esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Parametric Analyses
### Influence of the Bolt Parameters  
Under the combined action of bending moment \(M\) = 40 MN\(\cdot\)m and residual jacking force, this paper evaluated the LEBS of the shield tunnel by changing the bolt parameters in turns, including the bolt numbers and bolt radius. The value ranges of the bolt parameters are based on the conventional tunnel, and the remaining parameters are shown in Table 1.  
Figure 8 shows that the LEBS and effective ratio of LEBS for the shield tunnel are constant as the residual jacking force is 0 MN. As shown in Figure 8, the LEBS increases linearly with the bolt radius and nonlinearly as an S-curve with the residual jacking force. The LEBS of the shield tunnel increases by approximately 0.81 \(\times\) 10\({}^{7}\) kN\(\cdot\)m\({}^{2}\) for every 2 mm increase in bolt radius, and the maximum increment of the LEBS reaches 144.3%. Similar results are also presented by [PERSON] et al. [22], who analyzed the LEBS by performing a model test.  
It can be seen from Figure 9 that the LEBS and effective ratio of LEBS for the shield tunnel are constant, which are linearly proportional to the number of bolts when the residual jacking force is 0 MN. As shown in Figure 9, the LEBS of the shield tunnel increases by approximately 1.3 \(\times\) 10\({}^{7}\) kN\(\cdot\)m\({}^{2}\) for every 4-bolt  
Figure 7: Effects of lining thickness on the LEBS.  
increment of the LEBS reaches 77.3%. The LEBS is increased by 10.2 times as the number of bolts is 25.  
Figures 8 and 9 show that the LEBS and effective ratio of LEBS are variable constants, which are directly proportional to the number of bolts, the radius of a bolt, and residual jacking force. Under the same conditions, the bolt radius has more influence on the LEBS of the shield tunnel than the bolt numbers. When tunnels are planned to pass through existing tunnels, it is suggested to increase the radius and number of bolts to improve the LEBS of the existing tunnel.  
The study indicates that the residual jacking force changes the contact area between tunnel segment lining, which increases linearly at first, and then nonlinearly, so the LEBS and the effective ratio of LEBS increase linearly and nonlinearly in the 5-curve. When the contact area between tunnel segment lining reaches 85% (i.e., the effective ratio of LEBS is more than 0.85), the parameters of ring width and lining thickness, numbers, and radius of bolts have little influence on the stiffness.
## Abbreviations  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} ADR & Adaptive Data Rate \\ AFA & Adaptive Frequency Agility \\ BLE & Bluetooth Low Energy \\ BW & Bandwidth \\ CF & Carrier Frequency \\ CCA & Clear Channel Assessment \\ COTS & Commercial-off-the-shelf \\ CR & Coding Rate \\ CRC & Cyclic Redundancy Check \\ CSS & Chirp Spread Spectrum \\ e2 eD & End-to-end delay \\ ED & End-device \\ ETSI & European Telecommunications Standards Institute \\ FLoRa & Framework for LoRa \\ INET & Internet Networking \\ IoT & Internet of Things \\ IT & Information Technology \\ LBT & Listen Before Talk \\ LoRa & Long Range \\ LoRaWAN & Long Range Wide Area Network \\ LPWAN & Low Power Wide Area Network \\ MAC & Medium Access Control \\ MATLAB & MATrix LABoratory \\ MDPI & Multidisciplinary Digital Publishing Institute \\ OMNeT++ & Objective Modular Network Testbed in C++ \\ MDelivR & Message Delivery Ratio \\ MDropR & Message Drop Ratio \\ MLossR & Message Loss Ratio \\ RFID & Radio-Frequency IDentification \\ SDN & Software-Defined Networking \\ SF & Spreading Factor \\ SIR & Signal-to-Interference Ratio \\ SWOT & Strengths, Weaknesses, Opportunities, and Threats \\ TDMA & Time Division Multiple Access \\ ToA & Time on Air \\ TP & Transmission Power \\ \end{tabular}
## 3 Literature Classification and Analysis
### Mathematical Models  
Let us rewrite Equation (2) to describe the dynamic model of an open-chain robot with \(N\) rigid links in slightly different form [5]:  
\[D(q)\ddot{q}+h\big{(}q,\dot{q}\big{)}=F, \tag{20}\]  
where \(D(q)=\big{[}d_{ij}\big{]}\in R^{NxN}\) is the positive-definite inertial matrix, \(q\) is the vector of generalized coordinates, \(h(q,\dot{q})\in R^{N}\) is the coupling vector containing centrifugal and Coriolis, gravitational and frictional effects, and \(F\) is the joint forces/torques actuating vector.  
