esa-sceva/satcom-chunk-collection huggingface dataset:
# Partial Discharge Data Augmentation and Pattern Recognition Method Based on DAE-GAN +
## 4 Case Analysis  
### Subsection  
After extensive testing, it was found that setting the latent code dimension to 13 yields minimal reconstruction error upon convergence. With this result, the structure and parameters of each module within the DAE-GAN network have been established accordingly, as detailed in Table 2.  
Hardware Environment: Desktop computer (Windows 10 operating system, CPU model: AMD Ryzen 5 3600, GPU model: 1660 Super Ultra).  
Software Environment: The model development was carried out in the PyTorch framework. The Adam optimizer was employed for optimization, with learning rates  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{1}{c}{**Network Module**} & **Type** & \begin{tabular}{c} **Number of** \\ **Channels** \\ \end{tabular} & \begin{tabular}{c} **Activation** \\ **Function** \\ \end{tabular} \\ \hline \multirow{3}{*}{Generator} & Encoder & \begin{tabular}{c} Fully connected \\ layer \(\times\) 5 \\ \end{tabular} & \begin{tabular}{c} 200-5000-5000- \\ 4000-13 \\ \end{tabular} & LeakyReLU \\ \hline \multirow{3}{*}{Generator} & Decoder & \begin{tabular}{c} Fully connected \\ layer \(\times\) 5 \\ \end{tabular} & \begin{tabular}{c} 13-4000-5000- \\ 5000-200 \\ 13-500-1000- \\ 500-13 \\ \end{tabular} & Tanh \\  & Latent code generator & \begin{tabular}{c} Fully connected \\ layer \(\times\) 5 \\ \end{tabular} & \begin{tabular}{c} 13-500-1000- \\ 500-13 \\ \end{tabular} & ReLU \\ \hline \multirow{3}{*}{Discriminator} & \
\begin{tabular}{c} Fully connected \\ layer \(\times\) 4 \\ \end{tabular} &
\begin{tabular}{c} 200-5000- \\ 400-1 \\ \end{tabular} & ReLU \\ \hline \hline \end{tabular}
\end{table}
Table 2: Network structure and parameter setting.  
Figure 6: Data augmentation and pattern recognition for PD signals based on DAE-GAN.  
configured at \(5\times 10^{-6}\) for both the encoder and decoder, while the momentum factors were set to 0.9 and 0.99, respectively. As for the discriminator and latent code generator, the learning rates were adjusted to \(5\times 10^{-5}\), with momentum factors of 0.5 and 0.9. The pre-training phase consisted of 2000 iterations, followed by 5000 iterations for training the complete DAE-GAN network.
## 3 Application Community
### Application Community Detection Method  
According to the definition of an application community, the community detection method proposed in this paper is divided into two steps. The first step is to divide the network relationship diagram into multiple partitions according to the nodes' communication relationships. A partition consists of a leader node (usually a server), multiple member nodes (usually clients), and a connection between them (traffic). One or more services or applications may exist in a partition. Therefore, according to the statistical characteristics of the traffic flow, the nodes in the same partition are further divided to find a set of hosts whose connectivity and traffic characteristics are similar, which is the final community identification result.
## 2 Materials and Methods
### Dataset
#### 2.1.3 HY-2C and CFOSAT Satellite-Borne Scatterometers Dataset  
The microwave scatterometer accomplishes retrieving sea surface wind speeds by measuring the radar backscatter signal from the sea surface. The microwave scatterometers used for this purpose operate in the C-band or Ku-band. The formation and distribution of sea surface waves are influenced by wind speeds, which alter radar backscatter intensity from the sea surface. The sea surface wind speeds can be retrieved by measuring the intensity of the backscattered echo signals from the sea surface.  
