esa-sceva/satcom-chunk-collection huggingface dataset:
# Secure and Efficient Authentication Scheme in IoT Environments
## 5 Evaluation
### Comparison by Performance
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Operation** & **Terminology** & **Execution Time (ms)** \\ \hline Hash operation & \(T_{h}\) & 0.039 \\ ECC operation & \(T_{ccm}\) & 0.110 \\ Modular exponentiation & \(T_{expo}\) & 0.343 \\ Pairing & \(T_{pair}\) & 0.992 \\ Symmetric encryption + decryption & \(T_{s}\) & 0.686 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Primitive timing for cryptography operation. = 256 bits, where each parameter consumes 128 bits. The costs of the other parameters for the communication element, described in Table 5, are \(X_{i}\), \(Y_{i}\), \(CID_{i}\), \(X_{j}\), \(Y_{j}\), \(T\), and \(C_{i}\). Thus, the communication cost is \((X_{i},Y_{i},CID_{i})+\left(X_{j},Y_{j},T\right)+C_{i})\) (\(640+640+128\)) = 1408 bits. Similarly, we computed the communication costs for the recent schemes of existing approaches and compared the recent schemes with the proposed scheme. In comparison, as depicted in Table 4, we conclude that the communication cost of the proposed protocol is lower than the existing schemes. * Storage cost: Throughout this subsection, we measured the storage component cost depending on the PBC library applied on our machine, which meets NIST [48] and ECRYPR II [49], stored in the embedded device's memory. In the proposed scheme, the embedded device stores cookies as recommendations. To compute storage cost, we need to identify the number of components is well as their pseudo-identity (i.e., \(C_{k}^{\prime},CID_{i}\)), thereby consuming \(256+128=384\) bits of memory. Likewise, we counted a number of components stored in the embedded devices' memory for recent schemes and computed their storage cost. From Table 4, we infer that the proposed scheme consumes a very small amount of memory, 384 bits, compared to other existing schemes. Thus, from Table 4, we conclude that the proposed scheme has a lower running (i.e., operation) cost for IoT devices relative to other recent schemes. This lower operating cost helps the proposed scheme to eliminate the existing gaps in recent schemes in order to become more efficient and suitable for real-time scenarios.
## 2 Literature Review
### Related Studies
In this study, the overall accuracy of multiclass classification was 87.4% (84.2-90.1%). The sensitivities for classification of no change in VEP, increasing VEP, and decreasing VEP were 92.6%, 78.9%, and 83.7%, respectively, and their specificities were 80.5%, 93.3%, and 100.0%, respectively. Somatosensory evoked potential (SEP) is a modality that acts as the framework of intraoperative spinal surgery monitoring [4]. [PERSON] et al. [22] utilized least squares and multi-support vector regression models on 15 patients undergoing spinal surgery to intraoperatively interpret the SEP results. They defined the warning criteria as an amplitude reduction of \(\geq\)50% or a latency delay of \(\geq\)10%. Target outcomes were classified as successful, false-positive, or trauma cases. Their intelligent decision system lowered the false warning rate compared with their conventional method and enabled more accurate detection of spinal cord trauma. The multi-support vector regression model performed better than the least squared support vector regression model. \begin{table}
\begin{tabular}{c c c c c c c} \hline
**Author** & **Samples** & **IONNM Modality** & **Models** & **Target Outcome** & **Summary of Results** \\
**(Year)** & **Samples** & **IONNM Modality** & **Models** & **Target Outcome** & **Summary of Results** \\ \hline [PERSON] et al. [16] (2022) & 55 patients who underwent & & & & The proposed method was & \\  & lumbar surgeries & MEP & KNN and Bagged trees & Positive outcome & & interior to the existing criteria. \\  & & lumbar surgeries & & (motor improvement) & - & Sensitivity of 87.5\% \\  & & & & & - & Specificity: 33.3\% \\ \hline [PERSON] et al. [17] (2022) & 993 patients who underwent & & & Change in status & - & AUROC: 1.00 \\  & spinal surgery & MEP and SEP & Deep