esa-sceva/satcom-chunk-collection huggingface dataset:
# Full-Field Mode Shape Identification Based on Subpixel Edge Detection and Tracking
## 2 Theory and Methodology
### Detection of Pixel-Level Edge  
Edge detection is a basic technique in image processing for feature extraction. The popular pixel-level edge-detection methods are the [PERSON] [33], Sobel [34], and Canny algorithms. The Roberts algorithm is very sensitive to noise and produces weak responses for genuine edges, while the Sobel algorithm is less sensitive to noise but has a long computation time. However, the Canny algorithm performs better than all these operators in almost all scenarios [35]. Thus, it is used to acquire continuous dense pixel-level edge points, and it mainly includes the following steps: (1) preprocess the ROI in the image using the Gaussian filter as a smoothing technique for noise removal; (2) calculate the gradient direction and amplitude of all pixels of the ROI; (3) find the local maxima of gradient amplitudes for all the pixels of the ROI using the non-maximum suppression and set the gradient amplitudes of the remaining pixels to be zero; (4) set two thresholds, \(T_{1}\) and \(T_{2}\), to classify the pixels into three parts--the pixel with a gradient amplitude \(>\)\(T_{2}\) is defined as an edge point, the pixel with gradient amplitude \(<\)\(T_{1}\) is defined as a non-edge point, and the pixel with a gradient amplitude between \(T_{1}\) and \(T_{2}\) is retained when adjacent to the edge points.
## 3 Data Collection and Analysis
### Data Source  
At the end of 2017, the total permanent population of Shenzhen was about 12.53 million and about 65.3% are nonregistered migrants, according to the Shenzhen Statistics Bureau [28]. Due to the restriction of car ownership in Shenzhen, the total number of buses and cars was about 2.83 million at the end of 2017, and thus most people only use public transit vehicles for travel [28]. By the end of 2018, there were 985 bus lines with a total length of 20,000 km and a 285 km long metro network with nine lines. The coverage rate of the ground-level bus station within the scope of 500 m is up to 95.3%, and the proportion of public transportation passengers during peak hours amounts to about 60.5% of total travelers [29]. The AFCS by using a smart card for bus travel was deployed by Shenzhen transit agencies in 2004, and the AFCS for the metro system opened in 2005 [30]. Shenzhen's smart card has been widely used for multifunctional fare payments for bus, metro, taxi, and grocery purchasing. Besides the smart card system, ticket fees can also be paid by one-way tickets, cash, and Quick Response (QR) Code payment for the metro and bus. Particularly, the Shenzhen Traffic Operation Command Center (TOCC) is responsible for the operation and maintenance of the metro and bus SC system.  
To analyze the interchange between the metro and bus systems, this study derived the half-monthly SC transaction data of metro and bus system from TOCC on March, 2019. The collected metro data in this study relates to five metro lines (numbers of 1, 2, 3, 4, and 5) which have more than 2.78 million daily data points on average. Moreover, the bus data covers 985 lines and 6319 stations in Shenzhen, with the average daily records totaling more than 3.43 million data points. The collected dataset refers to the identification card number, card type, metro station, bus line number, transaction timestamp, etc. Each bus traveler's timestamp approximates the time after boarding, while the metro passenger's timestamp is equal to the leaving time when the passenger passes through the ticket gate. The unique card number for each passenger can help track her/his transfer trip from the metro to the bus system.  
Notably, different from other cites' SC systems, such as Singapore and Seoul where both boarding and alighting time and station are recorded, the bus SC system in Shenzhen only records the card number and swiping time. Thus, the missing boarding station and time can be estimated by comparing SC data with GPS data.  
In addition, this study also collected bus GPS data, which includes bus line, vehicle ID, the arrival and departure time at each bus station. The related Geographic Information System (GIS) data about metro lines and bus lines comes from open source Baidu Maps (API) in China.
