esa-sceva/satcom-chunk-collection huggingface dataset:
# Extended Smoothing Methods for Sparse Test Data Based on Zero-Padding
## 4 Numerical Verification  
### Smoothing of 4 \(\times\) 4 Two-Dimensional Discrete Data  
It is worth mentioning that the sampling data indeed satisfy the sampling theorem. For convenience, we use sampling datasets generated by numerical simulation instead of practical tests and assume that the sampling theorem is satisfied.  
In order to clearly show the detail of zero padding in the wavenumber domain, the method proposed in Section 3 is used to smooth the two-dimensional 4 \(\times\) 4 spatial discrete data. This numerical simulation is carried out under the premise that the sampling theorem is satisfied. The numerical results show that the smoothing method can increase the data density without changing the values of the original discrete data. The known two-dimensional discrete data shown in Figure 4a are randomly generated data. The number of \(x\) and \(y\) direction data is 4, and the space interval is 0.1 m.  
It can be clearly seen from Figure 4b that the spatial intervals of the two-dimensional discrete data in \(x\) and \(y\) directions after smoothing are reduced to half of the original, which is 0.05 m. To further verify whether the values of the discrete data changed before and after smoothing, the data need to be compared. The values of the original two-dimensional discrete data are listed in Table 1, and the values of the regenerated two-dimensional discrete data are shown in Table 2.  
Through comparative analysis in Tables 1 and 2, the corresponding values of the original two-dimensional discrete data can be found in the regenerated two-dimensional discrete data table. For example, the data of (3,3) spatial positions are all 1.10061. Therefore, through the above specific values, it can be verified that the value does not change, which is consistent with the characteristics of constant value of smoothing theory. It is of concern that the data in Table 2 produce unnecessary data outside the test range (i.e., 0-0.3 m). Combined with the above analysis, this part of the data does not affect the accuracy of the data after smoothing, so it can be directly ignored.
## 2 Background
### Wireless Sensor Networks (WSNs)  
Generally speaking, WSNs connect spatially dispersed and dedicated sensor nodes that are capable of sensing a massive amount of data and transmit them to the sink, usually referred to as the base station. Then, the sensed data are either transmitted to another base station or another sensor node in the base station. The main components of a WSN are illustrated in Figure 3 and described as follows:  
* Base stations: The base station transforms the data-to-data center or cloud for more processing. The sink node is responsible for analyzing and collecting data from sensors around. Choosing its best location near the sensors preserves their energy since it enables the sink node to centralize the system and receive the signals from all the sensors. The sink node is also known as a gateway when communicating to external networks. Besides the interface between the sink node and the sensors, the sink node also has a simple human interface [15].
* Sensor nodes: The sensor nodes are used to ease the network complexity of cables; furthermore, they are admitted to be energy efficient with a long-life cycle. The sensor nodes minimize energy consumption by assigning a sleep mode to a sensor that does not sense data and an active mode to one that does. Their state changes periodically according to the state of the sensing environment [30]. The sensor nodes are diffuse to sense data from the environment; then, the significant data is transmitted to the sink node. Finally, the sink nodes communicate with each other or with other sensors in the environment [30; 31].  
Figure 2: Industry 4.0 Layers.  
Within the industrial environment, WSNs prevent the problems of a wired network, such as installation and maintenance costs. Moreover, the recent improvements in the field of WSNs have allowed their implementation in industrial low-cost embedded systems. In such systems, sensor nodes offer customized jobs, ranging from area, structural, waste, temperature, machine health, and power quality monitoring to industrial automation, control feedback, disaster prevention, and emergency response [32; 33].