learning-based natural language processing & Change in status & - & F1 score: 0.80 \\  & & & & Impactivity establishing & - & AUROC: 0.97 \\  & & & & baseline & - & F1 score: 0.80 \\  & & & & & - & AUROC: 0.91 \\  & & & & & Stable course & - & F1 score: 0.93 \\ \hline [PERSON] et al. [18] (2021) & 34 patients who underwent & & & & & Accuracy: 97.6\% \\  & thyroid surgery & EMG & Bayesian CNN & Classification of & - & Precision: 97.7\% \\  & & & & action potentials & - & Recall: 97.5\% \\ \hline [PERSON] et al. [19] (2021) & 5 patients who underwent & & & & & The hybrid model could \\  & thyroid surgery & & & & EMG signal waveforms & automatically classify the \\  & & & & (quiet, evoked, irritation, & fore-running EMG, \\  & & thyroid surgery & & & burst, injury, and artifact) & - & Accuracy: 89.5\% \\  & & & & & - & Sensitivity: 94.23\% \\ \hline [PERSON] et al. [20] (2021) & 533 TOF samples from 35 patients & AMG & & & & AMG-based intraoperative \\  & & & & & & measurements of TOF outliers \\  & & & & & displayed an increased \\  & & & & & & monitoring consistency. \\  & & & & & - & F1 score: 0.86 \\  & & & & & - & AUROC: 9.91 \\ \hline \end{tabular}
\end{table} 
Table 1: The application of machine learning in the field of intraoperative neurophysiological monitoring.
## 4 Results and Discussion
### Flow Field and Stress Filed
#### 4.1.2 Stress Field after Fracture Initiation  
After the first step of simulation, the FSI code was executed, and the shale rock was studied using the restart solution. The element failure criterion was activated, and some elements were removed as the tensile stress reached the failure tensile stress. As shown in Figure 13, under the load of perforation pressure and in situ stress, the fracture can easily be generated at three locations. These locations are the root, the middle, and the tip of the perforation.  
There are different mechanisms of crack initiation in the three different positions of the perforation. Cracks were observed in both the root and the tip, but only one crack was observed in the middle of the perforation. It was noted that the root of the perforation was different from the others because it was the interface between the shale rock and the cement. Additionally, the length of the crack in the root was greater than the others. This is due to the material discontinuity and structure discontinuity between the cement and the rock. While comparing the cracks at three locations, it was found that there were more cracks in the tip. In the previous results of other scholars, there are many methods and models to demonstrate the crack initiation, including fracture criterion [24], stress distribution [25; 26], and so on.  
The stress distribution dominated the crack initiation, led to different lengths and styles of crack. Even though the shale was compressed by both in-situ stress and internal pressure, there was tensile stress region in the shale. The tensile stress contributes to crack initiation. In Figure 14, the stress distribution in the \(y\)-direction is listed in the first row, while that in the \(z\)-direction is listed in the second row. In the tip, it was found that the tensile stress in the \(y\)-direction was distributed in front of the crack tip. This means that the stress in the \(y\)-direction controlled the crack initiation and propagation of the tip, while the SC-CO\({}_{2}\) jet was in the perforation. However, in the middle and the root of the  
Figure 13: Flow field and stress field before fracture initiation.  
perforation, the maximum tensile stress in the \(z\)-direction was generated in front of the crack tip. Therefore, it was concluded that the stress in the \(z\)-direction controlled the crack initiation. On the surface of the perforation tunnel, the rock was compressed by internal pressure, so the extreme tensile stress was locally formed under the influence of both in situ stress and internal pressure. Furthermore, irregular perforation profiles and inhomogeneous rock properties also contributed to fracture initiation. At the end of the perforation, the stress concentration at the tip led to the occurrence of cracks.
