esa-sceva/satcom-chunk-collection huggingface dataset:
# A Survey on Intrusion Detection Systems for Fog and Cloud Computing
## 1 Introduction
### Contributions and Goals  
This paper provides a deep survey on fog and cloud computing and intrusion detection and prevention systems. As the paper aims to enhance the current cybersecurity strategies deployed in businesses, we focus on the organizational adoption of such solutions and present selected examples from health and education sectors in different countries: China, Poland, and the United Kingdom.  
In detail, the main objective of this survey paper is to provide an in-depth summary and survey of intrusion detection and prevention and cloud computing technology that can be adopted in the development of cloud security initiatives and productive intrusion detection and prevention solutions. The review of past research enables the investigation of several comparative facets of security policies involved in the cloud computing environment. It has also been a requirement for cloud providers concerning data security and, therefore, it aids in contributing to a scientific group of cloud computing security and development of cloud security policy.  
The paper also sets out to achieve the following goals:  
1. Proposing a methodology to identify new rules that should be operated in addition to cloud security policies and can be employed to evaluate the threats against cloud-based environments;
2. Reviewing detailed studies addressing fog computing and intrusion detection security issues;Reviewing the available threats which can affect fog computing environments and intrusion detection technology, with a specific focus on the ones that cannot be employed in conventional systems;
* Identifying security regulations and policies, particularly the most preferred recommendations for Software-as-a-Service security guidelines.
## 4 Proposed SFO Estimation Scheme  
In this section, we develop an effective frequency-offset estimation scheme that does not rely on any information about the channel and training symbols in the HomePlug GP system. By using a repeat coding specified in the HomePlug GP standard, the symbol at the \(k\)-th subcarrier through the ROBO interleaver is duplicated to the subcarrier spaced by \(F_{m}\), where \(F_{m}\) is the distance between two data subcarriers copied by cyclic shift at the \(m\)-th subset. To accommodate to tone mask requirements, the proposed estimation scheme utilizes the concept of carrier grouping in the computation of frequential correlation. In the proposed approach, \(N_{c}\) non-masked subcarriers are grouped into a number of subsets, so that two repeated replicas have an unique distance \(F_{m}\). Therefore, the \(m\)-th subset contains the elements of twin data that are separated by \(F_{m}\) subcarriers.
## 5 Results and Discussion
### Discussion on PSO Results
Benchmark problems may be embeded to demonstrate the performance of the low discrepancies sequence approaches, at various complex levels. Table 6 contains the experimental simulation results on benchmark functions. The exhaustive statistical results are explained in Table 7. From Figures 23-38, the experimental results of constrained benchmark test functions are only exhibited by having the surface with \(D\) = 10, 20, and 30. The experimental results of this work may not contemplate the entire competency of the new proposed low discrepancies sequence in accordance with all the possible conditions. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Functions** & **DIM \(\times\) Iter** & **DE** & **DE\(-\)H** & **DE\(-\)S** & **DE\(-\)TO** & **DE\(-\)WE** & **DE\(-\)KN** \\ \hline \multirow{4}{*}{F1} & 10 \(\times\) 1000 & 1.1464 \(\times\) 10\({}^{-44}\) & 2.1338 \(\times\) 10\({}^{-44}\) & 5.8561 \(\times\) 10\({}^{-44}\) & 7.4117 \(\times\) 10\({}^{-45}\) & 7.4827 \(\times\) 10\({}^{-39}\) & 5.7658 \(\times\) 10\({}^{-39}\) \\  & 20 \(\times\) 2000 & 3.3550 \(\times\) 10\({}^{-46}\) & 7.2338 \(\times\) 10\({}^{-46}\) & 1.3545 \(\times\) 10\({}^{-45}\) & 1.2426 \(\times\) 10\({}^{-45}\) & 9.6318 \(\times\) 10\({}^{-45}\) & 7.1501 \(\times\) 10\({}^{-45}\) \\  & 30 \(\times\) 3000 & 8.8946 \(\times\) 10\({}^{-47}\) & 1.2273 \(\times\) 10\({}^{-45}\) & 9.4228 \(\times\) 10\({}^{-46}\) & 1.6213 \(\times\) 10\({}^{-46}\) & 6.2007 \(\times\) 10\({}^{-46}\) & 5.7425 \  
The core objective of this section is to review the consequences of tested optimization approaches in high dimensionality, regarding the accuracy and reliability of achieved solutions at the time of solving complex and computationally-expensive optimization problems. From Figures 23-38, the performance of following methods: DE-KN, DE-WE, DE-TO, DE-S, DE-H, and traditional DE is compared for sixteen benchmark functions. In the graphs, the horizontal axis displays the total number of iterations, while on the other hand, the vertical axis displays the mean value of objective functions at the fixed number of function computations. Correspondingly, the value achieved in each iteration operates as a performance measure. As a result, the exploitation ability of traditional DE is moderately low, particularly for high-dimensional problems. The results are also disclosed that traditional DE, DE-S, DE-H are only effective in performance, while they tackle with expensive design problems having low dimensionality. algorithm and other algorithms in DE-KN, DE-WE, DE-S, DE-H and standard DE are given in the Table 7.
