esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
The task of localizing a target speaker in the presence of competing interference and background noise is investigated in this paper. A method combining a BLSTM-based target time-frequency selection scheme with the cWMM-based localization algorithm is proposed. The time-frequency neural network additionally relies on a reference utterance from the target and achieves speaker-awareness to predict the target-dominant time-frequency regions in the signal spectra. After time-frequency selection, the general cWMM-based localization method could be applied, and the localization results correspond only to the target. Experiments are conducted in adverse conditions, and the performance of the proposed algorithm remains robust in terms of the GER and MAE metrics.  
Combing source separation and source localization as in this paper would facilitate speaker tracking over time, which is another challenging task that could be investigated in future work. For providing the prior information of the speaker to be localized, others speaker-dependent representations, such as pitch and voiceprint, could be introduced to the time-frequency selection neural network.  
Methodology, validation and writing, original draft, Z.W. Review and editing and formal analysis, J.L. Supervision and project administration, Y.Y.  
This work is partially supported by the National Natural Science Foundation of China (Nos. 1146114004, 61271426, U1536117, 11504406, 11590770-4, 61601453), the Strategic Priority Research Program of the Chinese Academy of Sciences (Nos. XDA06030100, XDA06030500, XDA06040603), the National 863 Program (No. 2015 AA016306), the National 973 Program (No. 2013 CB329302), and the Key Science and Technology Project of the Xinjiang Uygur Autonomous Region (No. 201230118-3).  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
cWMMComplex Watson Mixture Model  
DNNDeep Neural Network  
DoADirection of Arrival  
FFFeed-Forward layer  
GCC-PHATGeneralized Cross-Correlation with Phase Transform  
GERGross Error Rate  
LHUCLearning Hidden Unit Contribution  
LSTMLong Short-Term Memory  
MAEMean Absolute Error
## 1 Introduction  
As the era of new communication such as 5G era comes, it is expected that there will be a lot of services that cannot be compared with what existed before. Examples include Internet of Things (IoT) [1], in which everything in everyday life is connected to the Internet, such as a refrigerator and a TV that can be remotely controlled, and services such as Tactile Internet. These services have network requirements [2; 3].  
The term Internet of Things was first introduced by [PERSON] in 1999 [4]. IoT network means that things such as sensors are connected to the internet and is freely controllable from the remote. Internet of Things is regarded as one of the emerging technologies in IT. This will enable new services [5]. It requires new network characteristics accessing for a high number of devices.  
Tactile Service or Tactile Internet is that providing real-time interactions with low delay that humans cannot recognize [6; 7]. If Tactile Service is realized, many novel services will appear: remote health care, virtual reality, robotics, etc. Each service needs a different delay.  
The network requirements for these new services are difficult to achieve at once. So, the core technology of future network proposed in 5G is to be able to dynamically and quickly provide the requirements of such a network [2; 3]. However, 5G's core enabling technologies, SDN and Network Functions Virtualization (NFV) [8], are designed exclusively for network administrators or managers. This is a powerful solution to many problems, but there are still problems that cannot be solved at the same time. In this paper, we investigate what these problems are and present functional-oriented networking as a solution and complement.  
In Section 2, related works are described. In Section 3, Function-Oriented Networking is proposed and how to implement it is explained. In Section 4, we illustrate how to implement Routing using ant colony optimization as FON Function. In Section 5, the previous FON Function is evaluated and network performance analyzed.
