esa-sceva/satcom-chunk-collection huggingface dataset:
# A New Sum-Channel Radiating Element for a Patch-Monopole Monopulse Feed
## 4 Discussion  
The radiation-pattern measurements correspond well to measurements at about the same solid angle as the illuminated reflector is located, so the taper efficiency is easily calculable. The spillover-efficiency calculation is more problematic, since spillover radiation measurements and simulation results are only rough approximations. The simulation model is too ideal and the measured weak backside-radiated power is sensitive to a nonideal measurement space, while also affected by the mechanical mounting structure, cabling, and a measurement probe (as seen in Figure 4). The same applies to determining the depth of the difference-channel minimum. The sum-channel radiation pattern tapers off by about 9 dB at the edge of the reflector. Including the edge attenuation due to the reflector shape (3 dB for \(\frac{\mathrm{F}}{\mathrm{D}}\) of 0.4), the edge attenuation amounts to about 12 dB. There is still confidence in the results around the reflector's edge, where the spillover radiation is the strongest. The weaker side and back radiation affect the spillover-efficiency approximation less.  
Compared to the elliptic-patch design [1], the diameter of the feed is slightly reduced, while the new design still increases the directivity. A feed of this size shades only 2% of the aperture area of a reflector that is only 5 \(\lambda\) in diameter. More than the new patch shape, the narrowband design is responsible for the increased directivity, and also a shallower cavity, to a lesser extent. The feed design is optimized for a relatively narrowband sum channel to see the achievable efficiencies of such a design. The crosstalk between the channels of \(-35\) dB is suitable for tracking operation. Since the tracking control loop is very slow compared to the sum-channel data rate, the difference channel is much less demanding in terms of bandwidth and the required signal-to-noise ratio.  
For future work, a wideband optimization at a minimal and acceptable performance loss in the middle of the band should be tested with the design to find its practical limits. The feeding circuit elements could be integrated directly into the feed structure for an even more compact, repeatable, and integral solution. It should also be possible to scale the design to at least the X band, make a dual-polarization version of the design, or develop a simple radome for such a feed. Finally, it is impractical to wholly measure the feed in an indoor test space, and the final antenna efficiency, gain, and noise temperature can only be predicted. In the future, we will also focus on field-testing the feed, illuminating a real ground-station antenna.  
Figure 9: Sum-channel simulation results of total directivity and LHCP gain frequency characteristics.
# Fusing Appearance and Prior Cues for Road Detection
## 2 Proposed Algorithm
### Estimate of Appearance Model
#### 2.2.2 Gaussian Mixture Model  
In this section, we estimate the appearance model of the road surface in log-chromaticity space and the S channel in HSV color space using the Gaussian mixture model (GMM) based on the selected seed superpixels. GMM is a classical and currently widely used method. Furthermore, unlike discriminative methods, GMM delivers a probability map which allows us to fuse it with road geometric prior, which will be discussed subsequently. Here, for log-chromaticity space or the S  
Figure 3: Seed superpixels selection. (**a**) Seed superpixels defined initially; (**b**) final seed superpixels selected.  
channel in HSV color space, the value \(P(X)\) which depicts the probability of the superpixel \(X\) with the mean value \(f_{X}\) in log-chromaticity space or the S channel belonging to road is defined as  
\[P(X)=\sum_{i=1}^{K}\omega\,_{i}\times\eta(f_{X},\mu\,_{i},\Sigma\,_{i}), \tag{6}\]  
\[\eta(f_{X},\mu,\Sigma)=\frac{1}{\left((2\pi)\times|\Sigma|\right)^{\frac{1}{2} }}exp(-\frac{1}{2}(f_{X}-\mu)^{T}\Sigma^{-1}(f_{X}-\mu)), \tag{7}\]  
\[\sum_{i=1}^{K}\omega\,_{i}=1, \tag{8}\]  
where \(K\) gives the number of Gaussian components, \(\omega\,_{i}\) is the weight assigned to the corresponding Gaussian component having the mean \(\mu_{i}\) and covariance matrix \(\Sigma\,_{i}\), and \(\eta\) is the Gaussian probability density function defined in (7). We optimize the parameters of \(\omega\,_{i}\), \(\mu\,_{i}\), and \(\Sigma\,_{i}\) with the expectation-maximization (EM) algorithm in log-chromaticity space or the S channel respectively based on the selected seed superpixels. In view of effectiveness and robustness, the value of \(K\) is set to three. Thus, we obtain \(P^{log}(X)\) and \(P^{S}(X)\) which correspondingly depict the probability of the superpixel \(X\) belonging to road in log-chromaticity space and the S channel in HSV color space. For the combination of color channels, we compute the average value of the color channels' \(P(X)\) as the final \(P_{a}(X)\) which depicts the probability of the superpixel \(X\) belonging to road surface according to the appearance cue. Additionally, each pixel in the superpixel \(X\) shares the same value, i.e.,  
\[P_{a}(x)=P_{a}(X)x=1,2,3,\cdots,n, \tag{9}\]  
where \(n\) gives the number of pixels in the superpixel \(X\). Hence, a probability map based on appearance cues denoted as \(P_{a}\) is obtained by calculating the \(P_{a}(X)\) of all the superpixels in the input image.