## 2 Materials and Methods  
### Sample Preparation  
Co\({}^{2+}\):MgAl\({}_{2}\)O\({}_{4}\) has a cubic crystalline structure, it is hard (Mohs hardness 8) and has a density of 3.58 g/cm\({}^{3}\). During the growth of a MgAl\({}_{2}\)O\({}_{4}\) crystal it is possible to dope it with cobalt, which occupies part of tetrahedral sites and induces two ground-state absorption bands at 590 nm and \(\sim\)1.4 um [22]. Such structural crystal modification is effectively used for Q-switching in erbium lasers. Co:MALO crystals also possess other important necessary properties, such as low scattering losses and a high inhomogeneity.  
Commercially same-batch-polished Co\({}^{2+}\):MgAl\({}_{2}\)O\({}_{4}\) crystals, having a 6 mm diameter and a 1.15 mm thickness were used for the experiments. The oxygen plasma etching of the crystal surface was performed using an RF plasma source Copra IS300 (CCR Technology, Troisdorf, Germany), installed within a PVD coating chamber VERA1100 (VTD Vakuumtechnik, Dresden, Germany). Higher ion energies might also introduce higher number of atomic subsurface defects such as vacancies and interstitials. Oxygen vacanciesdecreased the LIDT of HfO\({}_{2}\) films by a factor of 1.7 for 355 nm nanosecond pulses [23]. Similar trends were also observed for other oxide films, such as Ta\({}_{2}\)O\({}_{5}\) and ZrO\({}_{2}\)[24]. The thermal annihilation of oxygen vacancies in a Nd:YAG crystal allowed the increase of the LIDT for 1064 nm 10 ns pulses by 3 times [25]. Therefore, low-energy (<350 eV) oxygen plasmas were used for the etching. Different etching depths of 50, 100, 200 and 400 nm were selected. Previous etching experiments demonstrated that the typical etching depth precision was of the order of \(\sim\)\(\pm\)10%.
# Internet of Everything (IoE) Taxonomies: A Survey and a Novel Knowledge-Based Taxonomy
## 4 Proposed IoE Taxonomy
### Knowledge
#### 4.1.5 Action  
The Action dimension refers to knowledge creation. Actionable intelligence is meaningful for humans to promote automated processes [51], ranging from creating value when used in a specific usage context [25; 68; 117] to transforming and changing the state of their environment [24]. Big data analytics aims to improve the understanding of data, thereby supporting useful and timely decision-making with the refined information gathered [40; 42; 69].  
The goals of IoT systems range between general and specific and include monitoring, reducing costs, and improving processes [109].  
For [PERSON] et al. [117], even in the case of uncertainty, a rational agent is one that acts to achieve the best outcome or the best-expected outcome. There is a close interrelation-ship between intelligence and automation [55], or creating and pursuing goals through transformation. Sensor information in IoE applications provides either automation or transformation of the IoE environment, which are defined as follows:* Automation: the aptitude to make cognitive decisions related to a given situation, which guarantees the right action is performed. The automation of tasks and dependency on machines may reduce human abilities [105]. When combined with AI and machine learning, new applications will benefit from automated decision-making [106], with efficient usage of network resources, minimization of operational costs, coordination of computational resources, and efficient and effective data management mechanisms [60] associated with the quality of experience [104; 118].
* Transformation: an enormous number of raw observations (created by the machine and human sensors) can be transformed into higher-level abstractions [57] that are meaningful for human or automated decision-making processes [55]. When an IoE solution provides transformation, smart things act independently, with minimal or no human intervention [51]. With the support of wireless communications and AI, humans benefit from improvements in technological advancements [42; 101] by collecting, modeling, and reasoning the context [36].  
Considering how actions generate changes in the environment to achieve the desired goal, automation and transformation processes may occur in the short or long term or may represent a prominent solution. Some works have explored the implications of the IoE for value creation and decision-making provided by smart things and big data [15; 39]. However, our study is concerned with how humans respond and interact with the environment in assisting the evolutions of future systems (defined in [15]), which can be:  
* Reactive: having the ability to promptly react to a changing environment;
* Adaptive: having the steadier ability to adapt their behavior to changes;
* Predictive: having the ability to use computation and analytics techniques to identify relevant patterns, in-depth knowledge of the environment, and the most appropriate solutions or possible evolutions to each IoE system situation.