HY-2C, the third satellite in the Chinese Marine Dynamic Environment Satellite series, is a non-sun-synchronous (inclined) satellite. It is equipped with a Ku-band Scatterometer (HSCAT), primarily for remote sensing inversion of global sea surface wind fields. This study utilized the sea surface wind speed products derived from the HSCAT scatterometer onboard the HY-2C satellite ([[http://osdds.nsoas.org.cn/](http://osdds.nsoas.org.cn/)]([http://osdds.nsoas.org.cn/](http://osdds.nsoas.org.cn/)) (accessed on 19 June 2023)). Its orbital altitude was 782 km and its operating frequency was 13.256 GHz, with a working bandwidth of 1 MHz. The polarization modes were HH and VV, with an HH polarization swath width exceeding 1350 km and a VV polarization swath width of 1700 km. The measurement accuracy for s\({}^{\circ}\) was 0.5 dB, and the range for s\({}^{\circ}\) was from \(-\)40 dB to +20 dB. These products have a 25 km \(\times\) 25 km spatial resolution and an overall wind speed accuracy of approximately 1.2 m/s.  
CFOSAT is a collaborative project between China and France to develop an oceanographic satellite. China is responsible for satellite carrying, launching, and telemetry, and the fan-beam rotating scanning scatterometer (SCAT) operating at the Ku-band frequency (13.256 GHz). The swath width is greater than 1000 km and the measurement accuracy for the backscatter coefficient is better than 0.5 dB, with a measurement range of at least 45 dB (\(-\)21 dB to 24 dB). The sea surface wind speed products derived from the SCAT scatterometer onboard the CFOSAT satellite ([[http://osdds.nsoas.org.cn/](http://osdds.nsoas.org.cn/)]([http://osdds.nsoas.org.cn/](http://osdds.nsoas.org.cn/)) (accessed on 18 July 2023)) were utilized in this study. The wind speed retrievals from CFOSAT have a 25 km \(\times\) 25 km spatial resolution and a wind speed accuracy of approximately 1.3 m/s [44].  
In October 2021, there were 26 observations recorded using the HY-2C in over 80% of the study area, while CFOSAT made 20 observations recorded in over 80% of the study area. The transit times with intervals less than 3 h for both satellites are presented in Table 1.
# Design and Calibration of a Sensory System of an Adaptive Gripper
## 4 Conclusions  
The logical architecture of the sensory system is designed to allow independent mechanical, electrical, and software understanding. The implementation of the three-step self-adaptive grasping ensures precise object handling, avoiding their slipping and deformation.  
The sliding sensor system implemented in the self-adaptive gripper jaw uses a resistive encoder and a flat strip with translational and rotational movement to detect and manipulate objects. It ensures efficient and safe gripping, minimizing slipping and deformation of grasped objects.  
Software tests of the sensor system confirm the correct functionality of the resistive encoder in detecting the position and small errors. Implementation of a high-resolution optical incremental encoder and optimization of the translation and rotation mechanism are recommended to improve the performance and reliability of the system.  
The design of the advanced robotic gripper focused on the integration of a slip sensor to improve the ability to grip and manipulate objects, emphasizing the importance of geometric and material optimization for performance and durability.  
The implementation and testing of the advanced robotic gripper demonstrated its efficiency and functionality under real-world conditions, confirming its ability to adapt and perform precise operations in various industrial and object-handling applications.
## 2 Related Works and Contribution
### Related Works
#### 2.1.2 New User  
The second category of works tries to alleviate the cold-start problem from the new user side, as it was discussed above.  
To begin with, many works try to identify similarities among users and items. For example [PERSON] et al., 2014 adopt a graph-based approach to simulate the preference propagation among users, in order to alleviate the data sparsity problem for cold-start users [18]. They propose a Monte Carlo algorithm to estimate the similarity between different users. [PERSON] et al., 2018 propose a method that enables time-sensitive point-of-interest recommendation. Users' semantic and spatial similarities are considered. In semantic modeling, they calculate users' similarity scores by comparing users' temporal hierarchical semantic trees. In spatial modeling, they use Gaussian mixture model (GMM) to compute users' similarity scores. Finally, they combine the check-in data of the target user with those of her top-k most similar users in terms of both semantic and spatial similarities to train a personalized hidden Markov model (HMM) to predict the most probable venue category for each specified time slot [19].  
