esa-sceva/satcom-chunk-collection huggingface dataset:
# A Quantitative Study of Axial Performance of Rockbolts with an Elastic-Debonding Model
## 4 Parameter Study and Discussion
### Slippage at the Ultimate Load  
To analyse the impact of the slippage at the ultimate load on the axial performance of rockbolts, this article used the following values: 1 mm, 1.5 mm, 2 mm, 2.5 mm, and 3 mm. When the slippage at the ultimate load varied, the tensile performance of rockbolts is shown in Figure 13. Apparently, the slippage at the ultimate load had a more apparent impact on the axial performance of rockbolts in the elastic phase. When the slippage at the ultimate load was 0.5 mm, the peak force of rockbolts was 64 kN. When the slippage at the ultimate load increased to 2.5 mm, the peak force of rockbolts increased to 129 kN, increasing by 101.6%. As for the peak force, it also increased. However, the increasing trend of the peak force was not significant. In this scenario, when the slippage at the ultimate load increased from 0.5 mm to 2.5 mm, the peak force of rockbolts increased from 210 kN to 219 kN, increasing by only 4.3%.  
Figure 12: Axial performance of rockbolts when the b/g face strength varied: (**a**) force-deformation curves; (**b**) peak force variation trend.  
Figure 13: Axial performance of rockbolts when the slippage at the ultimate load varied: (**a**) force-deformation curves; (**b**) peak force variation trend.  
There was also a strong linear relationship between the peak force of rockbolts and the slippage at the ultimate load. The corresponding equation is shown below. The linearly dependent coefficient was 0.9995. This confirmed that the obtained equation was sufficient in calculating the peak force of rockbolts.  
\[F_{bp}=4.4s+208 \tag{13}\]  
[PERSON] et al. [28] experimentally studied the relationship between the grout shear strength and the b/g face strength. They found that with increasing the grout shear strength, the b/g face strength increased non-linearly. However, how the b/g face strength affected the axial performance of rockbolts was not studied. This article continued on this topic and analysed the impact of the b/g face strength on the axial performance of rockbolts. Therefore, this article remedied a previous experiment study.
## 4 Results and Discussion
### RQ7: What Types of Web Attacks Do the Proposed Approaches Detect?  
In this research question, we attempt to identify the types of web attacks that reviewed studies try to detect using Deep Learning models.  
As seen in Figure 15, most studies develop Deep Learning models for detecting web attacks without targeting a specific type of web attacks. Still, some studies focus on detecting specific web attacks, namely query injection attacks, XSS attacks, and file and path injection attacks. Moreover, 46 studies develop binary classification models, while only 15 studies develop multi-classification models (Figure 16).  
Since most if not all studies do not mention whether the proposed DL models were trained on a dataset that contains an even number of instances for each type of web attack, it is important to evaluate how well these DL models will perform in detecting specific  
Figure 14: Histogram of classification models used in selected PS.  
types of web attacks. This can be achieved by giving more attention to the development of multi-classification models.
## 3 ZigBee Protocol Stack Overview and Configuration
### Protocol Stack Configuration
#### 3.2.3. APL Layer  
At the APL layer, the impact of the RTO algorithm on the network performance is evaluated. Since there is not one universal RTO value that always delivers an optimal performance, there is a need to use an RTO algorithm that adapts to the requirements of the application and the network conditions. In this context, the performances of four alternative RTO algorithms are evaluated and compared to the default one. An RTO algorithm can be split into two main components: the initial RTO assignment and the backoff method. The initial RTO value is used for the first transmission of a packet. If a loss is detected, _i.e._, if the RTO timer expires and the packet needs to be retransmitted, a backoff mechanism can be applied, which increases the RTO value. In the following, a short introduction to the five RTO algorithms investigated is given. A summary of these five RTO algorithms can be found in Table 2.  
\begin{table}
\begin{tabular}{|c|c|c|} \hline Algorithm & Initial RTO & Backoff method \\ \hline ZigBee & 1.5 s & - \\ ZigBee + Backoff & 1.5 s & BEB \\ Constrained Application Protocol (CoAP) & \([2\;s,\;3\;s]\) & BEB \\ CL & 4\(\times RTT_{RE}\) & BEB \\ Request For Comments (RFC) 6298 & 1.5 s & BEB \\ \hline \end{tabular}
\end{table}
Table 2: The retransmission timeout (RTO) algorithms investigated and their main aspects.