## 4 Measurement Results
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Ref.** & \(\epsilon_{r}\) & **Design Configuration** & **Excitation Mechanism** & **Mode of Excitation** & **Usable CP Frequency** & **Usable CP Bandwidth** & **Gain (dBic)** & **Antenna Geometry** \\ \hline \multirow{2}{*}{[18]} & \multirow{2}{*}{\(10\) \& 3.5} & Rectangular multilayered DRA & Cross-slot with microstrip line & TE\({}_{113}\) & \(\&\) 10.5\(-\)11.5 GHz & 9.5\% & \(11\) & Complex \\ \hline \multirow{2}{*}{[19]} & \multirow{2}{*}{\(10\)} & \multirow{2}{*}{\(10\)} & Four metallic walls surrounded RDRA & Cross-slot with microstrip line & TE\({}_{111}\) & 2.2\(-\)3.5 GHz & 46.9\% & 4.37 & Complex \\ \hline \multirow{2}{*}{[20]} & \multirow{2}{*}{\(9.6\)} & Rectangular DRA with two annular via Triangular aperture with parasitic strip & \multirow{2}{*}{\(1\)} & \multirow{2}{*}{\(13\)} & \multirow{2}{*}{\(3.28\)\(-\)3.46 GHz} & \multirow{2}{*}{\(5.5\%\)} & \multirow{2}{*}{\(6\)-\(7.1\)} & \multirow{2}{*}{Complex} \\ \cline{1-1} \cline{5-5}  & & Rectangular DRA & Cross-slot-coupled & & & & & \\ \cline{1-1} \cline{5-5}  & & DRA & & & & & & \\ \hline \multirow{2}{*}{[21]} & \multirow{2}{*}{\(9.8\)} & Rectangular DRA with two annular via Triangular aperture with parasitic strip & \multirow{2}{*}{\(1\)} & \multirow{2}{*}{\(3.46\)\(-\)3.54 GHz} & \multirow{2}{*}{\(2.29\%\)} & \multirow{2}{*}{\(5\)} & \multirow{2}{*}{Complex} \\ \cline{1-1} \cline{5-5}  & & Rectangular DRA & Cross-slot-coupled & & & & & \\ \cline{1-1} \cline{5-5}  & & DRA & & & & & & \\ \hline \multirow{2}{*}{[22]} & \multirow{2}{*}{\(20.5\)} & Rectangular DRA & Cross-slot-coupled & & & & & \\ \cline{1-1} \cline{5-5}  & & DRA & & & & & & \\ \hline \multirow{2}{*}{Proposed CP 5G RDRA} & \multirow{2}{*}{\(10\)} & Rectangular DRA & **Unique conformal strip** & & & & & \\ \cline{1-1} \cline{5-5}  & & DRA & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison between proposed CP 5G RDRA with recently reported RDRAs in literature. Figure 16: Simulated and measured gain of the CP 5G RDRA.
# Rain Monitoring with Polarimetric GNSS Signals: Ground-Based Experimental Research
## 2 Experiments and Data
### Experiments  
From Equation (1), it can be seen that the horizontally and vertically polarized phases of GNSS signals should be received. However, there is no relevant equipment on the market now. Therefore, some devices have to be designed to receive dual-polarized GNSS signals with higher accuracy of phase measurement. Since receiving technology of GPS signals is relatively mature and its L1 (1.57542 GHz) is larger, L1 signal is regarded as our signal resource. A GNSS dual-polarized rain detection system is fabricated for the experiment, which consists of a GNSS dual-polarized receiving antenna, a dual-polarized receiver and a positioning antenna. The dual-polarized antenna is a conical horn antenna, developed by National Space Science Center, Chinese Academy of Science (NSSC-CAS) (see Figure 1) based on two vertical waveguides to receive horizontally and vertically polarized GNSS signals, respectively. Under this structure, the antenna patterns of horizontal polarization and vertical polarization have good consistency. The dual-polarized receiver is also supplied by NSSC-CAS, which is similar to the receiver of its GNSS occultation sounder (GNOS) onboard the FengYun 3 series C satellite [27]. The carrier phase and signal amplitude of horizontally and vertically polarized GPS signals can be obtained separately based on this kind of receiver. The positioning antenna is an ordinary omni-directional antenna produced by the manufacturer NovAtel in order to get accurate location of the receiving antenna.  
Figure 1: Outdoor antenna section of the Global Navigation Satellite System (GNSS) dual-polarized rain detection system.  
This experiment is implemented at a mountain in Nanjing where heavy rain occurs frequently during annual rainy season. Fortunately, a weather radar station is at the top of the mountain, where has an S-band dual-polarized doppler weather radar. The receiver and receiving terminal (computer and receiving software) are sheltered in a room, not far from the antenna. The dual-polarized antenna points to the northeast horizon, which can track all visible satellites simultaneously in the north-east field of view from 10\({}^{\circ}\) to 80\({}^{\circ}\) of azimuth and \(-3^{\circ}\) to 45\({}^{\circ}\) of elevation (see Figure 2, enclosed by red dashed line). Through the view, the antenna can receive signals from 22 GPS satellites (G1, G3, G7, G8, G9, G10, G11, G12, G13, G15, G16, G21, G22, G23, G24, G25, G26, G27, G28, G29, G30, G32) almost every day. However, observing period of each day is almost the same for a satellite but different for 22 satellites, ranging from several minutes to several hours. In Figure 2, the pointing triangle and downward pointing triangle represent ascending and descending GPS satellite respectively. The experiment is carried out twice, one from June to September in 2015 and the other one from July to November in 2016.
