esa-sceva/satcom-chunk-collection huggingface dataset:
# Target Identification via Multi-View Multi-Task Joint Sparse Representation
## 2 Related Works  
Recently, researchers have proposed various target identification methods: a typical method is finding the target position through the response map generated by the online learning correlation filter (CF) and determining the target scale using a fixed scale factor. The CF based trackers include Kernelized Correlation Filters (KCF) [20], multichannel features [21], adaptive scale estimation [22], the fusion of complementary learners [23], long short-term memory [24], support vector machines [25], sparse coding [26] etc. However, these CF-based trackers have some major shortcomings: First, the tracker relies excessively on the maximum response value when determining the target position. Second, the CF-based tracker uses a fixed scale factor to determine the size of the target, which is not suitable for the actual scale change of the moving target. Therefore, when the tracking scene is too complex, the generated model is not robust enough. Besides the optical flow method, Camshift and Meanshift are also popular target tracking methods used in recent years, showing their potential in identification in tracking applications [27; 28]. However, these methods identify the target according to its single feature, and it is impossible to identify a common single feature that can be applied to different situations.  
To improve the robustness of prediction, various ensemble methods have been studied. The Staple tracker [29] consists of a correlation filter and a model based on color histogram, which complement each other. In [30], three different feature trackers based on support vector machines are integrated, and the trackers are adaptively selected according to the consistency of front and rear tracks. In order to eliminate redundancy between weak models, efficient diverse ensemble co-tracking [31] trains different models by generating a set of effective manual data. Multi-cue correlation filters tracker [32] assigns the suitable weak classified based on their self-wise and pairwise relationships. Due to the limited training samples, sequent training convolutional tracking (STCT) solved the problem of overfitting existing convolutional neural networks (CNNs) in visual tracking [33]. STCT uses binary masks to force basic learners to learn different features. In the correlation filter method it is proposed to fuse response maps from different convolutional neural network levels. Therefore, integrating multiple individual CF [34] has become a common technology. Under the Siamese tracking pipeline, a twofold Siamese network trains an appearance model and a semantic model, respectively, for online combination [35]. The learning part-based model of joint tracking is also discussed in the Siamese network [36].
## 3 Proposed Framework: PE-DOCC
### One-Class Classifier for Anomaly Detection  
Once the unsupervised training is completed, the training data are rejected into the trained Periodicformer encoder and the latent representation (\(Z=\{z_{1},...,z_{|X_{tr}|}\}\)) is fed to the local outlier factor (LOF) as input where \(|X_{tr}|\) is the number of training data.  
LOF is a density-based outlier detection algorithm. The determination of the outlier is judged based on the density between each data point and its neighbor points. The lower the density of the point, the more likely it is to be identified as the outlier. The key definitions for the LOF are as follows:  
**Definition 1**.: \(k\)_-distance of data point \(z_{p}\). For a given dataset \(D\) and any positive integer \(k\), the \(k\)-distance of \(z_{p}\), denoted as \(k\)-distance(\(z_{p}\)), is defined as the distance \(d(z_{p},z_{o})\) between data point \(z_{p}\) and data point \(z_{o}\) in the following conditions:_  
1. _For at least_ \(k\) _data points_ \(z_{\prime}\in D\setminus\{z_{p}\}\)_, it holds that_ \(d(z_{p},z_{o^{\prime}})\leq d(z_{p},z_{o})\)_;_
2. _for at most_ \(k-1\) _objects_ \(z_{o^{\prime}}\in D\setminus\{z_{p}\}\)_, it holds that_ \(d(z_{p},z_{o^{\prime}})<d(z_{p},z_{o})\)_._  
**Definition 2**.: \(k\)_-distance neighborhood of data point \(z_{p}\). Given the \(k\)-distance of \(z_{p}\), the \(k\)-distance neighborhood of \(z_{p}\) contains every data point \(z_{q}\) whose distance from \(z_{p}\) is not greater than the \(k\)-distance, as described in Equation (10)._  
\[N_{k\text{-distance}(z_{p})}(z_{p})=\{z_{q}\in D\setminus\{z_{p}\}|d(z_{p},z_{ q})\leq k\text{-distance}(z_{p})\} \tag{10}\]  
**Definition 3**.: _reachability distance of data point \(z_{p}\) with respect to data point \(z_{o}\). Let \(k\) be a natural number. The reachability distance of \(z_{p}\) with respect to \(z_{o}\) is defined as Equation (11)._  
\[\text{reach-dist}_{k}(z_{p},z_{o})=\max(k\text{-distance}(z_{o}),d(z_{p},z_{o})) \tag{11}\]  
**Definition 4**.: _local reachability density of data point \(z_{p}\). The local reachability density of \(z_{p}\) is defined as Equation (12)._  
\[lrd_{k}(z_{p})=1/\frac{\sum_{z_{o}\in N_{k}(z_{p})}\text{reach-dist}_{k}(z_{p },z_{o})}{|N_{k}(z_{p})|} \tag{12}\]  
_where \(N_{k}(z_{p})\) is the \(k\)-distance neighborhood of data point \(z_{p}\)._  
**Definition 5**.: _local outlier factor of data point \(z_{p}\). The local outlier factor of \(z_{p}\) is defined as Equation (13)._  
\[LOF_{k}(z_{p})=\frac{\sum_{z_{o}\in N_{k}(z_{p})}\frac{lrd_{k}(z_{o})}{lrd_{k} (z_{p})}}{|N_{k}(z_{p})|} \tag{13}\]  
The contamination rate hyper-parameter determines the threshold based on the LOF of the training samples. During testing, data points with LOF values greater than the threshold are considered outliers.
