esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on Short-Term Prediction Methods for Small-Scale Three-Dimensional Wind Fields
## 2 WRF-CFD Coupled Small-Scale Wind Field Simulation
### Analysis of CFD Results
Figure 5: Temperature variables at the CFD east boundary obtained with two interpolation methods. (**a**) Nearest neighbour interpolation method; (**b**) inverse distance weighted interpolation method. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Error Assessment Indicator**} & \multirow{2}{*}{**WRF**} & \multicolumn{2}{c}{**WRF + CFD**} \\ \cline{3-4}  & & **Nearest Neighbour** & **Inverse Distance Weighted** \\  & & **Interpolation** & **Interpolation** \\ \hline Average error (BIAS) (m\(\cdot\)s\({}^{-1}\)) & 2.952 & 0.819 & 0.333 \\ Root mean square error (RMSE) (m\(\cdot\)s\({}^{-1}\)) & 4.684 & 2.844 & 2.724 \\ Mean absolute error (MAE) (m\(\cdot\)s\({}^{-1}\)) & 4.052 & 2.334 & 2.080 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Evaluation indicators of wind speed simulation at 30 m height of the Sha Chau Automatic Weather Station. features in wind speed on the lee side of the terrain and buildings. This also indicates that the grid partitioning and local refinement methods used in this study effectively capture the fluctuation phenomena caused by the terrain and buildings.
## 3 Analysis of Input Parameters and an Overview of the Effective Length Dataset
Importance scores of all three selected features on the effective length are depicted in Figure 4. Similar feature importance was also obtained by the ANOVA F-Test, but results were not provided here to avoid redundancy. Observing Figure 4, it can be seen that the soil resistivity dominates as the most crucial feature influencing the effective length of the grounding electrode, boasting an MRMR importance score of 1.7347. Rise time trails significantly behind, with a score of 0.0659, whereas the burial depth of the grounding electrode, as a factor affecting its effective length, was considered practically inconsequential, evident from its MRMR importance score of 0.0015.
## 1 Introduction
Adjusting the hyperparameters is significant to get a better model with high predictive power. The application of CNN in the petroleum industry has been undertaken to detect hydrocarbon zones in seismic images where 2D-CNN is used in the image segmentation process, and the model achieved more than 80% accuracy [26]. The developed model used 2D-CNN because the input of seismic images is in two dimensions (2D). Other than image segmentation, the implementation of CNN has also been applied for image recognition in the petroleum industry. [PERSON] et al. [27] developed a well pump troubleshooting model in which the 2D power card images are taken as input to classify different types and severity of pump troubles. The analysis of power card images helps to identify issues that could impact the oil well production and help in the configuration of pumping parameters. The accuracy of power cards equivalent to seven pump troubles achieved more than 96%, except for the undersupplying trouble type, which obtained 87% accuracy. Besides, the CNN model's development on the regression problem was carried out by [PERSON] et al. [28], where a radial composite reservoir model was developed using 2D-CNN and verified by the oilfield measurement data. The mean absolute error (MAE) was used as the performance metric to verify the developed model. All outputs showed a small error with less than 0.7 for both validation and testing sets. [PERSON] et al. [29] applied 2D-CNN to determine the location of an oil well under geological uncertainty by predicting the cumulative oil production of reservoir simulation. The performance of the 2D-CNN model was compared with a shallow machine learning model, ANN, and the results showed that the 2D-CNN model achieved 88% accuracy with a relative error of 0.035. In comparison, the ANN model achieved 84% accuracy with 0.24 relative error. The CNN model outperforms the ANN model in predicting cumulative oil production. [PERSON] et al. [30] applied the CNN method to simultaneously predict the volume flow rate of multiphase flow of oil, gas, and water phases. The results are very convincing, with more than 80% accuracy for all phases and were verified by the measurement of an individual well in the petroleum industry. Since the application of CNN in the petroleum industry attained promising results for both classification and regression problems, this method was applied on SRT to reduce the time and effort to run the laboratory test by predicting the plugging performance and sand production. The application of deep learning has yet to be imposed in SRT. Therefore, a one-dimensional convolutional neural network (1D-CNN) was developed for SRT modelling to classify plugging sign, predict sand production, and retain permeability using various sand distribution, stand-alone screen, screen slot size, and sand concentration. Classification and prediction of the SRT results were used to filter the most compatible SAS and screen slot size accordingly. Then, 1D-CNN hyperparameters were tuned manually to determine optimal hyperparameters combination that will provide higher predictive power for the developed model.
