esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Method
### Channel Correction Module  
The classes of the test set in the few-shot classification task are not seen in training compared to the general classification task. The importance of the quality of the image features learned in training is also illustrated in some works. [PERSON] et al. (2018) argued that the feature distribution of the test set will significantly affect the results when they are different from the training set. For example, animal images are often identified with plants as the background, and the discrimination is made according to some parts of the animal's body that are unrelated to the background. Then, the model will ignore the information about plants so that when it comes to classifying the plants, the results will be better.  
That also applies to the ACL tear classification, as shown in Figure 3. The MRI images of the ACL for each individual in the ACL tear classification task are significantly different. The images of different patients are very different, even if only the region labeled by the physician is used for classification, and this variability affects the model's effectiveness. The testing and training sets do not contain the same patients, and the image distribution differs. A function is then introduced for channel correction, inspired by [PERSON]'s approach to the few-shot classification task, to eliminate or reduce the effect of different patient image distributions. The function is shown below, where k is a hyperparameter:  
\[\phi_{k}(\lambda)=\left\{\begin{array}{cc}\frac{1}{ln^{k}(\frac{1}{\lambda}+ 1)}&\lambda>0\\ 0&\lambda=0\end{array}\right. \tag{1}\]  
The mean magnitude of the channel (MMC) represents the average value on each channel, and this function suppresses channels with high MMC and boosts channels with low MMC. This function is applied to each channel of the feature map to reduce the adverse effects caused by the different distributions of the patient's ACL images.  
Figure 2: The proposed framework in this paper, where Fc represents the fully connected layer, Avgpool represents the average pooling layer, and Max represents taking the maximum value in the first dimension of the feature map.
# Distance-Based Resource Allocation for Vehicle-to-Pedestrian Safety Communication
## 3 Network and System Model
### Network Architecture  
The network architecture consists of a vehicle surrounded by several road-side users. The vehicle is involved in direct D2D communication with the road users in the vicinity (V2P). In this network, we also have some cellular users involved in direct communication with the infrastructure. Figure 1 shows the vehicle with road-side users. If the distance between a cellular user and the vehicle is more than 50 meters, the cellular user is involved in direct communication with the infrastructure (CUE). When the distance becomes 50 meters or less, V2P communication starts.
## 3 Data and Method
### Wellbore Data  
The wellbore used in the study (Caravel-1) was drilled inside the Waka 3D survey (Figure 1) by Anadarko New Zealand Company (ANZC) in 2014, and penetrated the basement to a total True Vertical Depth Sub-sea (TVDSS) of 3092 m. The log data were acquired with different logs suites of gamma-ray (GR), deep resistivity (RDEP), density (DEN), neutron (NEU), and sonic (AC). Unfortunately, gamma-ray and deep resistivity logs on the study interval (Quaternary) read unconsolidated formation (Figure 3). Meanwhile the other logs and drill cuttings start below the study interval, from Paleogene to the basement. In addition, biostratigraphy data and seismic horizon information from Galleon-1 [45], Clipper-1 [44], IODP317-Site 1352B [46], and ODP1119 [44,47] wellbores were used for regional correlation.
# LoRaWAN Path Loss Measurements in an Urban Scenario Including Environmental Effects
## 5 Path Loss and Shadowing Modelling
### Multiple Linear Regression Model
It can be seen that (i) in the case of the LDPLM, the prediction error is not normally distributed, and (ii) in the case of the MLR model, it can be seen that the prediction error is normally distributed, fixing the problem of normality, and the errors are more concentrated around the mean. Furthermore, in Figure 14, it can be seen that the predicted values using the MLR CPLS model are more concentrated around the regression line because this method exhibits a higher R\({}^{2}\). Finally, we depicted the error distributions of each model regarding the distance in the box plots of Figure 15, where it can be noticed that distance does not affect the relative errors of the models, which are around 1.5%. Increasing the accuracy of the CPLS models by using the environmental variables can be used for energy reduction by applying TPC strategies and for localization or tracking tasks [29]. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Predictor** & **Variable** & **Value** \\ \hline \(\hat{K}\) & Intercept & \(-\)505.64 dB \\ \(\hat{\gamma}\) & Path loss exponent & 2.203 \\ \(\hat{B}_{1}\) & Temperature & 0.123 dB/\({}^{\circ}\)C \\ \(\hat{B}_{2}\) & Relative humidity & 0.0105 dB/\(\%\) \\ \(\hat{B}_{3}\) & Barometric pressure & 0.407 dB/hPa \\ \(\hat{B}_{4}\) & PM2.5 & 0.00222 dB/\(\text{ug/m}^{3}\) \\ \(\hat{B}_{5}\) & SNR & \(-\)0.635 dB/dB \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Model predictors for the MLR CPLS model. Figure 13: Shadowing distribution using the LDPLM and MLR models. It can be noticed that the errors are more concentrated around the mean when using the MLR model, and the distribution is bell-shaped.