Furthermore, many researchers propose to utilize external data from other sources or ask for user participation (e.g., answer some questions before using the system). [PERSON] et al., 2015 proposed a model for cold-start local event recommendation in social networks [20]. They get the event's organizer existing data (previous events, location, users and their friends, etc.) and by applying Bayesian Poisson factorization they recommend related events to new users. [PERSON] et al., 2019 get user data from other systems (transferred information from an ad platform to an online shopping domain) and apply deep learning techniques to solve the problem [21]. [PERSON] et al., 2020 utilize users' social media data to extract more info and build their profile [22]. Then, they apply classification techniques (decision trees, random forests) to recommend items. One example regarding approaches that requires user participation is that of [PERSON] et al., 2015. They present an algorithm (called ExcUseMe), which selects a predefined number of users that are more likely to be interested in interacting with new items [23].  
Apart from the above, there are some knowledge-based approaches or hybrid approaches. For example, [PERSON] et al., 2018 implemented a hybrid approach which combines association rules, probability metrics, and user's context to alleviate the cold start problem in rule-based context aware recommender systems [7].
# An Approach to Data Analysis in 5G Networks
## 4 Analyzer Inputs/Outputs  
By studying the Analyzer Module as a black box model it is possible to focus more on its inputs/outputs and their relationship with the rest of the SELFNET components [34]. From this perspective, their information sources, nature of the data and behaviour in different circumstances are described. As shown in Figure 5, the Analyzer Module depends on three sources of information. Two of them are external: the SELFNET Aggregation component and the use case operators; the last is internal: data generated by the Analyzer Module itself. The inferred conclusions are reported to the SELFNET Diagnosis Module as symptoms [60]. The role played by each of these elements is detailed below:  
* _Aggregation_. Observations in SELFNET come to the Analyzer through the Aggregation Layer (Perception capabilities within the [PERSON]'s model). The information provided by this source contains facts concerning Events \(Fa(Ev)\), Thresholds \(Fa(T_{H})\) and Key Performance Indicators \(Fa(KPI)\) related to the current network status.  
* _Use Case Operators._ The knowledge-base is specified from data acquired from the use case definitions. Because of this, use case operator may provide inference rules Ru (1), and declare the objects O (2), operations Op (3) and prediction metrics Ft (4) to be taken into account (i.e., what observations should be taken into account (2), how (3)what data must be forecasted (4) and how are they considered in order to acquire knowledge about the specific use case (1)). Optionally, the use case operator may describe the adaptive thresholds \(AT_{h}\) to be calculated, and if pattern recognition PR is required, then configuring how it must be addressed.
* _Analyzer._ An important part of the information necessary for proper Reasoning is generated by the Analyzer Module itself. It is gathered into a pair of groups: Perception and Machine Learning. The first block is imperative, and establishes facts Fa from pattern recognition \(Fa(PR)\), forecasts \(Fa(Ft)\) and adaptive thresholds \(Fa(AT_{h})\). On the other hand, machine learning may provide additional data to that provided by use case operators (definition of new rules Ru and description of prediction metrics Ft). Furthermore, it could generate information to improve the knowledge management (weight, prioritizations, fusion, smoothing, etc.).
* _Diagnosis._ the final conclusions and symptoms that compose the SELFNET Situational Awareness are sent to Intelligent Diagnostic Module (Autonomic Management Sublayer) [60], via reports Re.
# Crescent Microstrip Antenna for LTE-U and 5G Systems
###### Abstract  
The field of wireless cellular network technology has seen a significant development in recent years, allowing the emergence of many new applications in addition to the traditional mobile phone calls. We are currently implementing the 5G system, which is replacing the previous cellular technologies on the market. Parallel to the development of cellular technologies, wireless local networks based on the IEEE 802.11\(\times\) standards are rapidly spreading. The desire to use the advantages of both mobile telephony and wireless local networks has led to the idea of integrating the currently used communication systems in one device and requires a well-designed antenna, which should be given a lot of attention when designing the radio system. This article presents the proposed model of a two-band microstrip antenna for which the main assumption is its operating frequencies in the LTE-U (LTE-Unlicensed) band and one of the 5G system bands. The antenna dimensions and parameters have been calculated, simulated, and optimized using CST Microwave Studio software. The developed antenna has a compact structure with dimensions of (\(60\times 40\times 1.57\)) mm. The dielectric material RT Duroid 5880 with a dielectric constant \(\epsilon_{r}=2.2\) and thickness \(h=1.57\) mm was used as a substrate for the antenna construction. The article presents an analysis of the results of simulation and measurements of selected electrical parameters and radiation characteristics of the proposed antenna. The antenna described in the article, working in 5G systems and LTE-U systems, is characterized by two operating bands with center frequencies equal to 3.52 GHz and 5.37 GHz, a low reflection coefficient (for resonances \(-31.54\) dB and \(-23.16\) dB), a gain value of 4.45 dBi, a wide frequency band of 3.0 GHz (68.18%), and a high energy efficiency in the range of 80-96.68%.  