## 2 Related Research Work
The model includes two parallel CNN branch networks, whose inputs are the root mean square (RMS) of the short-time Fourier transform (STFT) of the two-view data, and then the improved D-S algorithm is used to fuse the diagnosis results of the two branch networks. [PERSON] et al. [5] constructed a stacked wavelet auto-encoder (SWAE) by utilizing the Morlet wavelet as the activation function of the hidden layer to more accurately map nonstationary vibration data and various working states. An enhanced voting fusion strategy is designed to synergize the diagnosis results of a series of base SWAEs on multi-sensor vibration signals. [PERSON] et al. [19] proposed a multi-sensor decision level fusion fault diagnosis method combining a symmetrized dot pattern (SDP) image method and the VGG16. First, the SDP method was employed to convert the vibration signal of a single multi-channel sensor into an imaging arm, which was input into the VGG16 convolutional neural network for model training. Then, the SDP images of the measured values of multiple multi-channel sensors were input into the fault diagnosis model, and the final diagnosis result was obtained through the D-S evidence theory. The above methods of decision-level fusion mainly use D-S and voting strategies, and some of them can also learn the weights of different views but ignore the relationship between views. At present, how to learn the view weight and how to combine multi-view learning is still an open problem. Considering that concatenating or splicing multi-view data into single-view data will destroy the spatial structure of multi-view data, inspired by the characteristics of image channels, this study has constructed a dataset with multichannel characteristics. Each channel corresponds to a sensor monitoring data in different directions, and channels are independent of each other. Then, the channel of the CNN model is used to correspond to the multi-channel of the dataset. The convolution kernels of the first convolution layer first extracts the features in the channel, and then extracts the features between channels. In the field of fault diagnosis, the application of designing multi-channel inputs at the input layer is rare. In recent years, attention mechanisms have been widely used in natural language processing, speech recognition, fault diagnosis and other fields because it can capture important information in data. This study proposes a CAM_MCFCNN model based on the attention mechanism and convolutional neural network. The model uses view level weight strategy to realize multi-view collaborative fault identification. At the same time, the model integrates data fusion, feature learning and fault recognition into a framework to avoid the sub optimal results caused by the step-by-step strategy.
## 2 Materials and Methods
### UW functions  
Load DICOM (square button with an arrow icon, in the top left): this function allows the operator to upload a DICOM file folder, automatically managing some possible issues related to file selection, such as the presence in the same folder of various scans even coming from different diagnostic  
Figure 1: EVAR-eaSE framework: the three interconnected processing modules (Upload Window—UW, Segmentation Window—SW, Display and Computation Window—DCW) and the main software functions implemented in each module.  
Figure 2: Graphical user interface of Upload Window (UW).  
modalities, or external files not relative to the case under examination. To address these possible issues, all available image scans are shown to the user in a preliminary window, in which each folder has a description. The user can select the folder to load, simply by clicking on it. During loading, the DICOM files with a reduced number of slices and/or with a too large slice thickness (low resolution) than the other files in the same folder, are recognized by the software and excluded.  
_Invert Volume_ (rectangular button with two-arrows icon, in the top left): this command is used to mirror the imported DICOM scans, in order to switch between \"head-foot\" and \"foot-head\" orientation. This may be useful to set the right orientation ensuring that in axial plane of the Segmentation Window, when scrolling the slider up the scan advances towards the head, when scrolling down it goes towards the foot.  
_Select ROI_ (rectangular buttons in the right side): it allows for selection of a ROI, both in the frontal and sagittal planes, by clicking and dragging a rectangular selection on the image. Once the ROI is selected in both planes, the central button (Show Selected Volume) becomes active.  
_Show Selected Volume_ (central button in the right side): when pressed, it opens the SW that provides the segmentation only for that portion of the image resulting from the intersection of the selected ROI in the two planes.  
_File menu:_ contains Save and Import functions to save the current EVAR-eaSE working file and import a previously created and saved file, respectively.  
_b. UW outputs_  
_Patient Info_ (box in the bottom left): in this section the patient information and exam data (e.g., exam type, exam date) are displayed as soon as the DICOM files are loaded.  
_Image frames_ (in the central part of the window): the loaded image scans are displayed along two planes: frontal (or coronal) and sagittal. The scans can be scrolled along ventral-dorsal (for frontal plane) and left-to-right (for sagittal plane) directions, using the nearby sliders.
# Long-Tailed Metrics and Object Detection in Camera Trap Datasets
## 2 Materials and Methods
### Long-Tailedness Metrics  
Deep learning models need to learn abstract features from large amounts of camera trap images, for animal classification and detection; therefore, the model performance heavily depends on the distribution of the dataset. Accurately and objectively quantifying the degree of long-tailedness in camera trap datasets is an important prerequisite for research and practical applications. By referring to existing studies [16; 17; 18; 20], we exploited four commonly used metrics: the Gini Coefficient (denoted as GC); the Imbalance Factor (denoted as IF); Standard Deviation (denoted as SD); and the Mean/Median (denoted as MM), to measure the long-tailedness of multiple camera trap datasets.