# An Efficient Adaptive Data-Link-Layer Architecture for LoRa Networks
###### Abstract  
LoRa is one of the most popular low-power wireless network technologies for implementation of the Internet of Things, with the advantage of providing long-range communication, but lower data rates, when compared with technologies such as Zigbee or Bluetooth. LoRa is a single-channel physical layer technology on top of which LoRaWAN implements a more complex multi-channel network with enhanced functionalities, such as adaptive data rate. However, LoRaWAN relies on expensive hardware to support these functionalities. This paper proposes a LoRa data-link-layer architecture based on a multi-layer star network topology that adapts relevant LoRa parameters for each and node dynamically taking into account its link distance and quality in order to balance communication range and energy consumption. The developed solution is comprised of multiple components, including a LoRa parameter calculator to help the user to configure the network parameters, a contention-free MAC protocol to avoid collisions, and an adaptive spreading factor and transmission power mechanism. These components work together to ensure a more efficient use of the chosen ISM band and end node resources, but with low-cost implementation and operation requirements.  
LoRa; data link layer; spreading factor; transmission power; MAC protocol Article  
An Efficient Adaptive Data-Link-Layer Architecture for LoRa Networks
## 2 Results and Discussion
### Surface Characterization of the Carbon Composites Containing Nano-ZrO\({}_{2}\) and Nano-ZrO
\(\pi\)-\(\pi^{\prime}\) satellites were also present in the spectra at a binding energy of approximately 290 eV. Chlorine or sulfur could not be detected in the L-Zr-Cl-T-t and L-Zr-S-T-t samples. The surface of RF-plasma-treated samples also contained 80-90 atomic% graphite and 1-8 atomic% ZrC\({}_{0.94}\). The quantity of ZrC0.94 is the highest in the L-Zr-N samples and the lowest in the L-Zr-S samples. In the case of H\({}_{2}\) plasma treatment, the surface ZrC\({}_{0.94}\) contents are always higher than in the case of He plasma treatment. The ZrC\({}_{0.94}\) content vs. the zirconium oxide content on the surface is the highest in the L-Zr-N samples. ZrO\({}_{2}\) and zirconium oxycarbide concentrations varied between 2 and 11 atomic% (Supplementary Materials, Table S5), and a low percentage of oxidized carbon derivatives were also detected. Figure 7: X-ray photoelectron spectra of the C\({}_{18}\) lines of the composites containing nano-ZrO\({}_{2}\)@C and nano-(Zr\({}_{0.58}\)C,ZrO\({}_{2}\))@C.  
Figure 8: X-ray photoelectron spectra of the Zr\({}_{\rm 3d}\) lines of the composites containing zirconium carbide prepared in RF plasma at various conditions.
###### Abstract  
We focus on dual-user operation, where two users control a single remote robot equipped with a force sensor using haptic interface devices. We employ a cooperative work in which the two users control the remote robot to collaborate with remote robot systems with force feedback to carry an object. By measuring the force acting upon the object, we aim to better understand the underlying mechanisms by which the user with lower network latency can help the other user, as observed in our previous work. We notice that with increasing network delays, the force exerted on the object tends to intensify, indicating that it becomes more challenging for users to operate the remote robot effectively as network delays increase. We also measure the force applied to the object by changing the network delays between the remote robot and the two users to clarify why the user with the lower network delay can assist the other user. We find that when the total network delay is the same, the average force magnitude and the average maximum force magnitude remain nearly identical. This is because, despite the challenges faced by the user with the larger network delay, the user with the smaller delay can operate the remote robot more easily and assist the other user. In order to reduce the force acting upon the object, we propose an enhancement method for the robot position control, which determines the position of the remote robot arm while accounting for network delay, and investigate the effects by experiment. Experimental results demonstrate that our proposed method is effective and can reduce the applied force. This is because the proposed method adjusts the ratio between the user with the lower delay and the user with the higher delay. The user with the lower delay can operate the remote robot more easily and respond to it more quickly. Our findings and proposed method can be useful in improving work accuracy and operability when designing a remote robot system with force feedback for applications.  
