esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Dataset
### Triplet Dataset Sampling  
The triplet loss function was initially presented in [16] and has since grown in popularity as a contrastive-based learning technique. A triplet-based loss function requires three images: an anchor image, a positive image that is a member of the same class as the anchor, and a negative image. This suggests that dissimilar pairs should be some margin away from similar ones. Figure 2 visualizes the triplet loss learning objective. Initially, the authors used the Euclidian distance to measure the distance, but we used cosine similarity in our study. Unlike the contrastive loss function, where we can randomly select an image sample and apply transformations to obtain two augmented images. Font images are typically grayscale and provide limited information, such as strokes, thickness, and serifs, which differentiate one font style from another. Applying augmentation in this scenario will result in different contents of the image, as illustrated in Figure 1. For this reason, here, we chose the hard positive and hard negative of an anchor in such a way that the distance between the anchor and negative is smaller than the distance between the anchor and positive. This process is known as triplet mining.  
In our test case, as shown in Figure 3, suppose we select a random image \(x_{i}^{a}\), that belongs to style class \(m\), with character class \(i\), and we select a positive image \(x_{j}^{p}\) from the same class \(m\) but with different character content, \(j\) from the anchor. However, in the  
Figure 2: Triplet loss training objective. The anchor and positive images have distinct characters in the same font style, whereas the negative image has a different font style and the same character as the anchor.  
case of the negative image \(x_{i}^{n}\), the image should belong to another class but have the same character content \(i\) as the anchor, as font images are grayscale, and very little information is available within the training images, such as strokes, thickness, serifs, etc., which separate one font style from another. It is a crucial aspect of triplet mining to select the hard-negative image of the anchor, where the distance between the anchor and the selected negative image should be the shortest compared to the remaining images. Therefore, we chose images with identical character labels from different style classes as the negative images. We followed the same triplet selection strategy for Chinese and English data loaders.
## 3 Parameter Extraction and Measurement Results  
### Path Loss Exponent and Shadow Fading  
In the widely accepted power-law path loss model, the change of the path loss along with the TX-RX distance is depicted by the path loss exponent. Such exponent and shadow fading are extracted from the measured results by using the following expression:  
\[PL(d)=PL(d_{0})+10 nlog_{10}(d/d_{0})+X, \tag{1}\]  
where \(d\) is the distance between the TX and RX, and \(PL(d)\) is the path loss (small-scale fading has already been filtered out) at \(d\). \(d_{0}\) denotes the reference distance and \(n\) is the path loss exponent. \(X\) is the shadow fading, which is well fitted by the log-normal distribution by passing Kolmogorov-Smirnov, Anderson-Darling, and chi-squared tests [16]. By using the least-square criterion, the path loss exponent \(n\) can be obtained.
## 4 Discussion  
We evaluated the AIA images that had undergone super-resolution processing and achieved results surpassing other models in terms of image quality metrics. To substantiate whether our approach could more accurately approximate Hi-C images compared to other models, we designed and conducted two experimental analyses for comparison. As shown in Figure 10, the profile lines [13] taken from three solar coronal images, after being processed by our model, perform closer to the original images in terms of image pixel intensity restoration. Especially in handling the pixels where the intensity rapidly decreases and then increases in the processed images, our model demonstrates superior recovery ability.  
