esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Results and Discussion  
### Pipeline Leak Detection  
We begin with the performance of the normal models, which are presented in Tables 1 and 2. Plainly, both models demonstrated very similar performances, with the exception of the training time. The time required to train the LSTM model is roughly 20 times the 1-D CNN model, which can be very significant depending on the size of the dataset. We thus select the 1-D CNN model over the LSTM model as our normal model, and these are the forecasts we provide to the AD component.  
[PERSON] advises using non-parametric tests when comparing learning algorithms. The Wilcoxon signed ranks test is most suitable in this case, as two machine learning algorithms are being compared [67]. Defining the null hypothesis as both algorithms performing similarly with an insignificant difference, the Wilcoxon signed ranks test will reject the null hypothesis if the computed \(p\)-value is less than the chosen significance \(\alpha\) (we adopt the common choice of \(\alpha=0.05\)). The test resulted in a \(p\)-value of 1.9381 \(\times\) 10\({}^{-18}\), and so the null hypothesis is rejected, and the Isolation Forest AD does significantly outperform OC-SVM based on the 2 mm data. Additionally, to evaluate the effect size, we compute [PERSON]'s delta [68], obtaining a value of 0.4489, indicating a moderately strong effect.  
We next compare our completed architecture based on our dataset against anomaly detectors from the literature. For both the computer-generated and lab-generated data we pool the different leak scenarios together (this seems reasonable since performances for all of them were extremely high). The comparators we selected are the soft-bound and one-class Deep SVDD, OC-NN, and OCAN. We conducted a parameter exploration for each algorithm following the same procedures as our AD. The out-of-sample accuracy, TPR, and FPR for each algorithm based on the computer-generated data are given in Table 8, with the lab-generated data in Table 9. What we find is that, overall, our design using the Isolation Forest AD is as sensitive as any of the other algorithms (TPR = 1.0), while the False Positive Rate is somewhat lower than any other comparator for this dataset.
# Secure Aviation Control through a Streamlined ADS-B Perception System
## 3 Related Work
### Securing ADS-B
#### 3.2.2 Based on Time and Location  
This subsection will discuss the solutions that utilize the time and location information and the intervals between signals to provide a realistic check of the authenticity of the sent and received signals to secure ADS-B. [PERSON] [27] discussed different types of anomalies that occurred within ADS-B messages and used a method to detect intrusion using a sample of 16,000 messages from 52 different aircraft; his method utilizes the time when the information was received, which is retrieved from the sensor clocks, and then he compared it with the actual position of the aircraft. This method allowed him to detect any possible anomalies in the ADS-B messages, such as injection, without any locating algorithms. His model enabled the detection of anomalies in ADS-B messages without solving the hyperbolic inversion problem, and he has shown that his method can improve the synchronization between the different ADS-B sensors within networks.  
[PERSON] and [PERSON] [28] developed Securing Open Skies (SOS), a security framework to secure ADS-B communications and high robustness where a packet loss occurs or a message overhead is missing within an ADS-B message. SOS integrates the well-known timed-efficient stream loss tolerant authentication (TESLA) protocol within the ADS-B message format.  
Similarly, [PERSON] and [PERSON] [29] focused on the attacks and interference of communications between aircraft in China, showing that the original intention of ADS-B technology was to make air traffic control easier as the number of aircraft constantly increases. It can also be used to communicate with ground stations. They proposed three different ADS-B verification methods based on the ADS-B interference in China and possible interference. The first is coverage verification, where the target is instantly discarded when it exceeds the coverage range. The second method is time difference of arrival (TDoA) verification, which requires multiple ground sensors to receive signals from the aircraft simultaneously. The third and final method was cross-verification, which used radar signals to verify whether an ADS-B message was authentic by verifying the target from the coverage area and TDoA.  
