esa-sceva/satcom-chunk-collection huggingface dataset:
# RMFNet: Redetection Multimodal Fusion Network for RGBT Tracking
## 3 Our Method
### RMFNet Overall Architecture  
RMFNet architecture is shown in Figure 2. In order to fully leverage the shared and specific information from both modality images and fully utilize the complementary strengths of the two modality information for more accurate and dependable target tracking, our RMFNet consists of two modality-specific branch-based and one fusion branch network. A pair of visible and thermal images of arbitrary size are taken as input to the network. In the two modality-specific branch networks, the backbone comprises the first three convolutional layers of VGG-M [6], ensuring accuracy while avoiding the problem of low efficiency caused by too many layers. The backbone network was modified to improve the representation quality of the region of interest (ROI) and obtain a larger receptive field. Specifically, the first layer has a convolutional kernel size of \(7\times 7\times 96\), the second layer has a convolutional kernel size of \(5\times 5\times 256\), and both layers have a ReLU activation function and a local response normalisation (LRN), removing the max pooling layer after the second layer in the original network. The third layer has a convolutional kernel size of \(3\times 3\times 512\) and is a dilated convolution with \(r=3\).  
In addition, to obtain the shared information between the two modalities, we adopted a similar two- and three-layer convolutional setting as that of the specific modality branches. The input two-modality images were first extracted by a \(7\times 7\) convolution to extract features and then were separately inputted into the second and third layers of the fusion branch to mine their shared information, where the shared weights can explore more shared information between the two modalities. Furthermore, utilizing the designed feature fusion module, the two features outputted from the fusion branch were adaptively weighted and fused to enhance the target's feature expression capability, ultimately achieving more accurate and precise object tracking.  
Finally, all three features we obtained use three fully connected classification layers to learn the instance perception of the target and a softmax layer to classify the optimized features. The \(FC6\) layer is a binary fully-connected layer with K domains (one domain represents a video sequence). The three classification scores obtained using the three features are summed to obtain the final sample score, and the mean of the top 5 scoring sample frames is the location of the predicted target.
## 1 Introduction
### Motivation  
These resource management challenges have motivated the research community to look for efficient ways to manage the operation of IoT devices. Thus, this opens up new opportunities for the machine learning (ML) paradigm. ML models can enable the classification, detection, and prediction of future events [18]. With such models, resource sharing [19], load balancing [20], channel access [21], and routing can be performed efficiently [22]. Recently, ML, particularly reinforcement learning (RL), has shown promising solutions in the field of network security [23], computer vision [24], natural language processing [25], cognitive radios (CRs) [26], 6G communication [27], and so on. RL is a powerful tool that provides machines with the ability to perform a specific action without being programmed. In RL, an agent learns how to perform actions within an environment to achieve certain goals by receiving feedback in the form of a reward [28].  
RL-based techniques have achieved remarkable success in various domains during the past few years. RL algorithms are able to utilize learning processes to adapt to changing environments and make intelligent, long-term decisions. For example, RL has been studied for wireless networking, particularly to enhance specific networking layers [29]. Similarly, an RL-based model for a workflow scheduling framework for edge-cloud environments was proposed to reduce congestion and execution delays [30]. RL-based algorithms have also shown their potential in fog computing environments [31]. In addition, the integration of deep learning with RL (DRL) has demonstrated an improved performance in complex control tasks [32]. DRL has also been applied for trajectory planning for the unmanned-aerial-vehicle-assisted IoT [33]. The RL algorithm is also utilized to simplify the NP-hard optimization resource management problem, demonstrating its effectiveness in optimizing renewable energy use within long-range (LoRa) networks [34]. RL-based approaches have shown their potential to enhance CR capabilities [35]. In a similar manner, RL-based approaches have been applied to solve the issue of limited licensed spectrum availability. RL algorithms address the challenge of achieving both efficient and fair coexistence between long-term evolution and Wi-Fi technologies [36; 37]. Numerous algorithms driven by RL have been suggested to enhance the efficiency of IoT devices, such as a computation offloading scheme for healthcare applications [38], spectrum access [39] for IoT networks, and target localization for IoT sensor selection [40]. The resource management problem is also handled by using RL for efficient networking protocols. These studies present a viable option for effectively managing resources within IoT devices through the application of the RL framework.
