esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
For circular apertures with diameter \(D\), the filter function for Zernike tilt and piston are as follows [3]:  
\[F_{t}(\gamma\kappa)=\left[8\,J_{2}(\gamma\kappa D/2)/\gamma\kappa D\right]^{2} \ \left(\text{Tilt}\right) \tag{5}\]  
\[F_{p}(\gamma\kappa)=\left[4\,J_{1}(\gamma\kappa D/2)/\gamma\kappa D\right]^{2 }\ \left(\text{Piston}\right) \tag{6}\]  
For a uniformly illuminated circular source with diameter \(D_{s}\) located at a distance \(L_{S}\), considering that the uniform circular source consists of incoherent point beacons, the source filter function is as follows [3]:  
\[F_{s}(\gamma\kappa)=\left[4L_{s}\,J_{1}(\kappa D_{s}z/2L_{s})/\kappa D_{s}z \right]^{2} \tag{7}\]  
The final filter function is the product of any produced filter functions. Make a change to the variables by replacing \(\kappa D\) with \(x\) and use the replacement \(F_{N}=D^{2}/\lambda L\), where \(F_{N}\) is the Fresnel number, to simplify the final expression of the PWFs for the tilt phase (i.e., substituting \(F(\gamma\kappa)=F_{t}(\gamma\kappa)\) * \(F_{s}(\gamma\kappa)\) into Equation (4)) and piston amplitude (i.e., substituting \(F(\gamma\kappa)=F_{p}(\gamma\kappa)\) * \(F_{s}(\gamma\kappa)\) into Equation (4)), noted as \(W_{C,T}(u)\), \(W_{C,P}(u)\), to the following:  
\[\left[ \begin{array}{c}W_{C,T}(u)\\ W_{C,P}(u)\end{array} \right]=D^{\frac{5}{3}}\!\int_{0}^{\infty}\left[ \begin{array} []{c}\left[8\,J_{2}(\gamma\kappa/2)/\gamma\kappa\right]^{2}\\ \left[4\,J_{1}(\gamma\kappa/2)/\gamma\kappa\right]^{2}\end{array} \right]^{2} \left[2\frac{J_{1}(\frac{y(D_{L}L_{s})}{2 DL_{s}})}{\frac{x(D_{L}L_{s})}{2 DL_{ s}}}\right]^{2}J_{0}(\gamma\kappa\frac{d}{D})x^{-\frac{5}{3}}\left[ \begin{array}{c} \cos^{2}(\frac{x^{2}\gamma u}{4\pi F_{N}})\\ \sin^{2}(\frac{x^{2}\gamma u}{4\pi F_{N}})\end{array} \right]dx \tag{8}\]  
From Equation (8), it is clear that the PWFs for the tilt phase and piston amplitude depend on four variables: \(\gamma\), \(d/D\), \(F_{N}\), and \(D_{S}\)/\(DL_{S}\) (i.e., the ratio of the angular diameter of the source to that of the receive aperture observed at plane \(z=L\)). In the following sections \(D_{S}\)/\(DL_{S}\) will be noted as \(R\) for simplicity. Any change in these four variables will result in a new PWF. Obviously, we can obtain more PWFs by using the covariance function instead of the variance function, which is the special case of the covariance function for \(d=0\). The geometry and variables of the covariance functions are shown in Figure 1 for reference. For convenience later, denote \(W_{T}(u)\) as the normalized PWF for the covariance of the tilt phase, and \(W_{P}(u)\) as the normalized PWF for the covariance of piston amplitude. To obtain qualitative knowledge of the effects of these four variables on the PWFs, next we will give the PWFs in a range of typical conditions by numerical evaluation.
## 4 Methodology
### Evaluation of Conventional Methods  
The initial stage involved implementing the conventional methods (MLD, MMSE, and ZF) in MATLAB. The performance of these conventional methods was assessed based on SER, SNR, and computational time (CT). Moreover, the impact of the number of transmitters (N) and receivers (M) on these metrics was analyzed.
# Fault Diagnosis of Rolling Bearings Based on Improved Kurtogram in Varying Speed Conditions
## 4 Experimental Evaluation
### Diagnosis of a Bearing with an Inner Race Fault
#### 4.3.1 Experimental Study #4  
As shown in Figure 30a,b, the vibration signal and the rotating speed are collected at the same time during the speed increase from 20 Hz to 25 Hz, and the acceleration a equals 4/3 Hz/s. The frequency spectrum and TFR of the signal are blurred due to the variable rotating speed and the background noise, as shown in Figure 30c,d.  
