esa-sceva/satcom-chunk-collection huggingface dataset:
# IoT Privacy and Security Challenges for Smart Home Environments
## 5 Some Existing Security Support for IoT  
Due to their low cost, IoT computing devices generally are not as powerful as traditional desktop and laptop computers. Most IoT devices are low energy, use a low-end microcontroller and have limited memory. Such controllers are well-matched to the requirements of standalone controllers in a washing machine or air conditioner.  
However, these characteristics have made the move to networked IoT controllers more challenging as the existing Internet protocols are not typically designed for these embedded devices. Several Internet Engineering Task Force (IETF) working groups have been created to tackle these problems. IETF standardization work on IoT has played a vital role in the establishment of the necessary light-weight communication protocols for constrained environments over the existing IP network. These include IPv6 over Low-Power Wireless Personal Area Networks (6 LoWPAN: RFC 6282) [(20)], IPv6 Routing Protocol for Low power and Lossy Networks (RPL: RFC 6550) [(21)] and Constrained Application Protocol (CoAP: RFC 7252) [(22)]. Figure 2 shows the comparison between IETF IoT and TCP/IP protocol stacks. Once devices are connected to the Internet, any of the security threats on the Internet could also compromise the security and privacy of IoT. In the following sections we review the current security implementations for these standard IoT protocols.
## 2 Methodology  
In this study, as shown in Figure 2, we go through three processes to achieve effective image restoration in UDC systems. The first process involves generating virtual degraded images, simulating the unique degradation characteristics specific to UDC systems. The second process focuses on restoring these images by learning the degradation phenomena in the frequency domain, while the final process involves image restoration through learning in the spatial domain. In this section, each process in our effective image restoration methodology is described in detail, outlining the specific techniques used to address the challenges in UDC imaging.
## 3 A Grand Challenge in EM Security
### Prediction and Evaluation of a Cyber Attack's Impact on EM  
Current EMIS systems provide tools for predicting the evolution of an emergency based on the physical characteristics of the environment and the nature of the emergency. They do not take into account the impact of cyber failures, intentional or not. We argue that EMIS need to evaluate the impact of cyber threats on the mitigation, preparedness, response and recovery phases of EM, from the likelihood of a disaster to occur, up to the financial loss and the number of casualties caused by it. A similar recently developed research area in the defence sector is the mission impact analysis of cyber threats. For example, a first example of a cyber impact dependency graph model has been provided in [122] to evaluate the effect that a cyber attack has on the operational capacity of an ongoing military mission, and [PERSON] _et al_. [123] have discussed mission impact and cyber threat assessment in the military context. However, these pieces of research are still at an early stage and do not necessarily translate well in the EM setting where the civilian population is directly affected and the technologies involved are different.  
Nearer this goal is the research carried out by [PERSON] _et al._[44], who have been using an EM simulation environment to evaluate the impact of cyber attacks on an actual building evacuation mission. In their simulations, they have measured the ratio of evacuated civilians and the average evacuee health against the number of malicious nodes participating in a cyber attack that aims to disrupt the coordination of the mission. We believe that the analysis of cyber threats in this manner is in the right direction, because impact is measured in real-world metrics, such as number of casualties, rather than only cyber metrics. The grand challenge is to scale up this type of analysis to be able to take into account the whole operational picture and the cyber-physical-human interdependencies between different technologies, different emergency services and agents, as well as the evolving physical environment. For this, new large-scale EM augmented simulation environments and mathematical models may be needed.
## 7 Defensive Strategies Against Deepfake-Driven Social Engineering
### The Current Defensive Measures
#### 7.1.1 Employee Training and Awareness Programs
* **Verification protocols:** Employees should be trained to verify how authentic communications are, where especially those requesting sensitive information or financial transactions are focused on. From the interview with Company A, we learned that it highlighted the importance of validating communications through known channels. * **Reporting mechanisms:** It has become evident to us that establishing clear procedures for reporting suspected deepfake content or other suspicious activities is crucial.
# pH Measurement of Cement-Based Materials: The Effect of Particle Size
## 2 Research Methodology
### pH Meter Selection and Other Instruments  
The main components of pH meter are an electrochemical system (a pH sensor and a reference electrode), a high input impedance meter (which processes the voltage difference in DC millivolt and displays them in mV or pH) and a temperature sensor. The reference electrode provides stable and constant reference voltage but is completely insensitive to the measured fluid. The junction of the reference electrode may be contaminated under adverse condition, such as high temperature, high pressure, high acidity and high basicity. An under-performing reference electrode can cause the pH reading to drift. Therefore, the reference electrode should be used carefully, according to the supplier's recommendation,  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**[PERSON] et al. [13]** & **[PERSON] and [PERSON] [19]** \\ \hline Particle Size & pH Measured & Particle Size & pH Measured \\ \hline \textgreater{}2 mm & 11.55 & \textgreater{}16 mm & 12.22 \\ \textless{}850 μm & 12.48 & \textless{}75 μm & 12.81 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of the pH value for biggest and smallest particle sizes in previous studies.  
