esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experimental Procedure
### Experimental Results
\begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**LAMBADA**} & \multicolumn{2}{c}{**CBT**} & \multicolumn{2}{c}{**WikiText**} & \multicolumn{1}{c}{**PTB**} & \multicolumn{1}{c}{**enwiki8**} & \multicolumn{1}{c}{**text8**} & \multicolumn{1}{c}{**1 BW**} \\ \cline{2-11}  & **PPL \(\downarrow\)** & **ACC \(\uparrow\)** & **ACC \(\uparrow\)** & **ACC \(\uparrow\)** & **(EN)** & **PPL \(\downarrow\)** & **(WikiText2)** & **PPL \(\downarrow\)** & **(WikiText103)** & **PPL \(\downarrow\)** & **BPC \(\downarrow\)** & **BPC \(\downarrow\)** & **BPC \(\downarrow\)** & **PPL \(\downarrow\)** \\ \hline \multicolumn{11}{l}{**Baseline**} & & & & & & & & & & & \\ GPT-2\({}_{medium}\) & 15.60 & 55.48 & 92.35 & 87.10 & 22.76 & 26.37 & 47.33 & 1.01 & 1.06 & 55.72 \\ \hline \multicolumn{11}{l}{**Ours**} & & & & & & & & & & \\ GPT-2\({}_{medium}\)(G)+ & & & & & & & & & & & \\ RoBERTa\({}_{large}\)(D) & **14.21** & **57.20** & **92.85** & 87.00 & **21.94** & **25.12** & **45.93** & **1.00** & **1.06** & **55.01** \\ RoBERTa\({}_{large}\)(D) & 14.91 & 56.21 & 92.84 & 87.15 & 22.40 & 25.49 & 45.99 & 1.01 & 1.07 & 55.82 \\ BERT\({}_{large}\)(D) & 14.28 & 57.14 & 92.83 & **87.32** & 21.97 & **25.12** & 45.94 & 1.00 & 1.06 & 55.03 \\ BERT\({}_{large}\)(D) & 14.92 & 56.17 & 92.81 & 87.02 & 21.44 & 25.32 & 45.98 & 1.01 & 1.07 & 55.80 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: After 156 minibatch-sized EvoText sessions, we evaluated the performance of various natural language understanding models (D) and the same language generation model (GPT\({}_{medium}\)) on 7 natural language processing tasks ZERO-SHOT. Each model was tested five times on each task, and the results were averaged. RLHF in terms of Chatgpt feedback on nearly every task. Based on the results presented in Table 8, it can be observed that the EvoText training approach is highly effective in rectifying a significant portion of the grammatical errors produced by the model, which is an impressive outcome.
# A Through-Transmission Ultrasonic Method for the Detection of Ferrite Tile Defects
## 4 Design and Experimentation of the Ultrasonic Transmission System
### Experimental Operation and Data Processing  
The experimental procedure is as follows: firstly, the sample is securely fixed with the probe using a fixture, ensuring precise alignment between the probe and the sample. Next, the PC data acquisition program and motion platform are activated to scan the sample surface and collect data to obtain ultrasonic transmission signals. By analyzing the signals, the sample intervals are determined, and their energy characteristics are computed.  
Figure 5: System composition.  
As shown in Figure 7, the analysis of ultrasonic transmission signals reveals their components and sources: part 1 corresponds to the emission signature generated by circuit coupling, indicating the initiation of ultrasonic wave transmission. Part 2 represents the sample transmission signal, which is the ultrasonic wave signal passing through the sample and coupling agent via the shortest path during transmission. Its energy reflects the presence of obstacles in the transmission path. Therefore, calculating the total energy of the transmission signal provides the ultrasonic transmission energy of the sample. The signals that appear between the second and the third part are generated by ultrasonic waves repeatedly reflecting between the sidewall and surface of the measured sample, forming a series of gradually decreasing signal peaks. This part of the signal is categorized into delayed waves and triangular echoes based on their generation causes [30]. Due to the prolonged transmission path, they appear later than the second part. Part 3 corresponds to the water transmission signal. This signal is formed when ultrasonic waves only pass through the coupling medium without entering the sample for propagation, as water has a slower sound velocity compared to the solid sample, resulting in a delayed appearance.  