# Generative Adversarial Networks in Histological Image Segmentation: A Systematic Literature Review
## 5 Results
### GANs in Histological Image
#### 5.4.6 Domain Transformation and Reconstruction of High-Resolution Image  
Domain adaptation and high-resolution image reconstruction are essential for improving cross-domain generalization, as they align heterogeneous data distributions and recover fine structural details. [PERSON] et al. [83] explored domain adaptation using the CycleGAN and DASGAN architectures. Supported by powerful hardware, including NVIDIA V100 (32 GB) and K80 GPUs, their approach demonstrated the feasibility of unsupervised domain adaptation for harmonizing histopathological data from diverse sources. However, specific quantitative metrics were not reported, limiting the objective assessment of the model's performance.  
Complementing this, [PERSON] et al. [92] performed a comprehensive comparison between cGAN and CycleGAN for domain transformation alongside color normalization and data augmentation. Their results indicated that cGAN marginally outperformed CycleGAN concerning mutual information (MI = 0.26 \(\pm\) 0.07 vs. 0.24 \(\pm\) 0.08), normalized mutual information (NMI = 0.10 \(\pm\) 0.03 vs. 0.09 \(\pm\) 0.03), hierarchical clustering (HC = 0.75 \(\pm\) 0.13 vs. 0.61 \(\pm\) 0.26), and Bhattacharyya distance (BCD = 0.31 \(\pm\) 0.06 vs. 0.35 \(\pm\) 0.15).  
In high-resolution image reconstruction, [PERSON] et al. [2018] proposed a super-resolution approach combining SRGAN with a ResNeXt backbone to enhance histopathological image quality. Executed on a system with NVIDIA RTX 2060 GPU and Intel Core i7-9750H CPU, their model achieved a Peak Signal-to-Noise Ratio (PSNR) of 32.13, Structural Similarity Index Measure (SSIM) of 0.93, and Mean Squared Error (MSE) of 2.75.
###### Abstract  
The global statistical median behavior of the electron temperature (\(T_{\mathrm{e}}\)) in the topside ionosphere was investigated through in-situ data collected by Langmuir Probes on-board the European Space Agency Swarm satellites constellation from the beginning of 2014 to the end of 2020. This is the first time that such an analysis, based on such a large time window, has been carried out globally, encompassing more than half a solar cycle, from the activity peak of 2014 to the minimum of 2020. The results show that Swarm data can help in understanding the main features of \(T_{\mathrm{e}}\) in the topside ionosphere in a way never achieved before. \(T_{\mathrm{e}}\) data measured by Swarm satellites were also compared to data modeled by the empirical climatological International Reference Ionosphere (IRI) model and data measured by Jicamarca (12.0\({}^{\circ}\)S, 76.8\({}^{\circ}\)W), Arecibo (18.2\({}^{\circ}\)N, 66.4\({}^{\circ}\)W), and Millstone Hill (42.6\({}^{\circ}\)N, 71.5\({}^{\circ}\)W) Incoherent Scatter Radars (ISRs). Moreover, the correction of Swarm \(T_{\mathrm{e}}\) data recently proposed by [PERSON] was applied and evaluated. These analyses were performed for two main reasons: (1) to understand how the IRI model deviates from the measurements; and (2) to test the reliability of the Swarm dataset as a new possible dataset to be included in the underlying empirical dataset layer of the IRI model. The results show that the application of the Lomidze correction improved the agreement with ISR data above all at mid latitudes and during daytime, and it was effective in reducing the mismatch between Swarm and IRI \(T_{\mathrm{e}}\) values. This suggests that future developments of the IRI \(T_{\mathrm{e}}\) model should include the Swarm dataset with the Lomidze correction. However, the existence of a quasi-linear relation between measured and modeled \(T_{\mathrm{e}}\) values was well verified only below about 2200 K, while for higher values it was completely lost. This is an important result that IRI \(T_{\mathrm{e}}\) model developers should properly consider when using the Swarm dataset.  