# PTMD Control on a Benchmark TV Tower under Earthquake and Wind Load Excitations
###### Abstract  
A bounding tuned mass damper (PTMD) is introduced by making use of the energy dissipated during impact. In the proposed PTMD, a viscoelastic layer is attached to an impact limitation collar so that energy can be further consumed and transferred to heat energy. An improved numerical model to simulate bounding force is proposed and verified through experimentation. The accuracy of the proposed model was validated against a traditional Hertz-based bounding model. A comparison showed that the improved model tends to have a better prediction of the peak bounding force. A simulation was then carried out by taking the benchmark Canton Tower, which is a super-tall structure, as the host structure. The dynamic responses of uncontrolled, TMD-controlled and PTMD controlled system were simulated under wind and earthquake excitations. Unlike traditional TMDs, which are sensitive to input excitations and the mass ratio, the proposed PTMD maintains a stable level of control efficiency when the structure is excited by different earthquake records and different intensities. Particularly, more improvement can be observed when an extreme earthquake is considered. The proposed PTMD was able to achieve similar, or even better, control effectiveness with a lower mass ratio. These results demonstrate the superior adaptability of the PTMD and its applicability for protection of a building against seismic activity. A parametric study was then performed to investigate the influence of the mass ratio and the gap value on the control efficiency. A comparison of results show that better control results will be guaranteed by optimization of the gap value.  
vibration control; bounding tuned mass (PTMD) damper; energy dissipation; super-high structure; earthquake excitation +
Footnote †: journal: Applied Physics Letters
# Enhancing Visual Data Security: A Novel FSM-Based Image Encryption and Decryption Methodology
## 3 Materials and Methods  
### General Finite State Machines  
Finite state machines undergo state transitions and generate output sequences based on input symbols [29]. The behavior of a finite state machine is characterized by a set of components, including the set of states, set of input symbols, set of output symbols, start state, and final state [30]. There exist two primary types of finite state machines: Moore Machines [31] and Mealy Machines [32]. These machines are collectively referred to as finite-state machines due to the finite nature of their states. Renowned for their application in cryptography, these machines generate output sequences corresponding to each input sequence. Cryptography applications often leverage cellular automata, transducers (both Moore and Mealy Machines), and state machines with accepting states, excluding output symbols. In essence, a finite state machine is defined as a system that yields an output sequence for each input sequence across distinct states. Moore and Mealy machines are commonly depicted using graphical notations suitable for digital circuit representation [33].  
Finite state machines, encompassing both Moore and Mealy variants, are denoted as \(M\) and defined by six tuples, i.e., \(M=(X,Y,S,\delta,\lambda,x_{0})\). Here, the tuples \(X,Y,S,\) and \(x_{0}\) signify the finite sets of states, input symbols, output symbols, and the initial state. The tuples \(\delta\) and \(\lambda\) represent one-way mapping functions. Specifically, \(\delta\) is defined as \(\delta:X\times Y\to X\), illustrating the transition function for both Moore and Mealy machines. Simultaneously, \(\lambda\) is articulated as \(\lambda:X\to S\) for Moore machines and \(\lambda:X\times Y\to S\) for Mealy machines, delineating the output mapping functions associated with each respective machine type.  
Since the finite automaton acts as an encoder/decoder, it must have invertibility properties. In our case, a weakly invertible state machine with delay is used. A finite state machine is called invertible if, knowing the output sequence and the initial state, the input sequence can be uniquely determined.