# Prediction and Analysis of Airport Surface Taxi Time: Classification, Features, and Methodology
## 3 Mainstream Models and Methods
### Analytical Modeling
#### 3.2.2 Neural Network
The results demonstrated that employing this combined model significantly improved prediction accuracy compared to using a single SVR model or a single BP neural network model in isolation. By integrating the complementary aspects of SVR and the BP neural network, the combined model effectively addressed the challenges posed by the diverse factors affecting taxi time and offered more accurate predictions.
# A Novel Relay Selection Scheme Based on Q-Learning in Multi-Hop Wireless Networks
###### Abstract  
In wireless communication systems, reliability, low latency and power are essential in large scale multi-hop environment. Multi-hop based cooperative communication is an efficient way to achieve goals of wireless networks. This paper proposes a relay selection scheme for reliable transmission by selecting an optimal relay. The proposed scheme uses a signal-to-noise ratio (SNR) based Q-learning relay selection scheme to select an optimal relay in multi-hop transmission. Q-learning consists of an agent, environment, state, action and reward. When the learning is converged, the agent learns the optimal policy which is a rule of the actions that maximize the reward. In other words, the base station (BS) knows the optimal relay to select and transmit the signal. At this time, the cooperative communication scheme used in this paper is a decode-and-forward (DF) scheme in orthogonal frequency division multiplexing (OFDM) system. The Q-learning in the proposed scheme defines an environment to maximize a reward which is defined as SNR. After the learning process, the proposed scheme finds an optimal policy. Furthermore, this paper defines a reward which is based on the SNR. The simulation results show that the proposed scheme has the same bit error rate (BER) performance as the conventional relay selection scheme. However, this paper proposes an advantage of selecting fewer relays than conventional scheme when the target BER is satisfied. This can reduce the latency and the waste of resources. Therefore, the performance of the multi-hop transmission in wireless networks is enhanced.  
O 2020; 10, 5252; doi:10.3390/app10155252
# 3D Monitoring of Buildings Using TerraSAR-X InSAR, DInSAR and PolSAR Capacities
## 3 Measuring the Height by Interferometry
### Height Estimation Results for Time-Series-Repeat-Pass Interferometry  
As stated in Section 2.2, the studied building are deforming daily, in particular because of weather changes. Therefore, a deformation \(\delta r\) impacting the shape of the buildings between two difference acquisition date will impact the phase difference following:  
\[\phi\equiv-\bar{k}_{z}h+\frac{4\pi}{\lambda_{0}}\delta r\quad[2\pi] \tag{2}\]  
For our data set, the term \(\frac{4\pi}{\lambda_{0}}\) is constant and equal to 405.3668 whereas \(|\bar{k}_{z}|\) ranges between about 0.01 and 0.66. Neglecting 1 cm of deformation will generate between a 6 m error when \(\bar{k}_{z}=0.66\) and a 375 m error when \(\bar{k}_{z}=0.01\). This can explain the unrealistic heights obtained in the previous section. It is, however, possible to measure the height of the buildings using the whole temporal stack in a different - and arguably more robust-way.  
To this aim, the deformations at the different dates are assumed to be independent and to result in a Gaussian noise on the phase. Figure 5 shows the unwrapped phase \(\phi\) difference given the multi-pass interferometric factor \(\bar{k}_{z}\) for the four buildings. The linear relationship between \(\phi\) and \(\bar{k}_{z}\) is clearly visible. Using a linear regression between the unwrapped phase \(\phi\) and \(\bar{k}_{z}\), the height can be retrieved with a high degree of fit, as presented in Table 3. The heights estimated by linear regression show a good agreement with the reference heights of the buildings.  
Figure 4: Measured height given the number of days separating the acquisitions. \(\bullet\) Eiffel Tower, \(\blacktriangledown\) Cristal Tower, \(\blacklozenge\) Keller Tower, \(\blacksquare\) Mirabeau Tower. The vertical dashed lines represent the years from the master image acquired the 24 January 2009.  