The energy of the ultrasonic transmission signal from each emission is defined as the square of the integral of the amplitude of the signal peak in the transmission signal. Comparing the energy of the sample's transmission signal peaks with that of intact samples from the same batch can reveal the presence of defects. Since ultrasonic waves undergo multiple reflections and superimpose at the probe-water interface and sample-water interface, only the first received transmission signal peak in the time domain is considered for energy calculation, as it is less affected by interference compared to other multiply-reflected ultrasonic signals. This signal is generated by ultrasonic waves propagating along the shortest path between the probes, experiencing less interference compared to other multiply-reflected ultrasonic signals.
# Meso-Scale Simulation of Concrete Based on Fracture and Interaction Behavior
## 2 Finite Element Modeling Method
### Aggregate Generation  
The aggregates are generated through modeling arbitrary polygon aggregates based on a pre-generated mesh [33]. The plane is meshed by triangular grids. The nodes' coordinate information is extracted as the basis to model aggregates, and check conflicts and overlaps. The distributions of aggregates accord with Fuller grading cumulative distribution. The function of Fuller grading curve can be decomposed into two dimensions [34] as:  
\[\begin{array}{l}P_{c}(D<D_{0})=\\ \begin{array}{c}P_{k}\big{(}1.065D_{0}{}^{0.5}D_{\max}{}^{-0.5}-0.053D_{0}{}^ {4}D_{\max}{}^{-4}\\ -0.012D_{0}{}^{6}D_{\max}{}^{-6}-0.0045D_{0}{}^{8}D_{\max}{}^{-8}-0.0025D_{0}{} ^{10}D_{\max}{}^{-10}\big{)}\end{array}\end{array} \tag{2}\]  
where \(P_{c}(D<D_{0})\) denotes the proportion of aggregates whose particle diameter \(D\) are smaller than the threshold value \(D_{0}\), \(P_{k}\) is the ratio of the total volume of aggregates to the concrete volume, and \(D_{\max}\) is the diameter of the largest aggregate particle.  
The geometrical shapes of aggregates are formed by the polygons' vertices. A random function expressed in terms of the polar coordinates \(r\) and \(\theta\) is used to generate the vertices, as shown in Figure 2. To enforce randomicity and ergodicity, random numbers are introduced while generating the radius and angle. The vertices location \((r,\theta)\) is defined as follows:  
\[\left\{\begin{array}{c}r=\frac{D}{2}\times[1+rand(-1,1)\times f_{r}],\;0<f_{ r}<1\\ \theta=\frac{2\pi}{\alpha}\times[i+rand(-1,1)\times f_{\theta}],\;0<f_{\theta }<0.5,\;i=0,1,2,\ldots\ldots,\alpha-1\end{array}\right. \tag{3}\]  
where \(rand(-1,1)\) is a random number less than 1 and greater than \(-1\), \(\alpha\) is the edge number, and \(f_{r}\) and \(f_{\theta}\) are the radius and angle fluctuations respectively. \(f_{r}\) and \(f_{\theta}\) are used to control the irregularity of aggregates. The greater \(f_{r}\) and \(f_{\theta}\), the more irregular the generated aggregates will be. If \(f_{r}=0\) and \(f_{\theta}=0\), the generated aggregates are regular polygons.
## 2 Materials and Methods
### Selection of Variables  
Correlation analysis plays a vital role in exploring the connections among different inputs in datasets. However, given the diverse nature of datasets, the assumptions associated with each correlation method often vary, influencing their suitability and reliability.  