The optimal frequency bands corresponding to different indexes are shown in Figure 31. The FK and the WPTK have the same optimal frequency band, which is [625, 1250] Hz as shown in Figure 31a,b. The Protrugram is paved in Figure 31c with BW equal to 400 Hz and a step of 100 Hz and the center frequency is 425.5 Hz. In Figure 31d, the optimal frequency band of the FOSCK is [3125, 3750] Hz. The envelope order spectra of the filtered signals corresponding to different indexes are displayed in Figure 32. The envelope order spectra do not contain any noticeable FCO in Figure 32a-c, which means that the FK, the WPTK and the Protrugram failed to identify the appropriate fault sensitive resonance frequency band. In Figure 32d, the FCO and its third octaves can be identified in the envelope order spectrum, although the third harmonics are masked by heavy background noise, and the fault component can still be identified. Therefore, the FOSCK has the best ability to detect bearing inner race faults in this case.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Acceleration (Hz/s)** & **Experimental Study \#4** & **Experimental Study \#5** & **Experimental Study \#6** \\ \hline a & 4/3 & 3/2 & 3 \\ \hline \end{tabular}
\end{table}
Table 6: Parameters of each experiment.  
Figure 30: The signal measured from an inner race fault bearing: (**a**) time-domain signal, (**b**) the shaft rotational frequency from 20 Hz to 25 Hz, (**c**) the frequency spectrum of (**a,d**) TFR by using STFT.
## 5 Experiments and Results
### Dataset Description  
We used the SWAT dataset and EPIC dataset provided by Singapore University of Technology and Design as our research data. Figure 4 shows the experimental environment for the acquisition of the two datasets. The SWAT dataset was collected from a real water treatment bench. The water treatment process includes water supply, storage, filtration, and backwashing, etc., and communicates using the ethernet/IP protocol [28]. The EPIC dataset was collected from a small power test bench that simulated a real power system in a small smart grid, communicating based on the IEC 61850-MMS protocol [29].  
Figure 3: The training and testing process of the decision tree classifier.  
The EPIC test bench was also used to power the SWAT test bench, resulting in a cascaded ICS using multi-protocol communication, which fits well with the research topic of this paper (classification of ICS protocol traffic based on multi-communication protocols). Therefore, these two datasets are selected as the experimental data of this paper. However, the amount of data in the SWAT and EPIC datasets is huge. In order to facilitate the experimental processing, we randomly select 70,000 protocol data streams from each of the two datasets, and a total of 140,000 samples are used for experiments.
## 3 Proposed System Model
### Offloading Decision Sub Problem  
In our analysis, we specifically focus on cost offloading without accounting for the energy consumed at the edge, in contrast to the cost incurred by local execution. This comparison is guided by the conditions outlined in Equation (11). Subsequently, we formulate the new offloading decision problem, as articulated in Equation (12). Equations (13) and (14) outline the essential conditions that must be satisfied for a computation task to undergo offloading. These conditions serve as pivotal criteria guiding the decision-making process regarding whether a task should be offloaded to the edge or executed locally. This condition stipulates that the latency incurred by offloading the task to the edge must not exceed a predefined threshold. By imposing such a constraint, we ensure that offloading tasks to the edge remains viable only if it results in latency improvements compared to local execution. This consideration is particularly crucial for latency-sensitive applications where responsiveness is paramount. Furthermore, it introduces another critical condition related to computational resource availability at the edge. This condition dictates that the edge server must possess adequate computational resources to execute the offloaded task effectively. By assessing the computational capabilities of the edge server against the computational requirements of the task, we ascertain whether offloading is feasible from a resource perspective. This condition plays a pivotal role in preventing resource overloading at the edge and ensuring the efficient utilization of available computational resources. Together, they establish a robust framework for evaluating the suitability of offloading computation tasks to the edge. By considering factors such as network latency and computational resource availability, we aim to make informed offloading decisions that optimize both performance and resource utilization in edge computing environments.  
\[min\sum_{i\;\epsilon\;l}\left((1-x_{i})\left(\frac{C_{i}}{V_{i}^{loc}}+\alpha C _{i}\left(V_{i}^{loc}\right)^{2}\right)+x_{i}\left(\left(\frac{D_{i}}{R_{nj}} \right)+\left(t^{off}*p_{i}^{tr}\right)\right)\right) \tag{11}\]  
\[min\sum_{i\;\epsilon\;l}\;\left(\left(cost^{off}-cost^{loc}\right)x_{i}\right) +min\sum_{i\;\epsilon\;l}\;cost^{loc} \tag{12}\]  
\[cost^{off\_total}<cost^{loc} \tag{13}\]  
\[t^{off}+t^{edge}<t^{max} \tag{14}\]  
In order for tasks to be offloaded effectively, several critical conditions must be met. First, as outlined in the equations, the cost associated with offloading computation to the edge server must be lower than the cost of local computing. This consideration is fundamental in ensuring cost efficiency and justifying the decision to leverage edge resources for computation. By conducting a cost-benefit analysis, we can determine the optimal allocation strategy that minimizes expenses while maximizing performance.
