esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Simulation and Experiment
The maximum voltage error is about 2 V and occurs at the mode switching point. Figures 15 and 16 show the simulation and experimental results, respectively, when the proposed compensation method is used in the ARSI. The output voltage distortion is reduced in Figures 15a and 16 without increasing the auxiliary current \(i_{Lr}\). Figure 15b demonstrates that the voltage error is reduced. Figure 14: Experimental waveforms of ARSI without dead-time compensation when the modulation index is 0.4 in an open-loop configuration. Figure 15: Simulation waveforms of ARSI with dead-time compensation when the modulation index is 0.4 in an open-loop configuration. (**a**) simulation results; (**b**) voltage error. Figure 13: Simulation waveforms of ARSI without dead-time compensation when the modulation index is 0.4 in an open-loop configuration. (**a**) simulation results; (**b**) voltage error. Figure 17 shows the magnitudes of the 2 nd-10 th harmonic components with respect to the fundamental component in the output voltage and current. The power analysis module DPO4 PWR is used to analyze the total harmonic distortions (THDs) of the current and voltage. Figure 17a demonstrates that THD of the output voltage with the compensation method is 3.48% which is less than 6.29% than without dead-time effect compensation. The magnitudes of the 2 nd-10 th harmonics are reduced, except for the 8 th harmonic. Figure 17b indicates that THD of the output current is reduced from 1.57% to 0.712% by using the proposed compensation method. Figure 16: Experimental waveforms of ARSI with dead-time compensation when the modulation index is 0.4 in an open-loop configuration. Figure 8:0 Magnitudes of the 2 nd–10 th harmonic components in respect to the fundamental component (**a**) output voltage; (**b**) output current. Figure 18 shows the experimental voltage and current THDs under different load conditions. As the modulation index increases, the THDs of the output voltage and current both decrease. Through using the dead-time compensation strategy, the THDs of the output voltage and current are clearly reduced.
# LogBD: A Log Anomaly Detection Method Based on Pretrained Models and Domain Adaptation
## 3 Methodology
### Anomaly Detection Based on Domain Adaptation
#### 3.4.3 Anomaly Detection  
The anomaly classifier determines whether a log sequence is anomalous according to the distance mapped to the center of the sphere. As shown in Figure 8, the blue normal data points are scattered haphazardly in the hyperspace. By training the feature extractor TCN, the data points are mapped to the hypersphere space, which is centered at c. The TCN is trained to make the normal data points close to the hypersphere sphere center. The anomaly detection is then performed by calculating the anomaly distance threshold using some normal and anomalous data.  
After the training phase, the feature extractor gains the ability to map both the source and the target system data to the vicinity of the hypersphere sphere center c, making the anomalous samples of both systems at a large distance from the center of c. To detect anomalies, a threshold \(\gamma\) needs to be set as the determination boundary to distinguish between normal and anomalous sequences. The validation set is used to find the optimal thresholds \(\gamma^{S}\) and \(\gamma^{T}\) in the source and target systems, respectively. Samples with a distance greater than \(\gamma\) from the center will be marked as abnormal sequences. The validation set is composed of log sequences from any day.  
The average values of normal log sequences and abnormal log sequences in the validation set after mapping positions are calculated and denoted as Mean\({}^{\text{normal}}\) and Mean\({}^{\text{abnormal}}\). The threshold is set to the average value of normal sequence positions Mean\({}^{\text{normal}}\), and the abnormal labels of log sequences are given by comparing the distance of log sequences mapped to the center of the hypersphere sphere and the magnitude of the threshold. If the AUC is greater than the current AUC value, the best threshold value is changed to the current threshold setting, and the best AUC is also changed to the current AUC value; otherwise, the best threshold value and the best AUC do not change, and the threshold value increases toward Mean\({}^{\text{abnormal}}\) at the end of judgment to expand the judgment range of normal log sequences. The above operation is cycled 100 times, and the best threshold value \(\gamma\) is finally obtained.