Lastly, authors in [30] developed a similar solution based on location verification using multi-iteration techniques based on TDoA. Specifically, they leverage multi-alteration techniques based on ADS-B signals to verify the reliability of information broadcasted by aircraft. Still, it is fair to say that these are unreliable solutions because if one packet is lost, the whole communication will not be validated and thus would cause any ADS-B signal received to be invalid or considered corrupt or malicious. We summarize the most relevant solutions utilizing time and location to secure ADS-B in Table 3.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Ref.** & **Approach** & \multicolumn{2}{c}{**Contributions**} & \multicolumn{2}{c}{**Limitations**} \\ \hline \multirow{4}{*}{[27]} & \multirow{4}{*}{\(\
## 4 Modeling and Prediction Results
### Data Collection  
This method utilizes the relationship between cable force and cable vibration frequency. In a known length of cable with fixed ends, mass distribution, and other parameters, a highly sensitive sensor is attached to the hunger to capture cable vibrations induced by ambient vibrations. The captured vibration signals undergo filtering, signal amplification, A/D conversion, and spectral analysis to determine the self-oscillation frequency of the hanger. By correlating this frequency with cable force, the actual cable force can be derived.  
To ensure accuracy, multiple measurements were conducted on the 11 pairs of hangers on the bridge. Several datasets were collected and averaged to obtain the precise cable strength values for the bridge hangers. Figure 5 illustrates the installation of the JMM-268 dynamic measuring instrument (Kingmach Measurement & Monitoring Technology Co., Ltd., Changsha, China) by site construction personnel for cable force measurements.  
Figure 4: Bridge side drawing and hanger number.  
Figure 5: Hanger cable force measurement.  
Figure 3: Bridge model diagram.
# Hybrid Impedance-Admittance Control for Upper Limb Exoskeleton using Electromyography
## 4 Materials and Methods
### Materials  
We employed several components to build and evaluate the proposed robotic arm. When choosing the materials, we prioritize the lowest cost components that met the project's requirements. Microcontrollers were selected due to their high availability in the market and the amount of documentation available. The choice of servos was due to the torque/force ratio sufficient so that the structure's weight did not impact the performance of the robotic arm. All these components--including the sensors--are easy to find in the current market, facilitating the reproduction of the proposed robotic arm. The bill of materials (BOM) used includes:  
* _EMG Sensor_: MyoWare Muscle Sensor Development Kit, an Arduino-powered sensor kit from Advancer Technologies. The kit also includes circuitry boards to measure the filtered and rectified electrical activity of a muscle [36]. The MyoWare sensor has an input impedance of 110 G\(\Omega\) and can operate with an input voltage ranging from 2.9 to 5.7 V. It has a potentiometer with resistance ranging from 0.01 to 100 k\(\Omega\) to adjust the gain at the outputs. The sensor has two outputs that present the integrated and rectified electromyographic signal.
* _Force Sensor_: A 0.5\" Force Sensitive Resistor (FSR) that senses applied force in the range from 100 g to 10 kg. It varies its resistance according to the pressure applied to the sensing area [37].
* _Gyroscope_: MPU-6050, a motion-tracking device that combines a 3-axis gyroscope, a 3-axis accelerometer, and a Digital Motion ProcessorTM in a small package [38].
* _Servo Motor_: Datan Analog S1213, an analog feedback servo providing a torque of 6.5 kg*cm with a speed of 0.21 s/60\({}^{\circ}\). This device provides feedback (potentiometer wiper) line that enables reading the servo's position through an analog interface [39].
* _Microcontroller_: ESP32-DevKitC V1 from Espressif, a low-footprint development board which integrates a 32-bit microcontroller (ESP32) and a 2.4 GHz Wi-Fi circuitry, running C code on bare metal [40]. ESP is an acronym for Espressif.
* _Embedded Microcomputer_: Raspberry Pi 3 Model B, a single-board computer with wireless LAN and Bluetooth connectivity, running C code on Raspberry Pi OS [41].
* _PC Microcomputer_: A laptop computer with an Intel i5 4210U processor and 12 GB of RAM.
## 5 Results and Discussion
### Structural Optimization Design  
Generally, machine-tool castings are often designed using the empirical method. If it is not optimized, an excess of materials may occur. To achieve high speed and precision, the structure is optimized using a computer, which effectively saves materials. Unnecessary material removal contributes to improved mechanical properties of the machine tool and reduced cost.  
In this study, the structure of the machine tool was optimized using topology optimization. The optimization goals were divided into maximizing rigidity, increasing natural frequency, and quality. Here, maximizing rigidity was chosen. The spindle head, worktable, and saddle of the machine tool were optimized. The model was then modified according to the optimized results, and the original and optimized models were imported into the finite element analysis software for static analysis. In addition, modal analyses were conducted, and the differences were compared. Finally, the results of the modified model and those of the entire machine analysis were compared.