# DFT-Spread Spectrally Overlapped Hybrid OFDM-Digital Filter Multiple Access IMDD PONs
## 2 Principle of DFT-Spread Spectrally Overlapped Hybrid OFDM-DFMA PONs  
A representative DFT-spread spectrally overlapped hybrid OFDM-DFMA IMDD PON architecture is depicted in Figure 1, in which only the more challenging multipoint-to-point upstream operation is considered. The additional \(K\)-point DFT and IDFT block at each ONU and OLT, combined with a subcarrier mapper and de-mapper module, are shown highlighted in yellow. In each ONU, either in-phase (I) or quadrature-phase (Q) M-ary quadrature amplitude modulation-encoded data symbols are grouped into blocks, each containing \(K\) symbols. The \(K\)-point DFT operation is applied to spread the symbols into the frequency domain. Localized mapping is then utilized to map the symbols onto an \(N\)-point (\(N>2(K+1)\)) IFFT with \(N\)-subcarriers. As the first subcarrier is unused, the \(K\)-signal-carrying subcarriers occupy the first \(N/2-1\) subcarriers; as such, after zero padding, the signal length is \(K\leq N/2-1\). To produce a real-valued DFT-spread OFDM signal, an \(N\)-point IFFT operation is applied after enforcing the Hermitian symmetry. Following the addition of the cyclic prefix (CP), each digitally encoded sub-band sample sequence is up-sampled by a factor of \(M\times\). After that, the digital shaping filters, after having fed the up-sampled sequence, generate a digitally filtered sub-band signal, which is passed through the DAC and then fed into an optical intensity modulator (IM) to perform electrical-to-optical (E-O) conversion. Similar to the treatment adopted in [7], the utilization of IM as the preferred light source in the numerical simulations is to completely eliminate the signal-signal beating interference (SSBI) effect [23]. It is noteworthy that, for practical implementation of the proposed PONs, different ONUs can use different wavelengths to transmit their OFDM sub-bands, provided that every two adjacent wavelengths have a minimum wavelength space of \(\sim\)0.28 nm to effectively mitigate the SSBI effects [24].  
After transmitting through an SSMF, in the OLT, the corresponding receiver DSP functions are as follows: signal detection by a photodetector (PD); signal digitization by an ADC; serial-to-parallel conversion; symbol timing alignment; CP removal; single \(L\)-point FFT operation, with \(L\) satisfying \(L=M\times N\) for generating complex-valued frequency domain subcarriers utilizing the received real-valued time-domain symbols; identification of lower sideband (LSB) and upper sideband (USB) subcarriers; independent channel estimation and channel equalization of LSB and USB subcarriers based on the pilot subcarriers; and sideband processing to demultiplex the spectrally overlapped spread-spectrum OFDM subcarriers. After the above DSP process, the resulting output sub-band subcarriers' data are passed through the subcarrier de-mapper. The output of the de-mapper is then subjected to the \(K\)-point IDFT and symbol demodulation to obtain data information corresponding to the transmitted I-phase and Q-phase ONUs' sub-band input data.
