esa-sceva/satcom-chunk-collection huggingface dataset:
# Hybrid Resonant Converter with Three Half-Bridge Legs for Wide Voltage Operation
## 2 Circuit Structure and Principle of Operation
The DC voltage gain in the high input voltage range is \(V_{o}\)/\(V_{in,H}=G_{H}\)(\(f\))\(n_{s}\)/(\(4n_{p}\)). Comparing the DC voltage gains for three different input voltage ranges, it is observable that \(V_{o}\)/\(V_{in,L}=2\)(\(V_{o}\)/\(V_{in,M}\)) = 4(\(V_{o}\)/\(V_{in,H}\)) if \(G_{L}\)(\(f\)) = \(G_{M}\)(\(f\)) = \(G_{H}\)(\(f\)). The theoretical PWM waveforms and the equivalent circuits in a switching cycle for high input voltage range are shown in Figure 5.  
**State 1 [\(t_{0}\)-\(t_{1}\)]:**\(C_{Q1}\) is discharged to zero voltage at \(t=t_{0}\). Due to \(i_{Lr1}\)(\(t_{0}\)) being negative, the body diode \(D_{Q1}\) is forward. \(Q_{1}\) can turn on after \(t_{0}\) to realize zero-voltage switching. Because \(i_{Lr1}\)(\(t_{0}\)) \(>\)\(i_{Lm1}\), \(D_{1}\) is conducting and \(v_{Lm1}\) = \(v_{Lm2}\) = \(nV_{o}\) and \(i_{Lm1}\) and \(i_{Lm2}\) increase. \(C_{r1}\), \(C_{r2}\), \(L_{r1}\) and \(L_{r2}\), are resonant with resonant frequency \(f_{r}\) = \(1/2\pi\sqrt{L_{r1}C_{r1}}\) in this state. Figure 4: Proposed DC converter under medium input voltage range: (**a**) PWM waveforms; (**b**) state 1; (**c**) state 2; (**d**) state 3; (**e**) state 4; (**f**) state 5; (**g**) state 6. **State 2 [\(t_{1}\)-\(t_{2}\)]:** At time \(t_{1}\), \(i_{Lr1}=i_{Lm1}\) and \(i_{Lr2}=i_{Lm2}\). The rectifier diode \(D_{1}\) becomes off. \(L_{m1}\), \(L_{m2}\), \(L_{r1}\), \(L_{r2}\), \(C_{r1}\), and \(C_{r2}\) are resonant in this state. **State 3 [\(t_{2}\)-\(t_{3}\)]:**\(Q_{1}\) is turned off at \(t=t_{2}\). Due to \(i_{Lr1}\) being positive, \(C_{Q1}\) (\(C_{Q2}\)) is charged (discharged). The rectifier diode \(D_{2}\) is forward biased due to \(i_{Lr1}(t_{2})<i_{Lm1}(t_{2})\). **State 4 [\(t_{3}\)-\(t_{4}\)]:**\(v_{CQ2}=0\) at \(t=t_{3}\). Owing to \(i_{Lr1}(t_{3})\) being positive, the body diode \(D_{Q2}\) is forward. Power switch \(Q_{2}\) can turn on after \(t_{3}\) to achieve zero-voltage switching. \(D_{2}\) is conducting owing to \(i_{Lr1}(t_{3})<i_{Lm1}(t_{3})\). Thus, \(v_{Lm1}=v_{Lm2}=-nV_{o}\) and \(i_{Lm1}\) and \(i_{Lm2}\) decrease. \(C_{r1}\), \(C_{r2}\), \(L_{r1}\), and \(L_{r2}\) are resonant in this state. **State 5 [\(t_{4}\)-\(t_{5}\)]:** At \(t=t_{4}\), \(i_{Lr1}\) equals \(i_{Lm1}\) and \(i_{Lr2}\) equals \(i_{Lm2}\). Then, the rectifier diode \(D_{2}\) becomes off. In this time interval, \(L_{m1}\), \(L_{m2}\), \(L_{r1}\), \(L_{r2}\), \(C_{r1}\), and \(C_{r2}\) are resonant. **State 6 [\(t_{5}\)-\(T_{sw}\) + \(t_{0}\)]:**\(Q_{2}\) turns off at \(t_{5}\). Because \(i_{Lr1}\) is negative, \(C_{Q1}\) is discharged and \(C_{Q2}\) is charged. Due to \(i_{Lr1}(t_{5})>i_{Lm1}(t_{5})\), the rectifier diode \(D_{1}\) is forward biased. At time \(T_{sw}\) + \(t_{0}\), \(C_{Q1}\) is discharged to zero voltage. Figure 2.0 _Cont._
## 3 Smart Collaborative Positioning Using CD Approach  
### Influence of Some Parameters in CD Process  
The goal of this paper is to propose a smart version of the CD approach. The developed algorithm will be able to operate the receiver optimally according to various parameters. For example, choosing the best satellites or selecting of the best reference station to assist the user. The influence of some parameters on the CD positioning ambiguity is shown in Figure 3. In this example, we compare the 3D correlogram by calculating the CD metrics corresponding to 6 best satellites and all visible satellites (10 satellites) by varying some simulated parameters (\(C/N_{0}\), elevation mask angle, GDOP). It is based on the presence of three strong signals, and the remaining satellites are too weak to justify the use of the CD approach instead of the conventional acquisition. So, we have three weak + three strong for six best satellites and seven weak + three strong for 10 satellites. Weak signals correspond to the strongest signals below the set threshold, and strong signals correspond to the strongest signals among all visible satellites.  