## 5 Results and Discussion
### Algorithm Training Settings  
In the experimental traffic flow settings, considering that highway traffic flow originates from the upstream mainline and on-ramps, the mainline traffic demand generally ranges from 3000 to 7000 vehicles per hour (veh/h), while the ramp demand generally ranges from 500 to 1200 veh/h. To simulate real-world traffic conditions, the simulation environment is set to a medium flow scenario, with a total traffic demand of 5100 veh/h.  
Considering that real-world traffic demand typically undergoes a stochastic variation process, random noise is defined as fluctuations around the traffic demand that do not exceed 10% of the maximum traffic demand. During the first 100 time steps of the simulation, the mainline traffic demand continuously increases, eventually stabilizing around 1700 veh/h/lane. The demands at both on-ramps are set to be the same, fluctuating around 800 veh/h/lane.  
The experiment sets the maximum training epochs for the RL algorithm to 1000. The algorithm terminates training when it reaches the maximum number of training rounds or a convergence condition. During each training epoch, the simulation steps in the CTM simulation environment include an environment warm-up period and a formal training period. At the beginning of each training session, the CTM initializes the simulation environment and warms up the traffic operation environment within 20 simulation steps. Then, over the following 600 simulation steps, agents interact with the stable CTM model environment to obtain observational information. In the traffic control system, the control cycle is set to 3 min, with the integrated controller updating the speed limits and lane change transition coefficients for each lane every 3 min.  
Additionally, by comparing the effects of different experiments, the parameters related to OU noise were selected as \(\theta=0.15\), \(\mu=0\), \(\sigma=0.4\).  
In Table 1, the settings for the algorithm-related parameters are provided:  
Figure 6: Traffic flow density scatter diagram.
# Optimizing Foreign Language Learning in Virtual Reality:
## 6 Discussion  
### Strengths and Limitations of the Framework  
#### 6.1.1 Major Advantages of the VR-CCL Framework  
The VR-CCL framework serves as a blueprint for optimizing language learning experiences in the realm of virtual reality. This framework marries the principles of constructivism and cognitive load theory, balancing immersive engagement and manageable learning pathways.  
One of the standout benefits of the VR-CCL framework is its holistic view of the learning process. Instead of focusing solely on content delivery, it prioritizes meaningful interactions. This emphasis on engagement is crucial, whether between the learner and content, amongst learners, or centered on specific tasks. Interactive engagement is often the difference between passive absorption and active understanding, especially in language learning where practical application is critical.  
The VR-CCL framework's attention to cognitive load management is a testament to its forward-thinking approach. With its endless possibilities, VR can easily overwhelm learners with excessive stimuli. However, by considering the learners' cognitive capacities and fine-tuning content to eliminate unnecessary complexities, the framework guarantees a streamlined and focused learning experience. This precision ensures that learners can efficiently process and internalize language constructs without distractions.  
One of the most salient aspects of the VR-CCL framework is its commitment to personalization. Recognizing that every learner is unique, with distinct needs, preferences, and pace, the framework adapts. It offers bespoke learning pathways, ensuring that the instruction resonates, personally enhancing motivation and comprehension.  
The VR-CCL framework is a comprehensive guide for shaping VR-based language instruction. Considering engagement, cognitive load, and personalization ensures that virtual learning environments are not just technologically advanced, but are also pedagogically sound, providing learners with an unparalleled language acquisition experience.  
The VR-CCL framework's adaptivity extends to its capacity for inclusivity, embodying the principles of universal design for learning. This means crafting VR experiences that are accessible to learners with varying abilities including those with dyslexia or learning disabilities. By integrating features such as modifiable text presentation, multimodal information delivery, and customizable learning paths, the framework is not just personalized, but also universally accessible.