# Floating Photovoltaic Plant Monitoring: A Review of Requirements and Feasible Technologies
## 5 Technologies for Autonomous Water Basin Mapping and Monitoring
### Issues Related to Robotic Systems
#### 5.2.3 Energy Autonomy and Environmental Impact  
Operational challenges include energy management and environmental impact too. Prolonged operation of robotic systems demands efficient energy use. However, energy storage and consumption remain critical concerns [113]. Battery life limits the operational time of both ASVs and ROVs/AUVs, requiring frequent recharging or battery replacement. Efficient energy management systems and the development of high-capacity, durable batteries are essential to extend operational missions. Designing docking stations for ASVs, ROVs, and AUVs is necessary to enable autonomous retrieval and docking for battery recharging, data upload, and maintenance checks, thus enabling continuous system operation. Additionally, robotic systems must minimize their environmental footprint to avoid disturbing aquatic life and prevent contamination or disruption of the water body [133]. System designs and operation should consider noise reduction, eco-friendly materials, and non-toxic lubricants to minimize environmental impact. Establishing guidelines and protocols for responsible robotic operations in aquatic environments is crucial for ensuring sustainable and ethical use. Surface and underwater drones can also carry multiparametric probes with the specific aim to monitor their own environmental footprint.
## 3 Methods
### Environment Attention Module
#### 3.4.2 Scene Semantic Module  
As shown in Figure 6, to obtain environmental information, we first extract the scene information using VGG19. Then, we determine the attention levels of pedestrians towards different areas of the scene through spatial probability density values. Finally, we summarize the environmental information that the target pedestrian is focused on using an attention module. We employ pre-trained VGG19 (a network of convolutional neural networks) to extract the features of the scene image so that we may fully utilize the scene information. The pre-trained VGG19 serves as our backbone network, while \(C_{i}^{t}\) represents the retrieved characteristics. The scene feature map \(C_{i}^{t}\) is shown by Formula (12).  
\[C_{i}^{t}=VGG19(I_{scene},W_{ggg19}) \tag{12}\]  
where \(I_{scene}\) is the processed image data in the dataset, and \(W_{ggg19}\) is the network parameters of VGG19.  
In order to make full use of the scene information, we calculate the scene attention \(SC_{i}^{t}\) at each moment \(\mathbf{t}\in\{1\), 2, 3, \(\ldots\), Tobs\(\}\) according to the scene feature map \(C_{i}^{t}\) and the pedestrian trajectory density map \(K_{i}^{t}\). The environment information vector \(SC_{i}^{t}\) calculated by scene attention is shown by Formula (13).  
\[SC_{i}^{t}=ScneAtt(C_{i}^{t},K_{i}^{t},W_{Att}) \tag{13}\]  
where \(K_{i}^{t}\) is the spatial probability density feature map, and \(W_{Att}\) is the network parameters of scene attention.  
Figure 6 shows the scene attention module structure.
## 3 Theoretical Analysis
### Case Study: Transmitter Localization by LDWuR for a Mobile Network
#### 3.6.2 Dynamic Antennas  
In this strategy, the receiver adopts multiple LDWuRs. Each LDWuR consists of a WuR built in an omnidirectional antenna. Multiple LDWuRs cooperate to steer a beam in different directions in turn. Therefore, the receiver can use a beam to detect the transmitter's direction.  
The average total power consumption is the same as WuRs Equation (15) with a larger cover range. The average total power consumption, \(P_{t}^{dyn}\), is given by:  
\[P_{t}^{dyn}=\frac{\sum\limits_{j=1}^{N_{all}^{beam}}N_{ant}^{amn}\frac{1}{N_{ ant}^{sum}}P_{t}T_{scan}^{beam}}{N_{dir}^{beam}T_{scan}^{beam}}=P_{t} \tag{19}\]  
where \(T_{scan}^{beam}\) represents the scan time for each direction of beam, \(N_{ant}^{amn}\) is the number of omnidirectional antennas and \(N_{dir}^{beam}\) is the number of different directions of beams. For example, the receiver adopts three LDWuRs to cooperate to form a beam in different directions with a certain degree of cover range. Equation (19) shows that the receiver adopts \(N_{ant}^{amn}\) LDWuRs with \(N_{ant}^{amn}\) antennas of \(1/N_{ant}^{amn}\) times power consumption to explore the transmitter's direction. Figure 3a shows that three LDWuRs cooperate to steer a beam in the south direction with a normalized spacing of five units, a related phase delay of seven units and beam pattern size of 90 \({}^{\circ}\) at a normalized signal strength of 0.7. Figure 3b shows the beam pattern size and signal strength level of 0.7. Figure 3c shows the 3D beam pattern with the same parameters.  