antenna; microstrip antenna; 5G antenna; LTE-U antenna; crescent antenna; 5G system; LTE-U system +
Footnote †: journal: Electronics Letters
## 2 Materials and Methods  
Research in the area of microwires is very extensive, especially that conducted during the last few decades. Due to their mechanical, electrical and magnetic properties, they have various application possibilities. Considering the amorphous glass-coated magnetic microwires with a metallic core diameter of 0.6-30 \(\upmu\)m covered by a glass coating with a thickness of 2-20 \(\upmu\)m, shown in Figure 1a,the dimensions together with their unique physical characteristics make them suitable for utilization also in sensorics, where microwires can be used as a sensing element in a microwire-based sensor. Their advantages are fabrication rate, manufacturing price and the fact that, during the fabrication process, various chemical compositions and dimensions, which strongly influence the resulting physical properties (for example, coercivity, magnetic anisotropy field or magnetic permeability), can be prepared.  
For the purposes of the tensile stress sensor design, amorphous glass-coated microwires with a high and positive magnetostriction constant were used. The domain structure in this case consists of one axial magnetic domain in the middle of the wire, whereas the cover is formed by the radial domains and closure domains, which are at the end of the wire. This structure means that the magnetization can switch between the states of positive and negative saturation through the single Barkhausen jump at the value of the magnetic field, called the switching field. This field is a carrier of information not only about the value of the external magnetic field, but it can be used also for the tensile stress calculation. These microwires are characterized by a rectangular bistable or orthogonal hysteresis loop. The mathematical model involving the mechanical properties is described in detail in [46]. In our case, in the role of the sensing element, a microwire with the chemical composition Fe\({}_{77,5}\)Si\({}_{75}\)B\({}_{15}\) (Figure 1b) was used. Sensitivity as determined from the sensor transfer characteristics obtained from the measurements performed in laboratory conditions was 0.20037 A\(\cdot\)m\({}^{-1}\cdot\)MPa\({}^{-1}\).
## 5 Simulations  
### Description of LTV Three-DOF System  
Numerical simulation experiments were conducted with a three-DOF time-varying mass. The test system defined is the same as that used in Ref. [36]. The equations of motion for systems with time-varying mass can be written as Equation (19):  
\[\begin{bmatrix}m_{1}(t)&0&0\\ 0&m_{2}(t)&0\\ 0&0&m_{3}(t)\end{bmatrix}\begin{bmatrix}\tilde{x_{1}}\\ \tilde{x_{2}}\\ \tilde{x_{3}}\end{bmatrix}+\begin{bmatrix}c_{1}+c_{2}&-c_{2}&0\\ -c_{2}&c_{2}+c_{3}&-c_{3}\\ 0&-c_{3}&c_{3}\end{bmatrix}\begin{bmatrix}\tilde{x_{1}}\\ \tilde{x_{2}}\\ \tilde{x_{3}}\end{bmatrix}+\begin{bmatrix}k_{1}+k_{2}&-k_{2}&0\\ -k_{2}&k_{2}+k_{3}&-k_{3}\\ 0&-k_{3}&k_{3}\end{bmatrix}\begin{bmatrix}x_{1}\\ x_{2}\\ x_{3}\end{bmatrix}=\begin{bmatrix}F_{1}(t)\\ 0\\ 0\end{bmatrix} \tag{19}\]  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Algorithm-Based** & **Time Complexity** & **Space Complexity** &
\begin{tabular}{c} **Modal** \\ **Exchange** & **Number of Matrix** \\ \end{tabular} & **Accuracy Comparison** & **Applicable** \\
**OMA** & \(o(N^{3})\) & \(o(N^{3})\) & Exist & \(T\) & Meet the demand & SLTV and FLTV \\ _ARPCAWF_ & \(o(N^{2})\) & \(o(N^{2})\) & Exist & \(T\) & Similar as PCAWF & SLTV and FLTV \\ _ERPCAWF_ & \(o(N)\) & \(o(N)\) & Avoid & \(1\) & Best & SLTV and FLTV \\ _LMPCA_ & \(o(NL),N<<L\) & \(o(NL),N<<L\) & Exist & \(T-L+1\) & Meet the demand & SLTV \\ _LMRPCA_ & \(o(N^{2})\) & \(o(N^{2})\) & Exist & \(T-L+1\) & Similar as LMPCA & SLTV \\ _MWSIPCE_ & \(o(rML),M<<N\) & \(o(NL),N<<L\) & Exist & \(0\) & Better than LMPCA & SLTV \\ \hline \hline \end{tabular}
\end{table}
Table 1: Performance comparison of multiple methods.  