# DDTMS: Dirichlet-Distribution-Based Trust Management Scheme in Internet of Things
## 2 Related Works
### TMS Based on Gaussian Distribution  
The Gaussian distribution is a very common continuous probability distribution. It is often used to represent real-valued random variables whose distributions are not known. A random variable with a Gaussian distribution is said to be normally distributed and is called a normal deviate. [PERSON] et al. proposed a Gaussian-based trust and reputation management system to use for fading MIMO (Multiple-Input Multiple-Output) WSN [11]. They used the multivariate Gaussian distribution and Bayes' theorem to construct this system, and furthermore, they considered the impact on the MIMO wireless fading channels. They also combined with direct and indirect reputation information, in order to calculate reputation and trust value. The simulated results demonstrated that the system could effectively isolate malicious nodes. However, the calculation process is too complex to be suitable for resource-constrained sensor nodes.  
In addition, [PERSON] and [PERSON] presented afternoon for a time and trust aware mechanism for inference and prediction of reputation using Heteroscedastic Gaussianprocess regression (HGPR) model [12]. The model accepted as inputthe reputation feedback from third party sources and predicts the real reputation value.  
In complex networks, the interactive behavior between nodes is not only cooperative and non-cooperative, but also the finer granularity of interaction behavior according to different measurement methods. For many types of such network interactions, Dirichlet distribution can better describe the reputation changes caused by the historical behavior of the nodes.
# 25 Years of Bluetooth Technology
## 6 Recent Research Results on Bluetooth
### Smart Homes  
Internet of Things (IoT) is having a huge impact on our daily lives. Several home gadgets now have the capability to connect to the Internet. BLE technology is playing a significant role in enabling the deployment of _smart homes_.  
Recent research efforts [58] have proposed an energy management approach for smart homes. Bluetooth Low Energy is utilized for communication among home appliances. The approach helps in reducing the peak load demand and electricity consumption charges and reaps huge savings. Researchers have been investigating the use of BLE with artificial neural network support for home energy management [59]. This approach helps to predict the home energy requirements at different times of the day throughout the week and helps to implement _smart grid_ applications.  
Other smart home applications [60] using BLE include home lighting, smart door locking, cloud monitoring, and others. Cloud applications provide remote monitoring and control of IoT devices. The applications allow the user to view heating history and make adjustments remotely to the temperature settings. The cloud application can read the temperature service from the sensors and the thermostat at all times but can only set the temperature when a Short Message Service (SMS) message confirms that user has instructed it to do so.  
One recent study [61] has also investigated how a Bluetooth mesh can be used for deploying smart building applications. The Bluetooth mesh permits gadgets, such as smart phones, having a 4.0 or higher BLE version, with GATT support to join the mesh network. The mesh network is used to deploy a smart doorbell application, which has many nodes connected in a mesh network to send messages amongst nodes. Devices located at building doors represent a doorbell and send messages to a central node, also referred to as an Internet gateway. The range of the network is extended via relay nodes. A message is generated by pressing a button on the node at the door. This message signals to the staff regarding incoming visitors. Upon receiving the message, the gateway transmits it to all network devices as a notification to the staff. The building's space and structure need to be taken in to account when designing a mesh network [60].
# Designing Multi-Agent System Organisations for Flexible Runtime Behaviour
## 1 Introduction
### Demonstration Scenario  
To highlight the behavioural requirements for agents to be addressed, we present a rescue situation within an emergency management scenario. The situation involves multiple agencies involved in rescuing injured individuals from a disaster area [32]. The scenario begins following a disaster event and an unknown number of individuals are injured. The rescue task involves searching for the injured and transporting them to ambulances ready to transport them to hospital for further medical treatment. The two rescue agencies involved in this example are a medical agency (medic agents) and a law enforcement agency (officer agents). Medics at the rescue scene are responsible for the rescue of injured parties and delivering them to a particular zone for ambulance pick up. Some of the injured can be rescued by one medic; however, some rescues require a stretcher to be carried by two agents, and thus two medics must work together to perform those rescues. The objective of the officers is to ensure safety and order at the rescue scene. Medic agents can delegate or request that officers clear away bystanders who are impeding rescue progress because they are in an area where a rescue must take place. Officers direct bystander agents into different areas so that they do not impede rescue operations.  