remote robot systems; force feedback; haptic interface device; dual-user operation; robot position control; network delay
## 3 Results
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Contrast Methods** & & **Accuracy \%** & **Sensitivity \%** & **Specificity \%** & **F1 Score \%** & **AUC** \\ \hline \multirow{3}{*}{LH-CON} & K-means & 86.55 & 63.68 & 93.15 & 64.81 & - \\  & FCM & 92.41 & 73.73 & 97.29 & 79.96 & 0.84 \\  & ANFIS & 90.34 & 72.07 & 95.28 & 76.8 & 0.82 \\ \hline \multirow{3}{*}{LH-XWW} & K-means & 89.31 & 70.05 & 96.37 & 76.44 & - \\  & FCM & **94.14** & 83.54 & 96.99 & 83.67 & **0.89** \\  & ANFIS & 91.72 & 79.39 & 95.35 & 81.34 & 0.86 \\ \hline \multirow{3}{*}{IFS} & K-means & 82.41 & 70.71 & 87.08 & 65.66 & - \\  & FCM & 91.72 & 69.89 & 97.29 & _77.46_ & 0.81 \\  & ANFIS & 90.69 & 67.89 & 95.88 & 73.19 & 0.75 \\ \hline \multirow{3}{*}{PFS} & K-means & 82.41 & 76.27 & 85.51 & 72.46 & - \\  & FCM & 93.45 & 73.59 & 97.87 & 79.84 & 0.82 \\  & ANFIS & 92.07 & 82.52 & 95.26 & 83.71 & 0.86 \\ \hline \multirow{3}{*}{FFS} & K-means & 85.86 & 73.26 & 91.05 & 74.87 & - \\  & FCM & 91.72 & 81.19 & 94.69 & 82.36 & 0.84 \\ \cline{1-1}  & ANFIS & 92.07 & 75.15 & 96.21 & 76.49 & 0.83 \\ \hline \multirow{3}{*}{OWA-GA} & K-means & 85.17 & 83.97 & 84.98 & 77.77 & - \\ \cline{1-1}  & FCM & 92.76 & 84.71 & 94.51 & 82.83 & 0.87 \\ \cline{1-1}  & ANFIS & 90.69 & 70.96 & 95.74 & 73.54 & 0.79 \\ \hline \multirow{3}{*}{OWA-HK} & K-means & 85.17 & 70.63 & 91.07 & 69.6 & - \\ \cline{1-1}  & FCM & 93.1 & 84.79 & 96.3 & **86.22** & 0.88 \\ \cline{1-1}  & ANFIS & 90.69 & 66.23 & 98.26 & 76.48 & 0.76 \\ \hline \end{tabular}
\end{table} 
Table 3: Indicators for type-2 fuzzy sets thresholding method (The best scores are bold). Advanced fuzzy sets (IFS, PFS and FFS) yielded satisfactory results, especially with global and Otsu thresholding methods.