and/or a double-junction electrode should be selected, instead of a single-junction electrode, to minimise contamination  
The quality of electrodes degrades overtime depending on usage and maintenance, as evident in response time, slope and offset of the pH electrode. Response time refers to the time taken to obtain a stable reading. In theory, a perfect pH electrode reads 0 mV at pH 7 buffer at 25 \({}^{\circ}\)C, with a slope of 59.16 mV/pH. For a clean, correctly calibrated, and in good condition electrode, at 25 \({}^{\circ}\)C, the slope shall be within 92% to 110%, where 100% is 59.16 mV/pH, while the offset shall be \(\pm\)30 mV [31]. Before the test, calibration was performed with at least one to five standard buffer solutions, depending on the capability of the pH meter.  
The suspension of CBMs consists of hard and sharp particles. The pH measurement was carried out in filtered aqueous solutions to protect the pH-sensitive glass sensor from physical damage. The Hanna Instrument [32] recommends the use of plastic beakers to minimise any EMC interferences during the measurement.  
This experiment used the Hanna edge(r) pH HI2002 and Hanna digital pH electrode HI1310, with a resolution of 0.01 pH and accuracy of \(\pm\)0.01 pH. The instrument has an automatic temperature compensation feature between \(-\)5 \({}^{\circ}\)C and 100 \({}^{\circ}\)C because of its built-in temperature sensor. This double-junction electrode has a 9.5 mm spherical tip.
## 3 Numerical Simulation
### Model Verification  
The reasonableness of the model's parameter settings is verified by comparing the measured tunnel deformation data with the numerical simulation results. Figure 9 presents both the measured values and numerical simulation results of the tunnel's horizontal and vertical displacements at the control section of the foundation pit under corresponding working conditions. Upon comparison of the measured values with the simulated values in the figure, it is evident that the simulated horizontal and vertical displacements align well with the measured values, and their trends exhibit rough consistency. The congruence between the simulated values and measured values under diverse working conditions indicates the reasonableness of the model's parameter settings, enabling accurate prediction of tunnel deformation induced by the unloading resulting from laterally adjacent foundation pit excavation.
# Micro-FL: A Fault-Tolerant Scalable Microservice-Based Platform for Federated Learning
## 4 Micro-FL  
Figure 2 contrasts the building blocks of a commonly used federated learning server design (monolithic architecture) with the microservices-based design proposed in this study. In a monolithic federated learning server design, all components (e.g., user interface and communication services) are encapsulated into a single process, using a single database. A fault in any of these components can completely halt the federated learning process. This issue, known as a'single point of failure' within the server, could cause substantial downtime and undermine system reliability. More importantly, scaling monolithic applications requires scaling the whole application, necessitating a considerable increase in resource requirements.  
Conversely, when employing a microservices architecture, these components are decoupled from each other (i.e., isolated), each operating as an individual scalable process (microservice) with its own dedicated database. Furthermore, a communication mechanism is implemented to allow these services to interact with each other. As these microservices are horizontally scalable, multiple instances of each microservice can be created to enhance the fault tolerance of the federated learning system. The proposed architectural framework is called Microservice-based Federated Learning (Micro-FL) ([[https://github.com/asgaardlab/Micro-FL](https://github.com/asgaardlab/Micro-FL)]([https://github.com/asgaardlab/Micro-FL](https://github.com/asgaardlab/Micro-FL)), (accessed on 21 February 2024)).  
Micro-FL accommodates both Linear and Deep Neural Network (DNN) models, leveraging TensorFlow and Keras. Additionally, as a microservice-based application, Micro-FL possesses several notable attributes:  
Figure 2: Comparison of monolithic and microservices-based federated learning systems architectures.  
* Micro-FL's distinct modules handle specific functions, contributing to a compact codebase and easier debugging, while also enabling incremental upgrades. These upgrades allow for the coexistence of old and new versions for compatibility testing, and changes in a module do not require a system-wide reset, thus reducing the re-deployment cycle.
* The framework enhances fault tolerance capabilities with Kubernetes; even with a communication microservice failure, the federated learning process continues without interruption.
* Using containerization, Micro-FL allows for an extensive customization of the deployment environment and facilitates the scaling of individual microservices without impacting the whole application. This functionality supports the easy deployment or retraction of services based on demand and accommodates both horizontal and vertical scaling.