To validate the superiority of our model compared to the baseline model in restoring solar astronomical texture features, we conducted a difference comparison experiment. Specifically, we set three different thresholds to measure the acceptable differences between pixels in the super-resolved images and the original images. Pixels that exceeded this threshold were considered to have significant differences and were marked in green in the images. To make the results easier to observe, we also label the four surrounding pixel coordinates--up, down, left, and right of the discrepancy points--as green. At the same time, we calculated the percentage of green-marked pixels in the total image pixels, namely the rate of difference. Figure 11 shows the experimental results; our model has a significantly lower rate of difference compared to traditional models, with a noticeable reduction in green-marked difference points. This indicates that our model is more accurate in restoring the texture features of the solar surface compared to the baseline model, and the frequency of erroneous pixels is lower.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & \multicolumn{5}{c}{**Choice**} \\ \hline Dense concat & w & w/o & w/o & w/o & w/o \\ RCAB & w/o & w/o & w & w/o & w \\ RCFAB & w/o & w/o & w/o & w & w \\ \hline PSNR & 32.97 & 33.23 & 35.78 & 35.12 & 36.91 \\ SSIM & 0.87 & 0.84 & 0.89 & 0.88 & 0.91 \\ NIQE & 5.95 & 5.92 & 5.54 & 5.76 & 5.31 \\ LPIPS & 0.1835 & 0.1833 & 0.1821 & 0.1823 & 0.1812 \\ Parameters (M) & 16.69 & 13.02 & 13.11 & 13.06 & 13.16 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The impact of different methods on the model.  
Figure 10: Compare the pixel intensity values along the line in coronal images for different models. Ours demonstrates better performance by more closely fitting the curves of the GT image in the annotation of pixel intensity curves.  
Figure 11: _Cont._
## 2 Results and Discussion
### Potential Applications of Mg NP/GW Nanoparticles
Additionally, investigating the molecular mechanisms through which these nanoparticles interact with biological systems will provide deeper insights into their effects and help optimize their use in various applications [76]. The study of Mg NP/GW nanoparticles in _Allium cepa_ root tips reveals significant cytotoxic and genotoxic effects, underscoring the need for cautious and informed application of nanotechnology. Despite these concerns, the potential applications of these nanoparticles in agriculture, environmental remediation, medicine, and industry are vast and promising. Responsible practices and comprehensive research are essential to maximize their benefits while minimizing potential risks, ensuring that nanotechnology contributes to sustainable development and technological advancement.
# Globally Conditioned Conditional FLOW (GCC-FLOW) for Sea Clutter Data Augmentation
## 3 Experiment Settings  
### Dataset  
The sea clutter data used in the experiments come from the _Dartmouth_ and _Grimbsby_ databases collected by the IPIX radar [32]. The following two datasets are used:  
1. High sea state data: The 3 rd range bin of data file #269 at Dartmouth labeled as _high_. The average wave height is 1.8 m (max 2.9 m);
2. Low sea state data: The 5 th range bin of data file #287 at Dartmouth labeled as _low_. The average wave height is 0.8 m (max 1.3 m);
3. Unidentified sea state data: The 11 th range bin of data file #155 at Grimbsby.  
All data samples are complex-valued, and the length of the high sea state data and the low sea state data is 131,072, whereas the unidentified sea state data length is 60,000. The detailed specification of the data, such as resolution and sampling rate, can be found in [32].
## 3 Design and Implementation of the Multi-Attribute PLA Scheme for LoRaWAN
### Data Preprocessing and Model Development  
The dataset was preprocessed and balanced to ensure effective training and evaluation of the proposed PLA scheme. RSSI, BL, and altitude values were standardized to achieve uniformity and improve model performance. To address data imbalance, legitimate entries (status = 0) and spoofed entries (status = 1) were balanced through random undersampling. The evaluation of the PLA scheme utilized a combination of ML and DL models to classify legitimate versus spoofed sensors. Standard ML models, including LR and KNN, were implemented due to their efficiency and interpretability in resource-constrained environments. These models were chosen for their ability to provide baseline comparisons in classification tasks. DL models, specifically MLP and CNN, were employed to explore the capacity of neural networks to capture non-linear relationships and complex patterns among the multi-attribute dataset. These architectures were selected for their proven effectiveness in handling multi-dimensional data while maintaining flexibility for varying input features. All models were trained and tested on the balanced dataset to ensure consistent evaluation metrics. The results were analyzed to compare the performance of traditional ML models and advanced DL techniques in detecting spoofing attacks based on the multi-attribute PLA framework.