Only the unwrapped phases of the Cristal Tower seems to deviate from linear model: this buildings has the biggest difference between the reference and the regression height, and its coefficient of determination \(R^{2}\) is the smallest. However, the deviation from the linear model occurs when the orthogonal baseline \(B_{\perp}\) (and thus \(\bar{k}_{z}\)) is small, as expected from Equation (2).  
Since the phase is more impacted by the deformation when \(B_{\perp}\) is small, large baselines could be considered as an easy way to measure the height without any impact of the deformation. However, when \(B_{\perp}\) is high and thus the ambiguity height \(\frac{2\pi}{\bar{k}_{z}}\) small, more fringes are visible on buildings and the phases are more difficult to unwrap automatically with the needed accuracy.  
In this method, the fact that the height and the deformation account conjointly in the interferometric phase is overcome by the diversity in the baseline values. The linear regression based on nearly 100 images enables to limit errors in the height estimation.
# Pattern-Based Decoding for Wi-Fi Backscatter Communication of Passive Sensors
## 6 Experimental Evaluation
### Environmental Setup  
We used two Ettus Universal Software Radio Peripherals (USRPs, N210 and X310) [34] to generate and receive Wi-Fi signals, as shown in Figure 6. The gr-ieee802-11 module of GNU Radio [35] was used to generate Wi-Fi carrier signals according to the OFDM frame format of IEEE 802.11g. Each Wi-Fi frame is composed of a 64 \(\upmu\)s physical-layer header and a following MAC frame of 1528 bytes. It is transmitted at a bit rate of 9 Mbps (QPSK modulation and 3/4 code rate) and 1.4 ms long. The USRPs operate at the center frequency of 2.432 GHz. The transmit power of the transmitting USRP is 14.78 dBm (as measured at the output antenna port by a network analyzer). The receiving USRP captures the I/Q signals of received Wi-Fi frames via two antennas, each at a sampling rate of 10 MHz (for moderate computational loads), and preprocesses them according to the \(\upmu\)MO procedure [9], so that communication distance is expanded thanks to diversity. Then, the resulting signal stream is smoothed via moving average with the window size same as the slot length and input to the processing block of bit decoding.  
Figure 5: Illustration of the pattern-based decoding algorithm.  
The backscatter tag was implemented using Analog Devices' RF switch ADG902 [36] for the reflection and absorption of ambient Wi-Fi signals. Information data were generated as alternating one and zero, and coded using FM0 coding [8]. Then, the backscatter tag transmits coded bits at a bit rate of 20, 50, 100, and 200 kbps. The total number of Wi-Fi frames generated for each experiment was 500, where each of received Wi-Fi frame is represented as 14,000 signal samples. The performance metric is the bit error rate (BER) of information bits for various tag distances and bit rates.  
Figures 6 and 7 show the experimental setup, and the placement of the USRPs and the backscatter tag in the indoor environment of a small office. The USRPs were one meter apart from each other. The backscatter tag was placed in the middle of the USRPs and moved to eight different positions, each 0.5 m apart from the adjacent ones. All were at a height of 0.5 m from the ground. The tag's reflection gain (calculated as the ratio of the backscattered signal amplitude of the tag's reflection state to that of the absorption state, i.e., approximately \(|h_{1}\rho+h_{2}|/|h_{2}|\) from Equation (1)) for different distances is given in Figure 8.  
Figure 6: Experimental setup.  
Figure 7: Experimental setup for evaluation with various tag distances.
## 2 System Model
### 5G New Radio 3D Scenarios
#### 2.2.3 Indoor Hotspot  
The indoor hotspot deployment scenario focuses on small coverage per cell and high user throughput or user density in buildings. This scenario represents indoor offices (InDs) with a total area of 120 m \(\times\) 50 m. There are 12 small cells that are deployed with an ISD of 20 m. In this case, the BS antenna height is 3 m. The carrier frequency options are 30 and 70 GHz (mmWaves). The bandwidth for both 30 and 70 GHz carriers is up to 1 GHz. A total of 10 users per cell are distributed uniformly, and all users are indoors with 3 km/h  
Figure 2: Urban micro scenario layout: Macro layer: ISD = 200 m = 3R; Micro layer: 3 micro TRPs per macro TRPs.  
velocity. Full buffer model is assumed. The layout of this scenario is illustrated in Figure 3. All parameters used correspond to the Table 7.2-2 in [36].  