## 2 Experimental Setup  
The Tm,Ho:YAP laser crystals employed in this study were grown using the Czochralski technique at the Shanghai Institute of Optics and Fine Mechanics. The crystals were grown along the crystalline c-axis for YAlO\({}_{3}\), while the laser crystal sample containing 5 at.% thulium and 0.3 at.% holmium was cut along the \(\alpha\)-axis. The crystal dimensions were measured to be 4 mm \(\times\) 4 mm \(\times\) 8 mm, with plane-parallel ends. Both end faces of the crystal were coated with a reflectivity of less than 0.5% at both 790-800 nm and 1.9-2.2 um.  
A schematic drawing of the laser setup used in these experiments is shown in Figure 1. A laser diode (nLight NL-PPS50-10030, Washington, DC, USA) with a maximum output power of 20 W and a central output wavelength of 794.1 nm was employed for pumping the Tm,Ho:YAP laser. The pump beam was coupled to a fiber with a core diameter of 100 um and a numerical aperture of 0.22, and afterwards was re-focused on the crystal's center using collimation and focus lenses, both having focal lengths of 75 mm. A pump spot with a diameter of approximately 850 um was placed on the center of the laser crystal with an overall coupling efficiency of approximately 80%. To ensure maximum laser output efficiency based on previous experimental results [18], we utilized a plane output coupler coated with partial reflectance (PR; R = 70%) for wavelengths ranging from 1.9 um to 2.2 um. Meanwhile, a plano-concave mirror with a 300 mm radius of curvature was used as the front mirror (M) with a high-transmittance coating (HT; T > 98.0%) at 790-798 nm as well as a high-reflective coating (HR; R > 99.5%) at 1.9-2.2 um. The overall cavity length of the resonator was approximately 90 mm.  
The laser crystal was mounted in an oxygen-free copper holder with indium foil to improve the heat-spreading efficiency and was placed in a vacuum chamber. We attached the copper block to the cold finger of the temperature-controlled cryostat (Janis Research SHI-4-2, Woburn, MA, USA) with a temperature-control stability better than 0.05 K. A calibrated copper-constantan thermocouple was affixed directly to the crystal block to enable direct monitoring of temperature. Two plane-parallel optical windows coated with 99.5% transmittance at a wavelength of around 2 um were placed in the vacuum chamber. The pressure gauge of the molecular pump indicated that the amount of vacuum was approximately 1.2 \(\times\) 10\({}^{-3}\) Pa, even though there was no accurate pressure sensor in the vacuum chamber.
# A Study on Finding Partners through Patent Network Analysis of UAM (Urban Air Mobility)
## 2 Theoretical Background and Hypothesis  
### City Traffic Problems in the City Center  
According to a 2019 report by the United Nations, urbanization rates were growing by 55.3% in 2018 and are projected to reach 68.4% by 2050. In addition, Tokyo recorded the highest number of urban populations based on administrative divisions for each city at 37.47 million. It was followed by Delhi, Shanghai, and Sao Paulo. Seoul recorded 34 th in the ranking with 9.9 million people living in the city center.  
The amount of SOC (Social Overhead Capital) investment required for solving urban traffic problems is astronomical. The cost of solving traffic problems is also taking up increasingly higher proportions of the nation's finances.  
Figure 1 shows that the construction of roads and subways in complex two-dimensional spaces--which are already mixed with people, buildings, and cars--is no longer an effective solution for urban traffic problems [6]. The population is likely to continue to be more concentrated in the city center and improving the efficiency of transportation such as public transportation, automobiles, and MaaS (Mobility as a Service) is surely the biggest workload for megacities. With the utilization of 3D space, UAM can be a meaningful alternative to solving megacity traffic problems [7].  
The UAM market is expected to grow at an average annual rate of 30% or higher and will be introduced in 31 cities by 2030. UAM development is most active in the United States, and purely battery-powered vehicles were observed as taking up the largest proportion in classification by energy source [8; 9].
## 3 Measurement Setup and Ferrofluidic Properties  
### Characterization of Tunable Inductor  
The fabricated wound and bonded structure inductor contains two ports to connect to the network analyzer, with sub-miniature version A (SMA) connectors. The network analyzer was calibrated up to the end of its ports' connectors (up to the ports of the device under test), so that the insertion and mismatch loss due to the ports and their corresponding connectors are eliminated. The calibration kit used for this work is SOLT, which is short, open, load and through. The wire-wound solenoid inductor was fabricated using Rogers 4003 laminate.  
In addition, to remove the connectors and lines effect of the PCB until the heads of the inductor, a SOLT calibration kit was designed and fabricated using FR-4 PCB. The FR-4 laminate is the identical material used for fabricated spiral inductor. The calibration kit fabricated for this work is shown in Figure 3.  
