esa-sceva/satcom-chunk-collection huggingface dataset:
# A 5G NR FR2 Beamforming System with Integrated Transceiver Module
## 3 Fabrication and Measurement  
### Measurement of 28 GHz RF Beamforming System  
To develop a highly directional beamforming antenna, an array antenna with a series-fed structure was fabricated using a low-loss RF PCB substrate. Figure 9a shows the antenna-side view of the assembled 28 GHz 8 \(\times\) 7 phased-array antenna beamforming module using an eight-channel CMOS BFIC, and Figure 9b shows the chip-side view of the beamforming antenna module. All the RF lines connecting the antenna and the BFIC were equal in length. An array of dummy antenna surrounded the 8 \(\times\) 7 array for improved antenna impedance at large scan angles. The spacing between each antenna was 5 mm at 28 GHz. To measure the antenna beam pattern, a non-soldered K-type connector with a small form factor was used. A test pattern of the RF beamforming system was created to integrate the SPI interface and DC power connector for beamforming chip control. The fabricated beamforming antenna measured 100 \(\times\) 70 \(\times\) 70 mm\({}^{3}\). The beamforming antennas were enclosed in the aluminum structure to minimize heat dissipation and establish a secure ground connection.  
The far field calibration method was used to calibrate each channel of the beamforming antenna. The calibration process involved individually activating each channel to verify the phase and attenuation of the antennas within the array. Following this, adjustments were made to the phase of each antenna element using the tuning phase bits of the beamforming IC. The 28 GHz RF beamforming system was measured in a far-field anechoic antenna chamber with a standard horn antenna, as shown in Figure 10. The phase shifter and attenuator of the eight channels was controlled by the SPI with far-field measurement configuration. The antenna beam was scanned at a step of 10\({}^{\circ}\) intervals within a \(\pm\)50\({}^{\circ}\) Field of View (FOV) at the frequency of 28 GHz. The variable attenuator of the beamforming chip was adjusted to apply Chebyshev weighting, resulting in sidelobe suppression of 15 dB. A half-power beam width (HPBW) of 13\({}^{\circ}\) was achieved. The normalized measured TX radiation patterns scanning up to \(\pm\)50\({}^{\circ}\) at 28 GHz are shown in Figure 11. Table 1 summarizes the performance of the 28 GHz RF beamforming system and compares it with previously published beamforming modules.  
Figure 11: Normalized measured TX radiation patterns scanning up to \(\pm 50^{\circ}\) at 28 GHz.  
Figure 10: Measurement setup for 28 GHz 8 \(\times\) 7 beamforming antenna module in anechoic chamber.  
Figure 9: (a) Antenna-side view and (b) chip-side view of 28 GHz phased-array beamforming antenna module using eight-channel CMOS BFIC.
# Performance Improvement by FRFT-OFDM for Visible Light Communication and Positioning Systems
## 3 Simulation Results and Discussion
Figure 7: Localization errors against \(E_{b}/N_{0}\) for FRFT-OFDM and FT-OFDM schemes with 3- or 4-LED positioning. Figure 9 illustrates the improvement in positioning accuracy of the FRFT-OFDM scheme with the optimal order \(p\) compared to the FT-OFDM scheme for various indoor locations. It is evident that the FRFT-OFDM scheme achieves varying degrees of improvement in localization error across nearly all positions. Table 2 presents the localization errors of the FRFT-OFDM scheme at specific locations, along with the improvement in positioning accuracy compared to the FT-OFDM scheme. Combining this with Figure 8, we observe that when the receiver is very close to the center of the room, the increased power from direct LED reception leads to better positioning accuracy for the FT-OFDM scheme, with minimal improvement from the FRFT-OFDM scheme in this scenario. When the receiver is positioned close to the wall, the power of the received direct light is significantly reduced, resulting in a marked increase in localization error for both the FRFT-OFDM and FT-OFDM schemes. However, even in this scenario, the positioning accuracy of the FRFT-OFDM scheme remains notably superior to that of the FT-OFDM scheme. Overall, the FRFT-OFDM scheme demonstrates strong positioning performance in most scenarios, achieving localization errors of less than 10 cm and improvements of over 1 cm compared to the FT-OFDM scheme. Figure 8: Localization errors at various positions throughout the room under the optimal order \(p\) of the FRFT. Figure 9: Improvement in positioning accuracy at various positions throughout the room under the optimal order \(p\) of the FRFT compared to the FT.