# Equivalent Circuits for Microwave Metamaterial Planar Components
## 3 Metamaterial Resonators
Following this approach, the schematic diagram of the entire network could be represented in Figure 4.  
Then, a two-step measurement (or theoretical evaluation) can be performed: (1) determine the I/O impedances of an ordinary TL and derive the \(Z_{1}\) and \(Z_{2}\) values corresponding mainly to an inductor and a capacitor, respectively; (2) determine \(Z_{r}\) moving the I/O ports  
Figure 4: Equivalent circuit for a resonator fed by two transmission lines. to the internal ones with a de-embedding procedure. On the other hand, a simplified approach can be followed, considering that the entire structure could be suitable for an ordinary or metamaterial response. We can define a \({Z_{2}}^{\prime}\) component, including part of the feeding system and the resonator itself. In this case, we can simplify the circuit in Figure 4 using the equivalent one presented in Figure 5.  
So far, the initial equations linking the \(Z\)-parameters to the impedances of the equivalent circuit can be re-written as follows:  
\[\left\{ \begin{aligned} & Z_{11}=Z_{1}+{Z_{2}}^{\prime}\\ & Z_{12}=Z_{21}={Z_{2}}^{\prime}\\ & Z_{22}=Z_{1}+{Z_{2}}^{\prime}=Z_{11}\end{aligned} \right. \tag{8}\]  
Currently, \({Z_{2}}^{\prime}\) is a more complicated network, but it still preserves the meaning of an equivalent impedance that helps determine the ordinary or metamaterial response of the entire configuration. At the end, it is as follows:  
\[Z_{1}=Z_{11}-Z_{21}\text{ and }{Z_{2}}^{\prime}=Z_{21} \tag{9}\]  
It means that having a metamaterial component is equivalent to fulfilling the following updated equations:  
\[ \begin{aligned} & Im(Z_{2}^{\prime})=Im({Z_{21}})>0\\ & Im({Z_{1}})=Im({Z_{11}}-{Z_{21}})<0\end{aligned}  \tag{10}\]
# Multilevel Regularization Method for Building Outlines
## 3 Experimental Design  
### Descriptions of the Datasets  
We evaluated the proposed method using the open-source WHU Building Dataset [25], which covers a 450 km\({}^{2}\) area in Christchurch, New Zealand. This dataset includes approximately 220,000 independent buildings of various types and structures. Owing to its wide coverage, high-quality imagery, large dataset size, and diverse building types, this dataset has become very popular for research on building extraction and regularization methods. A region within the coordinate ranges of 43\({}^{\circ}\)32\({}^{\prime}\)48\({}^{\prime\prime}\) to 43\({}^{\circ}\)33\({}^{\prime}\)17\({}^{\prime\prime}\) South latitude and 172\({}^{\circ}\)41\({}^{\prime}\)14\({}^{\prime\prime}\) to 172\({}^{\circ}\)41\({}^{\prime}\)58\({}^{\prime\prime}\) East longitude was randomly selected as Image #1. This image, shown in Figure 14, has an original spatial resolution of 0.075 m and contains 932 buildings.  
To assess the effectiveness of the proposed method comprehensively, Image #1 was down-sampled to a spatial resolution of 0.5 m, creating Image #2. Then, the deep learning technique proposed by [PERSON] et al. [26] was applied to both Images #1 and #2 to extract building outlines at two different levels of accuracy. These extracted building outlines served as the experimental data for the proposed regularization method. Figure 15 presents examples of the original 0.075 m resolution remote sensing image, manually labeled ground truth, and building outlines extracted from both resolutions using the deep learning technique.  
