esa-sceva/satcom-chunk-collection huggingface dataset:
# Range-Free Localization Approaches Based on Intelligent Swarm Optimization for Internet of Things
## 3 Proposed Localization Algorithms
### CSO-Based Enhanced DV-Hop Algorithm  
In this section, we discuss three localization approaches that are used in WSNs to enhance the accuracy and efficiency of sensor node position estimation. In Section 2, we introduced the W-DV-Hop and HW-DV-Hop algorithms. The DV-Hop, W-DV-Hop, and HW-DV-Hop approaches are further improved using an optimization technique based on the Chicken Swarm Optimization (CSO) approach. These improvements aim to accurately determine node locations in WSNs. We assess different scenarios to confirm the efficacy of several localization algorithms in comparison with DV-Hop: MDV-Hop [35], PSODV-Hop [48], W-DV-Hop [39], and HW-DV-Hop [40]. All of the proposed techniques were developed and implemented in MATLAB, and we analyze their performance in static WSNs. The three new enhanced localization schemes are denoted as DV-HopCSO, W-DV-HopCSO, and HW-DV-HopCSO. These approaches consist of four stages, introduced as follows: (i) the locations of anchors are broadcast; (ii) the computation of the average hop size is enhanced; (iii) the hyperbolic 2D technique is adopted to optimize the unknown nodes' positions; and (iv) the CSO approach is integrated to enhance the accuracy of the localization process. In the DV-Hop algorithm, the conventional three phases are kept and an additional fourth phase is introduced to integrate CSO. This phase results in improved accuracy when locating the positions of sensor nodes within the network. For W-DV-HopCSO, the first and third stages are equivalent to those of W-DV-Hop, which is discussed in Section 2. Throughout the first phase, the anchor nodes transmit their position information. The weighted mean approach is used in the second phase to find the average hop size for each anchor node. Multilateration is used in the third phase to approximate the locations of unknown nodes. Finally, CSO is used in the fourth phase to minimize the positioning error of unknown nodes. Similarly, HW-DV-HopCSO is divided into four stages: (i) the locations of the anchors are broadcast; (ii) the average hop size computation is enhanced; (iii) the hyperbolic 2D solution is selected to determine the node positions; and (iv) CSO is used to optimize and refine the unknown node locations. During the first step, the anchor nodes disseminate a message specifying their exact positions in the network. In the second step, we adopt the mean weighted method [42] to calculate the \(AHSize_{i}\) utilizing the novel formula discussed in Section 3. In the third phase, the 2D hyperbolic location approach [43] is employed instead of multilateration to precisely identify the unknown nodes' coordinates. In the fourth stage, we use CSO to improve and refine the estimated positions. Furthermore, as detailed in this research, we use CSO to improve the precision of unknown node positions. The flowchart shown in Figure 3 provides an overview of the proposed hybrid localization methods, and the comprehensive pseudocode is illustrated in Algorithm 1. Figure 3: Flowchart of the proposed algorithms.
# Fusion of Computer Vision and AI in Collaborative Robotics: A Review and Future Prospects
## 8 Discussion
### Challenges in Real-Time and Scalable Machine Learning  
Real-world deployment confronts various practical constraints: latency [13; 158], data heterogeneity [159; 160], and domain shift [161]. Techniques such as few-shot and continual learning are promising but immature in collaborative robotics. Scaling machine-learning architectures without compromising safety or interpretability is a major bottleneck [162], especially in cloud-edge hybrid architectures [163]. A promising strategy to mitigate data inefficiency in cobot vision systems is the adoption of self-supervised learning (SSL) approaches, which reduce reliance on expensive, manually labeled datasets [164]. SSL leverages large quantities of raw, unlabeled sensor data--such as RGB-D images, force-torque signals, or robot trajectories--to learn useful representations via pretext tasks [165]. For example, predicting future frames, reconstructing occluded regions, or aligning multimodal inputs. These learned representations can then be fine-tuned on smaller, task-specific labeled datasets, improving data efficiency while retaining their high-level performance. In the context of collaborative robotics, SSL can enable robust object detection, pose estimation, and scene understanding even in dynamic, cluttered environments with limited annotation. Moreover, SSL frameworks can facilitate continual learning on the shop floor, adapting to novel objects and layouts without costly data curation. Integrating self-supervised learning thus offers a scalable pathway toward more adaptable, data-efficient vision systems for safe and intuitive human-robot collaboration.