# Measurement of Soy Contents in Ground Beef Using Near-Infrared Spectroscopy
## 2 Materials and Methods
### Measuring Spectra  
Samples were scanned by two NIR instruments simultaneously, a Fourier transform system (4 cm\({}^{-1}\) resolution, model Vector22/N, Bruker Optics, Billerica, MA, USA) and a dispersive system (10 nm bandpass, model NIRSystems 6500, FOSS NIRSystems, Inc., Laurel, MD, USA). The dispersive data were collected over the range of 400 to 2498 nm using WinISI software. However, the FT data were collected over the range of 9999.51 to 3999.80 cm\({}^{-1}\) (equivalent to 1000-2500 nm) using OMNIC software (v. 3.0.19, Bruker, Madison, AL, USA, 2009). Dispersive and FT spectral data were collected in reflectance mode and triplicate spectra were collected from separately packed cells on each sample and then averaged.
## 3 System Model
### Problem Formulation  
In this study, we modeled the anti-jamming problem using a Markov Decision Process (MDP). The MDP model involves a set of interacting entities, namely, an agent and an environment. Within a time slot \(t\), the agent observes the current state \(\mathbf{s_{t}}\) from the envi  
Figure 1: The schematic diagram of the communication confrontation model.  
ronment, derives an action \(\mathbf{a_{t}}\) based on this current state, and inputs the action into the environment. The environment then determines the next state according to \(\mathbf{s_{t}}\), and \(\mathbf{a_{t}}\) yields a corresponding reward \(r\) based on the state value function. The following are the five key elements of the MDP model for solving decision-making problems:  
1. State space \(\mathbf{S}\) represents the set of all channel jamming states; \(\mathbf{s_{t}}\in\mathbf{S}\) is an element of the environmental state space, denoting the set of jamming states of all channels at time slot \(t\). The actual state of the entire target frequency band can be expressed as \(\mathbf{s_{t}}=[s_{t}^{1},s_{t}^{2},\cdots,s_{t}^{N}]\).
2. Action space \(\mathbf{A}\) is the set of all executable anti-jamming actions \(\mathbf{a_{t}}\in\mathbf{A}\) of the wireless communication system. The transmission power of the communication transmitter is constant. At time slot \(t\), the action of the system is to select one channel from \(N\) channels for communication; \(\mathbf{a_{i}}=[a_{t}^{1},a_{t}^{2},\cdots,a_{t}^{N}]\) represents the transmission channel selected by the transmitter at time slot \(t\).
3. Reward function \(r\) represents the return obtained from the environment after the wireless communication system selects and executes the anti-jamming action \(\mathbf{a_{t}}\). This reward function \(r\) is used to calculate the immediate reward. The immediate reward is measured by the normalized throughput and is defined as \[r(\mathbf{s_{t}},\mathbf{a_{t}})=\delta(\theta_{t}\geq\theta_{th}),\] (4) where \(\theta_{t}\) is the average SJNR received by the receiver; \(\delta()\) is an indicator function. The average normalized throughput is used to evaluate the performance of the decision-making algorithm from time slot \(t_{1}\) to \(t_{2}\): \[r_{t_{1},t_{2}}=\sum_{t_{1}}^{t_{2}}r(\mathbf{s_{t}},\mathbf{a_{t}})/(t_{2}-t_ {1}).\] (5)
4. The state transition probability \(p\) is the probability \(\mathbf{P}(\mathbf{s_{t+1}}|\mathbf{s_{t}},\mathbf{a_{t}})\rightarrow[0,1]\) that the intelligent anti-jamming communication system transitions to \(\mathbf{s_{t+1}}\) after selecting and executing the action \(\mathbf{a_{t}}\) in the jamming environment state \(\mathbf{s_{t}}\).
5. Policy \(\pi(\mathbf{a_{t}},\mathbf{s_{t}})\) represents the conditional probability distribution of selecting different anti-jamming actions \(\mathbf{a_{t}}\) in the current jamming state \(\mathbf{s_{t}}\).