## 3 System Models
### Threat Model  
The widely accepted \"Dolev-Yao (DY) threat model\" [43] was followed in the designing of LDAKM-EIoT. As per the guidelines of the DY model, two communicating entities communicate over an open (insecure) channel. The end-point entities such as \"IoT devices\" are not fully trusted in general. The existing network adversary \((\mathcal{A})\) can eavesdrop, update, or delete the communicated messages as the channel is insecure. We also followed [PERSON]'s adversary model, also named as the \"CK adversary model\" [40], which is the current de facto standard model in the designing and modeling of an \"authenticated key agreement\" security scheme. In the CK adversary model, \(\mathcal{A}\) can have all the capabilities like the DY model. In addition to that, \(\mathcal{A}\) can also compromise the secret credentials along with the \"session states and session keys\" during a session. Moreover, \(\mathcal{A}\) can physical capture some IoT devices and extract the stored information from the IoT devices by the application of a sophisticated power analysis attack [13]. The obtained credentials can be further used to perform other kinds of malicious activities, such as the computation of \"secret credentials\" and the \"session key\", and launch some attacks, such as \"IoT device impersonation\", \"replay\", \"privileged-insider\", and \"man-in-the-middle\". As per the information available in [44], we also assumed that the edge node \((EN)\) was fully trusted and could not be compromised by \(\mathcal{A}\). Otherwise, if the \(EN\) was compromised, the entire network would be compromised. For such a purpose, we followed the method discussed by [PERSON] et al.'s protocol [45]. We assumed that the \(EN\) was equipped with a tamper resistant hardware device so that all the sensitive confidential information (for example, stored cryptographic keys) should be protected from \(\mathcal{A}\). Therefore, the application of a tamper resistant \(EN\) attained strong enough security in LDAKM-EIoT. Though it is also true that the attacks are possible on tamper resistant devices, \(\mathcal{A}\) needs special equipment (device) to perform such an attack to acquire the stored information. This is because it is less expensive to install the \(EN\) than to use special equipment because \(\mathcal{A}\) does not have any economic benefits to perform such an attack on a security scheme [45]. Moreover, we could secure the \(EN\) by putting it inside a physical locking system so that the physical capture of the \(EN\) will be difficult for \(\mathcal{A}\) as compared to that for the unattended IoT smart devices. In addition, the \"trusted authority \((TA)\)\" was also assumed to be a \"fully trusted\" entity of the network and that it would not be compromised, although the \"cloud servers\" were considered to be \"semi-trusted\" entities.  
Figure 1: Network model of the edge based IoT environment.
## 3 Optimal Multichannel ANC for Multiple Noise Scenarios
### Optimal Control for Stochastic Disturbances  
In the above sections, we discussed optimal control for deterministic signals. In this section, we consider a general case with stochastic disturbances, i.e., \(\mathbf{D}\) and \(\mathbf{X}\) becomes non-stationary. In this case, we can rewrite (3) as  
\[\mathbf{Y}_{\text{opt}}(n)=-(\mathbf{S}^{\text{H}}\mathbf{S})^{-1}\mathbf{S}^ {\text{H}}\mathbf{D}(n), \tag{19}\]  
where \(n\) is the time index. Correspondingly, the optimal control filter \(\mathbf{W}\) shall also change from time to time to achieve the optimal performance. In the case where only a fixed filter is realizable, we could factorize all available time frames (say \(N\) frames) into the calculation of a fixed optimal filter, and obtain  
\[\mathbf{W}_{\text{stochastic}}=\dot{\mathbf{Y}}_{N}\dot{\mathbf{X}}_{N}^{ \text{\tiny{$\leftrightarrow$}}\mathbf{H}}\left(\dot{\mathbf{X}}_{N}\dot{ \mathbf{X}}_{N}^{\text{\tiny{$\leftrightarrow$}}\mathbf{H}}\right)^{-1}, \tag{20}\]  
where \(\dot{\mathbf{Y}}_{N}^{\text{\tiny{$\leftrightarrow$}}\mathbf{H}}=\left[ \begin{array}{cccc}\mathbf{Y}_{\text{opt}}(1)&\mathbf{Y}_{\text{opt}}(2)& \ldots&\mathbf{Y}_{\text{opt}}(N)\end{array}\right]\), and \(\dot{\mathbf{X}}_{N}^{\text{\tiny{$\leftrightarrow$}}\mathbf{H}}=\left[ \begin{array}{cccc}\mathbf{X}(1)&\mathbf{X}(2)&\ldots&\mathbf{X}(N)\end{array}\right]\). Combining (19) and (20), we can obtain the final solution for a fixed optimal filter in the time-varying case as  