## 3 Research Model and Hypotheses
### External Variables of TAM
#### 3.2.2 Risk Perception: Perceived Vulnerability and Severity  
Perceived vulnerability and perceived severity are each defined as an individual's perceived probability of a threatening event happening to them and the severity of the damage the event will cause to their life or health, respectively. In this study, these two constructs are defined as the probability of an accident occurring to the construction worker at their workplace, and the damage the accident would do to their health.  
Perceived vulnerability and perceived severity are often used in research to study an individual's protective behaviour toward a certain threat. They are also often integrated with TAM as external variables affecting an individual's perceived usefulness toward a health-related technology. For example, [PERSON] et al. [39] found that perceived health risk, which is a multi-dimensional construct composed of perceived severity and perceived susceptibility, has a positive effect on perceived usefulness in the context of health-related internet use. Furthermore, [PERSON] et al. [40] found that perceived risk significantly affected perceived usefulness when studying consumers' intention to use social media for transactions. [PERSON] et al. [PERSON] et al. [PERSON] et al. (2018) also confirmed the effect of perceived health threat on perceived usefulness in a study identifying the factors affecting the acceptance of M-health technologies.  
Because construction workers are especially vulnerable to life-threatening accidents in the workplace, the level of risk perceived by the worker could have a significant effect on their perceived usefulness of VR safety training. If they consider themselves to be at risk of accidents, there is a higher chance they will think that the training content would be useful in keeping them safe. Therefore, the following hypotheses are presented:  
**H6.**_Perceived vulnerability has a positive effect on perceived usefulness._  
**H7.**_Perceived severity has a positive effect on perceived usefulness._
## 2 Implementation Devices
### AR Implementation Device  
AR technology mainly utilizes wearable head-mounted display (HMD) devices and mobile devices. Representative examples of wearable HMD devices include Microsoft's HoloLens, Google's AR Glasses, and Magic Leap [32; 33]. These devices provide users with a realistic experience by offering a perspective similar to that of actual glasses. However, the display brightness is low, the field of view is limited, and there are issues with the accuracy and speed of hand-tracking functionality. Additionally, the high cost of each device presents challenges for widespread adoption. On the other hand, MAR does not require separate HMD devices but is implemented through individual mobile devices. This eliminates constraints on the field of view and provides users with portability and accessibility. Furthermore, it is relatively affordable and easily accessible. In terms of technological trends, it has been observed that companies like Google and Apple have recently discontinued their development of AR HMD devices. This indicates the challenges in commercialization due to the inconvenience and low utility of HMD-based AR technology in real-life scenarios. Moreover, there are technical limitations concerning the field of view. Table 1 provides a comparison between HMD devices and mobile devices.  
AR technology applied to railway vehicle maintenance requires extensive development of maintenance manuals, involving long-term time, high costs, and constrained environments. Ensuring safety amidst limited visibility and a surge in inexperienced personnel necessitates advanced technical support. Therefore, this study focuses on designing AR content based on MAR, universally deployed on cost-effective, portable smartphones and tablets [34; 35]. This approach ensures a broad field of view, stability, and real-time high-level technical support.
# A Survey of Planning and Learning in Games
## 4 Planning in Games
### Planning Techniques
#### 4.1.2 Hierarchical Task Networks  
Hierarchical task network (HTN) planning decomposes a planning problem into a set of tasks (e.g., activities or processes) [154]. These tasks can be either primitive, in which case they correspond to an action that may be performed by the planning operators, or non-primitive, in which case they must be decomposed into a set of smaller subtasks, using methods. During the planning procedure, all non-primitive tasks are decomposed recursively into increasingly smaller subtasks until primitive tasks are reached.  