# Towards a Highly Available Model for Processing Service Requests Based on Distributed Hash Tables
## 1 Introduction
### State-of-the-Art  
Any distributed service needs to process its clients' data in some manner, and one could conclude, by observing previous research results, that the clients' _data_ integrity and availability is crucial for the service's integrity. In order for a distributed service to be _highly available_, the _data_ that it processes need to be stored in a persistent and, preferably, consistent manner. This observation comes after a lot of study referring to distributed services' needs and requirements in order to offer high availability and this is the reason why, in this section, a very strong focus falls on highly available storage for data, on how peer-to-peer DHTs have been designed and used, besides studying other solutions for highly available distributed services, too.
# Evaluation of pH and Optical Properties of Dual Ringe HEDP Irrigating Solution
## 3 Results
### Spectral Analysis  
The transmission values of the different irrigating solutions in wavelengths from 190 to 1100 nm are presented on Figure 2. Differences in the absorption coefficient alpha (\(\alpha\)) values between all irrigating solutions in different wavelength ranges are presented in Table 3.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Irrigating Solution** & **pH** & **Potential Difference (mV)** & **Temperature (\({}^{\circ}\)C)** \\ \hline DW & 6.32 \(\pm\) 0.01 & 54.5 \(\pm\) 0.02 & 24.5 \(\pm\) 0.4 \\ NaOCI & 10.34 \(\pm\) 0.01 & \(-\)184.5 \(\pm\) 0.01 & 24.7 \(\pm\) 0.3 \\ HEDP & 11.29 \(\pm\) 0.01 & \(-\)239.0 \(\pm\) 0.01 & 23.6 \(\pm\) 0.6 \\ HEDP + NaOCI & 11.01 \(\pm\) 0.02 & \(-\)223.8 \(\pm\) 0.03 & 24.4 \(\pm\) 0.1 \\ EDTA & 9.99 \(\pm\) 0.01 & \(-\)164.4 \(\pm\) 0.02 & 24.6 \(\pm\) 0.2 \\ CA & 1.5 \(\pm\) 0.01 & 328.5 \(\pm\) 0.01 & 24.4 \(\pm\) 0.3 \\ CHX & 6.25 \(\pm\) 0.02 & 53.5 \(\pm\) 0.05 & 24.9 \(\pm\) 0.2 \\ \hline \hline \end{tabular} Abbreviations: DW=distilled water; NaOCI=sodium hypochlorite; EDTA=ethylenediaminetetraacetic acid; HEDP=-1-hydroxylethylidene 1,1-disphosphonate; HEDP + NaCl=1-hydroxylethylidene 1,1-disphosphonate + Sodium hypochlorite; CA—citric acid; CHX=chlorexidine digluconate.  
\end{table}
Table 2: Chemical properties of distilled water and endodontic irrigants presented as pH value, difference in potential, and temperature of the solutions. Results are presented as mean \(\pm\) standard deviation (SD) of five measurements.  
Figure 1: Illustration of pH values of each irrigating solution on a pH scale from the most acidic to the most alkaline. Abbreviations: NaOCI—sodium hypochlorite; EDTA—ethylenediaminetetraacetic acid; HEDP—1-hydroxylethylidene 1,1-disphosphonate; HEDP + NaOCI—1-hydroxyethylene 1,1-disphosphonate + sodium hypochlorite.