Figure 3: _Cont._  
According to the curves in Figure 3, we can see that the positioning ambiguity varies according to changes in certain parameters. For example, the position solution becomes less accurate when the mask angle increases. Similarly, the position error becomes greater when the geometrical configuration is poor. Moreover, it should be noted that, for the same parameters, the curves corresponding to the 6 best satellites and 10 satellites are approximately the same, i.e., they do not present a great difference.
# Advancing TinyML in IoT: A Holistic System-Level Perspective for Resource-Constrained AI
## 5 Opportunities and Cross-Cutting Themes
### Smart Cities and Infrastructure  
City planners worldwide are increasingly relying on sensor networks to monitor and manage critical infrastructure, spanning traffic flow, waste management, air quality, and energy distribution [74]. Implementing AI at the edge within these urban ecosystems enables real-time decision-making, such as rerouting traffic after an accident or identifying energy waste in smart grids [3]. By embedding intelligence in streetlights, traffic signals, and other municipal assets, cities can adapt dynamically to evolving conditions, optimizing resource allocation and reducing operational costs. Furthermore, localized AI processing reduces the bandwidth needed for collecting vast volumes of raw sensor data, thus making large-scale sensor deployments more feasible [76].  
Yet, the sheer scale of smart city infrastructures introduces complexities. Citywide sensor networks comprise thousands or even millions of devices with highly varied hardware profiles and communication methods [28]. Managing firmware updates, security patches, and consistent model deployment across such a heterogeneous landscape becomes a monumental undertaking [57]. Federated learning approaches might alleviate some challenges by enabling distributed model training, but guaranteeing reliability and security at such a scale also necessitates robust device identification, tamper-resistant hardware, and flexible policy enforcement [77]. If successfully implemented, resource-constrained AI in smart city environments can provide actionable insights, streamline municipal services, and promote sustainability [12].
# Transmit and Receive Diversity in MIMO Quantum Communication for High-Fidelity Video Transmission
## 2 Related Works
While significant progress has been made in entanglement-based quantum MIMO communication methods, such as MIMO-enabled quantum teleportation and QKD [33], these approaches primarily focus on leveraging quantum entanglement for secure information exchange. These studies have provided valuable insights into the integration of MIMO techniques within quantum communication systems. Table 1 summarizes the major existing applications of MIMO in quantum communication, along with their advantages and disadvantages. Notably, all existing works have been proposed with a focus on security using quantum entanglement, while our approach leverages quantum superposition to enable high-fidelity transmission. However, the specific role of MIMO in quantum superposition-based systems, particularly for media transmission tasks such as image and video delivery, remains largely unexplored. This gap underscores the need to investigate how quantum MIMO architectures that leverage superposition can enhance multimedia delivery under realistic channel conditions. While quantum superposition holds significant promise for improving communication capacity and robustness, its effectiveness in mitigating fading effects, especially for visual content, still requires deeper exploration to advance practical and high-performance quantum communication systems. Therefore, this research introduces a novel direction in quantum communication by exploring the use of MIMO techniques using quantum superposition-based systems for video transmission. Unlike traditional approaches, which often overlook media-specific requirements, the proposed method aims to improve the reliability and efficiency of transmitting high-resolution video content across realistic, noise-prone communication channels. By harnessing the advantages of both MIMO and quantum superposition, this work seeks to enhance transmission quality while reducing error rates. The outcome has the potential to significantly advance quantum communication technologies, offering a foundation for practical, high-performance multimedia delivery in future communication networks.