## 3 Proposed Method
### Definitions and Problem Statements  
The aim of PESO is to predict the vessel trajectory based on AIS data. In order to express the related operations in our method more clearly, we will use the following definitions:  
**Definition 1**.: _(Vessel trajectory): A track point is defined as a tuple \(x_{t}\) = \((LON_{t},LAT_{t},SOG_{t},COG_{t},DIS_{t})\) at the time of \(t\) in which \(x_{t}\) is composed of longitude \(LON_{t}\), latitude \(LAT_{t}\), speed \(SOG_{t}\), course \(COG_{t}\), and sailing distance \(DIS_{t}\) respectively, and a vessel trajectory \(X=(x_{t_{0}},x_{t_{1}},\ldots,x_{t_{n}})\) is defined as a sequence of these points arranged in chronological order where \(\{t_{i},i=0,1,2,\ldots,n\}\) is a set of timestamps._  
**Definition 2**.: _(Position sequence): Consider the location of a vessel only. A position of a ship is defined as a tuple \(y_{i}\) = \((LON_{i},LAT_{i})\) at the time of \(i\), and a position sequence of a ship is defined as \(Y=(y1,y2,\ldots,yt)\) at a timestamps \((1,2,\ldots,t)\)._  
**Definition 3**.: _(Vessel trajectory prediction): Given an observed trajectory \(X=(x_{1},x_{2},\ldots,x_{t})\) at timestamp \((1,2,3,\ldots,t)\), the scenario aims to predict the following position state of the vessel \(Y=(y_{t+1},y_{t+2},\ldots,y_{t+k})\) at the following timestamps \((t+1,t+2,\ldots,t+k)\)._
# Independent Double-Boost Interleaved Converter with Three-Level Output
###### Abstract  
This paper introduces a novel converter topology based on an independent controlled double-boost configuration. The structure was achieved by combining two independent classic boost converters connected in parallel at the input and in series at the output. Through proper control of the two boost converters, an interleaved topology was obtained, which presents a low ripple for the input current. Being connected in series at the output, a three-level structure was attained with twice the voltage gain of classic boost and interleaved topologies. A significant feature of the proposed converter is the possibility of independent operation of the two integrated boost converters, in both symmetrical and asymmetrical modes. This feature may be particularly useful in voltage balancing or interconnection with bipolar DC grids/applications. The operation principle, simulations, mathematical analysis, and laboratory prototype experimental results are presented.  
double-boost converter; interleaved; three-level voltage; renewable energy; voltage gain +
Footnote †: journal: Applied sciences
# A Narrow-Down Approach Based on Machine Learning for Indoor Localization
## 3 System Design
### Source Domain Refinement (SDR)
#### 3.3.1 Data Averaging Technique  
In order to reduce the training data in the source domain, which in turn reduces the classifier's computational complexity and offline data storage, we use the K-means clustering algorithm to make small groups of similar fingerprints in the source domain. K-means is an unsupervised clustering algorithm that is used to cluster given data into K number of clusters [31]. The algorithm iteratively assigns the data points to one of the K clusters based on how near the point is to the cluster's centroid. After making K clusters, we calculate the mean vector of each group. Those mean vectors from each group are stored in a source file and then used to train the classification algorithms. Assuming that we have \((x_{o}^{n})_{n=1}^{N_{0}}\) RSS vectors in the source domain and we want K clusters, the K means algorithm tries to pick K points as the initial centroids from the dataset. If each cluster's centroid is denoted by \(c_{k}\), then each data point x is assigned to a cluster based on  
\[Argmin_{(c_{k}\in\mathbb{C})}=euclidean(c_{k},x)^{2}, \tag{1}\]  
where \"C\" is the total number of clusters.
## 4 Numerical Experiment
### Spare Parts Inventory Analysis of Fleet System  
Table 2 shows the inventory optimization results of the system when the maintenance capacity is 0.65 under the constraint of 95% system availability.  
Figure 5: Joint iterative optimization method.  
By comparing the inventory results in reference [30], it is found that the optimization results reduce the inventory of LRU2 in our study with the inventory of LRU2's SRU increased slightly. Obviously, dynamic optimization of maintenance capacity could get more economical service logistics decisions.  
In terms of maintenance decisions, they are more inclined to repair LRU by replacing SRU, which reduces the cost of purchasing the LRU2. At this point, the inventory of LRU1 is appropriately increased. In comparison, we saved 6.4% on spare parts inventory cost.  