# Inverse Modeling of Seepage Parameters Based on an Improved Gray Wolf Optimizer
## 4 A Casebook Study
### Computation Model  
IGWO was used for iterative search. The maximum number of iterations is 200, and the initial population size is 30. The simulation is performed 20 times independently by IGWO, and the average values of simulated hydraulic conductivity are taken as the target results.  
The multi-physics field simulation software COMSOL Multiphysics was applied in this simulation. A two-dimensional finite element mesh was created for inverse modeling each medium's hydraulic conductivity. The seepage properties of the dam body and foundation were analyzed. The free tetrahedral grid is applied to build the mesh, composed of 11,635 domain elements and 14,400 vertices. The maximum and minimum widths of the cells are 20 m and 5 m, respectively. A right-hand cartesian coordinate system is constructed with the \(x\)-axis pointing to the downstream reservoir and the \(z\)-axis pointing to the sky vertically. The upstream and downstream are both extended by 2.5 times the dam's height on the \(x\)-axis. The depth of the foundation is taken as 400 m. In addition, the upstream and downstream water levels are, respectively, set at 2850 m and 2702 m. The two-dimensional finite element mesh is presented in Figure 7.  
Figure 6: Measurements of hydraulic head and seepage discharge during storage period: (a) hydraulic head; (b) seepage volume.  
Figure 7: 2D FE mesh of Kakiwa dam.
## 3 Analysis of Experimental Results
### Examination of Macroscopic Deterioration and Fracture Characteristics  
During the freeze-thaw cycle, the water contained within the pores of sandstone transitions from a liquid to a solid state. This change in phase results in an expansion of the pore volume, which subsequently alters the natural skeletal structure of the material and exacerbates the development of cracks and internal defects. As the degree of freeze-thaw erosion intensifies, these cracks become discernible to the naked eye at a macroscopic level,  
Figure 14: Decay rate of various mechanical parameters with freeze–thaw cycles.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Strength and Deformation** & **Number of** & **Uniaxial Loading** & **16 MPa Triaxial Loading** \\
**Parameters** & **Freeze–Thaw Cycles** & **Uniaxial Loading** & **16 Loading** \\ \hline  & 0 & 9.269 & 12.357 \\ Elasticity modulus/GPa & 3 & 7.706 & 11.314 \\  & 6 & 6.817 & 10.567 \\  & 9 & 6.291 & 10.115 \\ \hline  & 0 & 56.865 & 124.935 \\ Peak strength/MPa & 3 & 46.183 & 113.401 \\  & 6 & 42.556 & 107.193 \\  & 9 & 40.470 & 104.589 \\ \hline  & 0 & 13.78 & \\ Cohesion/MPa & 3 & 11.27 & \\  & 6 & 9.91 & \\  & 9 & 9.35 & \\ \hline  & 0 & 38.30 & \\ Internal friction angle/\({}^{\circ}\) & 3 & 38.01 & \\  & 6 & 37.84 & \\  & 9 & 37.76 & \\ \hline \end{tabular}
\end{table}
Table 3: Numerical analysis of the reloading strength and deformation characteristics of unloaded sandstone during freeze–thaw cycles.  
as illustrated in Figure 15. When subjected to freezing and thawing, the pore water migrates due to gravitational forces and accumulates in the radial surface area, where the frost heave force exerts the most significant influence, leading to a pronounced cracking pattern that may appear nearly continuous.  
Figure 16 presents a case study of a sample subjected to nine freeze-thaw cycles, illustrating the fracture characteristics under single triaxial reloading. The sandstone exhibits overall shear failure, with cracks propagating from the upper edge to the opposite edge. Notably, when the number of freeze-thaw cycles reaches six or more, transverse cracks emerge on the surface of the sandstone; however, these cracks do not significantly influence the final shear fracture morphology. As the number of freeze-thaw cycles increases, the brittleness of the rock samples under load is markedly diminished, and the strain-softening effect becomes increasingly pronounced. Upon unloading and subsequently reloading until failure, the acoustic response transitions from a crisp sound to a lower pitch, accompanied by a noticeable reduction in loudness. Concurrently, the morphology of the residual debris on the fracture surface evolves from a hard and sharp appearance to a softer and sandier texture.  