# An Analytical Design of Simplified Decoupling Smith Predictors for Multivariable Processes
## 1 Introduction
The great advantage of this combination is that it completely removes delay terms out of the diagonal elements of the decoupled apparent matrix. Therefore, a delay-free part can be used for feedback controller design. In some cases, however, the first-order lag filter, if needed, would be put behind the PI/PID controllers to improve the tracking performance as well as robustness. It is well known that the internal model control (IMC) method [23] is very effective for the design of PI/PID controllers. In this paper, a general form of controllers is presented, including a PI/PID controller and a FOLF in series [2; 3]. The proposed method is based on the direct synthesis approach in which the primary PI/PID controllers are designed based on the desired closed-loop transfer functions [24]. Due to the properties of the SP structure, only the delay-free parts of the diagonal elements of the decoupled apparent matrix were addressed when designing the multiloop PI/PID controllers. Consequently, the resulting analytical tuning rules were derived for some common industrial processes. In order to evaluate the robustness of the controllers, the \(M-\Delta\) structure was performed in the presence of diagonal multiplicative output uncertainty [23; 25; 26]. This paper is organized as follows. Section 2 briefly introduces the simplified decoupling Smith predictor structure for multivariable processes; the approximation method by using the modified PSO algorithm is also addressed to reduce the order of process models and solve the issues of realizability of the simplified decoupling technique. Further, the IMC-PI/PID approach for controller tuning rules of some common industrial processes are presented, and the performance and robustness of the proposed control strategies are also discussed in this section. Some case studies are illustrated by simulations in Section 3. Finally, conclusions are given in Section 4.
## 4 SAR Raw Signal Processing
### Omega-k Algorithm for LFM-CW SAR System
#### 4.2.4 Fast Fourier Transform in Azimuth  
The azimuth Fourier transform is applied to the data using fast Fourier transform in azimuth, then the data are transformed to the two-dimensional frequency domain as shown in Equation (37).  
Figure 9: Modified Omega-k algorithm for the LFM-CW SAR System.
# Cognitive Frequency-Hopping Waveform Design for Dual-Function MIMO Radar-Communications System
## 5 Simulation  
In this section, numerical results based on Monte Carlo simulations have been provided to validate the effectiveness of the proposed method. Without loss of generality, each entry of the channel matrices follows the standard complex Gaussian distribution. The simulation parameters are based on radar application with a high Pulse Repetition Frequency (PRF), such as in X-band radar. A data rate in the range of dozens of Mbps can be achieved. We provide a comparison between the proposed scheme and the method of [32]. To implement the method in [32], we consider a dual-function MIMO system operating in the X-band with carrier frequency \(f_{c}=8.2\) GHz and bandwidth \(B=500\) MHz. The sampling frequency is \(f_{s}=10^{9}\) sample/sec, which is taken as the Nyquist rate. The PRI is \(T_{0}=10\) us. We assume an arbitrary linear transmit array consisting of \(M=16\) elements. We further assume that the minimum transmit/receive antenna spacing is sufficiently larger than half wavelength (distributed MIMO configuration). Hence, the correlation introduced by finite antenna element spacing is low enough that the fades associated with two different antenna elements can be considered independent. To implement the radar function, we further assume that the FH step is \(\Delta f=10\) MHz, the length of  
Figure 3: The adaptive waveform design and selection scheme for target detection.  
the FH code is \(Q=20\) and the FH interval duration is \(\Delta t=0.1\) us. We generate a set of 16 FH pulse waveforms. The parameter \(J=50\) is used. Therefore, the 320 FH code is generated randomly from the set \(\{1,2,\ldots,J\}\), where \(J=50\).  
We employ orthogonal sequences of the FH pulse over the transmit antenna elements. The backscatter signals are matched filtered at the receivers and the transmitted signals are later modified by the waveform optimization module as shown in Figure 2. The optimized transmission sequence at one particular transmit antenna after the two-step optimization process. The target response extracted from the received target echoes after matched filtering at the end of 20 iterations of the algorithm, where an excellent performance of the target response estimation can be observed. At each iteration, the scattering coefficients for the target and non-target scatterers in \(\mathbf{H}_{i}\) vary as described by the Swerling III model [45; 46]. This causes the amplitude returns of the backscatter signals to vary at each instance. However, the amplitudes of the echoes from the target are always assumed to be stronger than those from the clutter sources.