In this example, we consider a system with mass \(m_{1}(t)=1\) kg for \(0\leq t\leq 50\) s and \(m_{1}(t)=e^{-0.00005(t-50)}\) for \(50\leq t\leq 2000\) s. \(m_{2}(t)=m_{3}(t)=1\) kg, stiffness \(k_{1}(t)=k_{2}(t)=k_{3}(t)=1000\) N/m, and damping \(c_{1}(t)=c_{2}(t)=c_{3}(t)=0.01\)Ns/m for \(0\leq t\leq 2000\) s. The external force \(F(t)=[F_{1}(t)\ 0\ 0]^{T}\) is white noise excitation with zero mean and unit variance. Figure 6 shows the three-DOF system model with external excitation.  
The dynamic response was acquired using the \(newmark-\beta\) method. The simulation had a sampling time of 2000 s and sampling frequency of 40 Hz. All experiments were conducted on a server running Microsoft Windows 10 64-bit with four CPUs and 32 GB memory. MATLAB version 2016a was used to run the simulations and multiple variable memories were pre-allocated.
## 2 Related Work
The lengths detected by DIDSON were 35.6% shorter than the ones obtained from manual measurements. Moreover, results showed that fish mostly swam in a circular motion and close to the net. [PERSON] performed stereo Image analysis to monitor fish in [28]. They were able to measure shape, size position and spatial orientation of a fish. Three different experiments were performed in fish tanks with a depth of 3 m and 1 m of cross-section. All experiments had four stages being calibration, image acquisition, identification of the measurement points and 3D position calculation. Results showed an error between 3% and 5% for determining the length of the fish. A GPS tracking device was employed in [29] by [PERSON] to monitor sunfish Mola mola. The system was able to monitor the position with an approximate accuracy of 70 m. Experiments were performed on three different fish with sizes varying from 0.6 m to 1 m. The route performed by the fish was able to be visualized in a map. Moreover, Data on the speed of the fish was acquired and displayed on graph locating each measure on the map by adding a dot on the route. [PERSON] et al. presented in [30] a guidance system for a fish behavior and water quality monitoring system that employed an autonomous underwater vehicle. The vehicle was provided with a conductivity and oxygen optode sensor, as well as a spectrometer. LED cameras were also attached to the vehicle to observe fish behavior. The vehicle was able to navigate through the cages without colliding with them. As a result, the vehicle was able to obtain information without any collision. The systems for water quality monitoring measure the same parameters not considering other important factors to monitor such as the turbidity of the water. Moreover, many of the papers do not specify which sensors they have utilized, or they employ expensive sensors, resulting in a high-cost system that is difficult to implement in fish farms with few resources. Furthermore, fish behavior monitoring is not usually incorporated in the same system increasing the investment owners must do to improve the efficiency of their fish farm. In this paper, we present a low-cost water quality and fish behavior monitoring system. The system can measure the water quality parameters, tank state factors and fish behavior during the feeding process.