# A Comprehensive Review and Benchmarking of Fairness-Aware Variants of Machine Learning Models
## 5 Fair Editions of Ensemble Models
### FairBoost  
FairBoost [29] is a boosting-based ensemble method that integrates fairness constraints during training while maintaining classification accuracy. The model is built upon AdaBoost.SAMME.R, a variant of the AdaBoost algorithm optimized for multi-class classification, and introduces fairness-aware instance weighting to regulate the trade-off between accuracy and fairness. Unlike similar models that focus on a single sensitive feature, FairBoost supports multiple sensitive attributes, each with one or more categories, making it more applicable to real-world scenarios. The authors evaluate FairBoost on four benchmark datasets--Adult, Arrhythmia, Drugs, and German Credit--each containing multiple sensitive features. The results demonstrate that FairBoost outperforms existing methods in fairness while maintaining competitive accuracy.
# Calibrating the SAR SSH of Sentinel-3A and CryoSat-2 over the Corsica Facilities
## Data and Methodology
### Methodology  
The main difference in the processing in this study, compared to previous studies [1; 2; 3; 6], is the difference in the footprint area in over which the geoid height is interpolated. In the case of LRM (low resolution mode, e.g., Jason missions), the footprint is defined as a circle using the significant wave height to define the diameter [14], while in the case of SAR (synthetic aperture radar, e.g., Sentinel-3A) the footprint is defined as a rectangle with a width of 300 m along-track and a length across-track that is also a function of SWH with the same formula as LRM (see Figure 5 or Figure 8 for an illustration).  
Our standard processing of the SSH bias uses (when available) the wet tropospheric correction derived from the radiometer in addition to the ionospheric correction derived from the dual frequency of the altimeter. We performed a comparison with numerical weather prediction (NWP) model corrections that are available in the products for the wet troposphere and the ionosphere corrections, from ECMWF and GIM respectively (see Section 0).
## 5 Experiments and Results
### Experimental Details
#### 5.1.2 Validating Tests in the MPE, MAMuJoCo, and RPE Environments
When a job is processing an operation using a particular resource, that resource is unavailable to other robots until the job releases it. This characteristic reflects the resource-limited nature of real-world manufacturing systems. The operation sequence and the initial resource distribution can change when different batches of jobs are processed. This variability mimics the dynamic nature of real-world manufacturing environments, where job requirements and resource availability can change over time. The total time for a job to complete an operation consists of the operation processing time, the robot transition time, and the waiting time. For instance, in automobile vice-frame processing, different operations have different processing times, and the time taken for a robot to move between different sites and the time a job has to wait for an available robot also contribute to the overall time. This makes the scheduling problem more challenging, as the scheduler needs to optimize all these time components to minimize the total makespan. In our experiments, we set the number of jobs to 6 or 8, considered as 6 or 8 agents. At each time-step, agents choose a certain operation and a certain site to perform the operation if available. There are eighteen operational sites and three exit sites in total. More detailed information can be accessed in [41]. The overall training steps total 30M for the G-QMIX and MASA-QMIX [41] methods. The \"steps-of-an-episode\" is 200 for all tests. We also use the average value of _evaluating averaged makespan_ over 16 testing episodes for every 5000 training steps for measuring. Three rounds of individual tests are taken with different random seeds. The curves of _evaluating averaged makespan_ changing over time are plotted with the \(p\)-value being 0.05 and the confidence intervals being 95%.
## 3 Field Experiments
### Experimental Results
#### 3.2.3 Ground Reflection Experiment  
A schematic diagram of the experiment is shown in Figure 8c. The experiment was to observe how ground reflections affect the communications of UAV BSs under conditions of two closely located UAVs. In this experiment, one of the UAVs worked as a BS while the other played the role of a UE. Here, the interference was evaluated by the quality of the indirect communication link via the ground reflection path between the two hovering UAVs. In other words, the higher the throughput, the stronger the signal arrives at the other UAV via the reflected path. In the measurements, the horizontal distance between the UAVs was set to 5 m, and the throughputs were measured when both UAVs gradually increased their altitudes. The snapshot of the experiment can be seen in Figure 11. The measurement results are shown by the green circle in Figure 12.  