More formally, a task network is defined as a pair \(w=(U,C)\), where \(U\) is a set of task nodes and \(C\) is a set of constraints. Each constraint in \(C\) specifies a requirement that must be satisfied by every plan that is a solution to the planning problem. In this context, a task is defined as \(t(r_{1},\ldots\ r_{k})\), where \(t\) is a task symbol and \(r_{1}\),..., \(r_{k}\) are terms. The task is said to be primitive if \(t\) is an operator symbol and non-primitive otherwise. A method is defined as a 4-tuple \(m=(name(m),\ task(m),\ subtasks(m),\ constr(m))\), where \(name(m)\) is an expression of the form \(n(x_{1},\ldots,x_{k})\), \(task(m)\) is a non-primitive task and the 2-tuple (\(\ subtasks(m),\ constr(m)\)) is a task network. In the expression \(n(x_{1},\ldots,x_{k})\), \(n\) is a unique method symbol and \(x_{1}\),..., \(x_{k}\) are all the variable symbols that occur anywhere in \(m\). A graphical depiction of an HTN method is presented in Figure 6.  
HTNs have found diverse applications in game AI. One such example concerns the problem of deriving agents that act in a more natural way. Examples of this include creating believable NPCs in serious games [7], generating characters with dynamic behavior in real-time video games [155],  
Figure 6: Depiction of the decomposition of task _kill-enemy_ (i.e., kill the enemy \(e_{1}\) using gun \(g_{1}\)) into the subtasks \(aim\)-\(gun(g_{1},\ e_{1})\) and \(shoot\)-\(gun(g_{1})\) via the hierarchical task network (HTN) method _use-gun_. In this case the agent must first aim the gun before shooting it at the enemy. The order of precedence is depicted by the rightward-pointing arrow. This decomposition forms an AND branch of the decomposition tree. A decomposition tree is a tree structure that represents derivations of plans and results from the combination of these AND-branches.  
and deriving agents that can act has companions of the human player in Minecraft [156]. Other application uses include tasks as diverse as strategic coordination of teams of agents [157; 158], endowing agents with planning capabilities in fighting games [159], computing plans offline that can be used as game scripts [160], interactive storytelling using a model of personality traits [161], and macromanagement in RTS games [162]. HTN planners have also been used in commercial games such as Killzone 3 and Transformers 3: Fall of Cybertron [155].
## 2 Ice Load Prediction Model
### Ice-Hull Contact Mechanism and Determination of Impact Load
#### 2.1.3 Circumferential Ice Breaking  
Circumferential cracks are assumed to form after any initial defect is created. The ice sheet starts to break up due to bending failure occurring at the maximum stress, \(\sigma_{\text{max}}\). In the ice bending breaking model, with the failure criterion, \(\sigma_{\text{xx\_max}}\geq\sigma_{t}\), the position of the maximum stress can be located and the maximum ice sheet breaking force, \(P_{\text{max}}\), can be found. The ice breaking length, \(l_{b}\), as shown in  
Figure 2: Illustration of the ice breaking model between a ship bow and level ice.  
Figure 2, occurs at a point where the maximum stress just exceeds the ice flexural strength. Here the computed value of ice breaking length from ice bending breaking model is denoted as \(l_{b1}\),  
\[l_{b1}\ =\ x\ at\ \sigma_{\text{max}}\ =\ \sigma_{t}, \tag{2}\]  
There are other empirical ways to calculate the ice breaking length. Usually it is proportional to the ice characteristic length, \(l_{c}\). [PERSON] et al. [6] assumed that the breaking length \(l_{b}\) is one-third of \(l_{c}\). This assumption originates from the ice resistance prediction method proposed by [PERSON] [1]. The empirical ice breaking length value is denoted as \(l_{b2}\).  
\[l_{b2}\ =\ \frac{1}{3}l_{c}, \tag{3}\]  
\[l_{c}\ =\ \left(\frac{E{h_{i}}^{3}}{12(1-\
u^{2})\rho_{w}g}\right), \tag{4}\]  
where \(\rho_{w}\) is the water density, \(E\) is the ice Young's modulus, \(h_{i}\) is ice thickness and \(\
u\) is the ice Poisson's ratio.