###### Abstract  
This paper presents the design of a bidirectional wireless power and information transfer system. The wireless information transfer is based on near-field technology, utilizing communication coils integrated into power transfer coils. Compared with conventional far-field-based communication methods (e.g., Bluetooth and WLAN), the proposed near-field-based communication method provides a peer-to-peer feature, as well as lower latency, which enables the simple paring of a transmitter and a receiver for power transfer and the real-time updating of control parameters. Using the established communication, control parameters are transmitted from one side of the system to another side, and the co-control of the inverter and the active rectifier is realized. In addition, this work innovatively presents the communication-signal-based synchronization of an inverter and a rectifier, which requires no AC current sensing in the power path and no complex algorithm for stabilization, unlike conventional current-based synchronization methods. The proposed information and power transfer system was measured under different operating conditions, including aligned and misaligned positions, operating points with different charging powers, and forward and reverse power transfer. The results show that the presented prototype allows a bidirectional power transfer of up to 1.2 kW, and efficiency above 90% for the power ranges from 0.6 kW to 1.2 kW was obtained. Furthermore, the integrated communication is robust to the crosstalk from the power transfer and misalignment, and a zero BER (bit error rate) and ultra-low latency of 15.36 us are achieved. The presented work thus provides a novel solution to the synchronization and real-time co-control of an active rectifier and an inverter in a wireless power transfer system, utilizing integrated near-field-based communication.  
wireless power transfer; active rectifier; near-field communication; electric vehicle +
Footnote †: journal: Accepted: 22 January 2024
# Cross-Technology Interference-Aware Rate Adaptation in Time-Triggered Wireless Local Area Networks
## 5 Results and Analysis
### Performance of CTINet based on the \(\alpha\).
#### 5.2.4 Rate Selection Ratios  
Figure 8 illustrates the proportion of MCS indices, i.e., the data rates selected during data packet transmissions, along with the PER for each algorithm. The MCS indices range from 0 to 8. In each subplot corresponding to a specific SNR, the most frequently selected MCS index reflects the typical choice of comparison algorithms under the given channel conditions.  
As shown in Section 5.2.1, packet error-based algorithms generally exhibit higher PERs than measurement-based algorithms. This is because packet error-based algorithms interpret the receipt of an ACK message as an indication of good channel quality, leading them to continually attempt to select higher MCS indices. Consequently, across all subplots, packet error-based algorithms show a higher frequency of selecting higher MCS indices compared to measurement-based algorithms. Notably, this tendency to select MCS indices higher than the optimal channel condition often results in relatively higher PERs, as shown in Figure 8b-d.  
At a WiFi SNR of 25 dB, measurement-based algorithms predominantly select MCS index 6, as it is determined based on the measured SNR under the given channel conditions. In contrast, packet error-based algorithms tend to select MCS indices 7 and 8, depending on the success or failure of packet transmissions.  
Algorithms incorporating the CARA framework demonstrate more conservative behavior in selecting MCS indices, as they account for the impact of CTI. Comparing across algorithm types, CARA-based algorithms consistently show a higher frequency of selecting lower MCS indices under all conditions. This difference becomes more pronounced as channel conditions improve. For instance, at a WiFi SNR of 30 dB, the difference in the proportion of lower MCS index selections between CARA-based algorithms and their non-CARA counterparts is 5.16% and 2.84%, respectively (proportions of MCS indices 4-6, highlighted in green). This higher frequency of selecting lower MCS indices explains why CARA-based algorithms achieve lower PERs while maintaining comparable or superior performance in terms of goodput and latency compared to conventional algorithms.
## 4 Field Data Integration  
These technologies provide the opportunity of automation and almost real-time remote access to data and information. However, most still require field-based ground truthing and local knowledge (sourced from those that live and work the land) to complete the puzzle [18; 75; 76; 76]. For example, the Quick Carbon project builds interpolated datasets of soil carbon at the property level by using 3-D printed spectrometers, remotely sensed satellite data, and spatially referenced soils data [75; 77; 78]. Likewise, models of plant aboveground biomass are based upon field collected data which are calibrated to remote sensing methods [14; 18; 66; 79; 80; 81]. UAS use necessitates ground control points and ground truth data. Ground-based LiDAR has been field-validated to estimate woody plant biomass on grazinglands [82; 83], and may even be useful for estimating browse important in stocking rate estimates [84].  
In spite of that, not all ground truth data are created equal. Special provisions for calibrating remotely sensed data products to biophysical landscape parameters can ensure research efforts are more successful. Thus, it would be wise to consider how the scale and resolution of field-based and/or satellite measurements might correlate to the scale and resolution of UAS imagery, considering the latter may be sub-centimeter [60; 70]. For example, GNSS-enabled collars have been used to track and monitor livestock behavior in grazinglands for some time [30; 31; 33; 85; 86; 87; 88]. Distinguishing resting and foraging behaviors [30; 89; 90; 91; 92], distance traveled to/from water [33; 93], seeking out high quality forage [31; 90], terrain/topographical preferences/avoidance [32; 93], response to disturbance and environmental stressors [94; 95; 96], birth detection [97], and circadian rhythms [98] have all been studied through GNSS tracking collars and accelerometers.