To illustrate the correctness of the model and algorithm of this study, we take the last iteration of the joint optimization model as an example. The inventory optimization process is shown in Figures 6 and 7.  
Figure 6 shows the relationship between system EBO, availability, and system cost. System cost increasing means the inventory is increasing, the system availability is increasing, and the EBO is decreasing. According to the downward trend of the EBO curve, it can be judged that this curve conforms to the definition of the \"convex\" function; that is, any point on the curve is optimal. According to the relationship between availability and EBO, any point on the available curve line is also optimal.  
Figure 7 shows the selection process of SRU, LRU1, and LRU2. The abscissa represents the number of iterations. If one iteration is completed, an additional inventory of parts is added. It can be seen in the first 40 iterations that the algorithm selects increasing the inventory of SRU as the optimal solutions.  
According to the marginal benefit factor analysis, the factor value of SRU was greater than LRU1 and LRU2 in the first 40 iterations. Furthermore, since the price of SRU is much lower than LRU, the impact of price factor is greater than EBO reduction in the first 40 iterations.  
When the iterations exceed 40, the algorithm selects increasing the inventory of LRU. This is due to the inventory of the SRU satisfying demand. At this time, \(\mathit{EBO}_{SRU}(S_{i,j}|mT)\) is small enough, and the price of LRU1 is much higher than SRU. Therefore, \(\delta_{SRU}\) is close to \(\delta_{LRU1}\).  
Similarly, since the price of LRU2 is much higher than LRU1 and SRU, when the iterations increase to 105, \(\delta_{LRU2}\) is close to \(\delta_{SRU}\),\(\delta_{LRU1}\).  
The same curve trends are also found in reference [30]. This proves the correctness of our model and algorithm. All cases that conform to the two-echelon two-indenture structure are applicable to this model and algorithm.
## 4 Performance Evaluation and Discussion  
### Evaluation Parameters  
To manage channel congestion, the 3 GPP standard defines a metric named CBR, as well as potential mechanisms for leveraging these metrics to mitigate channel congestion [20]. The CBR is a measure of the portion of time the channel is busy transmitting data. CBR is useful for quantifying the level of channel congestion and can be utilized to implement congestion control mechanisms. By monitoring the CBR, the network can dynamically adjust the allocation of channel resources and regulate the transmission of data to avoid congestion. The CBR is calculated every \(T_{CBR}\) = 100 ms as follows:  
\[\text{CBR}=T_{busy}/T_{CBR}, \tag{6}\]  
In Equation (6), the channel occupancy of \(T_{busy}\) is dynamically updated at the beginning or end of each transmission for every vehicle. The calculation of \(T_{busy}\) is determined based on whether the channel is sensed as busy or not. Specifically, the channel is considered busy if the received power level \(P_{r_{i}}\) is greater than the sensitivity threshold \(S_{busy}\). The sensitivity threshold \(S_{busy}\) is set to \(-94\) dBm [26].  
The degradation of PRR is a common issue in vehicular networks as vehicle density increases. Higher PRR guarantees more reliable communication and is calculated as below Equation (7):  
\[\text{PRR}=\frac{P_{r}}{P_{r}+P_{SL}+P_{TL}}, \tag{7}\]  
where \(P_{r}\), \(P_{SL}\) and \(P_{TL}\) represent the total received packets, SINR packet loss, and packet loss of transmitting, respectively.  
The degradation of PRR is a common issue in vehicular networks as vehicle density increases. DCC algorithms are commonly used to mitigate PRR reduction, which typically involves reducing the CBR. However, lowering the CBR could result in reduced throughput performance. Hence, the aim of this study is to determine the optimal packet transmission power and packet transmission rate that can maximize the aggregate PRR of vehicle user equipment (VUE), while also maintaining the CBR to a predetermined target, even in high-vehicle-density scenarios, through the HPR-DCC algorithm.