# Enhancing Visual Data Security: A Novel FSM-Based Image Encryption and Decryption Methodology
## 2 Literature Review
The paper [27] by [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON] presents an image encryption algorithm based on block scrambling and finite state machines. This paper aims to provide a comprehensive analysis of the algorithm, its advantages, and potential applications. The combination of block scrambling and FSM ensures a high level of security while maintaining a reasonable computational cost. The algorithm's performance was evaluated using various experiments and comparisons with other existing encryption algorithms, demonstrating its superiority in terms of security and efficiency. As a result, this algorithm holds great potential for practical applications in fields such as digital forensics, medical imaging, and secure communication. In recent years, the field of cryptography has seen significant advancements, particularly in the area of image encryption. This scientific paper [28] by [PERSON], [PERSON], [PERSON], and [PERSON] introduces an S-Box construction based on exponentiation in finite fields. The proposed method offers a balance between security and visual quality while maintaining a competitive encryption speed. The use of exponentiation in finite fields for S-Box construction provides increased security and reduced complexity, making it a promising approach for future research in the field of cryptography and digital image encryption. In papers [26; 27; 28] where images were encrypted and decrypted, the following methods were used: NIST STS Analysis, Histogram Analysis, and Correlation Analysis.
# Review and Simulation of Counter-UAS Sensors for Unmanned Traffic Management
## 2 Review of the State-of-the-Art Technology
### Detection through Video/Images
The moving target detector accurately subtracts the background from subsequent frames by using a sparsely estimated global perspective transform. The target tracker consists of a Kalmar tracker and was validated using public video data from multiple fixed-wing UAVs working in real time. Video surveillance has not yet been incorporated to our simulation models but is described here for completeness. Next, we describe two commercial PTZ cameras used for drone detection and tracking. On the one hand, there is Axis Q6215-LE PTZ Network Camera from Axis Communications [42], which is a camera with normal range. Its specifications can be seen in Table 9.  
On the other hand, there is Triton PT-Series HD Camera from FLIR Enterprise [43], which is a PTZ with very high range, whose specifications are detailed in Table 10.  
Indra also has a camera/optronic sensor to be integrated in its ARMS system. Some details on it are described next, in Table 11.  
Another company that markets this type of sensor is HGH USA, specifically with its product called Spynel Series [44]. Spynel is based on thermal imaging technology with a 360\({}^{\circ}\) thermal sensor, which works day and night. Spynel can track targets over a long range and wide area. The specifications of each sensor model that exists in this product series
# A Novel Hierarchical Adaptive Feature Fusion Method for Meta-Learning
## 4 Experimental Results
### Setup  
Server configuration: In terms of server configuration, a server with 32 GB memory, two GTX-1080 ti graphics processors, and a Windows-10 operating system was used for experimental verification. In terms of architecture, the PyTorch deep learning architecture is used [37].  
Optimization setup: For the three widely used few-shot image recognition datasets, Stochastic Gradient Descent (SGD) optimizer with a momentum of 0.9 and a weight decay of 5 \(\times\) 10\({}^{-4}\) were used. Each batch consists of 64 samples. The learning rate was initialized to 0.05 and decayed three times with a factor of 0.1, once at the start of training, once at the 30 th epoch, and once at the 60 th epoch. The exception is miniImageNet, in which only two decays are made, and the third decays has no effect. MiniImageNet used 100 epochs, while CRFAR-FS and FC100 used 90 epochs. For the MUB dataset, we used SGD optimizer with a momentum of 0.9 and a weight decay of 1 \(\times\) 10\({}^{-3}\). Each batch consists of 16 samples. The learning rate is initialized to 0.2 and decayed four times by a factor of 0.1. The MUB dataset used 150 epochs.  
Backbone architecture: Based on previous research, for the three public datasets, ResNet12 was experimentally selected as the backbone network which consisted of four residual blocks. After the first three blocks, 4 \(\times\) 4 self-adaptive pooling, 4 \(\times\) 4 self-adaptive pooling, and 2 \(\times\) 2 self-adaptive pooling were adopted, respectively, and 1 \(\times\) 1 self-adaptive pooling was adopted after the last block. In subsequent experiments, SEResNet-12 was used as the backbone network for comparison, and the residual blocks of the two backbones are shown in Figure 6. SEResNet-12 adds extrusion and excitation mechanisms based on ResNet-12. The first and the second layers are fully connected to reduce dimensionality, resulting in a significant reduction in the number of parameters and computation. For the MUB dataset, MobineNet-V2 was selected as the backbone, which contained two convolutional layers at the beginning and end, as well as several modules. The experiment output is the features in specific modules for feature fusion.  