\[\mathbf{W}_{\text{stochastic}}=-(\mathbf{S}^{\text{H}}\mathbf{S})^{-1}\mathbf{ S}^{\text{H}}\dot{\mathbf{D}}_{N}^{\text{\tiny{$\leftrightarrow$}}\mathbf{H}} \left(\dot{\mathbf{X}}_{N}^{\text{\tiny{$\leftrightarrow$}}\mathbf{H}}\right)^ {-1}, \tag{21}\]  
where \(\dot{\mathbf{D}}_{N}^{\text{\tiny{$\leftrightarrow$}}\mathbf{H}}=\left[ \begin{array}{cccc}\mathbf{D}(1)&\mathbf{D}(2)&\ldots&\mathbf{D}(N)\end{array}\right]\). For large \(N\), we can express the cross-spectral density matrix \(\mathbf{S}_{\text{dx}}=E\left[\dot{\mathbf{D}}_{N}^{\text{\tiny{$\leftrightarrow$}} \mathbf{H}}\dot{\mathbf{X}}_{N}^{\text{\tiny{$\leftrightarrow$}}\mathbf{H}}\right]\) and spectral density matrix \(\mathbf{S}_{\text{xx}}=E\left[\dot{\mathbf{X}}_{N}^{\text{\tiny{$\leftrightarrow$}} \mathbf{H}}\dot{\mathbf{X}}_{N}^{\text{\tiny{$\leftrightarrow$}}\mathbf{H}}\right]\), and express (21) as  
\[\mathbf{W}_{\text{stochastic}}=-(\mathbf{S}^{\text{H}}\mathbf{S})^{-1}\mathbf{ S}^{\text{H}}\mathbf{S}_{\text{dx}}\mathbf{S}_{\text{xx}}^{-1}, \tag{22}\]  
which is consistent with [22]. In a practical case, the statistical analysis of the spectral pattern of the noise will be useful in implementing the fixed optimal control filter. The stochastic optimal control solution, though having been well established in the literature, did reveal that the proposed MCANC framework with underdetermined system brings insights into ANC solutions. However, a full evaluation of this method, as it is beyond the scope of this paper, will not be reported in the following sections.
## 4 Experiment
### Data Generation Based on the Degradation Model and Sobol Sampling  
Five run-to-failure training data are used to fit the degradation model here. Bearing3_1 is discarded because the RMS of this bearing is not suitable for the degradation model in Equation (6). Then five sets of parameters \(a,b,e\) can be acquired by the proposed data augmentation method. 250 run-to-failure data are generated for the preparation of the Wiener-LSTM training. Ten examples of the generated run-to-failure data are shown in Figure 7. The lifetime of the generated run-to-failure data ranges from 1000 s to 3000 s. It should be noted that the RUL of the bearing is calculated from the Failure Occurrence Time (FOT), while the PHM2012 is an accelerated degradation experiment.  
In Figure 7, the number \(1,2,3,\ldots,10\) denotes the generated run to failure data of bearing 1 to 10. The horizontal axis shows the inspection time. The vertical 1 axis shows the value of RMS. The RMS of the generated run to failure bearing data is consistent with the exponential model. To simulate the run to failure data with a different lifetime, the generated data are different from each other.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Data Set**} & \multicolumn{3}{c}{**Operation Conditions**} \\ \cline{2-4}  & **Conditions 1** & **Conditions 2** & **Conditions 3** \\ \hline
**Load (N)** & 4000 & 4200 & 5000 \\ \hline
**Speed (rpm)** & 4800 & 1650 & 1500 \\ \hline
**Training set** & Bearing 1\_1 & Bearing 2\_1 & Bearing 3\_1 \\  & Bearing 1\_2 & Bearing 2\_2 & Bearing 3\_2 \\ \hline
**Testing set** & Bearing 1\_3 & Bearing 2\_3 & Bearing 3\_3 \\  & Bearing 1\_4 & Bearing 2\_4 & \\  & Bearing 1\_5 & Bearing 2\_5 & \\  & Bearing 1\_6 & Bearing 2\_6 & \\  & Bearing 1\_7 & Bearing 2\_7 & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Datasets of IEEE PHM 2012 prognostic challenge.  
Figure 7: Ten examples of the generated data by the exponential model. Different color curves represent different run-to-failure data. The number \(1,2,3,\ldots,10\) denotes the generated run to failure data of bearing 1 to 10. The horizontal axis shows the inspection time. The vertical axis shows the value of RMS.  
As for the testing data. Bearing 1_3 and Bearing 1_4 are chosen to verify the effectiveness of the proposed method. The RMS values of the Bearing 1_3 and Bearing 1_4 are calculated and the data of 1000 s before failure are used for RUL prediction on these two testing bearings.