## 3 Results
### Experimental Results
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Samples** & **Parameters** &
\begin{tabular}{c} **Four-Probe** \\ **Bridges** \\ \end{tabular} & **SEM** & **Error** \\ \hline \multirow{4}{*}{Sample 1b} & \(R_{S}\) & 22.07 \(\Omega\)/sq & - & - \\  & \(w\) & 10.074 \(\mu\)m & - & - \\  & \(t_{1}\) & 1.626 \(\mu\)m & 1.512 \(\mu\)m & 7.54\% \\  & \(t_{2}\) & 2.887 \(\mu\)m & 2.773 \(\mu\)m & 4.12\% \\  & \(\theta\) & 76.03\({}^{\circ}\) & 80.22\({}^{\circ}\) & 5.22\% \\ \hline \multirow{4}{*}{Sample 2b} & \(R_{S}\) & 21.75 \(\Omega\)/sq & - & - \\  & \(w\) & 10.134 \(\mu\)m & - & - \\  & \(t_{1}\) & 1.624 \(\mu\)m & 1.518 \(\mu\)m & 6.98\% \\  & \(t_{2}\) & 2.910 \(\mu\)m & 2.776 \(\mu\)m & 4.82\% \\  & \(\theta\) & 75.82\({}^{\circ}\) & 80.64\({}^{\circ}\) & 5.98\% \\ \hline \multirow{4}{*}{Sample 3b} & \(R_{S}\) & 22.17 \(\Omega\)/sq & - & - \\  & \(w\) & 15.290 \(\mu\)m & - & - \\  & \(t_{1}\) & 1.594 \(\mu\)m & 1.510 \(\mu\)m & 5.59\% \\  & \(t_{2}\) & 2.893 \(\mu\)m & 2.773 \(\mu\)m & 4.33\% \\  & \(\theta\) & 75.29\({}^{\circ}\) & 80.96\({}^{\circ}\) & 7.01\% \\ \hline \multirow{4}{*}{Sample 4b} & \(R_{S}\) & 21.89 \(\Omega\)/sq & - & - \\  & \(w\) & 15.108 \(\mu\)m & - & - \\  & \(t_{1}\) & 1.607 \(\mu\)m & 1.515 \(\mu\)m & 6.07\% \\  & \(t_{2}\) & 2.891 \(\mu\)m & 2.778 \(\mu\)m & 4.07\% \\  & \(\theta\) & 77.56\({}^{\circ}\) & 81.03\({}^{\circ}\) & 4.28\% \\ \hline \multirow{4}{*}{Sample 5b} & \(R_{S}\) & 21.93 \(\Omega\)/sq & - & - \\  & \(w\) & 20.090 \(\mu\)m & - & - \\ \cline{1-1}  & \(t_{1}\) & 1.619 \(\mu\)m & 1.511 \(\mu\)m & 7.18\% \\ \cline{1-1}  & \(t_{2}\) & 2.881 \(\mu\)m & 2.773 \(\mu\)m & 3.90\% \\ \cline{1-1}  & \(\theta\) & 76.15\({}^{\circ}\) & 80.88\({}^{\circ}\) & 5.85\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Experimental results of Poly 2 layer and stacked Oxide 1-Oxide 2 layer.
# Interpolating the Directional Room Impulse Response for Dynamic Spatial Audio Reproduction
## 4 Results and Analysis
### Comparative Analysis in the Classroom
#### 4.1.1 Example Interpolated DRIR Signals  
Taking row 3 as an example, the middle recording position labelled as \"30x10y\" is taken as the reference point, and the DRIR recording at this location is the ground truth for DRIR interpolation of the two side positions, \"15x10y\" and \"45x10y\". The proposed algorithm firstly separates the recorded DRIR into two parts, the specular reflections (including the direct sound) and diffuse reflections. After this, the algorithm further decomposes the specular reflections into different components and obtains their corresponding image sources and DOAs. By interpolating this information for each component in the direct sound and specular reflections at the selected two side positions in each considered row, the interpolated DRIR at the listening position in that row can be obtained by combining these interpolated components with a known FOA recording. The overall interpolated DRIR in row 3 is illustrated in Figure 5, which is compared with the known (ground truth) recording at the same position, with the \(x\)-axis being the number of discrete time samples and \(y\)-axis being the normalized amplitude of the \(W\)-channel FOA recording.  
Taking the \(W\) channel of the interpolated DRIR as an example, it can be visually seen that generally the interpolated DRIR obtained by utilizing the proposed method accurately matches the ground truth for both databases. In the real-world classroom, the \(W\) channel almost exactly matches the ground truth, whereas the amplitude is a little lower. As for the simulation, the interpolated DRIR highly matches the ground truth due to the ideal acoustics and recording arrangements in the room, whereas the amplitude of all interpolated DRIRs is a bit larger, which may be due to extra summations of interpolated components.