Figure 2: Implemented tunable wire bond inductor. (**a**) The micrograph of the fabricated wire bond solenoid inductor. (**b**) The micrograph of the fabricated wire bond solenoid inductor with Polydimethylsiloxane (PDMS)-based micro channel.  
The inductor was measured in an empty state and the state when the ferrofluid was injected to the channel. The measured S-parameter data were used to determine the inductance and quality factor. The measurement setup, including the network analyzer and injection tools used for measuring inductance and quality factors, is shown in Figure 3. The wire-wound solenoid inductor was first measured when its channel was empty. Then, the ferrofluids, such as EMG901, were injected into the channel gradually until it was completely filled. This causes the inductance to increase continuously. The maximum inductance is achieved when the channel is fully injected. The same procedure was done for another three ferrofluids with different permeability and magnetic saturation. This method was implemented for both structures.
## 1 Introduction
A real-time shipping container accident inference edge system using an artificial intelligence technology is investigated. In the existing works, there is no container accident inference system. However, the proposed system has the advantage of being able to monitor the status and risk of container accidents in real time. The proposed system also consists of a shipping container data collection function, a deep learning-based safety level estimation function, and a port yard management function. 2. A deep learning-based safety level estimation method is proposed to infer the safety levels of shipping containers. The proposed deep learning-based safety level estimation method is implemented using the YOLOv4 algorithm and is able to infer the safety levels according to the safety classification criteria based on the alignment status of shipping containers. 3. A real-world dataset related to the alignment status of shipping containers was collected, and a safety level estimation model was trained and optimized based on this dataset. The optimized inference model was used as a safety-level estimation model for the proposed system to improve its performance. The rest of this paper is organized as follows. Section 2 presents the related works, and Section 3 proposes the RSCAIES architecture and the safety level inference model for accident risk based on YOLOv3, YOLOv4, and YOLOv7 networks to predict the safety accident level of the alignment of shipping containers in detail. In Section 4, the performance evaluation of the proposed RSCAIES and inference model is presented. Finally, this paper is concluded in Section 5.
## 5 Application Case
### Simulation Values of the Equipment  
We will consider the optimization criteria formulated for the cost \(C(\mathbf{x})\) and profit \(P(\mathbf{x})\) in (43) and a year working interval of \(L=10\). The models for cost and profit depend on the frequencies of the PM activities (M1, M2, and M3), which act as decision variables. The definition of two parameters, \(w_{c}\) and \(M_{min}\), is required for each PM activity. Each \(w_{c_{i}}\) for \(i=1,2,3\) is the level of deterioration of the monitored component when a preventive maintenance (PM) action is performed. \(M_{min_{i}}\) represents the minimum time between two PMs, that is to say, if the maintenance age reduced in a component via PM is small, a replacement of the component will be performed, making the age to tend to zero again. Thus, \(w_{c_{1}}\), \(M_{min_{1}}\), \(w_{c_{2}}\), \(M_{min_{2}}\), \(w_{c_{3}}\), \(M_{min_{3}}\) are the components of the chromosome of this problem. Variables \(w_{c_{i}}\) are integer values ranging from 1 to 260 days (average number of working days per year). \(M_{min_{i}}\) is represented by an integer value between 1 and \(w_{c_{i}}\). No constraints are applied to the objective functions.  
Tables 1-4 show the relevant component reliability, preventive maintenance, corrective maintenance and cost data for this case study, respectively.
# Innovative Integration of Severe Weather Forecasts into an Extended Arrival Manager
## 3 AMAN Air Traffic Controller Support
### Diversion Route Finding  
The AMAN 4D-CARMA used in SINOPTICA first creates an individual waypoint list for the 4D trajectory calculation for each aircraft, which consists of both physical navigation points, such as Very High Frequency Omnidirectional Radios (VORs) or Distance Measurement Equipment (DME) and virtual Aeronautical Information Publications (AIPs), or Flight Management System (FMS) waypoints with all their altitudes and speed constraints. Usually, this waypoint list is based on the defined standard approach routes and transitions of the airport and the approach direction. If a conflict between a trajectory and a polygon of severe weather has been detected, the waypoints located inside the polygon are first determined. Afterwards, the points directly in front of and behind the polygon are searched for.  
The points inside the polygon are deleted from the waypoint list, and the points before and after it are set as the start and end points for the diversion. For the route, points to the right and left of the weather are now calculated in the direction of flight, which would allow for a triangular diversion (for example, waypoints IP \([P_{1}P_{2}]\) left or IP \([P_{1}P_{2}]\) right in Figure 2). If it turns out that the waypoints in front of or behind the polygon are too close to it that an aircraft would have to fly a steep turn, these points are accordingly moved further away from the polygon along the route (from waypoint \(P_{1}\) to \(P_{0}\) in Figure 2).  