Let \(d(q_{1},\ q_{2},\ \ldots,\ q_{N})\) denote a generic inertial coefficient, whose functional dependency on the joint coordinates may be expressed as  
\[d(q_{1},\ q_{2},\ \ldots,\ q_{N})=\sum_{i=1}^{M}k_{i}\alpha_{i}(q_{1},\ q_{2},\ \ldots,\ q_{N}), \tag{21}\]  
where each \(\alpha_{i}\) is written as a product of some sines and cosines (and their powers) of rotational joint coordinates and of some (normalized) translational joint coordinates. The constants \(k_{i}\) depend on the geometrical and mass properties of the links as well as the range of the translational joint coordinates. Without loss of generality, let us assume that the terms in (21) have been ordered so that \(|k_{1}|\leq|k_{2}|\leq\ldots\leq|k_{M}|=k_{max}\). It suffices to note that  
\[|\alpha_{i}(q_{1},\ q_{2},\ \ldots,\ q_{N})|\leq 1,\ \forall i. \tag{22}\]  
To simplify then let us rewrite (21) under consideration as  
\[d(q_{1},\ q_{2},\ \ldots,\ q_{N})=k_{max}\sum_{i=1}^{M}p_{i}\alpha_{i}(q_{1},\ q_ {2},\ \ldots,\ q_{N}), \tag{23}\]  
where \(p_{i}\equiv k_{i}/k_{max}s_{0}|p_{i}|\leq 1,\forall i\). Find the greatest integer value of \(L\leq M\) for which  
\[\sum_{i=1}^{L}|p_{i}|\leq\epsilon, \tag{24}\]  
where \(\epsilon\ (0<\epsilon<1)\) is tolerance. Then the simplified coefficient \(\hat{d}\) is evaluated as  
\[\hat{d}(q_{1},\ q_{2},\ \ldots,\ q_{N})=k_{max}\sum_{i=1}^{M}\hat{p}_{i}\alpha_{ i}(q_{1},\ q_{2},\ \ldots,\ q_{N}), \tag{25}\]  
where  
\[\hat{p}_{i}=\left\{\begin{array}{ll}p_{i}\ \ if\ i>L\\ 0\ \ \ if\ i\leq L.\end{array}\right. \tag{26}\]  
The final simplified coefficient \(d\) is verified by the numerical significance analysis of the dynamic coefficients. Under this procedure, numerically significant terms are retained while the computational complexity is reduced and the way towards real-time implementations is opened [5].
## 3 Materials and Methods
### Scope of the Research  
The research was conducted systematically since 2018. During the period, 5 imitated phishing attacks were executed (see Figure 2). Each email, used in the phishing attack imitation, was created taking into account some related institutions, but using publicly available data (upcoming organization event, name of the organization managing person, title of the organization department, etc.). Multiple versions of emails were created by the experiment authors trying to keep similar organizational specific data and the purpose of the email in order to eliminate the effect of the email type on employees' behavior. The email generation approach was oriented toward an invitation to register for a free present, refund claim for data breach accident and requirement to change a password--calling for actions which are targeted at common human vulnerabilities and not specifically oriented towards a specific target group.  
A different date was selected for each of the simulated spear-phishing attacks. This was to reduce the employees' ability to determine whether it was a real or imitated attack. However, all the employees were informed about planned security training initiatives at least one week before the mock attacks. This was necessary to comply with the legal requirements. A one-week period was selected to assure that the employees would forget about the announcement in the internal system and would not associate the attack with more general reminders of planned security-related training initiatives.  