# Container Volume Prediction Using Time-Series Decomposition with a Long Short-Term Memory Models
## 3 Method  
This section describes the data pre-processing technique followed by a time-series decomposition method. A flowchart of the proposed method is shown in Figure 4.  
Exploration and verification of appropriate external variables are essential to predict the volume of loading containers at Busan port. This is done by utilizing the characteristics of time-series data and external variables. First, we checked the trend similarity and correlation with external variables with those of the container volume data. After selecting external variables with a correlation of 0.85, a VAR model is implemented for each external variable and volume. The VAR model is a regression model that considers both the past of the past and the past of relative data. In addition, it analyzes dynamic relationships between variables by estimating the relationships between all available time-series data without theoretical constraints. This model is also capable of executing simple predictions without considering the theoretical relationships between variables.  
After selecting the external variables from the results of VAR model implementation, we performed time-series decomposition based on additive models of volume and each variable to improve the performance of deep learning prediction. We derived the predictions using a multivariate LSTM model for each decomposed time-series element including external variables. Finally, we compared the prediction performance of SARIMA, gated recurrent unit (GRU), bidirectional GRU, univariate LSTM, bidirectional LSTM, multivariate LSTM, and time-series decomposition-based multivariate LSTM. The key difference between GRU and LSTM is that GRU's bag has two gates that are reset and update while LSTM has three gates that are input, output, and forget. GRU is less complex than LSTM because it has less number of gates. If the dataset is small then GRU is preferred otherwise LSTM for the larger dataset. For the bidirectional approach, the bidirectional approach algorithm can manage inputs in two ways, one from past to future and one from future to past, and this approach differs from unidirectional.
###### Abstract  
Although the IEEE Wireless Access in Vehicular Environment (WAVE) and 3 GPP Cellular V2X deployments are imminent, their standards do not yet cover an important security aspect; the message content plausibility check. In safety-critical driving situations, vehicles cannot blindly trust the content of received safety messages, because an attacker may have forged false values in it in order to cause unsafe response from the receiving vehicles. In particular, the attacks mounted from remote, well-hidden positions around roads are considered the most apparent danger. So far, there have been three approaches to validating V2X message content: checking based on sensor fusion, behavior analysis, and communication constraints. This paper discusses the three existing approaches. In addition, it discusses a communication-based checking scheme that supplements the existing approaches. It uses low-power transmission of vehicle identifiers to identify remote attackers. We demonstrate its potential address in the case of an autonomous vehicle platooning application.  
V2V communication; message contents plausibility; power control Article
###### Abstract  
Using remote sensing to estimate evapotranspiration minute frequency is the basis for accurately calculating hourly and daily evapotranspiration from the regional scale. However, from the existing research, it is difficult to use remote sensing data to estimate evapotranspiration minute frequency. This paper uses GF-4 and moderate-resolution imaging spectroradiometer (MODIS) data in conjunction with the Surface Energy Balance Algorithm for Land (SEBAL) model to estimate ET at a 3-min time interval in part of China and South Korea, and compares those simulation results with that from field measured data. According to the spatial distribution of ET derived from GF-4 and MODIS, the texture of ET derived from GF-4 is more obvious than that of MODIS, and GF-4 is able to express the variability of the spatial distribution of ET. Meanwhile, according to the value of ET derived from both GF-4 and MODIS, results from these two satellites have significant linear correlation, and ET derived from GF-4 is higher than that from MODIS. Since the temporal resolution of GF-4 is 3 min, the land surface ET at a 3-min time interval could be obtained by utilizing all available meteorological and remote sensing data, which avoids error associated with extrapolating instantaneously from a single image.  
GF-4; evapotranspiration; SEBAL model; MODIS; minute frequency Article
# Generative Modeling for Imbalanced Credit Card Fraud Transaction Detection
## 3 Generative Models
### Generative Adversarial Network (GANs)
During training, the generator and discriminator engage in a dynamic process where the generator attempts to improve its ability to produce realistic data while the discriminator continually refines its ability to distinguish real data from fake data. This iterative process continues until the discriminator can no longer reliably differentiate between real and fake data, indicating that the generator has succeeded in producing highly realistic data. The feedback loop provided by the discriminator is crucial for the generator's learning process. After each batch of training, backpropagation is used to update the weights of both the generator and discriminator networks, optimizing their performance. Algorithm 3 shows our proposed architecture of the GAN model used to address the imbalance issue. The choice of hyperparameters in the GAN training algorithm is made to optimize both model performance and stability. The generator's architecture uses progressively smaller layers (128, 64, 50, 40, and 15 units) to effectively map a high-dimensional latent space to the target data distribution. The larger initial layers (128 and 64 units) capture more complex features, while the smaller layers reduce the dimensionality to match the output data. ReLU activation functions are employed throughout the generator to mitigate the vanishing gradient problem and speed up convergence. Dropout is set to 0.5 to regularize the model and prevent overfitting by randomly deactivating half of the units during training. Batch normalization is applied to stabilize training by normalizing layer inputs, ensuring more consistent gradients. In the discriminator, the choice of 128, 64, and 32 units, along with LeakyReLU activations, allows the model to effectively distinguish between real and fake data while mitigating the risk of dying neurons. Dropout is similarly set to 0.5 in the discriminator to avoid overfitting, and the use of binary cross-entropy loss ensures the proper evaluation of fake and real data. The Adam optimizer with a learning rate of 0.001 is selected for both models to ensure efficient training and prevent the instability often seen with other optimizers in GAN training.