# Performance Analysis of Cache Based on Popularity and Class in Named Data Network
###### Abstract  
The communication network is growing with some unique characteristics, such as consumers repeatedly request the same content to the server, similarity in local demand trend, and dynamic changes to requests within a specific period. Therefore, a different network paradigm is needed to replace the IP network, namely Named Data Network (NDN). The content store, which acts as a crucial component in the NDN nodes is a limited resource. In addition, a cache mechanism is needed to optimize the router's content store by exploiting the different content services characters in the network. This paper proposes a new caching algorithm called Cache Based on Popularity and Class (CAPIC) with dynamic mechanism, and the detail explanation about the static method also presented. The goal of Static-CAPIC was to enhance the total cache hit ratio on the network by pre-determining the cache proportion for each content class. However, this technique is not appropriate to control the cache hit ratio for priority class. Therefore, the Dynamic-CAPIC is used to provide flexibility to change the cache proportion based on the frequency of requests in real-time. The formula involves considering the consumers' request all the time. It gives a higher cache hit ratio for the priority content class. This method outperforms Static-CAPIC, and the LCD+sharing scheme in the total network cache hit ratio parameter and channels it to the priority class.  
Keywords:Named Data Network; caching; popularity; content class; priority +
Footnote †: journal:  
_Future Internet_**2020**, _12_, 227; doi:10.3390/612120227
## 4 Theoretical Investigation of Bond Behaviour
### ANN Comparison with Other Prediction Models  
The predictive capability of the newly developed ANN model is compared initially with the experimental data, and the results show close agreement of bond strength values. The ANN model is also compared against prediction models recommended by the American Concrete Institute ACI 440.1R-03, ACI440.1R-06, and the Canadian Standard CSA S806-02 guidelines, and a comparison plot is shown in Figure 28 below.  
Figure 27: Validation Performance of the ANN Model.  
It is evident from the above plot that the ANN model is effective in predicting the bond strength of the basalt-FRP-reinforced SCGC specimens, and performs well against all of the other models. It can be seen that the ANN model is efficient in predicting smaller datasets with R2 = 0.96, and can be trained with available sets of test data, providing better prediction for composites such as SCGC-BFRP that exhibit non-linear behaviour. This supports the use of ANN and similar computational techniques to predict this kind of dataset, with high randomness and irregularities. Unlike traditional mathematical models, there is no need for developing separate models, as ANNs provide a good fit for randomized data using intelligent algorithms.
# Co-Channel Interference Suppression for LTE Passive Radar Based on Spatial Feature Cognition
## 5 Conclusions  
The paper discussed the DMI and co-channel interference suppression problem in LTE based passive radar. We proposed a cascade cancellation method that can effectively suppress the DMI and co-channel interference with a small sized antenna array. A DOA estimation method based on the cyclostationary characteristics of the OFDM signal and the sparse reconstruction technique was also proposed. It can obtain good DOA estimation of strong interference when the number of interference signals far exceeds that of the array elements. As can be seen in our simulation shown in Figure 15, 45 interferences exist (15 DMI from the BS-IoO and 30 DMI from theis CCBSs), but the proposed method could still obtain effective interference suppression and achieve a detection probability of above 0.9 by only using a 12-element antenna array. The detection performance can be further refined by using tracking filter methods, such as the Kalman tracking filter, which can eliminate the false alarms caused by the residual interference.  
Conceptualization: H.W., X.L., and K.L. Methodology: H.W. Software: H.W. Writing--original draft preparation: H.W., X.L. Writing--review and editing: H.W., X.L., and K.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by [Guangxi special fund project for innovation-driven development] grant number [GuikeAA21077008], [Guangxi natural science foundation project] grant number [2020 GXNSFBA297078], and [Research project on basic ability of young and middle aged teachers in Guangxi] grant number [2021 KY0197].  
The study did not require ethical approval.  
Not applicable.  
The study did not report any data.  
The authors declare no conflict of interest.
