results and Discussion
### Implementation Details
#### 4.3.1 Learning Curve for the DIODE Dataset  
We trained our model on the DIODE dataset with random weights; after 40 epochs, the loss started to reduce, showing the trend that the model is training in the right direction as shown in Figure 10.  
During training, the learning rate was set to 0.001 with the Adam optimizer. Similarly, due to GPU occupancy, we set our batch size to 16 for the DIODE dataset. We can see that validation loss, as well as training loss, decreased with the number of epochs which means that the model is learning the features with minimal loss. During the training of the model, the input size of each image was 256 \(\times\) 256. The DIODE dataset took 11 h to train the model.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Components** & & **Title 3** \\ \hline \multirow{3}{*}{Hardware} & CPU & Intel(R) Core (TM) i5-11400F \\  & GPU & 8 GB GeForce RTX 3050 \\  & SSD & 500 GB Samsung SSD \\  & RAM & 16 GB \\ \hline \multirow{3}{*}{Software} & OS & Windows 11 Pro, 64 bits \\  & Programming Platform & Python 3.8 \\ \cline{1-1}  & Library & TensorFlow 2+ \\ \hline \hline \end{tabular}
\end{table}
Table 4: System information for model implementation.  
Figure 9: Qualitative results of our proposed model. The first row shows RGB images, while the second row shows their predicted depth maps.
# A Secure and Scalable Authentication and Communication Protocol for Smart Grids
###### Abstract
The secure operation of smart grids is dependent upon provision of various security services, including (i) authentication, which verifies the authenticity of smart grid nodes, service providers (SPS), and other users to prevent impersonation, replay, or insider attacks; (ii) integrity, which protects transmitted PMU/smart meter measurements and readings from unauthorized alteration or tampering: (iii) confidentiality, which ensures that sensitive information, such as energy consumption, generation data, and operational commands, remains confidential during transmission; (iv) non-repudiation, which prevents any party engaged in data exchange from denying actions conducted: (v) authorization mechanisms, which provide access control and must implement role-based access controls to restrict system privileges: and (vi) anonymity, which is essential to protect consumer identity and prevent data tracking. Security safeguards must ensure that data extracted from compromised devices do not compromise the security of the overarching SCADA/AMI system. The provision of these security services explicitly mitigates threats such as man-in-the-middle (MITM) attacks and denial-of-service (DoS) [12] interruptions, thus ensuring the resilience and reliability of smart grid operations. This study addresses the critical need for a secure communication protocol specifically designed for smart grid environments. The protocol incorporates a robust authentication method utilizing the Elliptic Curve Digital Signature Algorithm (ECDSA) [13] and Elliptic Curve Diffie-Hellman (ECDH) [14] for key establishment and AES [15] for encryption and decryption. It also emphasizes scalability and resilience against potential cybersecurity threats. The primary contributions of this research are as follows:  
1. Development of a secure and lightweight cryptographic protocol that addresses the evolving needs of smart grid infrastructure while ensuring strong protection against potential security threats, thereby maintaining the integrity and reliability of smart grids. Efficient implementation of the proposed protocol across diverse platforms to ensure a thorough evaluation of its performance for different smart grid nodes with varying computation capabilities. 3. Experimental evaluation of performance, scalability, and security of the proposed protocol under real-world conditions. Our evaluation includes testing across both high-performance and resource-constrained devices and assessing resilience against cyber threats to ensure the robustness and reliability of the protocol for effective deployment in smart grid systems. The structure of this paper is organized as follows: Section 2 provides the necessary background for the proposed study and reviews relevant literature. Section 3 outlines the design of the proposed cryptographic protocol. In Section 4, the development of the experimental setup is detailed. Section 5 presents the experimental results, while Section 6 concludes this paper.
## 5 Classification and Regression of the Runway Overrun Risk
### Further Discussion of the Results  
Through the study of various machine learning models, we identified several key factors that influence the risk of runway overrun during the landing phase, including the automatic deceleration rate, runway exit mode, 50-feet kinetic energy, and touchdown position. To the best of our knowledge, the factor of energy has rarely been considered in previous research. To reduce the risk of runway overrun, pilots should pay attention to the aircraft's kinetic energy when it reaches a height of 50 feet. Additionally, during the landing phase, an excessive runway distance at touchdown should be avoided depending on the situation. At the same time, when necessary, a higher automatic deceleration rate can be selected to reduce the risk of runway overrun.