Figure 16: Observable characteristics of the sample subjected to single triaxial loading following nine freeze–thaw cycles. (**a**) Single-axis loading; (**b**) 8 MPa perimeter pressure loading; and (**c**) 16 MPa perimeter pressure loading.  
Figure 15: Observable characteristics of the sample subjected to freeze–thaw erosion.
# A New Association Scheme for Handling Node Mobility in Cluster-Tree Wireless Sensor Networks
## 4 Simulation Assessment
### Results and Discussion  
Simulations were performed comparing IEEE 802.15.4 standard association mechanisms (Std) with the improvements proposed in this paper, namely the anticipation of handover decision-making (Ant) and adequate setup of MAC parameters (MAC). In order to analyze network behavior, three metrics were defined. The average packet delivery rate is calculated as the ratio between the number of packets received by the PAN coordinator, and the number of packets generated by the mobile nodes. The average end-to-end communication delay represents the average communication delay of successfully received packets at the PAN coordinator, measured as the time interval from the start of its generation at the application layer of the mobile nodes till the time instant when it is received at the PAN coordinator. The average disconnected time percentage accounts for the average time that the mobile nodes remain disconnected from the network.
## 4 Case Study- Business Process Innovation and Improvement
### Case Study Overview  
The case study considered here deals with a typical business process handled by a help desk department based on the case scenario from literature [14]. The purpose of the help-desk process is to take requests that may be problems, service, or computer access requests, and to satisfy them according to their type and priority. For the case study, the help-desk process has been adapted for a fuel retailing business such as a petrol service station company, where the customer service operations are becoming increasingly complex and rely on IT to enhance business performance [66; 67]. The service stations of the company could be franchised with different owners operating different stations. However, all of them are required to follow through the same procedures with the company's semi-automated processes and systems. For the convenience of its customers, these service stations are increasingly selling other products such as fresh food, and grocery items in addition to petroleum products. They also provide additional auto services such as car wash. Hence, there are different kinds of equipment used to provide all their services, which vary from one service station to the other. Some of the typical equipment used are petrol pumps, refrigerators, coffee-making machines, point of sale (POS) system, computers and other IT peripherals and systems. Any incident that occurs at a service station are reported to the 24/7 help-desk over phone. The help-desk process is required to address the service requests received from these clients (customers), who could be a service station employee (internal customer) or any other stakeholder of the company (external customer). The help-desk is responsible for restoring the service as quickly as possible.
## 10 Results and Discussion
### Discussion  
The successful implementation of the GBG-RPL framework hinges upon the collaborative efforts of diverse stakeholders. From healthcare data providers ensuring data quality to healthcare practitioners, each role is integral [104]. Data scientists optimize algorithms, auditors ensure security, and regulators monitor compliance [103]. Frontline healthcare  
Figure 13: Message overhead. providers utilize insights for enhanced care, and patient feedback contributes to ongoing refinement. This coordinated effort underscores the significance of each stakeholder in realizing the potential of the GBG-RPL framework in revolutionizing smart healthcare systems. An overview of the roles and responsibilities of the various stakeholders in a smart healthcare CPS is tabulated in Table 9.  