# Waveguide Manufacturing Technologies for Next-Generation Millimeter-Wave Antennas
###### Abstract
As will be shown, it still presents certain limitations, but it has the great advantage of allowing the monolithic (i.e., single part) fabrication of certain waveguide devices that could not be manufactured using conventional subtractive techniques, while still obtaining the great mechanical robustness of 3D metallic manufacturing. Finally, an approach based on stereolithography (SLA) manufacturing of the waveguide parts with a posterior metallization is presented for a conformal array. SLA is an additive manufacturing method based on UV curable resins that can provide a high-precision surface finish on plastic materials [26]. Therefore, a proper metallization technique is indispensable for SLA printing to become a cost-effective method of manufacturing hollow waveguide structures [27; 28; 29]. When working at high frequencies, special care must be taken in the post-metallization process. There can be problems with adhesion of the metal to the plastic. Therefore, this process must be well-controlled to obtain a high-quality surface finish. This approach can overcome certain roughness problems that appear in DMLS devices, while also producing lighter and less expensive devices. However, the lack of access to internal channels to metallize the part restricts the monolithic fabrication of some structures. During recent years, the authors have worked on the manufacturing of several high-performance waveguide-based antennas using different manufacturing techniques, many of which belong to the additive manufacturing family. The present study presents the knowledge acquired during these years. It covers many manufacturing details and strategies that are key to the successful manufacturing of these devices, which details are not typically discussed in papers focused on the electrical design of the antennas.
###### Abstract  
The limitations of hyperspectral sensors usually lead to coarse spatial resolution of acquired images. A well-known fusion method called coupled non-negative matrix factorization (CNMF) often amounts to an ill-posed inverse problem with poor anti-noise performance. Moreover, from the perspective of matrix decomposition, the matrixing of remotely-sensed cubic data results in the loss of data's structural information, which causes the performance degradation of reconstructed images. In addition to three-dimensional tensor-based fusion methods, [PERSON]'s minimum-volume belief in hyperspectral unmixing can also be utilized to restore the data structure information for hyperspectral image super-resolution. To address the above difficulties simultaneously, this article incorporates the regularization of joint spatial-spectral smoothing in a minimum-volume simplex, and spatial sparsity--into the original CNMF, to redefine a bi-convex problem. After the convexification of the regularizers, the alternating optimization is utilized to decouple the regularized problem into two convex subproblems, which are then reformulated by separately vectorizing the variables via vector-matrix operators. The alternating direction method of multipliers is employed to split the variables and yield the closed-form solutions. In addition, in order to solve the bottleneck of high computational burden, especially when the size of the problem is large, complexity reduction is conducted to simplify the solutions with constructed matrices and tensor operators. Experimental results illustrate that the proposed algorithm outperforms state-of-the-art fusion methods, which verifies the validity of the new fusion approach in this article.  
spatial-spectral smoothing; super-resolution; coupled non-negative matrix factorization; total variation; minimum volume +
Footnote †: journal: Applied Signal Processing  
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# Research Status and Development Direction of Formation Damage Prediction and Diagnosis Technologies
## 3 Laboratory Tests and Numerical Approach Method
### Digital Core Analysis
#### 3.3.1 Electron Microscope  
[PERSON] and [PERSON] proposed the idea of an electron microscope in 1932 to compensate for the lack of resolution of optical microscopes [39]. The de Broglie wavelength of electrons is 100,000 times shorter than that of visible light, so electron microscopes can produce images with a resolution of 0.05 nm and a magnification of 10,000,000 times.  
The first commercial transmission electron microscope was developed by the British company Metropolitan-Vickers in 1936 [40]. The transmission electron microscopy (TEM) technique can provide accurate information about the crystallography, microstructure, and components at the micron to nanometer scale in thin-section samples. The most recent application of TEM to formation damage studies was by [PERSON] et al., who used TEM, among other methods, to analyze the film structure, dimensions, and chemical properties of a new acid nano-capsule, developed to verify its ability to extend the life of the acid and reduce corrosion during production enhancement [41].  
Scanning electron microscopy (SEM) was developed by German physicists in the early 20 th century in parallel with conventional TEM [42]. The advantage of SEM over TEM is that thick samples can be used, because the electron beam only scans the surface without penetrating the sample to obtain images. Therefore, SEM plays an important role in the field of petroleum exploration and development, including pore-scale and working fluid intrusion analysis, sediment diagenesis, mineral alteration, structural and pore space characterization, and fluid performance evaluation [43; 44]. Even in low-permeability formations, SEM can provide high-quality images of porous media [45]. Therefore, SEM has been widely used for the qualitative analysis of formation damage [46]. Figure 2 shows the microscopic morphology obtained by [PERSON] et al. [47] using SEM to study the distribution of nano-emulsions before and after cycling on the surface of rock samples.