Several findings can be seen from these results. When the drone's altitude is low, that makes a large incident angle of the reflected path, and the throughput is poor since the ground-reflection path is out of the main lobe of the directivity antenna. When the altitude is high enough, the incident angle is smaller such that the ground-reflection path is within the main lobe of the directivity antenna, and we can observe improved communication quality (or worse interference conditions). Furthermore, increasing the altitude, the throughput decreases naturally due to longer distance attenuation.  
Figure 11: Experimental observations of ground reflection.  
Figure 10: Experimental results of the coverage measurement.
## 2 Experimental Programme
### Shear Capacity Calculation by Various Standards
#### 2.1.2 Uniform Building Code  
The Uniform Building Code (UBC), a standard influential primarily in the western United States and established in 1997, provides a framework for evaluating shear capacity in reinforced concrete members [40]. Within this code, Equation (4) defines the shear contribution from the concrete (\(V_{c}\)) based on factors including the compressive strength of the concrete (\(f_{c}\)), the shear span-to-depth ratio (\(a\)/\(d\)), and the main tensile reinforcement ratio (\(\rho_{w}\)). Similarly, Equation (5) addresses the shear contribution of dedicated shear reinforcement (\(V_{s}\)) by incorporating its cross-sectional area (\(A_{v}\)), the tensile strength of the shear reinforcement (\(f_{yt}\)), inclination angle (\(\alpha\)), and spacing (\(s\)). These equations offered a standardised approach to shear design within the UBC's domain.  
\[V_{C}=\left(0.158\sqrt{f^{\prime}{}_{c}}+17.1\rho_{w}\frac{V_{u}d}{M_{u}} \right)b_{w}d \tag{4}\]  
\[V_{s}=\frac{A_{v}f_{yt}(\sin\alpha+\cos\alpha)d}{s} \tag{5}\]
## 2 Materials and Methods
### Validation of the Origin Discrimination Table  
The origin discrimination formula was validated based on judgment values using the 11 markers applied to 30 domestic and 30 imported soybeans. For the discrimination formula, the sensitivity, selectivity, and efficiency were estimated through qualitative analysis [16; 17].  
\[\text{Sensitivity}=\left(\frac{\text{TD}}{\text{TD }+\text{ FD}}\right)\times 100 \tag{1}\]  
\[\text{Slectivity}=\left(\frac{\text{TF}}{\text{TF }+\text{ FF}}\right)\times 100 \tag{2}\]  
\[\text{Efficiency}=\left(\frac{\text{TD }+\text{ TF}}{\text{TD }+\text{ FD }+\text{ TE }+\text{ FF}}\right)\times 100 \tag{3}\]  
True Domestic Product (TD) indicates a domestic sample identified as domestic based on the discrimination result. False Domestic Product (FD) indicates an imported sample identified as domestic based on the discrimination result. True Foreign Product (TF) indicates an imported sample identified as foreign based on the discrimination result. False Foreign Product (FF) indicates a domestic sample identified as foreign based on the discrimination result. Sensitivity indicates the level at which the discrimination table can correctly identify a domestic sample. Similarly, selectivity indicates the level at which the discrimination table can correctly identify a foreign sample. To evaluate the prediction performance of the established discrimination table, the judgment values of the tested samples were applied to the table. The percentage of the discrimination of domestic samples as domestic was set as the domestic predictive rate and that of the discrimination of foreign samples as foreign was set as the foreign predictive rate.
## 2 Experimental Setup
### Components of the Test  
Common methods of collecting hand movement information include control handles [29], data gloves [30], mouse-pointing, and image acquisition [31]. However, a movement towards the hand joint cannot be restored finely by control handles. Data gloves are costly and must be combined with external spatial-location equipment. Mouse-pointing is fast, but it is difficult to provide multiple degrees of freedom. This research used a non-contact Leap Motion (Leap) sensor [32], as shown in Figure 2. Leap has two cameras that can detect the space at 25-600 mm above the sensor.  