# Compression Behavior of Rubber-Bentonite Mixture under Different Salinities
## 2 Results and Discussions
### Compression Coefficient Change of MX80 Bentonite/Rubber Mixture
#### 2.1.1 Influence of Different Mass Ratio on Compression Coefficient
Under a 1 mol/L NaCl solution, after the internal structure of the B4T6 sample stabilized due to vertical compaction, the compressibility coefficient reached a steady state between 200 kPa and 300 kPa. Overall, the compressibility coefficients of the samples with different mass ratios decreased as the load increased from 300 kPa to 400 kPa, indicating that higher vertical loads result in higher sample densities.
## 2 Theoretical Foundation  
Fluid-structure interaction (FSI) is a branch of engineering at the interface of fluid mechanics and solid mechanics. It is a discipline that studies the interaction and mutual influence between fluids and solids. It is currently widely used in ocean engineering, aerospace engineering, shipbuilding and other engineering fields. The calculation of fluid-structure interactions can be regarded as the simultaneous calculation of fluids and solids, neither of which can be ignored, because the characteristics of fluids and solids are considered at the same time. Compared with the calculation of fluids and solids separately, fluid-structure interactions can improve the calculation efficiency and save the calculation cost, and at the same time, the results can be closer to the actual project.
## 1 Introduction
Building on these findings, the current study extends our methodology using machine learning to explore more complex ternary and quaternary nitride systems, with the goal of identifying new compositions that achieve further enhancements in Tc. Most existing studies focus predominantly on simpler binary compounds (like NbN, TiN), with little attention given to the more complex ternary and quaternary nitride systems. This scarcity presents both a challenge and an opportunity: on one hand,  
Figure 1: Impact of titanium substitution on NbN superconducting films. (**a**) presents critical current (JC) as a function of the magnetic field (H) for NbN films with varying concentrations of titanium, demonstrating a decline in JC and the critical field (HC2) with increasing titanium content. (**b**) provides experimental data for two NbN films, one pure and the other with titanium substitution (Nb\({}_{0.5}\)Ti\({}_{0.5}\)N), detailing their respective critical temperatures (Tc) and other superconducting parameters. The measured Tc values, approximately 15 K for NbN and 15.5 K for Nb\({}_{0.5}\)Ti\({}_{0.5}\)N, indicate a slight increase with titanium addition. (Adapted with permission from [11]). it restricts the ability to develop highly accurate predictive models due to a lack of comprehensive experimental input; on the other hand, it motivates the exploration of these less-understood materials as potential high-temperature superconductors. The lack of extensive data necessitates leveraging broader datasets to identify potential candidates among these nitrides, but it also introduces challenges, such as increased uncertainty in model predictions and potential biases toward more well-studied materials. Therefore, this study aims to bridge the knowledge gap by using machine learning to identify promising new compositions and guide future experimental work.
## 3 Results
### Changes in Properties According to Storage Conditions
#### 3.1.2 Changes in GRP  
During the 126-day storage period, the GRP values in NW\({}_{25}\), NW\({}_{35}\), and NW\({}_{50}\) decreased, whereas they increased in NW\({}_{100}\), NW\({}_{200}\), NW\({}_{300}\), NW\({}_{500}\), and NW\({}_{1000}\). For NW\({}_{50}\) and NW\({}_{100}\), the storage conditions had no significant impact on GRP (\(p>0.05\)). However, the other six NW preparations demonstrated significant changes in GRP under L\({}_{\rm{RT}}\) (\(p<0.05\)). In addition, NW\({}_{25}\), NW\({}_{200}\), NW\({}_{300}\), and NW\({}_{1000}\) showed no significant differences between S\({}_{\rm{RT}}\) and S\({}_{\rm{LW}}\), whereas NW\({}_{35}\) and NW\({}_{500}\) exhibited the smallest change in S\({}_{\rm{LW}}\) (\(p<0.05\)) (Figure 3).  
Figure 4: Change rates in the properties of NW preparations after the 126-day storage period under three conditions (\(n=5\)): (**a**) comparison among eight NW preparations with different initial ACCs for each storage condition according to the Kruskal–Walls test; (**b**) correlation between the initial ACC of NW and the change rate for each storage condition. R values indicate Spearman’s rank correlation coefficients.  
Significant differences in ORP change rates were observed among the eight test NW preparations under all storage conditions (_p_ < 0.05), with NW\({}_{1000}\) demonstrating the highest change rate (Figure 4a). The R values were 0.921 (S\({}_{\text{LW}}\))-0.949 (L\({}_{\text{RT}}\)), indicating a strong positive correlation between the initial ACC of the NW and the ORP change rate under all storage conditions (Figure 4b). There were strong negative correlations between the pH and ORP values of the NW preparations after 126 days of storage, as well as between their rates of change. The R values were \(-\)0.876 and \(-\)0.806, respectively (Supplementary Figure S3).
