esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methods
### TransUNet  
The TransUNet [41] architecture has been used for the medical image segmentation on the Synapse multi-organ segmentation dataset for segmenting the abdominal CT images into eight abdominal organ classes as shown in Figure 5. Since the task of skin lesion segmentation is quite different from multiorgan segmentation, as in skin lesion segmentation,  
Figure 4: Attention U-Net architecture adapted from [40].  
Figure 3: V-Net architecture adapted from [39].  
the task is to predict the binary mask rather than the multiclass pixel-wise segmentation. Therefore, we modified the basic architecture of the TransUNet, which is a combination of the Transformer and U-Net architectures in order to address the challenges in skin lesion image segmentation. For an image, instance \(i\in\ \mathbb{R}^{H\times W\times C}\), with a spatial resolution of \(H\times W\) and a channel count of \(C\) is determined. The goal is to forecast the label map with size \(H\times W\) that corresponds to the related ground truth label map. The most common and conventional method is to train a CNN (such as U-Net) to encode images into high-level feature representations, which are subsequently decoded back to full spatial resolution. Unlike other approaches, this method uses Transformers to incorporate self-attention mechanisms into the encoder architecture.  
The input to the model is (512, 512, 3) images. The model has four down and upsampling levels-two convolutional layers per downsampling and upsampling layers, respectively. We used 12 transformers blocks and the number of attention heads was set to 12. The other changes that have been made to the basic architecture of the TransUNet are the number of nodes in the multi-layer perceptron (MLP) and the embedding dimensions. In the proposed model, each transformer has 1536 multi-layer perceptron (MLP) nodes that embed the images into 384 dimensions. The reason for modifying these was to make the network focus on more precise localization and produce accurate binary masks as compared to the multiclass segmentation in the base TransUNet. For the transformer, an MLPs Gaussian Error Linear Unit (GELU) was used as an activation function and upsampling through bilinear interpolation was carried out. Sigmoid was used as an output function.
# Hybrid Empirical and Variational Mode Decomposition of Vibratory Signals
## 2 Evolution of the Hilbert-Huang Transform
Its development reflects a continuous effort by the scientific community to understand and analyze the inherent complexity of dynamic systems. The global COVID-19 pandemic accelerated the adoption of digital health technologies and telemedicine, where the HHT found important applications in the analysis of biomedical signals for remote patient monitoring and treatment personalization. The combination of the HHT with emerging deep learning models emerged as a promising area of research, offering significant improvements in complex data classification and analysis and facilitating the development of advanced diagnostic systems [2,70]. Looking forward, the HHT is expected to play a crucial role in predictive analysis, leveraging its ability to decompose and analyze complex signals to predict future events and trends more accurately. Its ongoing development signifies its potential to expand its applications to complex systems in environmental sciences, quantum physics, and beyond. This historical overview underscores the ongoing role of the HHT as a dynamic and adaptable tool in data analysis, prepared to meet emerging challenges in science and technology. The HHT and its related developments continue to evolve, driven by the need for analytical tools capable of addressing the increasing complexity of data in the modern era.
## 3 Traveling Waves (TW)
### Fault Location Criteria
#### 4.1.2 A Type Faults  
The concept of time lag is employed here. Typically, dc fault generate two traveling waves which travels toward both ends of the dc-link. The first traveling wave reaches straight at primary protection relay while the second traveling wave reaches at PP relay after reflection from second end of the dc-link. As it is difficult to make a difference between two wave fronts, the local maximum value of integration is used for identification and mathematically given as;  
\[\textit{local max}\{i[t],t\in\left[t_{o}-t_{1}^{\prime},t_{o}-t_{2}^{\prime} \right]\}=I[t_{di}] \tag{5}\]  
where \(i=1,2,3,\ldots\) and \(t_{di}\) are the points of time of reflected and refracted wave fronts.  
Magnitude of first transient traveling wave is important because of the attenuation phenomenon in transmission lines. Thus, the location of fault can be detected easily via the first traveling wave reaching at relaying point from fault position with time \(t_{s1}\) and traveling wave reflected from the second end of dc-link with time \(t_{s2}\). The intensity of TW reflected from fault position is higher than the intensity of wave reflected from second end when fault position is near to the ends containing detectors or when the resistance is very small [60]. Mathematically, it can be expressed as:  
\[\left\{\begin{array}{l}I[t_{s1}]>\ \forall\textit{ local max}[i[t]]\\ t\in\left[t_{o}-t_{1}^{\prime},t_{s2}\right)\cup\left(t_{s2},\ t_{o}+t_{2}^{ \prime}\right]\end{array}\right. \tag{6}\]  
\[\begin{array}{l}I[t_{s2}]>\ \forall\textit{ local max}[i[t]]\\ t\in\left[t_{o}-t_{1}^{\prime},t_{s1}\right]\cup\left(t_{s1},\ t_{o}+t_{2}^{ \prime}\right]\end{array}\right. \tag{7}\]  
and  
\[t_{s2}>t_{s1}\]  
The distance of VSC station containing detector from fault point is calculated by:  
\[l=L-\frac{v(t_{s2}-t_{s1})}{2} \tag{8}\]  
Thus, dc fault location for type A faults in MT-HVdc systems having high grounding resistances can be found from Equation (8).