The primary criterion for selection involves eliminating variables that are challenging to obtain and opting for readily available data. In this study, Spearman, Kendall, and Pearson correlation coefficients are calculated among different parameters to examine the correlation between input and output parameters, as illustrated in Figure 9. The strength of the correlation between factors is depicted via numerical values ranging from 0 to 1, where a value of 1 indicates a robust positive correlation, while a value of -1 signifies a strong negative correlation. When comparing the three methods (Spearman, Kendall, and Pearson) across different datasets with different inputs, the variables \(SR\), \(\gamma_{d}\), and \(e_{0}\) consistently show the highest correlation coefficients. Regardless of the dataset's composition or the underlying presumptions of each correlation technique, this steady trend points to a solid and robust relationship between \(SR\), \(\gamma_{d}\), and \(e_{0}\) and \(E_{t}\) and \(q_{u}\). Consequently, \(LL\), \(Fs\), and these three parameters (\(SR\), \(\gamma_{d}\), and \(e_{0}\)) were selected as inputs. The high correlation coefficients observed for degree of saturation (\(SR\)), dry unit weight, and void ratio (\(e_{0}\)), with unconfined compressive strength and the elasticity modulus, can be attributed to their fundamental roles in soil mechanics. The degree of saturation (\(SR\)) reflects the soil's moisture content relative to its maximum water-holding capacity, directly impacting soil strength and stiffness via its influence on pore water pressure. Dry unit weight and void ratio directly influence soil density, porosity, compressibility, and shear strength, explaining their strong correlations with unconfined compressive strength and elasticity modulus. Conversely, the lower correlation coefficients observed for sand content (\(Fs\)) and liquid limit (\(LL\)) suggest their relatively weaker direct influence on soil mechanics. \(Fs\) primarily reflects the composition of soil particles, which may affect soil strength and stiffness to a lesser extent than density and porosity represented by dry unit weight and void ratio. \(LL\), indicating soil plasticity, may indirectly affect mechanical properties via its influence on soil structure and behavior under different moisture conditions. However, despite achieving low correlation coefficients, \(LL\) and \(Fs\) were selected as inputs due to their importance in soil classification and the ease of determination via simple tests. This decision aids machine learning models in comprehending the complex relationships within multidimensional data, ensuring a more comprehensive analysis of soil behavior. The statistical characteristics of the chosen variables are presented in Table 4.
# Chaotic Image Encryption: State-of-the-Art, Ecosystem, and Future Roadmap
## 5 Future Roadmap: The Promise of AI
### Neural Networks in Image Encryption
#### 5.5.4 Privacy  
Maintaining the privacy of images that are processed by AI algorithms is a novel, but important concept. In a proposal by [PERSON] et al. [244], a special privacy-preserving encryption algorithm was used that encrypts the image, but maintains its features, so that a DNN can still read them. In a different paper by [PERSON] et al. [244], this algorithm was tested on the well-known ResNet-18 network.
# Detecting Small Anatomical Structures in 3D Knee MRI Segmentation by Fully Convolutional Networks
## 3 Methods
### Hyperparameter  
The original size of the images is 400 \(\times\) 400 \(\times\) 400. We conduct the experiments of the four U-net variants on the dataset. The time of a single training epoch of U-net with residual blocks on the dataset of resolution 400 \(\times\) 400 \(\times\) 400 is about 7 h. Because of the constraints on GPU memory, we set the patch size to 128 \(\times\) 128 \(\times\) 128 and the batch size to 1. We found that the training with a larger patch size and a smaller batch size performs better than the training with a smaller patch size and larger batch size.  
DeepLabv3 plus variants utilise several techniques to reduce the number of parameters, enabling us to use a larger patch size to feed the networks. The patch size used for training DeepLabv3 plus variants has increased from 128 \(\times\) 128 \(\times\) 128 to 192 \(\times\) 192. Although we increase the patch size, the model parameters are still lower than U-Net, and the training time of an epoch for DeepLabv3 plus variants is less than 10 min.  
The MR images were obtained from 20 volunteers: 18 volunteers provide the training dataset and 2 volunteers provide the testing dataset. Each volunteer provides 30 images; the total number of images was 600, that is 90% (540 images) for training and 10% (60 images) for testing. The main hyperparameters of U-Net and DeepLabv3 plus are displayed in Figure 1. We directly concatenate the results together when we receive all the outputs from patches.  
All the networks are trained on GPU GeForce RTX 2080 Ti, with a GPU memory of 11 GB GDDR6 and by using an Adam optimiser. The initial learning rate is set to 0.01 for training with dice loss and is set to 0.001 for training with weighted dice loss. The learning rates were set to be reduced by a factor of 0.5 after two epochs if the validation loss is not decreasing. The training was stopped when the loss on the validation dataset had not decreased for at least three epochs.