# An Integration Framework for the Inpainting and Colorization of Arbitrary Masked Grayscale Images
## 3 Methodology
### Component Models
#### 3.2.3 Dual-Decoder Colorization Model (DDColor)  
DDColor [3] is a cutting-edge image colorization model designed to produce photorealistic and vivid colorization results by employing a dual-decoder architecture. This architecture consists of a pixel decoder and a query-based color decoder, enabling end-to-end learning without reliance on hand-crafted priors. The pixel decoder restores the spatial resolution of grayscale images, while the color decoder learns semantic-aware color queries from multi-scale visual features. Together, they establish robust correlations between colors and semantic representations, significantly mitigating issues like color bleeding and improving the richness of the generated colors.  
A key innovation in DDColor is the introduction of color queries, which adaptively capture semantic relationships in the image. These queries are refined through cross-attention with multi-scale image features, making the model effective in handling complex contexts and diverse object details. To further enhance color vibrancy, DDColor incorporates a novel colorfulness loss, which optimizes the vividness of the generated results while maintaining semantic consistency.  
The backbone of DDColor is built on ConvNeXt, a state-of-the-art model for image feature extraction. This enables the system to achieve hierarchical representations, crucial for modeling high-level semantic and low-level spatial details. Through extensive experiments, DDColor demonstrates superior performance over prior methods, achieving high-quality results on datasets such as ImageNet and COCO-Stuff, with minimal color bleeding and greater adaptability to varying contexts.
# Thermal Stability Design of Asymmetric Support Structure for an Off-Axis Space Camera
## 2 Low Thermal Deformation Optical Mechanical Structure  
### The Remote Sensing Camera  
A typical off-axis three-mirror optical system is used in the remote sensing camera. The optical system consists of three mirrors. The primary mirror and third mirror are as-spherical mirrors, and the secondary mirror is a spherical mirror. The distance between the primary mirror and the secondary mirror is 297 mm. The optical system diagram of the space camera is shown in Figure 1.  
For our purposes, the basic structure of the remote sensing camera system can be simply divided into the main support structure and optical components, as shown in Figure 2. The main support structure is composed of frames and the bearing cylinder made of CFRP. The optical component comprises the reflectors, metal plates and the metal con-nectors between the plate and the frame.  
In the working process of the remote sensing camera in orbit, the periodic change of temperature inevitably causes serious deformation of the mirror. The deformation not only comes from the expansion deformation caused by the thermal load, but also from the thermal stress between the support structure and the body of the mirrors. Therefore, the final thermal deformation control of the mirror should be realized in two aspects:  
* The surface deformation of reflectors caused by thermal stress: Thermal stress will inevitably occur when different materials are joined together, which ultimately reduces the accuracy of the mirror surface. In this case, it is important to ensure that the reflector body matches the thermal expansion coefficient of the mounted backplate. Studies have shown that the combination of a Zerodure reflector and an Invar backplate is effective, enabling remote sensing cameras to maintain good surface ac-curacy in periodically changing temperatures [21; 22; 23].
* Rigid body displacement of the reflectors: This means that appropriate technology should be used to minimize the thermal expansion of the support structure. Therefore, the components with zero or near-zero CTE are preferable, in order to achieve low thermal deformation, for example, in the supporting frame and central bearing cylinder. It is difficult to reduce the actual thermal deformation to zero in every direction, and the thermal expansion deformation is smaller in the direction with good symmetry, so we need to strictly control the thermal deformation in the asymmetric direction. This method can reduce the actual rigid body displacement of the optical  
Figure 1: TMA optical system.  
Figure 2: Support structure for the off-axis TMA camera.  
module and obtain the optimal thermal stability of the structure in the periodic temperature environment.  
In the following sections, we will discuss how to use CFRP materials to design a support structure with low CTE, so as to realize the extremely low thermal deformation of the optomechanical structure for the off-axis remote sensing camera.
## 2 BDS-2 Satellite Constellation Development  
BDS-2 has been providing Asia-Pacific regional service since 27 December 2012. Currently, there are 14 operational BDS-2 satellites, including five GEO, six IGSO, and three MEO, with their launching and deactivation status shown in Figure 1. During the six-year regional service provision period, C03 was updated by a new satellite on September 29, 2018. Meanwhile, C13 was launched as an MEO satellite from September 19, 2012 to October 21, 2014. After that, C13 was retired from the BDS-2 constellation. Two years later, a new C13 satellite started to provide service as an IGSO satellite [19]. All these satellites contribute to the BDS-2 satellite constellation. Since November 2017, nine MEO, two IGSO, and one GEO satellites have been launched to construct the BDS-3 preliminary system [20].