# Photovoltaic Solar Cells: A Review
## 2 Solar Cells
### p-n Junction Solar Cell
#### 2.4.2 p-n Junction Solar Cell under Applied Voltage  
If the diode is connected to a forward bias external voltage, the depletion region will shrink. The negative charge carriers (electrons) and positive charge carriers (holes) are repelled from the negative and positive terminal respectively toward the p-n junction. Consequently, the energy required for the charge carriers to flow across the depletion region is lowered, referring to Figure 4.  
Once the applied voltage reaches the barrier potential, electrons start to flow through the space charge region. The diode current in the forward bias mode is expressed by the ideal Shockley equation. The current through the diode exponentially increases with increasing forward bias external voltage [29].  
\[I_{d}=I_{0}(exp(eV/kT)-1) \tag{9}\]  
where '\(I_{d}\)' is the diode current as the mobile carriers move through the junction; '\(I_{0}\)' is the saturation diode current in the depletion area at room temperature '\(T\)', 300 K; '\(V\)' is the external voltage; \(k\): Boltzmann's constant, 1.3805 \(\times\) 10\({}^{-23}\) J/K, and '\(e\)' is the electron charge, 1.60217657 \(\times\) 10\({}^{-19}\) coulombs [30].
# Effect of Curing Conditions on Microstructure and Pore-Structure of Brown Coal Fly Ash Geopolymers
###### Abstract  
This study reports the effect of heat curing at 120 \({}^{\circ}\)C on the geopolymeric reaction and strength evolution in brown coal fly ash based geopolymer mortar and concrete. Moreover, an examination of this temperature profile of large size geopolymer concrete specimens is also reported. The specimen temperature and size were observed to influence the conversion from the glassy (amorphous) phases to the crystalline phases and the microstructure development of the geopolymer. The temperature profile could be divided into three principal stages which correlated well with the proposed reaction mechanism for class F fly ash geopolymers. The geopolymerisation progressed more rapidly for the mortar specimens than the concrete specimens with 12 to 14 h providing an optimum curing time for the 50 mm mortar cubes and 24 h being the optimum time for the 100 mm concrete cubes. The 50 mm and 100 mm concrete specimens' compressive strengths in excess of 30 MPa could be obtained at 7 days. The structural integrity was not achieved at the center of 200 mm and 300 mm concrete specimens following 24 h curing at 120 \({}^{\circ}\)C. Hence, the optimal curing time required to achieve the best compressive strength for brown coal geopolymer was identified as being dependent on the specimen size.  
geopolymer; brown coal fly ash; compressive strength; heat curing; porosity; X-ray computed tomography +
Footnote †: journal: Applied
## 2 Materials and Methods
### Laser-Scanned Process  
Multilayer WSe\({}_{2}\) was scanned via Raman spectrometer laser (Renishaw in via Raman microscope) to fulfill region-selectable p-type doping. To figure out the proper power and exposure time for p-doping, multilayer WSe\({}_{2}\) was scanned with 633 nm, 515 nm and 473 nm lasers. The laser power and exposure time were set in a series of gradients. The diameter (d) of the 633 nm laser beam was calculated as 0.61 \(\upmu\)m by d = K\(\cdot\lambda\)/NA, where K = 0.82 was used for a Gaussian beam, \(\lambda\) = 633 nm was the laser wavelength and NA = 0.85 was the numerical aperture of the objective; the diameters of 515 nm and 473 nm laser beam were 0.50 and 0.47 \(\upmu\)m, respectively. The laser scanning step size was set as 0.3 \(\upmu\)m. Then, the pristine WSe\({}_{2}\) and laser-scanned WSe\({}_{2}\) were measured via Raman spectrometer (WitecAlpha 300 Access, Ulm, Germany) with a 532 nm laser. Laser power was set to 0.5 mW to avoid doping effect on WSe\({}_{2}\). The integration time was 8 s, and the number of integrations was 3. The experiment details are shown in Supporting Information Figure S2 and Table S1. After the preliminary experiment with the laser-scanned process, another WSe\({}_{2}\) was scanned using 633 nm laser, with laser power of 4 mW, exposure time of 0.75 s and laser spot diameter of 0.61 \(\upmu\)m.