## 3 Simulation Results and Discussion
### Simulation Process

Table 2: Basic processing parameters of femtosecond laser ablation for TTM simulation. 40 scans were chosen as the initial state for the simulation calculation, and the laser confocal microscope was used to measure the initial hole location and the micro-hole depth. Table 3 shows the information presented above. The initial depth is the depth of the micro-hole for 40 scans of three different single pulse energies. Additionally, during the continuous scanning of the laser, the material is always ablated, which leads to a certain distance between the orifice and the original surface even at 40 scans, called the absolute distance. Given that the structure is applied to a turbine blade, the undulation of the material surface is too large to affect the blade's service life, so 320 scans are chosen as the termination state. Figure 4: Femtosecond laser-induced surface micro-structure morphology by LSCM and SEM: (a) 80 uJ, 40 scans, (b) 80 uJ, 80 scans, (c) 80 uJ, 160 scans, (d) 80 uJ, 320 scans, (e) 100 uJ, 40 scans, (f) 100 uJ, 80 scans, (g) 100 uJ, 160 scans, (h) 100 uJ, 320 scans, (i) 120 uJ, 40 scans, (j) 120 uJ, 80 scans, (k) 120 uJ, 160 scans, (l) 120 uJ, 320 scans. Figure 3: (a) Ablation depth variation with laser fluence by TTM; (b) Spatial position between micro-structure and laser pulse.
# Predictive Energy-Aware Routing Solution for Industrial IoT Evaluated on a WSN Hardware Platform
###### Abstract  
In industrial wireless sensors networks (IWSNs), the sensor lifetime predictability is critical for ensuring continuous system availability, cost efficiency and suitability for safety applications. When deployed in a real-world dynamic and centralised network, the sensor lifetime is highly dependent on the network topology, deployment configuration and application requirements. (In the absence of an energy-aware mechanism, there is no guarantee for the sensor lifetime). This research defines a conceptual model for enhancing the energy predictability and efficiency of IWSNs. A particularization of this model is the predictive energy-aware routing (PEAR) solution that assures network lifetime predictability through energy-aware routing, energy balancing and profiling. The PEAR solution considers the requirements and constraints of the industrial ISA100.11a communication standard and the VR950 IIoT Gateway hardware platform. The results demonstrate the PEAR ability to ensure predictable energy consumption for one or multiple network clusters. The PEAR solution is capable of intracluster energy balancing, reducing the overconsumption 10.4 times after 210 routing changes as well as intercluster energy balancing, increasing the cluster lifetime 2.3 times on average and up to 3.2 times, while reducing the average consumption by 23.6%. The PEAR solution validates the feasibility and effectiveness of the energy-aware conceptual indicating its suitability within IWSNs having real world applications and requirements.  
predicable lifetime; energy aware; IWSN; IIoT; energy margin; ISA100.11a; routing 2022222107. [[https://doi.org/10.3390/s22062107](https://doi.org/10.3390/s22062107)]([https://doi.org/10.3390/s22062107](https://doi.org/10.3390/s22062107))  
1
## 7 The Estimation Models Discussion
### Limitations and Future Recommendations  
Based on the experiment conducted in this study, it is crucial to emphasize that the quality of the fit and, consequently, the determination of which features are statistically significant in the regression depend on the set of features initially included in the step-wiffit.m function algorithm. The regression algorithm works to find a locally optimal fit. This implies that had we chosen a different combination of features for the initial model instead of starting with an empty vector, we would have guaranteed global optimal fit [44]. Moreover, our observations are based on a small sample of two subjects. Therefore, the generalization of our findings to a broader population is limited. The features we have identified as significant might have been different if the dimension of the training had encompassed data from diverse individuals with varying physiological characteristics and health statuses. We foresee that the accuracy of our model could be enhanced by incorporating additional physiological factors such as age, height, and weight, as these variables can influence blood pressure. PPG represents an optical sensor measurement technique employed in the prevention of CVDs. We anticipate a forthcoming trend in the development of optical biosensor algorithms, enabling the utilization of intracellular, stationary, and time-dependent information for disease diagnosis [45].