Keywords:electron temperature; topside ionosphere; ESA Swarm satellites; International Reference Ionosphere model; Langmuir Probes in-situ data; Incoherent Scatter Radar data +
Footnote †: journal: Remote Sensing
# Positioning Information Privacy in Intelligent Transportation Systems:
## 2 Solutions for Spotting of Vehicles on the Road
### Infrastructure-Based Methods  
Some vehicles are already equipped with conventional IEEE 802.11 wireless modules commonly referred to as Wi-Fi. By using previously estimated maps together with sensed Received Signal Strength Indicator (RSSI) along with the Base Station (or access point) Identificator (BSID), the vehicle can approximate its location in the complex metropolitan environment, which is generally a challenging task for GNSS [50]. This methodology would be widely used in DSRC since all of the infrastructure nodes positions would be known forming a full map of BSIDs [51].  
The main drawback of the technique is the need for continually updating the RSSI maps since the changes in the environment (such as new roads, new trees, new buildings, etc.) affect the RSSI, and Wi-Fi is operating in unlicensed spectrum affected by many interference sources. Therefore, the Wi-Fi network faces changes over time, and these changes need to be monitored continuously in order to rely on Wi-Fi positioning. Nonetheless, another drawback of using Wi-Fi for vehicular positioning is the lack of centralized infrastructures and the difficulty to achieve a high quality of positioning system, due to significant fluctuations in RSS of Wi-Fi APs. The advantage of using a Wi-Fi network is the possibility to serve in the metropolitan scenario with complex GNSS propagation characteristics, where GNSS is likely to fail. Similar issues could be encountered while utilizing any short-range wireless technology, such as Bluetooth Low Energy (BLE) [52] or Zigbee [53].  
The other technique that could be utilized by smart cars is so-called cellular-positioning that has been used for localization of phones over the decades. Already in 2G, the Cell-ID-based positioning with accuracies on the order of a few hundred meters was feasible [54]. The efficiency was improved to tens of meters in 3G using TDOA measurements. Current cellular based-solutions also make use of Angle of Arrival (AOA) or combined AOA-TOA techniques [55].
## 5 Simulation
### Experimental Setup
#### 5.1.1 Experiment Environment  
The experimental setup consists of a three-layer architecture: IoT devices, edge nodes, and a central server. We utilize the following hardware components:  
* **IoT Devices:** Four Raspberry Pi units, each representing an individual and responsible for local data collection and training.
* **Edge Nodes:** Two PCs act as edge nodes. Each edge node aggregates model updates from two connected Raspberry Pi devices.
* **Central Server:** One PC serves as the central server, performing global aggregation of models received from the edge nodes.  
The devices connect through a local wireless network. IoT devices are connected to the corresponding edge nodes, while the edge nodes communicate with the central server. Each IoT device collects local human activity data, performs local model training, and sends encrypted model updates to its assigned edge node. The edge nodes then aggregate the local models and forward the results to the central server for final aggregation.  
There are 4 IoT devices, 2 edge nodes, and a cloud server in an experiment of the hierarchical federated-learning framework. In the actual experiment, 4 IoT devices are represented by Raspberry Pi units to demonstrate the feasibility of the system on real hardware. The Raspberry Pi devices primarily demonstrate performance in encryption and data transmission. Edge nodes and the cloud server are simulated using personal computers. The edge nodes are responsible for collecting and aggregating model updates from their assigned IoT devices, and the cloud server is responsible for aggregating the models received from all edge nodes and providing feedback information.  
Since the Raspberry Pi units are limited, it is challenging to scale the experiment to a larger number of devices. To address this, we adopted the scalability experiment with multiprocessing simulations under a PC with 32 GB RAM and Xeon w2123. To simulate a heterogeneous device environment, we introduced different delays for each IoT device, with these delays following a normal distribution to accurately emulate variations in processing times. This approach effectively models the asynchronous behavior resulting from differences in device performance and network conditions, demonstrating the advantages of our proposed Algorithm 2.