Figure 14: Image from the WHU Building Dataset and the randomly selected test area.  
Figure 13: Schematics of parallel and perpendicular enhancements. (**a**) Perpendicular enhancement; (**b**) Parallel enhancement; (**c**) No enhancement.
# A Goal-Oriented Evaluation Methodology for Privacy-Preserving Process Mining
## 4 Case Study
### Data Gathering
* _Precision_: It concerns a model's ability to accurately represent observed behavior while effectively preventing the inclusion of unobserved behavior, thereby mitigating the risk of underfitting. * _Generalization_: It concerns a model's ability to incorporate previously unobserved behavior while mitigating the risk of overfitting. To assess the performance of privacy-preserving machine learning algorithms, both accuracy and computational time metrics from the scikit-learn Python library were utilized, offering a comprehensive evaluation of model utility and efficiency. The accuracy metricmeasures the fraction of correct predictions generated by the model when applied to a labeled test dataset, serving as a fundamental criterion for assessing the performance of classification models within the constraints of differential privacy. For unsupervised learning tasks, specifically clustering, the inertia metric was employed to quantify the sum of squared distances between data points and their respective cluster centroids, serving as a measure of clustering cohesion. This metric quantifies cluster compactness and serves as an indirect measure of clustering quality, facilitating the assessment of how privacy-preserving transformations influence the structural integrity and coherence of the data. Furthermore, the time metric (in seconds), computed based on execution time measurements, provides valuable insights into the computational overhead imposed by privacy mechanisms, which is essential for evaluating the practicality of implementing such models in real-world scenarios. All experimental procedures were executed on a personal desktop computer equipped with an Intel Core i7-13700K CPU and 32 GB of DDR5 RAM. An execution timeout of 30 min was established for each anonymization task, defined as the application of an algorithm, configured with specific parameters, on an individual event log.
## 3 Results and Discussion
### Simulation for Data Accuracy in Randomly Placed Non-Hierarchical Bidirectional Graph
#### 3.3.4 Network Latency  
Using Equations (21) and (22), network latency of bidirectional ad hoc networks that separately uses AVG, EBDKF, and proposed statistical information fusion methods can be calculated. All parameters used in analysis of network latency for chain-based networks are substituted in  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Fusion Function** & **CPU Cycles** & **Time ((\(T_{A}\))(\(\mu\)s))** \\ \hline Average & 60 & 3.75 \\ Proposed method & 180 & 11.25 \\ EBDKF & 1387 & 86.6875 \\ \hline \hline \end{tabular}
\end{table}
Table 6: CPU overhead of different information fusion functions for randomly placed non-hierarchical graph.  
Figure 21: Network Lifetime comparison of three different fusion techniques for an ad hoc-type network.  
Equations (21) and (22) except for parameters given in following text. As there are only one sixth of packets or frames/packets waiting to be served, it can be assumed that for networks with proposed method and AVG that \(t_{qu}=0.1/6\)\(ms\). K is kept as a generic value without setting a value. Fusion times (\(t_{fu}\)) are obtained from Table 6. Table 3 summarizes the network latency values calculated in this manner.  
It can be observed from the results in Table 7 that when comparing the total latency value of latency due to information fusion (\(t_{fu}\)) can be neglected. None of the fusion techniques violate the latency requirement for both HANs and FANs up to K = 6. If K values are equal, the fusion methods in the increasing order of network latency will be Average method, proposed method and EBDKF. AVG method very slightly outperforms the proposed method by 7.5 us such that network latency of proposed method is almost equal to that of AVG in network with same K values. However, as it was described in sensor networks using chain and tree structured networks, to achieve the same level of low accuracy as the proposed method under high BER conditions that exist in Smart Grid environments, retransmissions have to be allowed in networks deploying AVG and EBDKF. In that case, Proposed method will outperform both other fusion methods.