# Mapping Aquaculture Ponds for the Coastal Zone of Asia with Sentinel-1 and Sentinel-2 Time Series
## 4 Results
### Hotspot Analysis  
To enable a viable representation of the results for the entire study region, the areas of the single-object pond features were aggregated to a hexagonal grid. In Figure 15, areas with high aquaculture density in the hexagon plots can be identified by blueish colors and are mainly concentrated in the greater delta regions of the Krishna River (IND), Godavari River (IND), Ganges River (BGD), Irrawaddy River (MMR), Chao Phraya River (THA), Mekong River (VNM), Pearl River (CHN), Yangtze River (CHN), and Yellow River (CHN). More hotspot regions are located in the southeast of Sumatra (IDN), along the northern coast of Java (IDN). The highest densities of pond aquaculture are concentrated in the largest aquaculture shares, which can be found along the shorelines of the coastal Chinese provinces of Hainan, Guangdong, Fujian, Zhejiang, Jiangsu, Shandong, Hebei, and Liaoning.  
Figure 14: Comparative boxplots of the calculated area in ha (top) and compactness (perimeter\({}^{2}*\) area) of the mapped coastal aquaculture ponds for each country in the coastal study region.  
Figure 15: Pond aquaculture summarized into a 5 km hexagon grid vector dataset (interlocking hexagon cells with a side length of 5 km and an area of \(\sim\)65 km\({}^{2}\)). Area of aquaculture ponds per hexagon (area of ponds in ha) for the entire coastal zone of Asia. Top: Overview with three subregions and corresponding coastal spots (blue boxes): (**A**) South Asia with a focus on the Krishna River Delta and Godavari River Delta, India; (**B**) Southeast Asia with a focus on Lampung Province, Sumatra, Indonesia; (**C**) East Asia with a focus on Qiongzhou Strait between Leizhou Peninsula and Hainan Island, China.
# Minimum Variance Distortionless Response--Hanbury Brown and Twiss Sound Source Localization
## 3 Simulation Analysis
### Frequency  
To investigate the influence of frequency on the localization results, single-frequency and multi-frequency signals were selected to conduct simulation analysis using MVDR-HBT. The simulation results for the sound source position (8, 10) at 600 Hz and at 600 Hz, 700 Hz, 800 Hz are shown in Figure 6, and the effect of different frequencies on the localization accuracy of MVDR-HBT was further studied. The results are summarized in Table 3 and compared with the localization results of HBT. As shown in Table 3, at an SNR of \(-\)5 dB, the localization accuracy of MVDR-HBT became more precise with an increase in spectral width, as the sound source signal contained more information. Although the localization accuracy of HBT for multi-frequency signals improved compared to that for single-frequency signals in low-SNR situations, there was still a gap between HBT and MVDR-HBT in terms of localization error.  
Figure 5: Simulation results for sound sources at (**a**) (5, 5); (**b**) (8, 10).  
Figure 6: Simulation Results of MVDR-HBT from Single-Frequency to Multi-Frequency: (**a**) 600 Hz; (**b**) 600 Hz, 700 Hz, 800 Hz.
###### Abstract  
In the process of data services, compressing and indexing data can reduce storage costs, improve query efficiency, and thus enhance the quality of data services. However, different service requirements have diverse demands for data precision. Traditional lossy compression techniques fail to meet the precision requirements of different data due to their fixed compression parameters and schemes. Additionally, error-bounded lossy compression techniques, due to their tightly coupled design, cannot achieve high compression ratios under high precision requirements. To address these issues, this paper proposes a lossy compression technique based on error control. Instead of imposing precision constraints during compression, this method first uses the JPEG compression algorithm for multi-level compression and then manages data through a tree-based index structure to achieve error control. This approach satisfies error control requirements while effectively avoiding tight coupling. Additionally, this paper enhances data restoration effects using a deep learning network and provides a range query processing algorithm for the tree-based index to improve query efficiency. We evaluated our solution using ocean data. Experimental results show that, while maintaining data precision requirements (PSNR of at least 39 dB), our compression ratio can reach 64, which is twice that of the SZ compression algorithm.  
Keywords:data service; lossy data compression; indexing algorithm +
Footnote †: journal: Medical Imaging