# All-in-Focus Three-Dimensional Reconstruction Based on Edge Matching for Artificial Compound Eye
## 5 Experiments
### Evaluation
#### 5.2.4 Image Reconstruction
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**eCley Image** & **Metrics** & **Sub-Image 1** & **Sub-Image 2** & **Sub-Image 3** \\ \hline \multirow{4}{*}{BP [6] 2003} & MSE & 0.0823 & 0.0593 & 0.0766 \\ \cline{2-5}  & \(D_{b}\) & 2732 & 1910 & 1783 \\  & \(D_{t}\) & 11682 & 7427 & 11386 \\  & \(D_{s}\) & 665 & 367 & 489 \\  & \(D_{p}\) & 3982 & 2417 & 3142 \\ \hline \multirow{4}{*}{Cost Filter [8] 2012} & MSE & 0.0843 & 0.0654 & 0.0829 \\ \cline{2-5}  & \(D_{b}\) & 2662 & 1821 & 1704 \\  & \(D_{t}\) & 10821 & 7309 & 10923 \\  & \(D_{s}\) & 587 & 321.4 & 501 \\  & \(D_{p}\) & 3811 & 2398.2 & 3028.7 \\ \hline \multirow{4}{*}{G3D [41] 2018} & MSE & 0.048 & 0.0536 & 0.0535 \\ \cline{2-5}  & \(D_{b}\) & **2883 1** & 1987.2 & 1847 \\  & \(D_{t}\) & 11861 & 7654.5 & 11556 \\  & \(D_{s}\) & **684.4** & 385 & 519.8 \\  & \(D_{p}\) & **4099** & 2501.8 & 3179.8 \\ \hline \multirow{4}{*}{EdMP [this work] 2024} & MSE & **0.0412** & **0.0507** & **0.0476** \\ \cline{2-5}  & \(D_{b}\) & 2520 & **2288.1** & **2045** \\ \cline{1-1}  & \(D_{t}\) & **16,446** & **11,983** & **17,116** \\ \cline{1-1}  & \(D_{s}\) & 463.6 & **622.7** & **648.9** \\ \cline{1-1}  & \(D_{p}\) & 3105 & **3273.8** & **3522.7** \\ \hline \hline \end{tabular} \({}^{1}\) The bold means the best.  
\end{table} 
Table 2: Reconstructed image evaluation. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **EdMP** & **Cost Filter** & **BP** & **G3D** \\ \hline Time (s) & 180.24 & 197.92 & 312.77 & 169.53 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The average execution time. Figure 15: Reconstructed image comparison with different depth estimation methods. From top to bottom: BP-based method, Cost Filter-based method, G3D-based method, EdMP, details of the corresponding box in the reconstructed images (dashed green box: BP, solid green box: Cost Filter, dashed red box: G3D, solid red box: EdMP).
# A Fast Indoor/Outdoor Transition Detection Algorithm Based on Machine Learning
## 2 Materials and Methods  
### System Overview  
We divided complex environments into four types: deep indoors, shallow indoors, semi-outdoors and open outdoors, as shown in Figure 2. Deep indoors is the area far from windows, balcony and glass curtain wall, while shallow indoors is the area opposite of deep indoors. Semi-outdoors is a semi-open area covered by modern buildings, overpasses and patios, while open outdoors is in the non-covered area with better sky exposure.  
Figure 1: Indoor and outdoor transition areas.  
We constructed a dataset by associating the GNSS measurements from Android smartphones and the truth value of the abovementioned scenarios. We extracted 36 features under three types of spatial geometry distribution, time sequence and statistical from GNSS measurements. Depending on the scenarios, the indoors and outdoors scenarios were marked as positive sample and negative sample, respectively. Thus, the IO detection task was transformed into a supervised binary classification problem. Multiple single classification models and a classification model based on stacking were used for training and testing. After the classification model forecast, a hidden Markov model inferred the current environment types from the predictions as final detection results. We evaluated the accuracy and context switch latency of our proposed algorithm under complex scenarios using four different datasets. Figure 3 illustrates the algorithm framework of the proposed IO detection.