## 7 Discussion  
In this paper, the DSM data of multitemporal images were used to identify the elevation changes of illegal buildings with remote sensing data of UAV in different periods. With the excellent spatial information analysis ability and data management ability of WebGIS, this research constructed the network information management system for monitoring illegal buildings. By utilizing the timeliness and convenience of mobile terminal, the system was constructed and the grid management operation level of the system was improved. Table 1 compares the proposed method with other studies; when remote sensing data of UAV is used, the images with a data accuracy of 0.06 m/pix can be obtained, which is much better than that of satellite remote sensing images of 2.5 m/pix. The improvements of images' resolution are beneficial to the extraction information of illegal buildings and higher resolutions can lead to having higher extraction accuracy. In terms of the extraction methods of illegal buildings, this research started from the height determinations of the buildings, screened out the changes of the buildings, and then extracted the illegal buildings by human-computer interaction to ensure the accuracy of the illegal building extraction, but the level of automation was low. Through the acquisition and processing of dynamic aerial images, relative information releasing by network, grid management, and collection of relevant building information by mobile APP, the effective supervision of urban illegal buildings can be reached. A three-dimensional model monitoring system based on sky, ground, and Internet can provide strong technical supports for urban constructions.  
The identification and monitoring of illegal buildings is a complex task involving multiple disciplines and the resolution of various key issues, such as the corrections of images' geometry and radiation, feature extraction and description, knowledge expression and integration analysis of multisource data, and knowledge. Along with the support of basic theories such as computer technology, remote sensing technology, image processing, and pattern recognition, the geographic information system for the unified management of geographic information and other attributes' information has also developed rapidly. Based on these technologies, carrying on spatial information mining and knowledge discovery are the problems that need to be solved at present, and they are some of the hotspots and difficulties of GIS research. How to achieve high-level integration of existing technologies and methods to achieve more efficient work is also an important issue for further research.
# Controlling Industrial Robotic Arms Using Gyroscopic and Gesture Inputs from a Smartwatch
## 1 Introduction
In contrast to conventional wearable human-machine interfaces (HMIs) such as instrumented gloves, electromyographic armbands, or vision-based tracking systems, which typically require specialized hardware, calibration procedures, and constrained environments, the proposed smartwatch-based control system offers a compact, accessible, and cost-effective alternative. By utilizing commercially available smartwatches equipped with inertial measurement units (IMUs) and wireless connectivity, the system enables intuitive gesture-based interaction with industrial robotic arms without the need for external sensors or infrastructure. This approach enhances portability, reduces system complexity, and lowers the entry barrier for adoption in both industrial and assistive scenarios. The innovation of this work lies in the integration of gesture recognition, real-time control, and cloud-based monitoring within a single off-the-shelf wearable device, aligned with the principles of scalable and human-centric automation promoted in Industry 5.0.
# First In-Orbit Validation of Interferometric GNSS-R Altimetry: Mission Overview and Initial Results
###### Abstract  
Sea surface height (SSH) serves as a fundamental geophysical parameter in oceanographic research. In 2023, China successfully launched the world's first spaceborne interferometric GNSS-R (iGNSS-R) altimeter, which features dual-frequency multi-beam scanning, interferometric processing, and compatibility with three major satellite navigation systems: the BeiDou Navigation Satellite System (BDS), the Global Positioning System (GPS), and the Galileo Satellite Navigation System (GAL). This launch marked the first in-orbit validation of the iGNSS-R altimetry technology. This study provides a detailed overview of the iGNSS-R payload design and analyzes its dual-frequency delay mapping (DM) measurements. We developed a refined DM waveform-matching algorithm that precisely extracts the propagation delays between reflected and direct GNSS signals, enabling the retrieval of global sea surface height (SSH) through the interferometric altimetry model. For validation, we employed an inter-satellite crossover approach using Jason-3 and Sentinel-6 radar altimetry as references, achieving an unprecedented SSH accuracy of 1.7 cm at a 40 km resolution. This represents a breakthrough improvement over previous GNSS-R altimetry efforts. The successful demonstration of iGNSS-R technology opens up new possibilities for cost-effective, wide-swath sea level monitoring. It showcases the potential of GNSS-R technology to complement existing ocean observation systems and enhance our understanding of global sea surface dynamics.  
g.2025, 17, 1820
## 7 Module III: Temporal Privacy
### Simple Global Anti Temporal Scheme (SGAT)
#### 7.1.3 Energy Cost  
We presume in this scheme that every node would send one message at the end of each interval. The message could be either real or fake. If we have (N) nodes in the WSN, then we expect (N) messages during each interval \(I_{i}\). The energy spent for transmission is almost constant since we have fixed size messages. However, we can evaluate how expensive it would be to use fake messages for privacy enhancement. If we have (Q) percent of the nodes send real messages at each interval, then we are wasting (1-Q) percent of the energy and of the bandwidth.  