# Measurement of Strain and Strain Rate during the Impact of Tennis Ball Cores
## 4 Discussion
For strain, these distributions have multiple peaks rather than one distinct peak around zero, whilst the strain rate distributions are skewed in the negative direction, with negative strain rates accounting for around 60% of the distribution. The strain rates presented in this investigation are in the 100-1000 s\({}^{-1}\) order of magnitude, at which strain-rate-dependent behaviour has been observed in vulcanised rubbers [8; 9; 10]. To test at rates in this order of magnitude, a bespoken test rig (similar to that used by Hoo Fatt & Bekar [9]) or more complex testing equipment, such as a split Hopkinson pressure bar, is required. Characterisation at strain rates between 100-1000 s\({}^{-1}\) would confirm if this rate-dependent behaviour is present in ball core rubber under the conditions experienced during use. Should significant rate dependence be observed, the incorporation of more complex material models (compared to hyperelastic models) incorporating this behaviour can be utilised in finite element models of ball impacts. Furthermore, should minimal strain rate dependence be observed, the process of artificial stiffening of hyperelastic material models to represent ball core rubber, as utilised previously [3; 4; 5], should no longer be employed. Additionally, different material compounds can be compared at representative strain and strain rates when assessing potential new compounds for use as ball core rubber. GOM is limited by the surface coverage, which is dependent on camera positions and relative placement. Clearly with one camera pair, it is not possible to obtain full surface coverage, particularly as the underside of the ball is obscured by the impact plate. Furthermore, it is only possible to measure parts of the object that are visible, so strains through the thickness of the material and on the inside surface of the cavity cannot be measured. Increased ball coverage could be achieved using additional cameras. A calibration of each camera 'pair' would be required in addition to the calibration of a global reference frame, to which all surface analysis could be transposed and combined to achieve more complete coverage. The time required to capture and process the data would greatly increase, however, and access to multiple high-speed cameras with each pair of cameras requiring matching lenses would be required.
## 3 Harvester Structure and Concept
### Design and Evaluation of the Planar Spring  
In this work, the aim of using the planar spring is to ensure a more compact structure for the converter as well as more flexibility. This section is devoted to investigating the design of the planar spring, in particular its geometry and size, and their influence on the resonance frequency. The main evaluated parameter is the resonance frequency of the spring relative to the geometry. The planar spring has a diameter of 50 mm, and the spring geometry is presented in Figure 4.  
The influences of the thickness, the number of beams, and the material of the spring on the resonance frequency are investigated based on finite element analysis. As the first evaluation, the effect of the material is evaluated for a spring thickness of 0.2 mm and using four beams. The results show that resonance frequencies of 12 Hz, 156 Hz, and 193 Hz can be reached for FR4, aluminum, and copper materials respectively (Figure 5a).  
Figure 3: Designed energy harvester system: (**a**) schematic, (**b**) 3D illustration.  
Figure 2: Scenario with the identification of possible failures from 0.25 g of acceleration.  
Further, an increase in the number of beams leads to a decrease in the resonance frequency. For example, for an FR4 spring with a thickness of 0.2 mm, the resonance frequency decreases from 21.7 Hz to 12 Hz for two beams and four beams, respectively (Figure 5b). This is due to the stiffness decrease when increasing the number of the beams, and hence the decrease in the resonance frequency. A part of that, i.e., the spring thickness effect while maintaining the same number of beams, was evaluated (Figure 5b). This showed that the increase in the spring thickness was proportional to the frequency increase due to the increase in the spring stiffness.