Our 3D simulation channel model considers the above-mentioned indoor and outdoor wireless propagation environments in terms of physical aspects of mmWave frequencies while numerous 5G NR channel models issues are adopted [36; 37]. For any considered operating frequency and scenario, the number of clusters, the number of subarrays (scatters) per cluster and the positions of the clusters can be determined by the detailed steps and procedures of [36; 37]. According to the 5G 3 GPP 3D channel models, the numbers of clusters and scatterers are determined using the Poisson and uniform distributions with specific parameters. Since 5G NR extends the operating frequency range by comprising the frequency bands below 7 GHz (i.e., FR1) and in the range from 24.25 to 52.6 GHz (i.e., FR2), different antenna solutions and techniques need to be employed depending on the utilized spectrum. For lower frequencies, up to a moderate number of antennas can be activated (i.e., 64). In higher frequency bands, the transmission is characterized by a considerable signal attenuation that limits the network coverage. To overcome this limitation, one of the key features is the adoption of a large number of multiantenna elements having a given aperture to increase the transmission/reception capability of MU-MIMO and beamforming. Since managing transmissions in higher frequency bands is complicated, beam management is necessary to establish the correspondence between the directions of the transmitter- and receiver-side beams by identifying the most suitable beam pair for both downlink and uplink.
# Metallurgical Aspects in the Welding of Clad Pipelines--A Global Outlook
## 3 Microstructure Evolution
### Weld Metal Microstructures
#### 3.3.3 Root Pass Welding with Ni-Based Filler Wire and Inconel 625 Clad  
For the combination of a Ni-based filler wire (Alloy 59/Inconel 625) and Inconel 625 clad layer, the weld metal microstructure should not be much influenced by the mixing of clad melting and filler wire due to their similar chemical compositions.  
The solidification mode is influenced to some degree by the cooling rate [34]. There is also local variation in chemical composition at the subgrain level due to the microsegregation of alloying (Fe, Cr, Nb, Mo, and Si) and impurity elements (S, P, O, and C) during solidification, leading to the second phase formation (e.g., carbides and Laves phase for Inconel 625). These phases form at the end of solidification in the interdendritic regions. Composition analyses show that dendrite cores consist of Ni, whereas interdendritic areas mostly include the precipitation of Nb (carbides, Laves phase) and Mo (Laves phase) [65]. An example is shown in Figure 13. The Laves phase is an intermetallic compound of the type A\({}_{2}\)B, where A is Ni or Cr and B is constituted by Mo or Nb.  
Due to the segregation of alloying and impurity elements, there is a possibility for liquation or solidification cracking (Figure 14a) to occur in the welding of solid-solution-strengthened alloys, as well as ductility-dip cracking (DDC); see Figure 14b. DDC is particularly common for multipass welding due to reheating [65]. Regarding nickel-based alloys, [PERSON] et al. [66] identified that the constitutional liquation of M\({}_{23}\)(B,C)\({}_{6}\) (M can be Mo, Cr, or Fe) carbides is responsible for the liquation cracking in WM. It was noted that the liquation cracking was reduced with a decrease in heat input; the heat input must, therefore, be optimized during welding.  
Figure 14: (**a**) Solidification crack: the crack path and eutectic constituents are coexisting. The eutectic constituent forms from the terminal solute-rich liquid at the end of solidification. (**b**) Ductility-dip cracking (DDC) in multipass welding [65].  
Figure 13: Primary weld metal microstructure with Inconel 625 filler wire.  
Recently, it was found that significant reduction (by \(\sim\)90 vol %) in Laves phases in welds can be achieved by applying an acoustic device which induces the vibration of the weld pool through a high-frequency current (the ultrasonic Ampere's force) at a specific frequency rate [67]. Here, two mechanisms are involved during ultrasonic treatment: reduction of the local concentration of Nb and specific (distorted) Ni-dendrite growth directions.