## 5 Results
### Tightening Performance Analysis
The final torques in the first and second stages were applied in order to quantitatively evaluate the degrees of tightening difficulty, and they were named the first final torque (FFT) and the second final torque (SFT), respectively. The values of FFTs and SFTs were obtained, as shown in Table 3. The FFT and SFT of the eccentric double nut were both the largest, followed by regular double nuts and the double-thread bolt connection. This demonstrates that the interference and eccentricity in an eccentric double nut increase the difficulty of tightening. It is interesting to discover that it is more convenient to tighten a double-thread bolt connection than regular double nuts.
# Channel Characterization and SC-FDM Modulation for PLC in High-Voltage Power Lines
## 6 Conclusions  
A comprehensive new model was presented for the frequency-response estimation of a PLC on HV transmission lines, incorporating the effects of coupling equipment and wave traps on channel modeling. Based on the analysis of the results, we found that the use of the SC-FDM-PLC systems offers greater transmission capacities (10 to 30 times) than the maximum possible with conventional HV-PLC systems. The results presented here show the superiority of the SC-FDM modulation in comparison with the conventional OFDM, obtaining a lower BER and greater spectral efficiency to the low SNR levels. The proposed system is capable of reducing distortion effects in the frequency-selective PLC channel, offering a solution for operating over HV power lines with wider bandwidths.  
Conceptualization, J.A.D.P.-F. and J.L.N.; methodology, J.A.D.P.-F., J.L.N. and F.P.-C.; software, J.A.D.P.-F.; validation, F.P.-C., R.P.-M., L.J.V. and C.D.-V.-S.; formal analysis, J.A.D.P.-F. and J.L.N.; investigation, J.A.D.P.-F., J.L.N., F.P.-C., R.P.-M., L.J.V. and C.D.-V.-S.; resources, J.A.D.P.-F., J.L.N., F.P.-C., R.P.-M., C.D.-V.-S. and L.J.V.; writing--original draft preparation, J.A.D.P.-F.; writing--review and editing, J.L.N., L.J.V. and C.D.-V.-S.; supervision, L.J.V. and C.D.-V.-S.; project administration, J.A.D.P.-F. and J.L.N.; funding acquisition, J.A.D.P.-F., L.J.V. and C.D.-V.-S. All authors read and agreed to the published version of the manuscript.  
The authors are thankful for the funding from the Universidad Panamericana.  
Not applicable.  
Not applicable, the study does not report any data.  
The authors declare no conflict of interest.
## 2 Materials and Methods
### Proposed Scheme
#### 2.5.1 Pre-Deployment Phase  
The system parameters are selected in this phase, and it pre-loads information in deployed sensor nodes and gateways before being deployed in a target field. This phase is carried out in a stand-alone mode. The system administrator (SA) is responsible for and manages the pre-deployment phase. Each cluster has n sensor nodes that are deployed randomly or manually in the preceding stage with a target field; each cluster also contains (HGWN). In this work, we assume that every sensor node chooses the nearest HGWN. The SA, on the other hand, selects the system parameters in the following manner:  
Figure 5: The proposed scheme communication models.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Notations** & **Description** \\ \hline SA & System administrator. \\ HGWN & Home gateway. \\ FGWN & Foreign gateway. \\ \(SNi\) & Sensor. \\ \(U_{i}\) & Agriculture professional. \\ \(SN_{ID}\) & Sensor identity. \\ \(SN_{MSK}\) & Sensor master key. \\ \(GW_{ID}\) & Gateway identity. \\ \(GW_{MSK}\) & Gateway master key. \\ \(HGW_{ID}\) & Home gateway identity. \\ \(HGW_{MSK}\) & Home gateway master key. \\ \(FGW_{ID}\) & Foreign gateway identity. \\ \(FGW_{MSK}\) & Foreign gateway master key. \\ \(UP_{PW}\) & Agriculture profession Password. \\ \(U_{BIO}\) & Agriculture profession biometric. \\ \(PID_{i}\) & Pseudo-identity. \\ \(PWR_{i}\) & Pseudo-password. \\ \(SN_{ID}^{n}\) & New sensor identity. \\ \(SK_{i}\) & Secret key. \\ \(h_{(}\cdot)\) & One-way hash function. \\ \((Gen)\) & Generation procedure of fuzzy extractor. \\ \((Rep)\) & Reproduction procedure of fuzzy extractor. \\ \(A\parallel B\) & Concatenation. \\ \(A\oplus B\) & Exclusive-OR. \\ \hline \hline \end{tabular}
\end{table}
Table 2: Notations.  