# Unsupervised Offline Changepoint Detection Ensembles
## 2 Materials and Methods
### Ensembles of Offline Changepoint Detection Procedures
#### 2.1.4 CPD Ensemble Procedure
A variety of scaling and aggregation functions for outlier, changepoint, classification ensembles, as well as the related issues can be found in papers [12; 13; 14; 16; 18; 19; 20; 27; 28]. Though scaling can be included in and considered part of aggregation procedure [4], we treat it separately from the aggregation function. Common scaling procedures are as follows:  
* _MinMax_: normalizing all values to the range \([0,1]\), also known as MinMax scaling;
* _Znorm_: zero mean, unit variance scaling, also known as Z-normalization or standard scaling;
* _MaxAbs (MinAbs)_: scaling by maximum (minimum) absolute value, also known as MaxAbs (MinAbs) scaling;
* _Rank_: using ranks of the criterion \(V_{n}\) points from minimum to maximum value. All procedures translate each criterion \(V_{n}\) individually. Among aggregation functions, also called combination functions, combination methods, or combination algorithms, the popular ones are as follows:  
* _Average_: averaging scores of all cost functions;
* _WeightedAverage_: weighting cost functions and then averaging weighted scores. Difference between static and dynamic weighting is presented in [29]. Commonly, the  
Figure 4: Categorization of ensemble techniques. Figure 3: An illustrative example of the cost functions aggregation procedure. weights for various models or cost functions are predetermined [16; 29]. For unsupervised offline ensembles, the weights can show the degree of confidence of each separate detector. * _MajVote_: the point that the majority of all cost functions has selected is a changepoint. Though majority voting is primarily used in classification ensembles [28; 29], it can be applied for CPDE, for example, together with _Rank_ scaling or using the threshold [4];
* _Max (Min)_: selecting maximum (or minimum) among scores of various cost functions;
* _Sum_: summarizing scores of various cost functions;
* _ThresholdSum_: discarding (pruning) scores below the selected threshold and then summarizing scores. Pruning can be applied either to the scores of every single model by using the threshold or to the scores at each point by selecting only the top \(M<N\) models [12];
* _AverageOfMax_: dividing cost functions into groups, taking maximum of scores of each group, and then averaging;
* _MaxOfAverage_: dividing cost functions into groups, averaging scores of each group, and then taking maximum of averages;
* _FeatureBagging_: applying cost functions to feature subsets and averaging of the obtained scores. The exact formulations of the scaling and aggregation functions selected for the experiment are presented in the next subsection.
# Finite Element Simulation of HELICOIL(r) Inserts
## 8 Discussion
### Validation by DIC  
Data obtained from DIC measurements were used for validation. The values of strain on the vertical axis (\(z\)-axis for finite element simulation) were compared. Colour maps were compared at four selected time points; specifically, 8 [s], 12 [s], 16 [s], and 20 [s]. The colour scale was adjusted according to the values measured by DIC at the locations marked L and KY. The comparison for time points 8 [s] and 12 [s] is shown in Figure 22, while the comparison for time points 16 [s] and 20 [s] is shown in Figure 23.  
It can be seen that the values agreed very well at the top (point L). This is due to the correction to point 72; see Section 6.3. The measured (DIC) and calculated (FEM) colour maps present a similar character. The value measured at the KY point was about two-thirds of the height of the FEM simulation. Considering the results in Figure 14, this result was worse. We assume that this difference was due to the following simplifications used for the simulation:  
* The boundary conditions (attachment) of the FEA model were too close to the area being compared, and the vertical displacement of the real sample was not the same throughout the whole cross section, as it was simulated using the fixed support;  
Figure 21: Comparison of the experiment with three mesh variations for the output curve.  
Figure 20: Shear stress \(yz\) for the specimen, the screw, and the three meshes.  
* The bolt was modelled as a cylinder, but the threads increase the part of the deformation that belongs to the bolt. Therefore, the solution error was increased;
* The hole in the sample was smaller, as part belongs to the threaded insert and part belongs to the threads. This also changed part of the total deformation of the sample.  
Point one showed a greater effect on the distribution of deformations in the monitored area. Points 2 and 3 then had an effect on the deformation value corresponding to the simulated interface.  
It is also possible to directly use the data obtained by DIC measurements to determine the material parameters (see, e.g., [33]). A similar approach could be utilized to determine the cohesive contact parameters; then, however, it would not be possible to use the DIC measurements to verify the results.
## 3 Security Attacks on AV Sensors: A Closer Examination of GPS Systems
### The Vulnerability of AV Cameras to Security Attacks  
Cameras serve as the optical eyes of autonomous vehicles, providing digital video feeds of the external world. They are employed in various capacities in AVs, including lane detection, traffic sign recognition, and headlight detection, among others [40, 41, 42].  