## 1 Introduction
However, the study suggested that there is a need for future research studies to develop an STS approach that can particularly address cybersecurity challenges, including human errors and organizational structural and procedural shortcomings. Hence, it can be argued that the application of STS theory to the cybersecurity domain, particularly cybersecurity challenges, has not received much attention. This research will add value to the cybersecurity challenges literature, particularly emerging cybersecurity challenges amidst the major crisis period in the HERS, by proposing an STS cybersecurity framework. This study will classify identified cybersecurity challenges using the STS theoretical lens amidst the major crisis and give examples from real case study scenarios from HERS. The overarching research question for this study is as follows:  
1. What sociotechnical cybersecurity challenges have emerged in the Higher Education and Research Sector (HERS) during major crises between 2019 and 2023, such as the COVID-19 pandemic or other global disruptions?
## 3 Results
### Comparison of ANN Model with Different Input Data
The model using the combination of meteorological data and one remote sensing factor, NDVI, shows intermediate results (RMSE = 19.25-19.84 W m\({}^{-2}\) (39.94-41.05%), MAE = 13.05-13.51 W m\({}^{-2}\), and R\({}^{2}\) = 0.81-0.82). The model using meteorological data and three remote sensing data factors (NDVI, NIRv, and SWIR) showed comparable performance with that using meteorological data and two remote sensing data factors (NDVI, NIRv). Therefore, it can be concluded that remote sensing data in the ANN model facilitated the improvement of the estimates of croplands ET.
## 3 Results and Discussion
### Thermal Conductivity of the Hybrid Nanofluids
#### 3.2.6 Mechanism Model of Thermal Conductivity of the Hybrid Nanofluid
The values of the four parameters could be determined as \(L=2.167\times 10^{-5}\) m, \(t=6.312\times 10^{-9}\) m, \(\eta=0.3211\) and \(R_{k}=3.113\times 10^{-9}\) m\({}^{2}\)Kw\({}^{-1}\). It is demonstrated that \(R_{k}\) of the GO-Al\({}_{2}\)O\({}_{3}\) aqueous hybrid nanofluid was less than \(1.5\times 10^{-8}\) m\({}^{2}\)kw\({}^{-1}\) of graphene and water interfacial thermal resistance [54]. This is because GO had better hydrophilicity and better wettability at the solid-liquid interface, which led to a lower interfacial thermal resistance. On the other hand, the interaction between the GO nanosheet and Al\({}_{2}\)O\({}_{3}\) nanoparticles might change the properties of the interface and reduce the interfacial thermal resistance. Figure 19 compares the calculated results using the new model with the experimental data; the average error was 0.18%, and the maximum deviation was 3.8% at 0 \({}^{\circ}\)C. The main reason is that the model underestimates the influence of Brownian motion at a lower temperature, as many researchers [83] found the temperature dependence of this model (\(\propto\sqrt{T}\)) is still small and presents little agreement with several experimental studies, which means that the impact of Brownian motion is greater than that obtained in this work. Figure 19: Comparison of new hybrid model values and experimental data.
# Potential of Homomorphic Encryption for Cloud Computing Use Cases in Manufacturing
## 2 Fundamentals
### Cloud Computing  
The National Institute of Standards and Technology (NIST) defines cloud computing as \"[... ] _a model for enabling ubiquitous, convenient, on-demand network access to a shared pool of configurable computing resources [... ] that can be rapidly provisioned and released with minimal management effort or service provider interaction\"_[20]. According to the NIST, cloud computing, thus, refers to access at any time to a pool of computing resources that can be rapidly provisioned and released with minimal management effort or service provider interaction [20].  
Cloud computing can be categorized according to the cloud deployment model, of which NIST defines four: private cloud (1), public cloud (2), community cloud (3), and hybrid cloud (4). Deployment models basically differ regarding cloud ownership and cloud accessibility, which in turn influences the cloud's functionalities and the cloud cost [13; 20]. A private cloud (1) is owned by one organization. Access to the private cloud is restricted to this organization's employees. A private cloud allows the highest flexibility regarding cloud functionalities but is the most expensive deployment model. In contrast, a public cloud (2) is accessible by the broad public. It is owned and provided by a cloud provider. In general, it is the cheapest deployment option, but also limits the cloud's functionalities. A community cloud (3) is accessible for a defined group of organizations that share the same interests or goals. It is either owned by one of the community members or an external cloud provider. If two or more of the deployment models mentioned are combined, this is referred to as hybrid cloud (4) [3; 13; 20; 21]. For further analysis, the tradeoff among functionality, cost, and accessibility is important.  