1. Sensor node pre-deployment: * The SA randomly chooses a unique identity \(SN_{ID}\) and master key \(SN_{MSK}\). For each deployed sensor node in the cluster \((1\leq j\leq m)\), then, SA calculates \(A_{j}=h(SN_{ID}\parallel SN_{MSK})\) for each sensor node. It also generates a distinct master key \(SN_{MSK}\), with all the generated \(A_{j}\), which are distinct throughout the WSN. Now, the credentials \((SN_{ID},A_{j})\) are pre-loaded into the sensor node memory within its corresponding cluster priorly. 2. Gateway Pre-deployment: * First, the gateway selects an identity \(GW_{ID}\), and \(GW_{MSK}\) as gateway master key for the deployed GWNs in the cluster. In the proposed scheme, there are two different GWNs: HGWNs, those located in a specific cluster, and those located outside a cluster called FGWN. The SA then generates an identity \(HGW_{ID}\) and \(HGW_{MSK}\) as gateway master key. The same goes for the FGWN generating \(FGW_{ID}\) and \(FGW_{MSK}\). * Later, the SA computes \(A_{HGWN}=h(HGW_{ID}\parallel SN_{ID}\parallel HGW_{MSK})\oplus h(SN_{ID} \parallel SN_{MSK})\) for all n sensor nodes \(SN_{i}\) within HGWN, for example. The SA finally pre-loads the information \(HGW_{ID}\), \((SN_{ID},A_{HGWN})\leq j\leq m\),\(HGW_{MSK}\) into the memory of the HGWN priorly to its deployment in the target field.
# Quantum-Key-Distribution (QKD) Networks Enabled by Software-Defined Networks (SDN)
###### Abstract  
As an important support for quantum communication, quantum key distribution (QKD) networks have achieved a relatively mature level of development, and they face higher requirements for multi-user end-to-end networking capabilities. Thus, QKD networks need an effective management plane to control and coordinate with the QKD resources. As a promising technology, software defined networking (SDN) can separate the control and management of QKD networks from the actual forwarding of the quantum keys. This paper systematically introduces QKD networks enabled by SDN, by elaborating on its overall architecture, related interfaces, and protocols. Then, three-use cases are provided as important paradigms with their corresponding schemes and simulation performances.  
Keywords:quantum key distribution (QKD); networks; soft-defined networking (SDN) +
Footnote †: journal: Applied
# Detecting Symptoms and Dispersal of Pine Tortoise Scale Pest in an Urban Forest by Remote Sensing
## 2 Material and Methods
### Wind Dispersion Dataset and Model  
Wind dispersion of PTS in the study site was evaluated as the probability of a pine forest location being labeled as symptomatic as a function of its distance from an infestation hotspot, under the assumption that the likelihood of a pine tree being infested decreases with increasing distance.  
A balanced binary dataset of pine forest locations was randomly sampled from the detection map by including 525 points for each binary class (total: 1050 points) and fitted to a binomial response generalized linear mixed model (GLMM [54]) that was used to link a binary (presence/absence of PTS symptoms) response variable to its physical distance to the infestation hotspot. A GLMM is an extension of the ordinary linear regression to non-normal data in which the linear predictor contains random effects in addition to fixed effects [55].  
The random component of the GLMM included two random intercepts: the forest stands each pixel was sampled in and a spatial term. The expectation that closer observations, in terms of latitude and longitude, should be more correlated than those further away was accounted for by the spatial term using a Matern covariance function.  
The effect of the dominant wind on PTS dispersion was evaluated by comparing two GLMMs differing only by how the distance explanatory variable was computed.  
A uniform dispersion model (UDM) included straight distances between observations and the initial infestation hotspot as the explanatory variable. This model assumes a random uniform PTS dispersion direction and a likelihood of infestation decreasing with distance from the infestation hotspot (Figure 1B).  
A prevailing wind dispersion model (PWDM) included straight distances between the observations and the dominant wind direction as the explanatory variable. This model assumes that PTS predominantly disperses along the dominant wind direction in the study site (Figure 1B).  
The magnitude of the error affecting the estimate of the distance coefficient and the proportion of variance explained by each model (R-squared based on the likelihood ratios) contributed to defining which model could better predict the diffusion of PTS [56]. A lower standard error of the distance coefficient led to more precise estimates, whereas a greater R-squared resulted in a higher contribution of distance toward explaining the variability of the presence/absence of infestation in each model.