We can adjust the amount of energy consumed by increasing the interval period \(\omega_{i}\). However, increasing \(\omega_{i}\), would increase the delays. If a SN receives multiple messages in one interval, then it will _queue_ the messages for transmission. Because the SN needs to wait until the end of the interval, it could arrange the messages in a queue and send them randomly at the end of interval. This should also increase the privacy and security of the data. It could also select a different forward node for each message. In conclusion, SGAT is energy-expensive due to sending fake/real messages by every node per each interval of time. However, SGAT provides the maximum message _entropy_. Figure 12 exhibits the network transmissions for two consecutive intervals.  
Figure 11: Total delay required to send a message from source to the BS through (\(U\)) hopes [42].
# Tracking Extended Targets: Novel TPMB Filter Driven by Model and Data Collaboration
## 3 Model-Driven IMM-TPMB Filter
### Implementation of an IMM-TPMB Filter Based on Gate-Control
#### 3.2.2 Update
Pruning and resampling: To reduce the number of global hypotheses, pruning and resampling of the MBM components are necessary. By approximating MBMs with low existence probabilities as PPPs, the algorithm increases computational speed without significantly impacting accuracy. The calculation of distortion caused by minimizing the KLD under the PPP approximation is as follows: \[D_{KL}\left(f^{s,t}_{k|k-1}\left|\left|f^{u,b}_{k|k-1}\right.\right)=r_{k|k-1} +\left(1-r_{k|k-1}\right)\log\left(1-r_{k|k-1}\right)\] (57)
6. Target state estimation: Select the MB components with existence probabilities above a set threshold to estimate the current target state at time step \(k\), from a set of weighted particle collections, represented as the outputted target trajectory \(X^{i,j,(l)}_{k}=\frac{1}{\sum\limits_{l\in L_{k}^{i}}w^{i,j,(l)}_{kk}\sum \limits_{l\in L_{k}^{i}}w^{i,j,(l)}_{k|k}\overline{w}^{i,j,(l)}_{k|k}}\). New target estimates are achieved using a Poisson point process, outputting a set of new target trajectories, denoted as trajectory \(X^{b}=\left(t^{b},x^{1,w}\right)\), where \(t^{b}=k\) is the birth time, and \(v=1\) is the trajectory length.
# Polyp Generalization via Diversifying Style at Feature-Level Space
## 4 Results
### Experimental Results of Kvasir-SEG  
Quantitative results are presented in Table 2, while qualitative results are illustrated in Figures 6 and 7. Observing Table 1, we find that our proposed method achieves comparable performances across all metrics compared to other state-of-the-art (SOTA) methods. Notably, it demonstrates approximately a 3% improvement in the Dice score and IoU compared to PraNet and HardNet-MSEG. Additionally, the proposed method exhibits enhancements in other metrics such as FM, SM and MAE. In contrast, while FRCNet and HardNet-MSEG achieve MAE scores of 0.024 and 0.028, respectively, these values are slightly higher than those achieved by our proposed method. The model's performances on challenging images can be observed in the accompanying figure, showcasing its effectiveness in polyp segmentation and generalization.  
\begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{2}{c}{**Kvasir-SEG**} & \multicolumn{2}{c}{**CVC-ClinicDB**} & \multicolumn{2}{c}{**CVC-ColonDB**} & \multicolumn{2}{c}{**ETIS Dataset**} & \multicolumn{2}{c}{**KvasirCapsule**} \\ \cline{2-11}  & **mDice** & **mIoU** & **mDice** & **mIoU** & **mDice** & **mIoU** & **mDice** & **mIoU** & **mDice** & **mIoU** \\ \hline Efficient UNet & 0.892 & 0.805 & 0.840 & 0.73 & 0.728 & 0.580 & 0.643 & 0.492 & 0.612 & 0.436 \\ Efficient UNet + Stylized Dataset & 0.902 & 0.834 & 0.853 & 0.749 & 0.778 & 0.689 & 0.746 & 0.610 & 0.804 & 0.719 \\ Efficient UNet + Stylized Dataset + Re-train & 0.909 & 0.846 & 0.858 & 0.751 & 0.785 & 0.693 & 0.766 & 0.636 & 0.824 & 0.732 \\ SCM & 0.903 & 0.841 & 0.852 & 0.759 & 0.781 & 0.697 & 0.773 & 0.683 & 0.836 & 0.739 \\ SGM & 0.897 & 0.835 & 0.868 & 0.776 & 0.788 & 0.704 & 0.781 & 0.696 & 0.848 & 0.759 \\
**NSSM** & **0.920** & **0.867** & **0.881** & **0.798** & **0.808** & **0.730** & **0.779** & **0.694** & **0.854** & **0.760** \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparision of baseline model, traditional augmentation method (stylized dataset) and the proposed method in different settings on the five datasets.  
Figure 5: (**a**) Overview of stylized dataset. By applying Adaptive Instance Normalization, we generate 30,000 stylized images using target style images. In this process, we transfer the style characteristics of the target images onto the original images while preserving their original content features. (**b**) A few samples of target style images, which were taken manually from five datasets.  
Figure 6: Qualitative comparison of the different methods on the challenging images from Kvasir-SEG testing subset and CVC-CliniCDB when trained on Kvasir-SEG.  
Figure 7: Qualitative comparison of the different methods on the images from challenging CVC-ColonDb **(top)**, ETIS-LaribPolypDB **(middle)** and KvasirCapsule-SEG **(bottom)** when trained on Kvasir-SEG.
# Unleashing the Potential of 5G for Smart Cities: A Focus on Real-Time Digital Twin Integration
## 2 Current Trends for 5G in Smart Cities
### Limitations of Applications of 5G in Smart Cities
\begin{table}
\begin{tabular}{c c c c} \hline
**Limitation** & **Description** & **Implications** & **Current Solutions** \\ \hline High Infrastructure & 5G requires dense and expensive infrastructure. & Limits rollout in low-budget areas. & Shared funding and phased deployment. \\ Costs & Ongoing maintenance & Delays the full & Cost-efficient hardware \\  & is required. & implementation. & in development. \\ \hline Limited Coverage & 5G signals are short-range, blocked easily. & Requires many base stations & Smart antennas and hybrid networks. \\  & Hard to maintain strong, city-wide signal. & Reduces network reliability. & AI-based \\  & More base stations and data & Increases energy costs and environmental impact. & Energy-saving hardware and renewable sources. \\ High Energy Use & May conflict with green & Limits sustainability. & Sleep modes and smart grids in testing. \\  & & More devices and real-time data & Threatens privacy and safety. & Encryption and secure 5G frameworks. \\ Security and & increase cyberattack risks. & & \\ Privacy Risks & Sensitive data need & High cost to protect & Blockchain and intrusion \\  & better protection. & infrastructure. & detection tools. \\ \hline \multirow{4}{*}{Interoperability Concerns & Devices and platforms often use & & Open standards and unified APIs. \\  & different standards. & Hard to integrate systems. & Cross-vendor frameworks \\  & Vendors use & Increases customization & in development. \\  & proprietary protocols. & costs and limits scalability. & Policy alignment \\ \hline \multirow{4}{*}{Regulatory and Policy Barriers} & Deployment restricted by local & Slows infrastructure rollout. & and reforms. \\  & rules and spectrum policies. & & May block installation of & Awareness campaigns and \\  & & cause resistance. & base stations. & safety reviews. \\ \hline \multirow{4}{*}{Device Readiness} & Older devices lack 5G support. & Costly to upgrade or & Backward-compatible \\  & & replace systems. & upgrades. \\  & Few universal device standards. & Slows smart city integration. & Industry push \\  & & & for standardization. \\ \hline \multirow{4}{*}{Digital Divide and Accessibility} & High costs and limited rollout & Unequal access to smart & Subsidies and \\  & may exclude low-income areas. & city benefits. & inclusive policies. \\ \cline{1-1}  & Coverage often favors wealthy & Worsens inequality in & Community-driven \\ \cline{1-1}  & or commercial zones. & tech access. & deployment models. \\ \hline \end{tabular}
\end{table} 
Table 1: Summary of the limitations in 5G deployment for smart cities and proposed mitigations.