The hand's space-state information can be recorded when the hand moves into this space. This study adopted Tobii Eye Tracker 4c (Tobii) [33] eye track sensor produced by Sweden to collect eye movement data, as shown in Figure 2. The range of action is 500-900 mm. The sampling rate of Leap is 120 Hz, and that of Tobii is 90 Hz. After calibration, Tobii obtains the spatial position and gaze point of the eye through a near-infrared camera. In general, Leap collects hand information while Tobii collects eye information. The robot is displayed and run in unity. The control algorithm of the robot is c  
Figure 1: The human–robot interaction model.  
The size of the monitor used was 27 inches. During the experiment, the user faced 70 \(\pm\) 10 cm in front of the screen, and the eye level was close to the middle of the screen. As each participant's eyes are different, such as their inter-pupillary distance, Tobii was calibrated in advance for each user according to the manufacturer's instructions. Leap was placed 30 \(\pm\) 5 cm forward from the outside of the shoulder of the user, which is a comfortable position and did not block the Tobii equipment.  
In brief, both the eye's line of sight and the finger's direction can produce an intersection on the screen. The system is designed to collect eye and hand information to improve interaction performance. Our method has two modes, namely \"Instruction\" and \"Mapping.\" Here is an example of the \"Instruction\" mode. First, the user faces the screen and puts his hand into the Leap identification area. Then, they point to a block using the index finger (Figure 3(G1)), and their eyes also stare at the same block. The robot can automatically plan the trajectory to grasp the indicated block. When the users want to stop the grabbing action, they only need to make another gesture (Figure 3(G3)). In \"Mapping\" mode, the user makes a gripping gesture (Figure 3(G2)). In this mode, eye information is not needed, but the spatial position of the hand is transmitted to the robot, and the hand directly controls the robot's movement and rotation. A tablet is used to remind users to choose the block according to the shape of the picture. The reason why we combined eye-hand is that adding eye movement information can improve reliability and speed up the interactive response.  
Figure 3: Nine gestures that are easy to use and distinguish.  
Figure 2: Users control the robot stacking according to the image requirements in the iPad through the eye–hand combination.
# DA-GAN: Dual Attention Generative Adversarial Network for Cross-Modal Retrieval
## 5 Experiments
### Competitors  
We compare the proposed DA-GAN with 13 competitors, including 6 traditional cross-modal retrieval approaches, i.e., CCA [69], KCCA [11], MCCA [70], MvDA [71], MvDA-VC [72] and JRL [73], as well as 7 deep learning-based approaches, i.e., DCCA [42], DCCAE [24], CCL [74], CMDN [75], ACMR [57], DSCMR [44], CM-GANs [76]. The brief introductions of them are listed here.  
* **CCA**[69] is a statistical method that is to learn linear correlations between samples of different modalities.
* **KCCA**[11] is a non-linear extension of CCA, which employs kernel function to improve the performance of common subspace learning.
* **MCCA**[70] is a generalization of CCA to more than two views, which is used to recognize similar patterns across multiple domains.
* **MvDA**[71] jointly learns multiple view-specific linear transforms so as to construct a common subspace for multiple views.
* **MvDA-VC**[72] is an extension of MvDA with with view consistency, which utilize the structure similarity of views corresponding to the same object.
* **JRL**[73] uses sparse projection matrix and semi-supervised regularization to explore correlations of labeled and unlabeled cross-modal samples.
* **DCCA**[42] is implemented by deep neural networks to learn non-linear correlation. It has two separated DNNs, one branch per modality.
* **DCCAE**[24] is a DCCA extension that integrates CCA model and autoencoder-based model to realize multi-view representation learning.
* **CCL**[74] realizes a hierarchical network to combine multi-grained fusion and cross-modal correlation exploiting. It includes two learning stages to realize representation learning and intrinsic relevance exploiting.
* **CMDN**[75] contains two learning stages to model the complementary separate representation of different modalities, and combines cross-modal representations to generate rich cross-media correlation.
* **ACMR**[57] is a adversarial learning-based method to construct a common subspace for different modalities by generating modality-invariant representations.
* **DSCMR**[44] exploits semantic discriminative features from both label space and common representation space by supervised learning, and minimizes modality invariance loss via weight-sharing to generate modality-invariant representation.  
Figure 3: Some image and text samples of Wikipedia, NUS-WIDE and Pascal Sentences.  
* **CM-GANs**[76] models cross-modal joint distributions by two parallel GANs to generate modality-invariance representations