The attacks were conducted on Lithuanian education institutions and employees only (experiments on how students respond to phishing attacks are not included in the research). Three attacks were carried out on Vilnius Gediminas Technical University (technical university) [40]. This was conducted repeatedly, and between attacks, dedicated training sessions were arranged [8]. One imitated attack was executed on another higher education institution (arts university). This organization has a different profile--more focused on arts than technologies. The last attack targeted all Lithuanian secondary schools, but only the main staff, not all the employees.  
The arts university and secondary school had no systematic training, while the technical university practiced phishing management using experiments and training sessions. Based on these three examples of different types of education institutions in Lithuania, it should be possible to identify the differences between them as well as the impact of phishing training on them. Based on the systemic literature analysis of [PERSON] et al. [23], our research falls into a large-group-sample-size simulated phishing experiment. It sent 6054 emails during the whole period to all the institutions. In this kind of experiment, the medium size of emails is 273, which is smaller than any of our individual experiments.
# Structure and Principles of Operation of a Quaternion VLSI Multiplier
## 1 Introduction  
The development of the theory and practice of data processing as well as the expansion and complexity of the range of problems being solved stimulate the use of increasingly advanced numerical systems. If complex-valued data processing was considered something exotic until quite recently, then there was no talk of the practical application of hypercomplex-valued data processing. Quaternions are being used in a wide range of scientific and engineering fields, such as deep neural networks, computer graphics and machine vision, robotics, navigation, data encoding, image and signal processing, multiresolution analysis, and graph theory (see, for example, [1; 2; 3; 4; 5; 6; 7; 8; 9; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21]). It should be noted, however, that hypercomplex-valued arithmetic operations ultimately still come down to performing elementary operations on real numbers.  
In the case of real-valued data processing, multiplication has also always been the most time-consuming operation. To reduce the computation time, developers sought to elaborate algorithms with a minimum number of multiplications. Another solution to the problem of accelerating calculations was the introduction of hardware multipliers into the data processing system. This is how hardware multipliers appeared: first as separate chips, and then built into the chip. Since even the common multiplication of two complex numbers necessitates four multiplications and two additions of real numbers, it is the most time-consuming operation for processing complex-valued data. In the case of hypercomplex numerical systems, the problem of multiplication implementation time is even more acute. This is because 16 multiplications and 12 additions of real numbers are needed to complete a single multiplication of two quaternions. A lot has been written about hardware real-valued multipliers--it has already become a classic [22; 23; 24]. A lot has also been written about hardware complex number multipliers [25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35; 36; 37]. But only a few works have been written about hardware multipliers of hypercomplex numbers, particularlyquaternions [38; 39]. In addition, as an analysis of known solutions has shown, they do not use all the possibilities of computer optimization. We want to fill this gap.
# Coverage Area Decision Model by Using Unmanned Aerial Vehicles Base Stations for Ad Hoc Networks
## 3 Proposed UAVs-BS Network Coverage Model
### Proposed Coverage Area Decision Model  
The coverage ranges of the UAVs-BS for the CUs are based on data demand in the network. The proposed model uses the entropy method for optimization to measure the field distribution. The proposed model calculates the CA centre point by calculating the threshold of the potential value and distance. The threshold value is adopted from the [42]. According to this study, the area with a certain radius on a CA is considered an average CA. The proposed algorithm spots the CA of any size, achieves the maximum CA, and serves in a cellular network. After initializing the input, the next step is broadcasting the messages to call the drones, which have a central point CA. If drones get the minimum distance from the demand point, then they obtain the threshold value and end the session. The centre point determines and waits for a random time and repeats the process. All these steps in the CA decision process are shown in Algorithm 1.  