In a smart healthcare system utilizing a Gini index-based trust framework, scalability requirements are crucial to ensure that the system can handle an increasing number of nodes, devices, and data transactions. The brief description of the scalability requirements is described below:  
1. Node Growth: As the smart healthcare system expands, more medical devices, wearables, and IoT-enabled devices are added, increasing the number of nodes in the network [105]. The proposed framework will accommodate the growing number of nodes without significant degradation in performance due to its distributed nature [106]. 2. Data Volume: With the proliferation of healthcare data generated by various devices, the system experiences an increase in the volume of data transactions [107]. The proposed framework efficiently handles the increased data volume, ensuring timely trust assessments without introducing delays due to the blockchain [108]. 3. Real-Time Monitoring: The smart healthcare system requires real-time monitoring of patient data, vital signs, and device interactions for prompt decision making [109]. The proposed framework is capable of performing real-time trust assessments, adapting to dynamic changes in the network, and providing timely feedback as the fog layer is closer to the end-user devices. 4. Geographic Expansion: The smart healthcare system extends its services to new geographic locations, leading to a geographically distributed network [100]. The proposed framework is designed to support geographic expansion, considering potential latency issues and ensuring consistent trust management across distributed nodes. Moreover, it is also important to define resource-management strategies. By implementing these strategies, the GBG-RPL framework ensures efficient and reliable trust management in the evolving landscape of smart healthcare systems. A brief description of the resource management strategies for implementing the GBG-RPL framework in smart healthcare CPSs is described below:  
1. The proposed framework implements a distributed architecture for the Gini index where trust calculations and data processing are distributed across multiple fog
# SPEKS: Forward Private SGX-Based Public Key Encryption with Keyword Search
## 2 Background  
Intel Software Guard Extension (SGX) is used for designing our construction for forward secure searchable encryption. SGX is an extension of the x86 instruction set architecture (ISA) introduced since the 6 th generation Intel Skylake Processor. SGX provides Memory Isolation, Enclave Page Cache, and Software Attestation, which are major functionalities that we rely on to construct our scheme. In this section, we briefly introduce the SGX structures and basic PEKS algorithms upon which our scheme is built.
# A Simple and Efficient Method for RSS-AOA-Based Localization with Heterogeneous Anchor Nodes
## 4 Simulation Results
### Computational Complexity  
Table 1 lists the complexity of all the considered methods. \(N\) and \(T\) denote the numbers of the anchor nodes and the time steps. \(K\) represents the number of iterations in the bisection method used in solving the GTRS problem. As shown in this table, the complexity of LS, WLS-d, ECWLS, TSLS, and ENWLS is linear, while the proposed algorithm has the largest complexity among the LWLS methods. This is because the proposed method requires \(TN\) iteration calculations for computing the weight matrix, while the other methods only require \(N\) iteration calculations. But this does not affect the real-time implementation of the proposed method, as will be demonstrated in the average runtime table. The SDP-SOCP method has the highest computational complexity among the compared methods, which may hinder its real-time implementation.  
To provide a more intuitive understanding, the average runtime for the considered method is also investigated in Table 2, which was obtained using a MacBook with an M1 chip. Clearly, the results in Table 2 support the key findings about the computational complexity of the considered methods in Table 1. Although the proposed method has higher complexity and longer runtime than the compared LWLS methods, it can still achieve real-time localization. This is because its runtime is very low in practice (less than 1 ms), and it scales linearly with the number of anchor nodes and time steps, making it suitable for real-time applications.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Algorithm** & **Complexity** \\ \hline the proposed algorithm & \(\mathcal{O}(TN)\) \\ LS in [11] & \(\mathcal{O}(N)\) \\ WLS-d in [12] & \(\mathcal{O}(N)\) \\ ECWLS in [13] & \(\mathcal{O}(N)\) \\ TSLS in [14] & \(\mathcal{O}(N)\) \\ ENWLS in [15] & \(\mathcal{O}(N)\) \\ SDP-SOCP in [18] & \(\mathcal{O}(N^{3.5})\) \\ GTRS in [19] & \(\mathcal{O}(KN)\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: The complexity of the considered localization methods.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Algorithm** & **Time (s)** \\ \hline The proposed algorithm & \(7.46\times 10^{-4}\) \\ LS in [11] & \(7.00\times 10^{-5}\) \\ WLS-d in [12] & \(9.13\times 10^{-5}\) \\ ECWLS in [13] & \(2.64\times 10^{-4}\) \\ TSLS in [14] & \(1.80\times 10^{-4}\) \\ ENWLS in [15] & \(3.03\times 10^{-4}\) \\ SDP-SOCP in [18] & \(7.65\times 10^{-1}\) \\ GTRS in [19] & \(7.40\times 10^{-4}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: The average runtime of the considered algorithms.