# The Long-Range Biomimetic Covert Communication Method Mimicking Large Whale
## 1 Introduction
In this paper, we propose a novel long-range biomimetic modulation method that has robustness against large multipath delay and higher DoM performance than the conventional methods. Since the whistle is a non-linear chirp signal, it has strong robustness against the distortion of large multipath delay. Thus, if the bit can be modulated while maintaining the shape of the whistle, the modulated signal has tolerance to the large multipath delay and the high DoM performance. Whales generate whistles with various frequency contours, but they have a habit when generating the sounds of the whistle. The whistle-generating habit is that the following frequency values are limited by the frequency value of the previous time. The proposed biomimicking modulation method utilizes this frequency-change habit of the whistle. The main contributions of the paper are summarized as follows:  
1. We modeled the time-dependent frequency change of the whistle of the right hump-back whale (RHW) using the Markov chain (MC). The modeled results can be employed to select the frequency values used by the whistle. Note that we considered the time-frequency orthogonality of the symbol to preserve low BER when modeling the frequency change of the whistle. 2. The proposed modulation method preserves the signal shape of the whistle because it modulates bits using frequency values that whales use to generate a whistle. Thus, the whistle-mimicking communication signal generated by the proposed modulation method shows improved performance of the DoM and tolerance of large multipath delay. 3. The proposed demodulation method estimates the multipath delay profile using a preamble and increases received signal gain using the estimated profile. Thus, the proposed method achieves low BER in a long-range underwater environment with a large multipath delay. 4. Computer simulations and practical ocean experiments were conducted and demonstrated that the proposed method had a lower BER than conventional covert communication methods. 5. Since the proposed modulation method makes a whistle using modeling results, a machine learning-based DoM assessment was conducted. The assessment results show that the trained machine learning classifier recognized the whistle-mimicking signal generated by the proposed modulation method as the whistle of the RHW. Thisresult shows that machine learning can be used as an effective evaluation method for the DoM performance of biomimetic communication. This paper is organized as follows. Section 2 describes how to model the time-dependent frequency change of the whistle of the RHW using MC. In Section 3, the proposed biomimicking modulation and demodulation methods are described. Section 4 shows the result of a machine learning-based DoM assessment. In Section 5, through computer simulations and ocean experiments, it is shown that the proposed biomimicking communication method has a lower BER than conventional methods. Section 6 concludes the paper.
###### Abstract  
Designing an efficient decoder is an effective way to improve the performance of polar codes with limited code length. List flip decoders have received attention due to their good performance trade-off between list decoders and flip decoders. In particular, the newly proposed dynamic successive cancellation list flip (D-SCLF) decoder employs a new flip metric to effectively correct high-order errors and thus enhances the performance potential of present list flip decoders. However, this flip metric introduces extra exponential and logarithmic operations, and the number of these operations rises exponentially with the increase in the order of error correction and the number of information bits, which then limits its application value. Therefore, we designed an adaptive list flip (ALF) decoder with a new heuristic simplified flip metric, which replaces these extra nonlinear operations in the original flip metric with linear operations. Simulation results show that the simplified flip metric does not reduce the performance of the D-SCLF decoder. Moreover, based on the in-depth theoretical analyses of the combination of the adaptive list and the list flip decoders, the ALF decoder adopts the adaptive list to further reduce the average complexity.  
polar code; list flip decoder; flip metric; adaptive list; high-order error correction capability Article  
Adaptive List Flip Decoder for Polar Codes with High-Order Error Correction Capability and a Simplified Flip Metric. **2022**, 24, 1806. [[https://doi.org/10.3390/e24121806](https://doi.org/10.3390/e24121806)]([https://doi.org/10.3390/e24121806](https://doi.org/10.3390/e24121806))
# Service Life Prediction and Life Cycle Costs of Light Weight Partitions
## 4 Field Survey Results and Discussion
### Initial Component Performance (ICP)  
The initial component performance (ICP) for partitions, which reflects their baseline performance, is derived by using Equation (1).  
Figures 2-4 present the ICP values gathered from sample observations, highlighting any substantial deviations from the average ICP value. These deviations are critical for identifying and excluding outliers in the data set, ensuring a more accurate and representative analysis of the partition's initial quality and expected performance.  