# SPCL: A Smart Access Control System That Supports Blockchain
## 3 The Proposed Scheme SPCL
### Registration Phase  
In the registration phase, the mobile client (\(MC\)) and the smart lock (\(SL\)) will register with the cloud server (\(CS\)).  
1. Smart lock registration. \(CS\) generates an identity identifier for \(SL\) and calculates \(P_{SL}=d_{SL}\times G\), \(N_{SL}=H_{1}(ID_{SL}||u||v||G||P_{SL})\). \(CS\) stores \((ID_{SL},d_{SL},P_{SL},N_{SL})\) in its registry and sends it to \(SL\) via secure channel. \(CS\Longrightarrow SL:M_{1}=(ID_{SL},d_{SL},P_{SL},N_{SL})\)
2. Registration information access. The user uses \(MC\) to scan the QR code on \(SL\) and obtains \(M_{2}=(ID_{SL},P_{SL},N_{SL})\) from \(SL\). \(MC\) stores \((ID_{SL},P_{SL})\) in its registry. The process of scanning the QR is considered a secure channel. \(SL\Longrightarrow MC:M_{2}=(ID_{SL},P_{SL})\)
3. Mobile client registration. 4 \(MC\) generates its unique identifier \(ID_{MC}\) and sends \(M_{3}=(ID_{MC},ID_{SL},P_{SL})\) to \(CS\) via secure channel. \(MC\Longrightarrow CS:M_{3}=(ID_{MC},ID_{SL},P_{SL})\)  
2 \(CS\) checks \(ID_{MC}\) and matches \(MC\) with \(ID_{MC}\). If there is already \(MC\) matching \(ID_{MC}\), \(CS\) checks whether \(ID_{SL}\) is in its registry; otherwise, \(CS\) picks a public-private key pair \(P_{MC}\) and \(d_{MC}\) for \(MC\): \(CS\) picks a random number \(d_{MC}\in\mathbb{Z}_{n}^{*}\), calculates \(P_{MC}=d_{MC}\times G\), \(N_{MC}=H_{1}(ID_{MC}||u||v||G||P_{MC})\) and checks whether \(ID_{SL}\) is in its registry.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Notations** & **Description** \\ \hline \(F_{q}\) & Finite field with prime order \(q\) \\ \(E(F_{q})\) & Additive group over finite field \(F_{q}\) \\ \(G\) & Base point of \(E(F_{q})\) \\ \(\mathbb{Z}_{n}^{*}\) & The additive group of order \(n\) \\ \(u,v\) & Curve parameters \\ \(d_{SL}\) & Private key of smart lock \\ \(d_{MC}\) & Private key of mobile client \\ \(ID_{MC},ID_{SL}\) & Identifiers for mobile client or smart lock \\ \(RID_{MC}\) & Pseudonym identifier for mobile client \\ \(P_{SL}\) & Public key of smart lock \\ \(P_{MC}\) & Public key of mobile client \\ \(H_{1}(\cdot),H_{2}(\cdot)\) & Cryptographic secure hash functions \\ \hline \hline \end{tabular}
\end{table}
Table 1: Symbols used in SPCL.  
3 After that, \(CS\) stores \((ID_{MC},d_{MC},P_{MC},N_{MC},ID_{SL},P_{SL},N_{SL})\) in its registry and sends it to \(MC\).  
\[CS\Longrightarrow MC:M_{4}=(ID_{MC},d_{MC},P_{MC},N_{MC},ID_{SL},P_{SL},N_{SL})\]  
4\(SL\) receives \((ID_{MC},P_{MC},N_{MC})\) from \(CS\) and completes the registration phase of \(MC\).  
\[CS\Longrightarrow SL:M_{5}=(P_{MC},N_{MC})\]
# Energy-Aware Control of Error Correction Rate for Solar-Powered Wireless Sensor Networks
## 3 Energy-Aware Reed-Solomon Scheme
### Determining the Parity Length for the Next Period
#### 3.3.1 Determining the Parity Length for a Node Itself
Therefore, even the minimum parity length cannot satisfy Equation (10), so the minimum parity bit value is selected as it is. In this case, the node attempts to conserve the energy consumption by minimizing the work for data recovery.