# Algorithm Development for the Non-Destructive Testing of Structural Damage
## 3 Damage Visualization
### Developed Image Processing Algorithms
#### 3.1.2.1 Segmentation of Steel Plate (SSP) Images
4 Computation of the inverse Fourier Transform to get the resulting image \"l\". 5 Computation of multi-thresholding on \"l\" to obtain \"S\" as a segmented image. 6 Performing the morphological operation (erosion) on \"S\" to remove small objects. ```  
_Algorithm 2_: The second proposed algorithm, in this section follows the Pseudocode 2 procedure, in which the Wavelet transform function will be replaced by a Gaussian function. _Algorithm 3_: The third algorithm is based on fuzzy c-mean (FCM) clustering, followed by Multi-thresholding and erosion. This algorithm has been formed in this paper only for a comparison with Algorithms 1 and 2, as it uses the FCM procedure. 3.1.2.3. Segmentation of Metal Plate (SMP) Images  
Three algorithms are proposed for this segmentation. The first and second algorithms almost completely follow the traditional approach, and are investigated for comparison. _Algorithm 1_: In the first algorithm, the performance of the traditional gradient magnitude based on Sobel's gradient operator [36] in the x and y axes is investigated. The edges of the notch were returned at those points where the gradient of the image was at its maximum. Therefore, the edges were achieved by using the Sobel approximation to the derivative. **Algorithm 2**: The second algorithm calculates the gradient by using the derivative of a Gaussian filter, and it employs two thresholds; the local maxima of the gradient are considered to be edges. **Algorithm 3**: In the third algorithm, the effective selection of the threshold value is performed on pre-processed MP images. The threshold value is obtained by [PERSON]'s thresholding method, and it is employed, to improve the threshold value of the Fuzzy c-mean technique. The center value of the thresholds was considered to be the desired value, which is more likely to detect true edges. The edges that were not stronger than the threshold value were ignored. Figure 1 shows the flowchart of this algorithm. The approaches, introduced above, allow experts to accurately visualize the defects that are significantly associated with any subset or linear combination of cracks. The methods can greatly simplify health monitoring in steel, composite materials, and metal structures, respectively. These algorithms can be generalized by investigations on other materials. In the following section, the experimental setup and results are discussed.
# Development and Preliminary Evaluation of a Lower Body Exosuit to Support Ankle Dorsiflexion
## 3 Preliminary Evaluation
### Experimental Design  
To measure lower body kinematics during gait, a mobile Xsens MTw Awinda System was used (Xsens Technologies BV, Enschede, Netherlands). This device shows an acceptable to excellent reliability and excellent validity [42]. With a Myon 320 system (Myon AG, Schwarzenberg, Swiss), the muscle activity of the m. tibialis anterior was recorded. The system exhibits good to acceptable reliability [43]. The sensor placement was conducted following SENIAM (Surface Electromyography for Non-Invasive Assessment of Muscles) guidelines. To synchronize both systems, a trigger signal from Xsens was used to mark start and stop into Myon Software.  
At the beginning, all subjects underwent a maximum voluntary contraction test for m. tibialis anterior. For Xsens System, a calibration walk of eight steps going back and forth was needed. Afterwards, each participant put the exosuit on the left lower leg and adjusted the size of the hip belt and supporting structure, as well as the length of the Bowden cable to get a sufficient foot lift. Turning the system on, they were able to familiarize with the activated exosuit for five minutes to overcome unwanted protective posture during gait  
Figure 4: Control scheme.  
and achieve a steady state. They completed a level walkway with 22 strides with and without wearing the exosuit on the left leg while walking with self-selected gait speed and maintaining their usual gait pattern. The with and without support trials were randomized and stopped after the first signs of stumble or any abnormal functions of the exosuit.