The situation demands that the rescue agents behave with some flexibility and potentially improvise in order to rescue the injured in a timely way. For example, if an agent enacting the role of a medic is ready to rescue an injured party on a stretcher and another medic agent is not available to help carry the stretcher, a nearby agent in the officer role, possessing stretcher-carrying capabilities, could be asked to help carry the stretcher, even though that agent is not officially assigned or expected to fulfil the medic role.
# Directional Core Drilling as an Approach to Reduce Uncertainty in Tunneling Construction
## 3 Methodology for the Uncertainty Economic Valuation
### Costs Related to Drill and Blast Excavation
#### 3.2.2 Performance Decreases and Delays  
Quality decreases in a rock mass along a tunnel's layout result in lower excavation advances, increasing the duration of tunnel construction. Equation (5) defines the advancing rate of a tunnel excavated by drill and blast techniques [44], based on the RMR [46]:  
\[a_{b}=\frac{\mathrm{RMR}-20}{10}, \tag{5}\]  
where \(a_{b}\) is the advancement per blast (m/blast).  
The construction of a tunnel, as with any other economic activity, entails fixed costs, such as salaries, insurance policies, machinery rentals, or fees related to water usage, among many other common factors. Usually, the most important fraction of fixed costs comprises staff salaries, and this cost is considered as the cost of uncertainty associated with duration increases in tunnel construction. Equation (6) defines the number of days required to excavate a tunnel as a function of the number of blasts, and Equation (7) determines its associated fixed costs.  
\[T=\frac{L_{f}-L_{0}}{n_{b}\cdot a_{b}} \tag{6}\]  
\[C_{f}=c_{f}\cdot T \tag{7}\]  
where T is the time required for the tunnel excavation (days), \(n_{b}\) is the number of blasts per day (blasts/day), \(C_{f}\) is the total fixed costs for a given period (EUR), and \(c_{f}\) is the fixed unit cost (EUR/day). A common daily fixed cost is EUR 12,000.
## 6 Results
### The Analysis of Confusion Matrices  
Besides comparing the general classification performance, we wanted to see how the methods classify images of specific sports and what are the most common misclassifications of one sport for another. For this purpose, we prepared a confusion matrix of test data classification from all ten folds for each of the methods (Figure 6).  
The proposed CNN-TL-DE method achieved the best results in the vast majority of situations. When compared to the CNN method, there is only one situation (out of 16 possible) where it lags slightly behind--namely, it misclassified a few more American football images as soccer (26 vs. 17 mistakes). In all other situations the CNN-TL-DE performed much better. When compared to CNN-TL, there are four situations (out of 16) where CNN-TL-DE performed slightly worse--when misclassifying American football images as field hockey (15 vs. 13 mistakes), field hockey images as rugby (20 vs. 16 mistakes), and soccer images as rugby (19 vs. 11 mistakes), and also when classifying soccer images correctly (275 vs. 279 cases).  
It is interesting to see that all three methods made the most mistakes when misclassifying American football as rugby or vice versa. Mistakenly identifying one instead of the other is quite natural because of their similar game concepts, formations and postures of athletes, shape of the ball, etc. On the other hand, field hockey can be considered as the most different of the four sports, with hockey sticks, smaller ball, etc. While this proved to be the case with the CNN-TL-DE method, the other two methods (especially the CNN) made more misclassifications of field hockey images--the  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Metric** & **Inception-V3** & **DenseNet-121** & **VGG19** \\ \hline accuracy & 76.82 \(\pm\) 5.77 & **82.49**\(\pm\) 5.26 & 81.31 \(\pm\) **3.51** \\ AUNP & 84.69 \(\pm\) 3.77 & **88.33**\(\pm\) 3.53 & 87.56 \(\pm\) **2.30** \\ precision & 80.38 \(\pm\) 3.59 & 82.49 \(\pm\) 5.26 & **83.13**\(\pm\) **3.33** \\ recall & 77.30 \(\pm\) 5.60 & **82.29**\(\pm\) 5.38 & 81.30 \(\pm\) **3.41** \\ \(F\)-1 & 78.81 \(\pm\) 4.59 & **82.39**\(\pm\) 5.32 & 82.20 \(\pm\) **3.31** \\ kappa & 69.20 \(\pm\) 7.61 & **76.62**\(\pm\) 7.04 & 75.08 \(\pm\) **4.64** \\ time & 4579.74 \(\pm\) 341.49 & 5408.79 \(\pm\) **77.73** & **3430.43**\(\pm\) 315.78 \\ epochs & 720.6 \(\pm\) 104.21 & **678.6**\(\pm\) **51.90** & 790.3 \(\pm\) 85.49 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparison of classification performance of CNN-TL-DE on different CNN architectures.  