# Particle Probability Hypothesis Density Filter Based on Pairwise Markov Chains
## 2 PHD Filter Based on the PMC Model  
### PMC Model  
Let \(\mathbf{x}_{k}\in\mathbf{R}^{m}\) express the state at time \(k\), and the corresponding observation is \(\mathbf{y}_{k}\in\mathbf{R}^{q}\). The couple \((\mathbf{x}_{k},\mathbf{y}_{k})\) is a PMC if and only if the joint probability density function (pdf) of \((\mathbf{x}_{0:k},\mathbf{y}_{0:k})\) can be factorized as follows:  
\[p(\mathbf{x}_{0:k},\mathbf{y}_{0:k})=p(\mathbf{x}_{0},\mathbf{y}_{0}){\prod_{i=1}^{k}}\,p_{i |i-1}(\mathbf{x}_{i},\mathbf{y}_{i}|\mathbf{x}_{i-1}\,,\mathbf{y}_{i-1}) \tag{1}\]  
where \(p(\mathbf{x}_{0},\mathbf{y}_{0})\) is the state distribution at the initial time, and an HMC model is a PMC model which satisfies  
\[\left\{\begin{array}{l}p(\mathbf{x}_{k}|\mathbf{x}_{k-1},\mathbf{y}_{k-1})=p(\mathbf{x}_{k}| \mathbf{x}_{k-1})=f(\mathbf{x}_{k}|\mathbf{x}_{k-1})\\ p(\mathbf{y}_{k}|\mathbf{x}_{k},\mathbf{x}_{k-1},\mathbf{y}_{k-1})=p(\mathbf{y}_{k}|\mathbf{x}_{k})=g( \mathbf{y}_{k}|\mathbf{x}_{k})\end{array}\right. \tag{2}\]  
where \(f(\mathbf{x}_{k}|\mathbf{x}_{k-1})\) and \(g(\mathbf{y}_{k}|\mathbf{x}_{k})\) are the target Markov transition density and the sensor likelihood function, respectively.  
The defined target motion model follows the PMC model as  
\[(\mathbf{x}_{k},\mathbf{y}_{k})=\varphi\big{(}\big{(}\mathbf{x}_{k-1},\mathbf{y}_{k-1}\big{)},\mathbf{w}_{k}\big{)} \tag{3}\]  
where \(\mathbf{w}_{1},\cdots,\mathbf{w}_{k}\) are independent zero-mean Gaussian noises, and  
\[E(\mathbf{w}_{k}\mathbf{w}_{k}^{T})=\Sigma_{k}=\left[\begin{array}{cc}\Sigma_{k}^{1 1}&\Sigma_{k}^{21}\text{T}\\ \Sigma_{k}^{21}&\Sigma_{k}^{22}\end{array}\right] \tag{4}\]A classical example of Gaussian PMC model can be expressed as [11]  
\[\left[\begin{array}{c}\mathbf{x}_{k}\\ \mathbf{y}_{k}\end{array}\right]=\underbrace{\left[\begin{array}{cc}F_{k}^{1}&F_{k }^{2}\\ H_{k}^{1}&H_{k}^{2}\end{array}\right]}_{B_{k}}\left[\begin{array}{c}\mathbf{x}_{k- 1}\\ \mathbf{y}_{k-1}\end{array}\right]+\mathbf{w}_{k} \tag{5}\]
## 4 Evaluation Proposal and Analysis
### Simulation Parameters  
The simulations parameters can be seen in Table 1. Nine different scenarios were performed, with each scenario using a particular topology and number of nodes. These topologies used combinations of transmission range and interference. Scenarios 1, 4, and 7 were composed of one sink node and nine source nodes; Scenarios 2, 5, and 8 were composed of one sink node and 19 source nodes; while Scenarios 3, 6, and 9 were composed by one sink node and 29 source nodes; with all scenarios performed in 10 min. The three different numbers of nodes were chosen to evaluate the network scalability and its impact on energy consumption.