Figure 6: Schematic comparison of the residual blocks of ResNet-12 and SE ResNet-12.  
Data augmentation: During the training of the embedding model, random clipping, color dithering, and random horizontal flip [38] are used for data augmentation on the three public datasets. For the MUB dataset, data augmentation methods such as random horizontal flipping and center cropping are used to enhance the generalization performance of the embedding model.
## 3 Existing Pathfinding Techniques
### Informed Search or Heuristic Approach Algorithms
#### 3.2.5 Jump Point Search Algorithm (JPS)  
The Algorithm expands by choosing (pruning) certain nodes in the grid map (called Jump Points) while the intermediate nodes are not expanded (skipping the exploration of a lot of nodes). The technique used by JPS is based on avoiding useless searches of symmetrical paths that A* searches, saving lots of time with the same memory load. Moving from a Jump Point to another is done by travelling in a concrete direction while recursively applying two neighbor pruning rules (one for horizontal and vertical steps and the other for diagonal ones) until reaching a dead-end, a non-walkable area, or the next Jump Point. To keep going with JPS, it is important to understand the concept of \"pruning\", which is the elimination of nodes to expand. That is to say the way in which the Algorithm decides which nodes to explore. After the pruning of nodes, the ones that remain in the tree are called \"natural neighbors\", which are the only ones that we want to consider. However,sometimes we need to consider one or two nodes that are not natural. Those are called \"forced neighbors\" [70]. In Jump Point Search, our objective is to avoid symmetric paths by \"jumping\" all nodes that can be optimally reached by a path that does not visit the current node. This means that we chose a Jump Point if the optimal path must, obligatorily, pass through that node. Once it has reached an obstacle or another Jump Point, the recursion stops. So, a Jump Point y with a neighbor z will be a successor of a Jump Point x only if to reach z we need to visit x and y. To make it real, we need to consider two pruning rules, one for straight moves (horizontal and vertical) and another one for diagonal moves.  
In Figure 9 (Left), from a Jump Point x (with parent p(x)), we recursively go straight until y and set it as a successor Jump Point of x because z cannot be reached (optimally) if we do not pass through x and y. The nodes in the middle are not evaluated or explored. In Figure 9 (Right), from a Jump Point x (with parent p(x)), we recursively go diagonally until y and set it as an x Jump Point Successor (the same than left). In this case, after each diagonal step, we do a straight recursion (marked with discontinue lines) and only if the two straight recursions fail, we keep going diagonally [71]. Also in the image, the node w is shown, which is a forced neighbor of x because of being in a place in which, to reach it optimally from the coming direction (p(x)), we need to pass through x [72].  
The heuristic function, time, and space complexities of pathfinding Algorithms are detailed in Table 2.
## 5 Experimental Data Analysis Based on Linear Regression
### Data Analysis of Disturbance Force and Lateral-Line-System Data on Yaw  
The third group of experiments is to find out the relationship between the disturbance torque in the yaw direction and the data of the artificial lateral-line system. In this set of experiments, the rotation axis in yaw of the robot and the force-measuring device are not coincided with the geometric center of the underwater robot. It is used to simulate the yaw torque of an underwater robot when it is subjected to an unbalanced lateral force. After interpolating the data of the artificial lateral-line system, \(p^{\prime}\) is obtained as in the yaw direction. The left and right disturbance parameters of torque are defined as \(B_{left}\) and \(B_{right}\); furthermore,  
\[B=\int_{0}^{L}r(l)p^{\prime}\Delta dl \tag{16}\]  
\(\Delta l\) is the distance from the point being integrated to the yaw axis.  