## 3 Application of MOFs as Sorbents in Selected Techniques of Preparing Liquid Samples for Analysis
### Stir-Bar Sorptive Extraction (SBSE)
[140] to extract and detect 5 non-steroidal anti-inflammatory drugs (ketoprofen, flurbiprofen, ibuprofen, naproxen, and diclofenac sodium) in sheep muscle, chicken wing, and milk. UiO-66 was prepared on a frosted glass rod (FGR) through the coordination interaction of Zr-OH groups and carboxyl sites on FGR (Figure 21). The optimized method, coupled with UHPLC, showed excellent results, giving another example in favor of using MOFs as modern sorbents [140]. Another example of using MOFs as sorbents in MSPE technology is based on the use of amino-modified MOF (NH\({}_{2}\)-MIL-101(Al)) coating on monolith onto a conventional Teflon-coated magnetic stir bar [141]. The external surface of the Teflon stir bar was first vinylized to immobilize a glycidyl methacrylate (GMA)-based polymer onto the magnet. Then, MOF was covalently attached to the GMA-based monolith. The designed stir bar was evaluated as an SBSE sorbent for the extraction of three estrogens (estrone, 17\(\beta\)-estradiol, and estriol) and also synthetic 17\(\beta\)-ethinylestradiol from water and human urine samples. Researchers obtained satisfactory detection limits in the range of 0.015-0.58 \(\upmu\)g/L, with recoveries ranging from 70 to 95% [141]. A summary of MOF materials used in SBSE is presented in Table 7.  
Figure 21: UiO-66@FGR application to extract selected NSAIDs, presented by [PERSON] et al. [140]. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline
**MOF** & **Analyytes** & **Matrix/Sample Technique** & **Analytical Technique** & **LOD** & **LOQ** & **Linearity Range** & **R\({}^{2}\)** & **Recovery** & **RSD** & **Source** \\ \hline LDH-2 RF-8 & Benaryl-pemicindind & Biological and food samples & HPLC & 0.05 ug/L & 0.15 ug/L & 0.5-500 ug/L & 0.9921 & 95.0-106.1\% & 2.8-5.9\% & [139] \\ \hline UD-466 RG & Five NSAIDs & Food samples & UHPLC & 0.92 ng/mL & – & 10-1500 ng/mL & & 808.3-172.7\% & \textless{}8.0\% & [140] \\ \hline NH\({}_{4}\)-ML & Entron, & Water and & & & & & & & \\ \(101
# Failure Prediction of the Rotating Machinery Based on CEEMDAN-ApEn Feature and AR-UKF Model
## 3 Experiment and Results  
### Experimental Setup  
To verify the ability of the proposed method on failure prediction of the rotating machinery, vibration signals data collected by the NSF I/UCR Center are utilized in this paper. The framework of the experimental system is shown Figure 3. By using the spring mechanism, the system was run fixed at 2000 r/min, and a 6000-lb radial load was placed onto the shaft and bearing. Four bearings (bearings 1-4, model: ZA-2115, 16 rollers in each row, pitch diameter: 2.815 in, roller diameter: 0.331 in, tapered contact angle: 15.17\({}^{\circ}\)) were installed on the shaft, and lubricated with an oil circulation system that regulated the flow and the temperature of the lubricant. A magnetic plug installed in the oil feedback pipe was adopted to collect debris from the oil as evidence of the bearing degradation. When the accumulated debris adhered to the magnetic plug exceeded a certain level, the test terminated by turning off the power. Accelerometers (model: PCB 353B33 high sensitivity quartz ICP) were mounted on bearing housing and four thermocouples were attached to the outer race of each bearing to record the temperature. The vibration signals were collected by a data acquisition system. The sampling rate was fixed to 20 KHz, and 20,480 data points were recorded in each data file.  
From 10:32, 12 February 2004 to 6:22, 19 February 2004, vibration signals of bearings were recorded in a number of 984 files with a time interval of 10 min. At the end of experiments, an outer race fault occurs in bearing 1.  
Figure 2: The flow diagram of RUL prediction based on Autoregressive (AR)-UKF model. PST = prediction starting time.
# An Equilibrium Strategy-Based Routing Optimization Algorithm for Wireless Sensor Networks
## 4 Simulation Results and Analysis
### Comparison with Other Algorithms
#### 4.2.1 Average Hops and Average Energy Consumption  
Figures 7 and 8 show the average hops and average energy consumption of the network at different network sizes. It can be seen from the figures that the two have similar trends since hops can reflect the energy consumption of the path in some way. In addition, the FAF-EBRM algorithm has the highest average hops and energy consumption. This is because FAF-EBRM detours for selecting nodes with higher energy as the next hop when routing, resulting in higher hop count and energy consumption. The average hops of FLEOR relative to FAF-EBRM is smaller, this is because FLEOR considering the selected path as close as possible to the shortest path, thus reducing the data forwarding times and average energy consumption. As for the ESRA algorithm of this paper, when \(\beta\) is taken as 0.4, the single-hop energy consumption has a greater influence on the selection of next hop. Simultaneously, as shown in Figure