## 2 Materials and Methods
### Measuring Impedance in Non-Newtonian Materials
#### 2.2.1 Impedance Spectroscopy  
When non-Newtonian fluids are treated as electrochemical systems, they can be studied from impedance measurement data [4; 5; 12]. The measurement involves the application of a small perturbation, alternatively to classical identification methods based on linear sweep or potential step which perturbs the nonlinear dynamics far from a piecewise linear equilibrium. A small imposed perturbation can be of applied potential, of applied current or, with hydrodynamic electrodes of convection rate. The fact that the perturbation is small brings advantages in terms of the solution of the relevant mathematical equations, i.e., the systems behave piecewise linear time-invariant systems with the relative simplifying assumptions when applying frequency domain analysis [12]. The response to the applied perturbation, which is generally sinusoidal, can differ in phase and amplitude from the applied signal. Measurement of the phase difference and the amplitude, i.e., the impedance, permits analysis of the electrode process in relation to contributions from diffusion, kinetics, Debye effects, coupled homogeneous reactions, etc. [27; 28].  
The main flowchart of the procedure used in this work, called _Phase-sensitive detectors and transfer function analysers_, is showed in Figure 1. The detector compares the signal applied to the system and its response, giving the phase difference and the ratio of the amplitudes. In our case the reference signal is applied by means of a potentiostat (PSTAT) and then through the frequency response analyser (FRA) the actual measurement of the impedance can be carried out, processed and cleaned. When used in online estimation mode, the high frequency sampling raw data recordings are downloaded on a computer where a spectral analysis algorithm estimates time and frequency domain response for further use such as parametric modeling. The user-defined range of test frequencies is relatively wide, i.e., from \(\upmu\)Hz to MHz.
# Research on Trajectory Planning and Tracking Methods for Coal Mine Mobile Robots
## 4 Experiments on Trajectory Planning and Tracking of CMRRs  
### Experiments on Trajectory Planning of CMRRs  
In order to verify the practicality of the MINCO trajectory planning method, a multiscenario experiment was conducted in a simulated tunnel in the Gas and Coal Dust Explosion Laboratory of China University of Mining and Technology. The mobile robot platform and experimental environment used for the experiments are shown in Figure 14. The mobile robot is realized by the differential speed control of the left and right tracks to realize the robot's motion.  
Figure 14: Test robot platform and test environment.  
The simulated tunnel is extremely similar to a real coal mine lineaway in terms of structure, walls, and other features. In order to simulate the obstacles in a manner that resembles a real underground environment, different experimental scenarios are designed by setting static obstacles, such as buckets, and dynamic obstacles of characters in the simulated tunnel to conduct robot-trajectory-planning and -tracking experiments. The experiments of a single obstacle scenario, multiple obstacle scenario, dynamic obstacle scenario, and right-angle turn scenario were carried out sequentially to examine the practicality and robustness of the robot trajectory planning algorithm.  
We conducted experiments to fully simulate an underground tunnel environment using a coal mine rescue robotic platform carrying a special sensor configuration with two Velodyne VLP-16 LiDARs. One of the horizontally deployed LiDARs was used to execute the LOAM [31] algorithm, which was used to further construct voxel maps and in turn execute the MINCO trajectory-planning methods. The tracking module used by the robot is the MPC controller from the previous section. The localization and mapping algorithm used by the robot is LOAM, which can output the mapping results and its own position information in real time.
## 8 Results and Discussions
### Uncertainty Analysis from Initial Stage to Stable VANET
#### 8.2.2 The Effect of Malicious Attacks  
Here, we have simulated the effect of malicious attacks i.e., half of the nodes are malicious under different mobility scenario i.e., node speed is 0 to 10 m/s. The results are plotted in Figure 9. From Figure 9a, we can see that the throughput of these protocol degraded with increase of malicious nodes. Since 3 VSR has better ability to detect and tackle malicious attacks it outperform other routing protocols. Similarly, in Figure 9b our proposed scheme has less packet loss ratio than standard AODV and AASR. Since AODV is blind to the malicious attacks and takes no additional actions, its delay does not vary in the presence of different numbers of malicious nodes. Since AASR spends time in the route discovery after making security processing, their delay is higher than AODV, while 3 VSR minimize this effect by using trust information exchange between neighbor nodes as can be seen in Figure 9c. Similarly, the probability of detection is higher in 3 VSR as they denied the malicious users based on their threshold valued as can be seen in Figure 9d.