Multiple linear regression is performed using the parameters \(B_{left}\), \(B_{right}\) and the torque \(M_{yaw}\) obtained from the torque-measuring platform, and the regression equation is derived, as is shown in Equation (16).  
\[M_{yaw}=a_{3}B_{left}-a_{3}^{\prime}B_{right}-b_{3} \tag{17}\]
## 3 Results
### Group 1--Use of Er:YAG Laser in Peri-Implantitis Treatment (Table 1)
Implant failures were found after 4.9 \(\pm\) 1.9 years of treatment (on average), with no substantial differences between the three groups (\(p\) = 0.303): six implant failures (8%) after laser decontamination, six implant failures (13%) after implantoplasty and four implant failures (17%) after combining the two therapies. The results founded by the authors suggest that success rates of peri-implantitis therapy did not differ between laser decontamination and/or implantoplasty surgery. [PERSON] proposed the use of Er:YAG laser to assist surgical regenerative peri-implantitis therapy in the presence of bone defects [16]. Twenty-four patients with infrabony defects caused by peri-implant disease were randomly divided into two groups (test group and control group). All patients underwent surgical therapy with implantoplasty and bone graft covered with dermal matrix membrane. In addition, in the test group was performed Er:YAG laser irradiation processes, such as adjunctive therapy, to remove inflammatory tissue and to decontaminate the implant surface from the presence of pathogens. Six months after surgical therapy, patients of both groups showed improvements in PD, gingival index (GI) and CAL. Between the two groups, no statistical differences were found in CAL gain (test group, 1.90 \(\pm\) 2.28; control group, 1.47 \(\pm\) 1.76 mm), GI reduction (test group, \(-\)1.14 \(\pm\) 1.15; control group, \(-\)1.04 \(\pm\) 0.89), radiographic linear bone gain (test group, 1.27 \(\pm\) 1.14; control group, 1.08 \(\pm\) 1.04 mm) or proportional defect size reduction (test group, \(-\)24.46 \(\pm\) 19.00%; control group, \(-\)15.19 \(\pm\) 23.56%). Instead, a higher PD reduction was found in the test compared to the control group (test group, 2.65 \(\pm\) 2.14; control group, 1.85 \(\pm\) 1.71 mm; \(p\) = 0.014).
## 3 System Model  
### Basic Assumptions  
We make the following basic assumptions for WSNs in this paper:  
1. All sensor nodes are fixed after deployment;
2. Each sensor node has a unique ID;
3. Links are symmetric;
4. There are no obstacle objects between communication pair;
5. Sensor nodes are location-aware and can adjust their transmission power based on distance.  
As can be seen from the assumptions above, the network is not assumed to be homogenous. It can be heterogeneous with various types of sensors and sink nodes (static or mobile ones). Some advanced sensor nodes and mobile sink nodes with more powerful energy supply can be applied here.
## 4 Simulations and Results
### Case 1
Figure 7: Comparison between the modeling errors \(\triangle S(n)\) of Method 1, Method 2, and the proposed method for the fixed acoustic path \(S(z)\) shown in Figure 4a. (3) Compared with Method 2, the proposed method can achieve a better modeling accuracy. Although Method 1 achieves good modeling accuracy, the low convergence and poor steady-state performance seriously degrade the overall performance of the system, as indicated in Figure 7.  
(4) The curves of the mean variable forgetting factor of the proposed method clearly indicate the status of convergence, as shown in Figure 8. Generally, it is better if the scaling strategy has the ability to quickly vary the auxiliary noise in the convergence stage; moreover, small auxiliary noise power is better when the algorithm is approaching the steady state. In addition, large jumps to adjust the auxiliary noise will adversely affect the convergence performance. \(\lambda(n)\) is a time-varying forgetting factor, and its value can be computed by weighting the historical autocorrelation errors. The scaling strategy of \(|e(n-1)|\) uses only the current error information. The fluctuation of \(|e(n-1)|\) will significantly affect the convergence speed. Since the weighting of the historical autocorrelation errors plays an important role in reducing the volatility, the convergence process of the system using \(\lambda(n)\) in the SPM subsystem can be faster and more stable.