Average ICP levels, which indicate a partition's initial quality performance, show variability across partition types. Gypsum board partitions have an average ICP of 82.7%, reflecting common issues such as surface waviness and visible seams that can impact their functional longevity. In contrast, autoclaved and hollow concrete block partitions exhibit higher average ICP levels, at 88.82% and 91.0%, respectively. These types often demonstrate  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Geographical Location** & **Gypsum Board** & **Gypsum Block** & **Aerated Concrete** & **Hollow Concrete** \\  & **Partitions** & **Partitions** & **Block Partitions** & **Block Partitions** \\ \hline Ashdod & & 3 & & \\ Be’er Sheva & & & 3 & 5 \\ Giv’atayim & 4 & & & 2 \\ Gedera & 4 & & & \\ Hadera & 4 & & & 1 \\ Haifa & & 3 & & 6 \\ Carmiel & 9 & & & 7 \\ Modi’in & 6 & & & 7 \\ Nesher & & 3 & & 3 \\ Kiryat Bialik & & & & 7 \\ Kiryat Gat & 7 & & & 7 \\ Kiryat Tiv’on & & & 7 & 10 \\ Kiryat Yam & 2 & & 11 & 2 \\ Rishon LeZion & 1 & 6 & 13 & \\ Rehovot & 1 & 6 & 13 & 3 \\ Shoham & & & & 3 \\ Tel Aviv & 2 & & & \\ Total & 35 & 26 & 30 & 42 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Geographic distribution of field surveyed partitions by type.  
Figure 2: Initial Component Performance (ICP) of Gypsum Board Partitions (by Age).
# Performance Evaluation of a Mesh-Topology LoRa Network
## 5 Proposed Approach: LoRaMesh
### LoRa-Mesh MAC  
Nodes in the LoRaMesh topology will follow a TDMA protocol. Figure 2 provides an overview of the Real-Time Clock (RTC) synchronisation and up-link procedures followed in the network. The network in Figure 2 consists of six nodes with a maximum of three hops, the overview of which can be seen on the right-hand side of the figure. The following two sections will provide an overview of the RTC synchronisation and the up-link transmissions.
# STAVOS: A Medaka Larval Cardiac Video Segmentation Method Based on Deep Learning
## 2 Dataset
### Dataset Construction  
The ventricles in the original videos exhibit varying degrees of occlusion, as illustrated in the subplots of Figure 1, where the visibility of the ventricles differs. Based on the clarity of the ventricles, we categorized the 63 videos into three levels:  
1. In the first level, the ventricles are essentially unobstructed, and their edges are clear and visible, as shown in Figure 1b,f. The quantity of videos in this level is 21.
2. In the second level, the ventricles experience some degree of obstruction, or their edges are blurred and challenging to distinguish, as depicted in Figure 1c,g. The quantity of videos in this level is 17.
3. In the third level, the ventricles have a significant amount of occlusion, and the edges are indistinguishable, as seen in Figure 1d,h. The quantity of videos in this level is 25.  
Due to the extremely poor quality of the videos in the third level, preventing accurate labeling, they were discarded. The videos in the second level also exhibited suboptimal quality, and considering the prevalence of ventricular occlusion and blurred edges, coupled with the limited dataset, we chose to use the first and second levels of quality videos for dataset construction. For differentiation, we labeled the video data from the first level as \"better\" and the second level as \"lower\".  
Our process for creating a dataset is as follows: Firstly, we extract frames from the medaka heart beat video, with approximately 207 frames per N-direction video and 165 frames per R-direction video. Next, we used the pixel level image annotation tool LabelMe (version 5.0.1) developed by the Massachusetts Institute of Technology to label medaka frame by frame, generate JSON label files with medaka ventricular edge coordinate information, and generate corresponding ventricular mask PNG images. Finally, we classify according to the folder format requirements of the DAVIS dataset [16].  
We have created a total of 7225 label images, including 4569 in the Lateral right view of the medaka heart and 2656 in the Ventral view of the medaka heart. The specific quantity information of the dataset is shown in Table 1.  
Figure 1: Schematic diagram and video sample display of medaka heart. (**a**) Ventral view of medaka heart; (**b–d**) Video capture of ventral view of medaka heart. (**e**) Lateral-right view of medaka heart; (**f–h**) Video capture of lateral-right view of medaka heart.