# Transcriptomic Analysis of the Candidate Genes Related to Aroma Formation in _Osmanthus fragrans_
## 2 Results
### Analysis of DEGs  
During the flowering process, the petals of \(O\). _fragrans_ release many aromatic compounds. To characterize gene expressions at the S1, S2, and S3 flowering stages, a global expression analysis was performed for each stage using three biological replicates. The DEG libraries were constructed and the unigenes were then mapped to the \(O\). _fragrans_ reference transcriptome after sequencing by the Illumina platform. The expression of each gene was calculated in RPKM (Reads per kilobase of exon model per million mapped reads). We stratified the relationship of the samples based on the expression level of the whole gene. A dendrogram was plotted and all biological replicates were clustered together (Figure 3). In addition, the results of two parallel experiments to evaluate the reliability and operational stability of the experiments (Figure S5) showed that the correlation index between the two experiments for the same sample was close to 1, indicating a high reproducibility. These revealed that all biological replicas were well correlated and this was similar to S2 and S3.  
For a better understanding of the gene expression profiles, Short Time-series Expression Miner (STEM) software was used to cluster gene expression patterns. Eight gene clusters with distinct expression patterns were identified (Figure 4). The trend blocks with color indicate significant enrichment trends; clusters7 and 8, containing 5976 and 4821 transcripts, respectively, showed opposite patterns (Figure 4). Cluster 7 showed a gradual increase from S1 to S3, while cluster 8 showed a gradual decrease, indicating that some genes had opposite roles during the flowering process. Cluster 4 (6128 genes) remained constant, then gradually increased, but cluster 6 gradually increased first and then stayed constant. The gene expression profiles revealed that the clusters were involved in biological events during the flowering process.  
To further investigate the DEGs among the groups, transcriptomes generated from different stages were compared. The DEGs were filtered by using a false discovery rate (FDR) \(<\) 0.05 and \(|\log_{2}\)FC\(|>\) 1. Comparing S1 with S2 resulted in 16,087 DEGs: 5455 were up-regulated and 10,632 were down-regulated. When comparing S1 with S3, there were 29,631 DEGs: 16,386 were up-regulated and 13,245 were down-regulated. The comparison of S2 with S3 showed 16,072 DEGs: 10,404 were up-regulated and 5668 were down-regulated (Figure 5).  
Figure 3: Global analysis of the transcriptome datasets of biological replicates and samples is shown. The dendrogram depicts the global relationships of the samples. The bar plot describes the number of expressed transcripts after filtering.
# LEO Precise Orbit Determination with Inter-satellite Links
## 3 Simulation and Method
### ISL Observation Simulation  
Since there is no actual measured data of the LEO ISLs, we simulate the ISL observations of three constellations mentioned above. Simulation of ISL observations usually adopts the approach that adding Gaussian noises to the actual calculated inter-satellite ranges [25; 40; 41], which can be formulated as follows:  
\[S_{ij}=\sqrt{\left(x_{j}-x_{i}\right)^{2}+\left(y_{j}-y_{i}\right)^{2}+\left( z_{j}-z_{i}\right)^{2}} \tag{9}\]  
\[{S_{ij}}^{\prime}=S_{ij}+\sigma_{ij} \tag{10}\]  
where \(S_{ij}\) is the real inter-satellite range between satellite \(i\) and satellite \(j\) which is calculated by the simulated 3D coordinates (\(x_{i}\), \(y_{i}\), \(z_{i}\), \(x_{j}\), \(y_{j}\), \(z_{j}\)) of each satellite in the Earth-centered inertial (ECI) frame; \(S_{ij}\)' denotes the simulated ISL measurements between two satellites computed by the sum of \(S_{ij}\) and Gaussian noise \(\sigma_{ij}\). In this study, the coordinates \((x,\ y,\ z)\) of the polar-orbiting satellites in ECI frame are calculated as follow:  
\[x=Rcosfcos\Omega\ y=Rcosfsin\Omega\ z=Rsinf \tag{11}\]  
where \(R\) denotes the spatial distance from the satellite to the center of the Earth; \(f\) denotes the true anomaly and \(\Omega\) denotes the right ascension of the ascending node (RAAN) of the satellite. \(R\), \(f\), and \(\Omega\) can be obtained according to the configuration of the designed constellation.  