Independent of the deployment model, three different states of data exist during cloud computing being relevant for encryption [13; 14; 15]:  
* _In-storage data_: data that are currently stored in the cloud,
* _In-transit data_: data that are currently sent from the cloud to the user's storage,* _In-use data_: data that are currently being used, e.g., for analyses. As not every encryption method is applicable for each state, this differentiation is important for encryption, as further described in Section 2.2.1.
# Intelligent Ship Scheduling and Path Planning Method for Maritime Emergency Rescue
## 2 Intelligent Ship Action Planning Method for Ocean Targets
### Evaluation Metrics  
In order to effectively evaluate the planning effect of the two stages of navigation planning and regional search, the evaluation indicators shown in Table 2 are used. The ship's voyage planning stage involves pre-planning the path from the large scale of space. Therefore, compared with other stages, only the path economic benefit and trip safety are considered, that is, the path length, the number of turns, and the average turning Angle. In the area search stage, it needs to search the area of interest in an all-round way, so it needs to consider the area search coverage rate and task completion rate and other indicators. The ship target observation stage needs to take into account the real-time planning of sensors and the detailed and comprehensive exploration of the area of interest, so it is also the stage with the most considerations in the whole navigation process, which needs to take into account both timeliness and task completion.
# Graph-Augmentation-Free Self-Supervised Learning for Social Recommendation
## 4 Experiments and Results
### Parameter Sensitivity Analysis  
#### 4.4.1 Influence of \(\lambda\) and \(\varepsilon\)  
We observed the effect on the recommendation performance when the combination of \(\lambda\) and \(\varepsilon\) was changed. When \(\lambda\) was [0, 0.01, 0.05, 0.1, 0.2, 0.5, 1], \(\varepsilon\) was [0, 0.01, 0.05, 0.1, 0.2, 0.5, 1], and the number of model layers was set to two.  
As shown in Figure 7, when fixing \(\varepsilon\) at 0.1, all parameters showed similar trend changes, and the best performance was achieved at specific \(\lambda\) values (Douban-Book dataset \(\lambda\) = 0.1, Yelp dataset \(\lambda\) = 0.05, Ciao dataset \(\lambda\) = 0.01). However, when \(\lambda\) was too large (\(\lambda\) = 0.5, 1), a significant performance drop could be seen.  
We fixed \(\lambda\) at the best values for the three datasets, as reported in Figure 8, and then adjusted \(\varepsilon\) to observe the performance change. When \(\varepsilon\) = 0.01, the best recommendation performance (Douban-Book dataset and Yelp dataset) was achieved. However, when \(\varepsilon\) was too large or too small, the recommendation was especially vulnerable to changes in \(\varepsilon\), so the performance declined faster. What can be seen from this is that GAFSRec was more sensitive to changes in \(\lambda\), and with \(\varepsilon\) = 0.01, the model could maintain stable performance.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Dataset** & \multicolumn{2}{c}{**Douban-Book**} & \multicolumn{2}{c}{**Yelp**} & \multicolumn{2}{c}{**Ciao**} \\ \hline
**Metric** & **Recall** & **NDCG** & **Recall** & **NDCG** & **Recall** & **NDCG** \\ \hline \multicolumn{7}{c}{**Top 20**} \\ \hline Without & 15.318\% & 13.371\% & 9.926\% & 6.175\% & 5.704\% & 4.265\% \\ \hline CL-c & 17.716\% & 16.716\% & 12.870\% & 8.152\% & 6.644\% & 4.942\% \\ \hline
**CL-ours** & **17.806\%** & **16.370\%** & **12.922\%** & **8.256\%** & **6.922\%** & **5.219\%** \\ \hline \multicolumn{7}{c}{**Top 40**} \\ \hline Without & 15.958\% & 11.379\% & 14.123\% & 7.147\% & 8.633\% & 5.402\% \\ \hline CL-c & 23.038\% & 18.055\% & 18.178\% & 9.815\% & 8.987\% & 5.737\% \\ \hline
**CL-ours** & **22.943\%** & **17.724\%** & **18.516\%** & **10.045\%** & **8.821\%** & **5.628\%** \\ \hline \hline \end{tabular}
\end{table}
Table 5: Performance comparison of different GCLSRec variants.