# Popularity-Aware Closeness Based Caching in NDN Edge Networks
## 4 Performance Evaluation
### Results
#### 4.2.3 Overhead Analysis  
To better show the pros and cons of the conceived solution, we summarize in Table 4 the main differences between the compared schemes in terms of incurred overhead, i.e., additional signalling bytes piggybacked in the exchanged NDN packets. A high signalling overhead introduced by a caching strategy could deteriorate the content retrieval performance, e.g., by increasing the content retrieval delay due to the transmission of larger packets that occupy the channel for a longer time and may generate a higher channel load and resulting congestion. It is worth observing that the blind CEE and Prob schemes incur no additional overhead. Similarly to PaCC, BEP foresees one additional field in the Data packet to convey the maximum betweenness to be compared with that of the nodes along the backforwarding path. It is the PaC metric in our proposal. Three additional fields per Interest are foreseen by PaCC, for a total of 10 bytes (less than 1% overhead per packet). For BEP, the overhead per Interest is a function of the number of nodes traversed along the path (\(|\Pi|\)) by the packet carrying the BetweennessArray field. For a path made of 5 nodes the overhead gets equal to 24 bytes. Under the majority of settings (\(|\Pi|>1\)), PaCC is also more efficient than BEP in terms of exchanged bytes in the network per packet. Therefore, the signalling overhead introduced by PaCC is extremely low, which contributes to limit  
Figure 8: Percentage of Interest packets that are forwarded to the origin server.
# Unsupervised Anomaly Detection and Segmentation on Dirty Datasets
###### Abstract  
Industrial quality control is an important task. Most of the existing vision-based unsupervised industrial anomaly detection and segmentation methods require that the training set only consists of normal samples, which is difficult to ensure in practice. This paper proposes an unsupervised framework to solve the industrial anomaly detection and segmentation problem when the training set contains anomaly samples. Our framework uses a model pretrained on ImageNet as a feature extractor to extract patch-level features. After that, we propose a trimming method to estimate a robust Gaussian distribution based on the patch features at each position. Then, with an iterative filtering process, we can iteratively filter out the anomaly samples in the training set and re-estimate the Gaussian distribution at each position. In the prediction phase, the Mahalanobis distance between a patch feature vector and the center of the Gaussian distribution at the corresponding position is used as the anomaly score of this patch. The subsequent anomaly region segmentation is performed based on the patch anomaly score. We tested the proposed method on three datasets containing the anomaly samples and obtained state-of-the-art performance.  
anomaly detection and segmentation; unsupervised method; noise resistance +
Footnote †: journal:  
0000-0001-9991-4000-0000-0000-0000-0000-0000-0000-00000-0000-00000-00000-00000-00000-00000-00000-00000-000000-00000-000000-00000-00000-00000-00  
located and exclude the samples that contain patches in low-density regions, as shown in Figure 1. Secondly, we obtain the final selected normal samples through the iteration module. Lastly, we fit patch-level Gaussian distributions based on the final selected normal samples. In the testing phase, the Mahalanobis distance between each patch of the test sample and the center of the corresponding Gaussian distribution is used as the anomaly score to perform anomaly detection and segmentation. This paper calls this the framework PaIRE (Patch Iterative Robust Estimation) method. The main contributions of our work are as follows:  
* Unsupervised anomaly detection methods degrade in performance due to noise. We propose a framework PaIRE to address unsupervised anomaly detection when the training set contains noise;
* A slope sliding window method is designed to reduce the effect of noise for improving the noise resistance of the proposed model;
* An iterative module is proposed to make the model estimate the distribution of normal samples more accurately from the dirty dataset;
# Reducing the Uncertainty of Lidar Measurements in Complex Terrain Using a Linear Model Approach
## 2 Materials and Methods
### Test Site and Measurement Campaign  
The experiment was carried out in south-west Germany on the Swabian Alb. The Swabian Alb extends for around 100 km and can be categorized as a very hilly area consisting of high plateaus surrounded by a pronounced 100-150 m tall wooded escarpment known in the region as the _Albrtauf_. The measurements described here are centred on a 100 m high met mast in relatively flat land less than 1 km from a section of _Albrtauf_ (Figure 1).  
Two wind lidars were installed near the mast. These lidar were the SWE Scanner [17] and a Galion all sky long-range scanning wind lidar. The measurement campaign ran from March 2015 until February 2016. Not all systems were available during the whole measurement period. The SWE Scanner measured from March 2015 to February 2016 and the Galion system measured from March 2015 until June 2015.  
Ideally the lidar and met mast would have been co-located or the lidar would been positioned a few 10s of meters upwind of the met mast to avoid wakes. However, this was not possible in this study because the lidars required around 2 kW of power, which could not be supplied directly at the mast's location. Instead the lidars were located at a nearby farm building (visible in Figure 1a), approximately \(\approx\)300 m from the met mast.  
In a second short measurement period from November 2015 to February 2016 the SWE Scanner was installed at the foot of the met mast guy wires, approximately 48 m from the met mast on a bearing of 330\({}^{\circ}\). During this time the same configuration as in the main measurement period was used, but the number of pulses were lowered from 10,000 to 3000 to reduce the the scanning time. The met mast and both lidar systems were time synchronized with a GPS time signal.  