```
1.Initializing
2.Input {UAVs-BS, BS (a,b,c,d,e...,n), IDs & Location Information
3.Output: Success/all Coverage
4.do Broadcast messages to call all the drone
5. obtain the CA center point
6.//min distance for demand point then
7. Obtain threshold value else
8.endif
9. Determine the CA centre point
10. Select CA else
11. Wait for a random time and repeat the process
12. end if
13.end process
```  
**Algorithm 1**CA decision process
## 3 Methodology
### Proposed Improved Genetic Algorithm (SDSGA)  
In implementing the GA, it was discovered that, if the population is too low, there appears to be little to no diversity in the population after a few generations. Although increasing the mutation probability can inject diversity, the GA tends to diverge and is unable to exploit a promising region of the search space more effectively.  
The strategy employed in this study is to enhance the highly exploitative property of GA by modifying the selection algorithm in such a way that the entire solution/chromosome set, including the fitness values, is considered rather than only the fitness value being used as a selection criterion as was previously done. On the other hand, we note that there have been several proposals in the literature for improving GA by specifically modifying the selection algorithms used for selecting parents who will participate in crossover operation [33; 34]. Our proposal is different since it only involves a simple selection of individuals sorted in a decreasing order of fitness values making sure that all selected individuals are unique. Although this can help in highly exploitative search, it reduces diversity in the population, which makes the algorithm unable to search globally in the search space.  
This challenge was addressed by increasing the mutation probability. It may be counter intuitive to increase the mutation probability as it causes divergence in the algorithm. However, since the system is already highly exploitative, this shows that the global search and the local search can be balanced using this method. The proposed algorithm for the modified GA selection algorithm is shown in Algorithm 1, whereas Figure 1 presents the flowchart for the proposed SDSGA. The performance of the proposed SDSGA was tested and evaluated by comparing with other well-known and popular metaheuristic optimization algorithms, particularly the GA, PSO and BBO algorithms, which will be described in Section 4. They were all tested for both benchmark function optimization and the tuning of the HPs of the ML models, specifically the CNN and RF algorithms. For tuning the hyperparameters, Bayesian Optimization, as described in [11], was used as a basis for comparison.  
```
0: population
0: parent array
1: Sort the population in decreasing order
2: Create array of zeros to contain parents
3: Set added, count to 0
4:for each individual in population do
5:if individual does not exist in parent array then
6: Add copy of individual to parent array
7: Increment added
8:endif
9:if added is equal to parent size then
10: Break
11:endif
12:endfor
```  
**Algorithm 1** Pseudocode of the selection algorithm.
## 4 Experiments
### Case Study  
In order to better understand our model, we compared it with the AEN and ASGCN models in several test examples. The experimental results are shown in Table 4. The attention visualization column in the table shows the attention scores of each model, and we use darker to lighter colors according to their scores. In the first example, \"delicious food but terrible environment\", because the sentence contains two entities \"food\" and \"environment\" and two opinion words \"delicious\" and \"terrible\". The AEN model based on the attention mechanism cannot capture the connection between the two very well, leading to incorrect prediction results. Due to the complexity of the sentence, in the second example, the AEN model is still unable to correctly model the connection between aspect words and emotional words, and the attention mechanism focuses on the wrong point. In the ASGCN model, since \"lovely\" is also close to \"son\" in the sentence structure, it is also updated to the sentence representation when the node information is updated, which causes the model to also assign a high weight to \"lovely\" when calculating the attention score. When the dependency tree contains noisy information or syntactic information is not obvious, the ASGCN model cannot correctly model the relationship between the aspect words and the opinion words, which leads to prediction errors.  
Our model correctly predicted the sentiment polarity of the two samples, which means that our model can make better use of the semantic and syntactic information of the sentence when processing complex sentences, thereby improving the model's performance to a certain extent, and can maintain good stability on different datasets.
## 3 Results and Discussion
### Proof of Concept: Quantifying Real Noise
#### 3.3.1 Cancer Methylation Signatures  
The first dataset consists of methylation profiles of 242 different cancerous and non-cancerous samples [7] (Figure 3). The classifications look very sparse and the groups are torn apart in many subgroups distributed along with the data's VP. We detected 25.1% of noise and a _PQA_ score of 0.53 (Figure 4, with a Z-score of 8.2 and a _p_-value of 9.6 \(\times\) 10\({}^{-17}\)), both numbers imply that even though there may be disordered in the VP, there is not a very high noise proportion nor a high _PQA_ score. These results suggest that, like any other statistical test, the longer the number of items in the partition the more diluted is the effect of disorder in the VP, and the results also lead to a greater statistical significance as shown in the analysis of the number of items and classifications. Moreover, the authors concluded that their clustering analysis results made sense from their molecular and biological background, as well as the perspectives about the analyzed profiles; they only assessed grouping just by visual inspection and concluded the grouping was done well. However, understanding the noise in the cluster can help to pursue better markers since it could help to narrow the search space in these kinds of studies.  