CNN-TL primarily confused them with soccer, and the CNN confused them with soccer and American football (see Figure 6).  
Overall, the CNN-TL-DE method turned out to be the most balanced, with very similar results on all four sports and all possible misclassifications. The CNN-TL classified rugby noticeably worse than other sports, while the CNN over-predicted American football and performed much worse on the other three sports. Thus, the CNN-TL-DE performed the most up to one's expectations.
# Channel Correlation-Based Adaptive Power Transmission for Free-Space Optical Communications
## 3 Simulations and Results
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Link Distance**} & \multirow{2}{*}{\begin{tabular}{c} **Uplink** \\ **Scintillation** \\ **Index \(\sigma_{I}^{2}\)** \\ \end{tabular} } & \multicolumn{2}{c}{**Corresponding**} & \multirow{2}{*}{\begin{tabular}{c} **Channel** \\ **Correlation** \\ **Index \(\sigma_{I}^{2}\)** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **Channel** \\ **Correlation** \\ **Coefficient \(\rho_{1}\)** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Channel** \\ **Correlation** \\ **Coefficient \(\rho_{2}\)** \\ \end{tabular} } \\ \hline
30 km & 0.37 & 0.15 & 0.6384 & 0.6404 \\
30 km & 0.5 & 0.2 & 0.5984 & 0.5979 \\
30 km & 0.65 & 0.28 & 0.5218 & 0.5225 \\ \hline
40 km & 0.42 & 0.17 & 0.6342 & 0.6338 \\
40 km & 0.6 & 0.23 & 0.5792 & 0.5808 \\
40 km & 0.7 & 0.35 & 0.4847 & 0.4871 \\ \hline \hline \end{tabular} \(\rho_{1}\) and \(\rho_{2}\) represent the channel correlation coefficients between the uplink and downlink, which are used as a metric to improve the signal power of the uplink and downlink transmission as reverse transmission, respectively.  
\end{table} 
Table 2: Channel correlation coefficients between the uplink and downlink for different link distances and turbulence intensities. Figure 4: Comparison of the BER performance of the proposed technique with FTD and ATD techniques under different turbulence intensities for a 30 km FSO link. (**a**) Uplink transmission, (**b**) downlink transmission.
## 2 Experiment Description
### Aerial Experiment  
#### 2.2.1 Measurement Setup  
The measurement setup and the aircraft used during the experiment is shown in Figure 2a,b, respectively. The measurement configuration consists of four geodetic-grade JVD GNSS receivers (type: Delta TRE-G3T(H)), which were running on the same firmware version. In addition to the four receivers, there were three external atomic clocks, IGI's AEROcontrol unit and an active GNSS splitter, which was connected to the Antcom G5 antenna placed at the top of the aircraft fuselage.  
The internal circuitry of three JVD Delta receivers (0081, 0082 and 0993) were driven by the three external oscillators (Microsemi MAC SA.35m, Spectratime LCR 900 and SRS SC10), respectively. On the contrary, the fourth JVD Delta receiver (0346) was driven by its internal TCXO. The IGI's AEROcontrol is a navigational grade IMU integrated with a high precision Septentrio GNSS receiver. From the data captured with the IGI's AEROcontrol unit, a precise kinematic reference trajectory was computed for the complete duration of the flight experiment.  
The whole measurement setup except for the GNSS antenna was fixed within a camera sensor pod, as shown in Figure 3a. Later, this sensor pod was placed inside the aircraft on a passively dampened aerial photogrammetry mount, as depicted in Figure 3b. The passive dampening on the mount reduces the impact of sudden jerks, mechanical shocks and vibrations on the external oscillators and prevents them from permanent damage.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**GNSS System** & **Geodetic Receiver** & **High Sensitivity Receiver** \\ \hline GPS & L1C, L1W, L2X, L2W, L5X & L1C \\ \hline GLONASS & L1C, L1P, L2C, L2P & L1C \\ \hline Galileo & L1X, L5X & L1X \\ \hline \hline \end{tabular}
\end{table}
Table 2: Receiver independent exchange format (RINEX) specifiers of recorded phase observations on different frequencies with geodetic and high sensitivity receivers.  
Figure 2: Flight experiment. (**a**) Measurement configuration, (**b**) aircraft used during the experiment.