Figure 1: (**a**) The test site topography. An aerial picture (©2016 Google) is overlain with elevation contours (©2009 GeoBasis–DE/BKG, LGL (Geobasisdaten ©LGL Landesamt für Geoinformation und Landentwicklung Baden-Württemberg, Az.: 2851.9-1/19)). The locations of the met mast (\(\blacktriangledown\), lidars (\(\bullet\)), wind turbines (\(\star\)), and vertical profiles from [15, 16] are also shown. The _Albruaf_ is between the two thicker elevation contours. (**b**) Terrain cross section. The locations of the met mast (\(\blacktriangledown\)) and lidars (\(\bullet\)) are shown. The experiment site. (**a**) Topography and structures (**b**) Terrain section along red dashed line.
# Bacterial Aerosol in Ambient Air--A Review Study
## 3 Discussion
### Interactions with Air Pollutants
#### 3.1.2 Ozone (O\({}_{3}\))  
Ozone is an effective and practical antibacterial agent [82; 83] used in buildings. However, the relationship between O\({}_{3}\) and bacterial aerosols exhibits both positive and negative correlations, with context and seasonal variations playing significant roles. [PERSON] et al. [64] found a moderate positive correlation (0.5252, \(p<0.05\)), indicating that higher ozone levels ranging from 44.35 to 88.6 \(\upmu\)g/m\({}^{3}\) (Table S1) may be associated with an increase in bacterial aerosols under certain conditions. High concentrations of O\({}_{3}\) can be toxic to bioaerosols after reacting with atmospheric olefins to form so-called open-air factors [84; 85]. Figure 3 presents statistically significant correlations between O\({}_{3}\) and bacterial aerosols. [PERSON] et al. [76] highlighted that the physical and biological properties of bioaerosols change significantly after exposure to ozone and water vapor. However, the exact mechanisms for these observed changes were not reported and require future investigation. The role of temperature is particularly evident in the range from \(-\)1.5 to 9 \({}^{\circ}\)C, which was significantly correlated with bacterial aerosols (0.5468, \(p<0.05\)). In contrast, two other studies report significant negative correlations, particularly [PERSON] et al. [48] (\(-0.6\), \(p<0.01\)) and [PERSON] et al. [47] (\(-0.795\), \(p<0.01\)) during winter, indicating that higher ozone levels are associated with reduced bacterial aerosol concentrations. This suggests that ozone might have an inhibitory effect on airborne bacteria, possibly due to its oxidative properties. However, the O\({}_{3}\) concentrations in these studies were between 5 and 60 \(\upmu\)g/m\({}^{3}\) and between 4 and 41 \(\upmu\)g/m\({}^{3}\), which are lower than those reported by [PERSON] et al. [64]. Additionally, temperature ranges in these studies were \(-8.1\) to 23.9 \({}^{\circ}\)C and \(-2.5\) to 21.0 \({}^{\circ}\)C, respectively, and also showed a negative correlation with bacterial aerosols (Table 1).  
Figure 3: Graphical summary of statistically significant correlation between bacterial aerosol and O\({}_{3}\).
# 8-W 2-Stage GaN Doherty Power Amplifier Module on 7 \(\times\) 7 QFN for the 5G N78 Band
## 4 Conclusions  
This paper presents a GaN 2-stage DPAM on a 7 \(\times\) 7 QFN package for the 3.4-3.8 GHz band. The input matching network and the interstage matching network were implemented using IPD and integrated with GaN-HEMT bare chips as MCM within the 7 \(\times\) 7 QFN for the main and driver stages. To minimize insertion loss, the load network was implemented on a PCB, and the low-pass and high-pass structures were initially designed using lumped passive components. These structures were subsequently replaced with TLs featuring high characteristic impedance and high-Q multi-layer chip capacitors to enhance output power and efficiency. Additionally, to reduce circuit complexity, multiple shunt components located at the same node were merged in both the interstage and load matching networks. The DPAM features an output PBO of 8.5 dB using an asymmetric structure. The overall dimensions of the proposed DPAM are \(9.5\times 14.8\) mm\({}^{2}\), including the load network. A gain of over 26.8 dB and a PAE of over 37.8% were achieved at an average output power of 39 dBm in the 3.4-3.8 GHz frequency band in the measurement results when using the downlink 5G NR signal with a bandwidth of 100 MHz and a PAPR of 9.3 dB.  