The next step is to determine the route lengths that involve a right- or left-side diversion. In addition, the moving direction and speed of the severe weather is considered when choosing the diversion's direction. Subsequently, the newly determined waypoints are integrated into the current waypoint list and used for arrival sequencing and trajectory calculation.
# Optimization of Machining Parameters Using Fuzzy Taguchi Method for Reducing Tool Wear
## 3 Research Design
### Combination of Rules and Fuzzy Operation  
According to the three levels (low, medium, and high) defined in the Taguchi method, the membership functions corresponded to the highest point in each fuzzy region. The membership function of the input was determined using the intersection method. The expected average values of each fuzzy region are shown as follows.  
1. When the fuzzy region denotes \"minimal\" tool wear \[\text{Average value}=\frac{1\times 0.84+2\times 0.68+3\times 0.52+4\times 0.36+5 \times 0.2+6\times 0.04}{1+0.84+0.68+0.52+0.36+0.2+0.04}=1.769\]
2. When the fuzzy region denotes \"small\" tool wear \[\text{Average value}\] \[=\frac{5\times 0.04+6\times 0.2+7\times 0.36+8\times 0.52+9\times 0.84+10 \times 1+11\times 0.68+12\times 0.52+13\times 0.36+14\times 0.2+15\times 0.04}{0.04+0.2+0.36+0.52+0.84+1 +0.68+0.52+0.36+0.2+0.04}\] \[=9.966\]
3. When the fuzzy region denotes \"moderate\" tool wear \[\text{Average value}\] \[14\times 0.04+15\times 0.2+16\times 0.36+17\times 0.52+18\times 0.68+19 \times 0.84+20\times 1+21\times 0.84\] \[=\frac{+22\times 0.68+23\times 0.52+24\times 0.36+25\times 0.2+26 \times 0.04}{0.04+0.2+0.36+0.52+0.68+0.84+1+0.84+0.68+0.52+0.36+0.2+0.04}\]  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**No.** & **Minimal** & **Small** & **Moderate** & **Large** & **Greatest** \\ \hline
9 & 0 & 0.84 & 0 & 0 & 0 \\
10 & 0 & 1 & 0 & 0 & 0 \\
11 & 0 & 0.68 & 0 & 0 & 0 \\
12 & 0 & 0.52 & 0 & 0 & 0 \\
13 & 0 & 0.36 & 0 & 0 & 0 \\
14 & 0 & 0.  
When the fuzzy region denotes \"large\" tool wear Average value \(26\times 0.04+27\times 0.2+28\times 0.36+29\times 0.52+30\times 0.68+31 \times 0.84+32\times 1+33\times 0.84\) \(=\dfrac{+34\times 0.68+35\times 0.52+36\times 0.36+37\times 0.2+38\times 0.04}{0.04+0.2 +0.36+0.52+0.68+0.84+1+0.84+0.68+0.52+0.36+0.2+0.04}\) 5. When the fuzzy region denotes \"greatest\" tool wear Average value \(=\frac{34\times 0.04+35\times 0.2+36\times 0.36+37\times 0.52+38\times 0.68+39 \times 0.84+40\times 1}{0.04+0.2+0.36+0.52+0.68+0.84+1}\) \(=38.23\) The results are configured by the Taguchi method to establish the orthogonal array as shown in Table 5.  
Using Fuzzy values, the main effects plot for SN ratios can then be plotted by Minitab 15 (Figure 6). The slope in Figure 6 represents the effect of the input in the condition. Therefore, the optimal combination of cutting parameters is A3 B3 C2.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**EXP**} & \multirow{2}{*}{**Cutting Speed (m/min)**} & \multirow{2}{*}{**Depth of Cut (mm)**} & \multirow{2}{*}{**Feed Rate (mm/rev)**} & \multicolumn{2}{c}{**Target Fuzzy Ranking Values**} \\ \cline{3-4}  & & & & **Tool Wear (mm\({}^{-2}\))** \\ \hline
1 & 1 & 1 & 1 & 32 \\
2 & 1 & 1 & 2 & 38.23 \\
3 & 1 & 1 & 3 & 32 \\
4 & 1 & 2 & 1 & 20 \\
5 & 1 & 2 & 2 & 32 \\