In accordance with the studies of predecessors, the measurement noise of BDS-3 ISL is confirmed to be less than 10 cm [29]. Because of the more complex space environment in which the LEO constellation operates, we mainly adopt numerically larger 20 cm-level noise scheme in this study.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Orbit** & **LEO** & **LEO** & **LEO** \\ \hline Satellite number & 60 & 120 & 192 \\ Constellation & 10 planes & 10 planes & 12 planes \\ Orbit type & Polar & Polar & Polar \\ Inclination [deg] & 90 & 90 & 90 \\ Altitude [km] & 1000 & 1000 & 1000 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Detailed orbital characteristics of the designed low Earth orbit (LEO) constellations.  
For comparison, 10 and 40 cm-level noise scheme are also used to verify the impact of link accuracy on orbit determination.
# Solder Joint Defect Detection in the Connectors Using Improved Faster-RCNN Algorithm
## 5 Conclusions  
This paper has captured images of connectors containing the five solder joint types, achieved data expansion and trained, verified, and tested them with data augmentation. After training, the algorithm proposed in this paper has been verified by comparison experiments to be more accurate in the detection of solder joint defects. The main contributions of this paper are as follows: First, there are few studies on solder joint quality inspection at present, and most of them are detected by workers' eyes during welding. The method proposed in this paper has obtained high detection accuracy, which provides a new way of thinking for solder joint quality detection of connectors. Secondly, a data set containing five solder joint types is produced to train the solder joint quality inspection network. At the same time, the data set will be filled in the following research process to enable the network to detect more connector solder joint defect types; Thirdly, the network model performance and the final detection accuracy have been significantly improved after optimization of the algorithm proposed in this paper. Therefore, the optimization method can also provide a new optimization strategy for solving similar target detection problems.  
Conceptualization, H.S.; methodology, K.Z.; software, K.Z.; validation, K.Z. and H.S.; formal analysis, K.Z.; investigation, K.Z.; data curation, H.S.; writing--original draft preparation, K.Z.; writing--review and editing, K.Z.; project administration, H.S.; All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
We declare that we have no conflict of interest.
## 2 Effectiveness of Eddy Current Damping in Seawater  
### Eddy Current Damping  
Eddy current damping is generated by magnetic inductions. When a conductive plate moves through a stationary magnetic field, or vice versa, eddy currents will appear in the conductive plate to hinder the movement of the conductive plate [24; 25; 26], as shown in Figure 1. The strength of the damping depends upon the strength of the magnetic field, the surface area, and conductivity of the conductive plate, and the rate of change of the magnetic flux [29].
# Comprehensive Study of IoT Vulnerabilities and Countermeasures
## 2 Preliminary Materials
### Commonly Used Protocols
#### 2.7.2 Dash7  
Dash7 (D7 AP) is an open source subGHz wireless sensor and actuator network protocol (WSAN) that complies with the ISO/IEC 18000-7 standard. It has a medium range of up to 2 Km, low power consumption (multi-year battery life), low latency, and is encrypted using AES with a 128-bit key. D7 AP operates in unlicensed ISM bands of 433.92, 868, and 915 MHz [19; 20]. A few use cases of D7 AP can be found in agriculture IoT, IoMT, and smart cities [21].
On the other hand, fuzzy logic [18], general regression neural network (GRNN) [19], neural-fuzzy (NF) [20], random forest [21], support vector machine (SVM) [22], principle component analysis (PCA), and analytical hierarchy process (AHP) [23], are among the available AI models that have been studied in previous works of HI. These models require extensive data to ensure a promising result in terms of prediction accuracy of the condition of the transformers. The main motivation of this study is to introduce a simplified method in order to predict the HI of transformer population that is based on Statistical Distribution Model (SDM) utilizing the individual condition parameter data as a key approach to determine the HI. SDM is chosen, due to its simplicity and adaptability to analyze any sample size data [24]. In addition, it can also identify the independent variables (13 condition parameters) that can affect the predicted HI. Hence, further investigation can be performed on the abnormal trend of the individual condition parameters data to improve the interpretation of overall transformer's HI. First, the representative distribution model is identified for the individual condition parameters data of transformers. Next, the SDM is implemented to the condition parameters data to determine the predicted HI of transformer population. The final part is on hypothesis testing through the Chi-square statistic to determine the best-of-fit and absolute error percentage between the predicted and computed HI.