Conceptualization, S.B. (Sooncheol Bae); methodology, S.B. (Sooncheol Bae) and K.K.; software, S.B. ([PERSON]), H.J. and K.B.; validation, S.B. (Sooncheol Bae) and Y.Y.; formal analysis, S.B. ([PERSON]) and Y.C.C.; investigation, S.B. ([PERSON]); resources, Y.W.; data curation, H.K. and W.C.; writing--original draft preparation, S.B. ([PERSON]); writing--review and editing, Y.W. and Y.Y.; visualization, S.B. ([PERSON]); supervision, Y.Y.; project administration, Y.Y.; funding acquisition, Y.Y. All authors have read and agreed to the published version of the manuscript.  
This work is supported by Samsung Electronics Co., Ltd. This research was supported by the SungKyunKwan University and the BK21 FOUR (Graduate School Innovation) funded by the Ministry of Education (MOE, Republic of Korea) and National Research Foundation of Korea (NRF).  
Data is contained within the article.  
The Authors [PERSON], [PERSON], [PERSON], and [PERSON] were employed by the company Samsung Electronics Co., Ltd. Authors [PERSON], [PERSON], and [PERSON] were employed by the company para-PA Inc. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
# Symmetric-Key-Based Security for Multicast Communication in Wireless Sensor Networks +
## 6 Efficiency
### Delay  
The registration phase for both the unicast and multicast methods is identical, as can be observed in Figure 5. The time required to finish the registration phase only increases with the number of motes in the multicast group and with the number of hops between the motes and the NMM.  
\begin{table}
\begin{tabular}{l l} \hline \hline OS and Simulator & Contiki (version 2.7) and COOJA \\ \hline \hline Motes & COOJA modes \\ \hline \hline Radio Medium & Unit disk graph medium (UDGM) \\ \hline \hline Ranges & Transmit: 50 m \\  & Interference: 50 m \\ \hline Topology & See Figure 1 \\ \hline Number of members & 2 to 5 \\ \hline Number of intermediate motes & 0 to 3 \\  & (hops (h): 1 to 4) \\ \hline PHY and MAC & IEEE 802.15.4 and CSMA \\ \hline Iterations & 10 for each configuration \\ \hline RNG seeds & New seed each iteration \\ \hline Traffic & According to protocol \\ \hline RDC protocols & NullRDC \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation configuration parameters.  
Figure 5: Average registration and distribution phase delay. The number of members in the group is denoted as \(n\) and the number of hops between the publisher and the members is denoted as \(h\).  
In contrast, Figure 5 also shows that the distribution phase of the two approaches is not the same. Whereas both approaches are equally dependent on the number of hops between the publisher and the other members, we observe that the method using unicast transmissions is less dependent on the number of motes in the multicast group. The latter can be explained by the fact that the multicast method does not duplicate its transmission into different packets until the last hop, while the transmission of the unicast method is duplicated by the publisher. This means that the unicast packet for the last mote in the group cannot be sent until all previous unicast packets were sent, resulting in additional delays.
# Traffic Prediction of Space-Integrated Ground Information Network Using the GTCN Algorithm
## 2 Related Work
However, the accuracy of the ESN network parameters and the time complexity depends on the reserve pool and its parameters have to be constructed artificially; thus, there is no guarantee of the stability of the forecast and the performance in different environments. Moreover, various model generalization ability is weak and not applicable to the Space-Integrated Ground Information Intelligent Network forecasting task. Other network traffic prediction algorithms [16; 17; 18] cannot be directly applied for the Space-Integrated Ground Information Intelligent Network due to the limited suitable environment and weak generalization ability. The main difference between the proposed algorithm in this paper and the existing technologies is as follows. Inspired by the TCN algorithm in [19], the Convolutional Neural Networks (CNN) are optimized to perform the traffic prediction task of the Space-Integrated Ground Information Intelligent Network, instead of the common approach of using RNN and its variants to solve the time series prediction. TCN algorithm has the advantages of CNN algorithm, namely, high parallelism, in which sequences can be processed in parallel without the need for sequential processing such as RNNs. The receptive field of TCN is very flexible and its size is determined by the number of convolution layers, the size of the convolution kernel, and the expansion coefficient. TCN does not have the gradient disappearance and gradient explosion problems of RNNs, thus its gradient is relatively stable. Additionally, TCN has lower memory consumption because it does not need to save every step information such as RNNs. However, in the intelligent network of the Space-Integrated Ground Information Intelligent Network, the network traffic sample is large, so more memory is needed to directly apply the TCN algorithm to predict the network traffic. The proposed algorithm in this paper extracts the network traffic data by using TCN in sequence and saves the eigenvalues of each segment by using gated unit screening to reduce the number of neurons and memory consumption and to predict the intelligent network traffic of the Space-Integrated Ground Information Intelligent Network more accurately and rapidly.