One significant vulnerability of these camera systems lies in their susceptibility to being temporarily or permanently obscured by targeted light interference. Such a compromise could pose a substantial risk to passenger safety, particularly in instances where the vehicle's ability to detect essential road signage or traffic signals is undermined [31]. Notably, this has been acknowledged as a potential area of concern by leading industry stakeholders, with instances recorded of autonomous vehicles, such as those developed by Google, experiencing difficulties in low-light conditions [43].  
A similar form of attack capitalizes on the period of recovery required by cameras after exposure to high-intensity light. During this interval, the autonomous vehicle may be more vulnerable to unperceived obstacles. Such an attack could be orchestrated by intermittently switching a light source on and off and could be initiated from any direction-- the front, back, or side of the vehicle [31].  
Several mitigation strategies could be employed to counteract these forms of attacks. For instance, employing a configuration of multiple cameras, each capturing the same visual field, could present an added layer of complexity for an attacker aiming to confuse all cameras at once. Alternatively, the integration of a detachable near-infrared-cut filter could provide the ability to selectively filter near-infrared light, enhancing the camera's resilience to light-based attacks. Moreover, photochromic lenses, with their unique capacity to change color and block specific light wavelengths, could also serve as a potential protective measure [31].  
In the broader context of emotional intelligence, these threats underscore the need for autonomous vehicles to possess a degree of perceptual intelligence. This would enable them to recognize and adjust to potentially harmful inputs, similar to how humans process and react to threats in their environment. This aspect of emotional intelligence in AVs is critical for mitigating safety concerns and advancing the technology's development and acceptance.
# SACRB-MAC: A High-Capacity MAC Protocol for Cognitive Radio Sensor Networks in Smart Grid
## 2 SACRB-MAC Framework
### System Model
As description in [27], detection and false alarm probabilities for the \(i^{th}\) channel in the \(j^{th}\) band are given by:  
\[P_{d}^{j,i}=Pr\left\{E^{j,i}\geq\sigma\mid S_{busy}^{j,i}\right\}=Q\left(\frac {1}{\sqrt{2}}\frac{\sigma-2n_{i}\left(\gamma_{i}+1\right)}{\sqrt{4n_{i}\left( 2\gamma_{i}+1\right)}}\right) \tag{3}\]  
\[P_{f}^{j,i}=Pr\left\{E^{j,i}\geq\sigma\mid S_{idle}^{j,i}\right\}=Q\left(\frac {1}{\sqrt{2}}\frac{\sigma-2n_{i}}{\sqrt{4n_{i}}}\right) \tag{4}\]  
Figure 2: The schematic of FA-OFDM based PHY layer. where \(Q(\cdot)\) accounts for the Q function, which is the complementary error function, \(\gamma_{i}\) denotes the primary signal-to-noise ratio and \(n_{i}\) denotes the bandwidth time product of the \(i\)-th channel.
## 4 Results and Discussion
### Integrated Discussion  
#### 4.2.1 Establishment and Analysis of the Evaluation System  
The evaluation system of AI painting software based on GAN constructed in this research is based on a large number of relevant studies at home and abroad, combined with the actual situation in the field of AIGC to amend and transform; we formulated the initial index of the structure and indicators after three experts and scholars in related fields evaluated and provided their modification opinions; then, five experts and scholars completed the Delphi questionnaire correction adjustment index. The questionnaire was then screened according to the plan, and a principal component analysis and reliability analysis were conducted to screen out the indicators with higher explanatory power before forming the official indicators. The development process was very rigorous, so this indicator has good content validity; the Cronbach's alpha coefficients of the subscales and the full scale were all greater than 0.8, which is also a very good level of reliability.  
The factor loadings of the principal components and the questionnaire items were all higher than 0.7, indicating a high degree of representativeness for the indicators. The evaluation indexes developed in this study show that excellent AI painting software should not only have rich functions, but also allow users to achieve a sense of coordination and esthetics when operating, and the software should maintain stability and security during usage. Therefore, AI technology enterprises are required to meet the above indicators when developing related software.