## 2 Theoretical Background
### Vertical Coupling Vibration Model of Train-Rail-Bridge
#### 2.2.1 Train-Rail Vertical Coupling Relationship  
The train-rail-bridge interaction relies on wheel-rail contact. It is not only the train and lower rail's connection manner but also the two subsystems' primary excitation source. A Hertz spring with the stiffness of \(k_{\text{H}}\) is used to imitate this contact in a mobile wheel-rail interaction element, as shown in Figure 3. According to the linearized [PERSON] elastic contact theory, the contact stiffness is calculated using the tangent slope method [37].  
\[k_{\text{H}}=\frac{\text{d}[P(t)]}{\text{d}[\delta z(t)]}=\frac{3}{2G}P_{0}^{1 /3} \tag{3}\]  
where \(G\) is the wheel-rail contact constant (m/N\({}^{2/3}\)), taking \(G=3.86r^{-0.115}\times 10^{-8}\); \(R\) is the radius of the wheel rolling circle (m); \(P_{0}\) is the static axle load of the train.  
The standard elastic compression of the wheel-rail contact point is:  
\[\delta z(t)=z_{\text{w}}(t)-z_{\text{r}}(x_{\text{w}},t)-r(x_{\text{w}}) \tag{4}\]  
where \(z_{\text{w}}(t)\) is the vertical displacement of the wheel at \(t\) time, \(z_{\text{r}}(x_{\text{w}},t)\) is the vertical displacement of the rail where the wheel acts on \(x_{\text{w}}\) at \(t\) time, \(x_{\text{w}}\) is the position coordinate of the wheel, and \(r(x_{\text{w}})\) is the value of rail irregularity at the \(x_{\text{w}}\) of the wheel.  
The vertical displacement of the rail at the contact point of the wheel can be obtained from the node displacement vector \(\mathbf{x}_{\text{r}}^{\text{e}}\) of the rail element and the corresponding cubic Hermite interpolation shape function, that is:  
\[z_{\text{r}}(x_{\text{w}},t)=\mathbf{N}_{x=x_{\text{w}}}\mathbf{x}_{\text{r}}^{ \text{e}} \tag{5}\]  
\[\delta z(t)=z_{\text{w}}(t)-z_{\text{r}}(x_{\text{w}},t)-r(x_{\text{w}})\]  
where \(\mathbf{N}\) is the cubic Hermite interpolation vector of the displacement of the rail and bridge elements. The indirect contact force between the wheel and the rail can be expressed as [38]:  
\[F_{\text{in}}=-a_{\text{in}}k_{\text{H}}\delta z(t) \tag{6}\]  
\[a_{\text{in}}=\begin{cases}1&,\delta z(t)\geq 0\\ 0&,\delta z(t)<0\end{cases}\]
# Performance Analysis of Post-Quantum Cryptography Algorithms for Digital Signature
## 1 Introduction
To accomplish this, it is necessary to implement a selected post-quantum cryptography algorithm to be used for this mechanism. Such an approach can verify the potential of algorithms that face the computing power of quantum computers, ensuring the proper functioning of widely used solutions such as digital signature mechanisms or other services based on asymmetric cryptography. Furthermore, it is particularly helpful to present a usage example and provide results regarding the time efficiency of the tested solutions. This article consists of six sections. The motivation, objective, and scope of this work are presented in Section 1. Section 2 introduces modern cryptography, including the idea and application of asymmetric ciphers. Section 3 describes the risk of breaking currently used encryption algorithms by quantum computers and introduces the concept of post-quantum cryptography. The testbed and some details regarding implementation are described in Section 4. Section 5 is dedicated to a performance analysis of post-quantum cryptography. The results of the efficiency analysis are presented, including the performance of key pair generation, file signing, and signature verification for various post-quantum cryptographic algorithms. This article is summarized in Section 6.