Figure 2: Z-scores of the _PQA_ scores from partitions varying in the number of classifications and the length of the partition.
# Automated Method of Road Extraction from Aerial Images Using a Deep Convolutional Neural Network
## 2 Methodology
### Dataset  
The dataset used in this work is the high-resolution images set from [PERSON] et al. [36], who collected the data from Google Earth [37]. [PERSON] et al. manually labeled the ground truth for each image. The images have a resolution of 1.2 m per pixel, and the dataset consists of 224 differently sized images. The smallest image size is \(600\times 600\) pixels, and the road width is approximately 12-15 pixels. The images have substantial occlusion from buildings, trees, and cars, which makes the task of extracting road networks from these images difficult.  
Before feeding this dataset to the proposed model, it is randomly divided into two sets with 80% of the images assigned to the training set and 20% to the test set.  
Since the dataset consists of different image sizes and since 224 images are not enough to train a deep neural network, the images and ground truths that are used in the training and test sets were cropped to \(224\times 224\) pixels to correspond to the input images' size; this cropping was done by using a sliding window with a stride of 64 pixels [35; 38]. The cropping technique alone increases the number of images in the dataset to 8064 images, where 6480 images are used for the training set and 1584 images were used for the test set. All the cropped images were preprocessed using the on-the-fly data augmentation technique in the Keras framework [39], which helps to increase the number of images within each epoch in the training process. The number of images reached 1,146,938 for the 88 th epoch. The standard Keras data augmentations include random rotations, random width and height shifts, shearing, zooming, and horizontal and vertical flips. Data augmentation is the most powerful technique to prevent the overfitting problem.
# Depth-Adaptive Deep Neural Network Based on Learning Layer Relevance Weights
## 1 Introduction  
Machine learning techniques have been deployed in a variety of areas, such as computer vision [1; 2], pattern recognition [3], natural language processing [4], and speech recognition [5; 6; 7]. These techniques have granted computers the ability to learn automatically predictive and descriptive models by exploiting the available data, with no need for explicit programming. Recently, deep learning-based solutions [4; 5; 6; 7; 8] have emerged as promising alternatives and demonstrated their effectiveness in several fields [9; 10]. In particular, Convolutional Neural Networks (CNNs) [9] have been extensively applied to address image classification [10], pattern recognition [11], image segmentation [12], and image captioning [13] problems. Similarly, Recurrent Neural networks [9] have been exploited to tackle video classification [14], human activity recognition [15], speech enhancement [16], and audio tagging [17] challenges. Moreover, in the context of Hyper-Spectral Image (HSI) analysis, CNNs are adopted for graph-structured data [18; 19]. The latter employ semi-supervised information to guide the learning process. Furthermore, autoencoders are exploited for HSI clustering techniques, where special graph convolutional embedding is incorporated to preserve the adaptive layer-wise feature locality [20; 21].  
The effectiveness of deep learning paradigms has been attributed to their learning ability, achieved through non-linear processing of massive input data and multiple network hidden layers in order to extract relevant features and learn their appropriate representations.  
The design of a Deep Neural Network (DNN) with an acceptable level of complexity remains challenging [22] and requires intensive experimentation and domain knowledge [2; 5]. Particularly, two key hyper-parameters control the architecture of DNNsand affect their performance [23]: the depth and the width of the network. Specifically, the network depth represents the number of layers in the network. On the other hand, the network width corresponds to the number of processing units (nodes) in a given layer. Since these two parameters can span over a large range of values, the scaling of DNN models becomes arduous and even more acute.