The total latency is \(N_{dir}^{beam}\) times the total latency of WuRs Equation (16), as the receiver spends more time on the scans of each direction. The total latency is given by:  
\[T_{t}^{dyn}=N_{dir}^{beam}T_{t} \tag{20}\]
## 1 Introduction
However, for heavy traffic areas, travel times play a very crucial role in achieving an efficient and practical solution. For example, choosing a route with less traffic, more customers can be served by a single vehicle. Furthermore, the route balance is frequently considered in the courier industry for balancing the working time among delivery vehicles. Solving a dynamic routing problem (i.e., with the whole time-dependent matrix) would require to make requests for the traffic-time distances from a point to every other point in an online manner. This can be very computationally expensive in practice for problems with a large number of customers. This aspect is of particular importance in contexts when the daily delivery requests from potential customers are not known beforehand but available in the morning, and a good decision must be made as soon as possible (within 30 min in our case). Therefore, the goal of this research is to demonstrate the potential of using the proposed route adjustment approach to adjust a solution (given by a regular, pre-determined driver-area delivery zone) that is primarily infeasible in terms of vehicle working time. The updated solution will have fewer number of vehicles with a working time violation, and as many as possible driver's locations in the driver-area delivery zone are retained so as to maintain the driver experience. As the process of acquiring traffic data in the urban logistics problem with time-dependent traffic information is the most time-consuming step, the proposed heuristic route adjustment method is initially applied to single time-dependent traffic information in order to reduce the overall processing time. The route time of the solution obtained with single time-dependent traffic information is subsequently updated with point-to-point actual traffic time from map services to get the actual vehicle time. In our business case studies, a vehicle working time limit is fixed due to drivers' working hours. We found, however, that by tuning the vehicle working time parameter, the obtained route solutions could reduce the overall time violation and achieve better route balance with the time-dependent traffic information. Finally, since solution methods in the literature focused mainly on creating a new route without taking into consideration the driver-area delivery zone, those methods are not directly comparable to ours. The rest of this paper is structured as follows. The review of related and relevant literature is given in Section 2. A problem description is described in Section 3. Proposed solution methods are presented in Section 4. Numerical experiments are provided in Section 5. Finally, we give conclusions and perspectives in Section 6.
## 4 Results and Discussion  
### Radiative Regime Description of PROSAILH-BGC  
The performances of PROSAILH-BGC and BIOME-BGC in describing the radiative transfer regime within the canopy were tested by comparing the observed and modeled \(\mathrm{PAR}_{\mathrm{t}}\) with the two models. Results showed a reduction in the bias of \(\mathrm{PAR}_{\mathrm{t}}\) using the PROSAILH-BGC (Figure 4). In fact, although the r\({}^{2}\) did not improve using PROSAILH-BGC, \(\mathrm{PAR}_{\mathrm{t}}\) simulated with the coupled model was closer to the 1:1 line than the \(\mathrm{PAR}_{\mathrm{t}}\) modeled by BIOME-BGC, thus leading to a reduction in RMSE between modeled and observed data (from 144.9 \(\mathrm{\SIUnitSymbolMicro mol}\) m\({}^{-2}\)s\({}^{-1}\) to 111.2 \(\mathrm{\SIUnitSymbolMicro mol}\) m\({}^{-2}\)s\({}^{-1}\)).
## 2 Related Works  
The topic of ensuring high-throughput and low-latency wireless communication--particularly in industrial and mission-critical contexts--has gained increasing attention in both academic and commercial research. Numerous studies have aimed to address these challenges by proposing novel solutions at various layers of the communication stack. However, the landscape remains fragmented, with varying approaches, technologies, and performance assumptions depending on the use case. Therefore, in this section, we aim to organize and contextualize key aspects of the problem. We begin by reviewing relevant use cases and their communication requirements, followed by an overview of related work and promising technical directions. This structured analysis provides the necessary foundation and motivation for our proposed research approach.  
In every field around us, we identified numerous use cases that clearly warranted deeper investigation. Since addressing all of them simultaneously was not feasible, we focused on a subset that stood out--particularly those involving industrial drones equippedwith components that generate high uplink traffic. For instance, a camera mounted on a remotely operated drone may require continuous 4K streaming, resulting in high-throughput uplink transmission, while control commands require low-throughput but ultra-low-latency communication. This simple setup illustrates the dual nature of such traffic demands.
# PPG and Bioimpedance-Based Wearable Applications in Heart Rate Monitoring--A Comprehensive Review
## 5 Heart Rate Monitoring
### Limitations  
Although numerous literature sources on MA removal exist, their reliability outside the laboratory conditions can be questioned. First of all, few cases of intensive movements of test subjects were considered in a majority of them. In [101], EEMD in conjunction with principal component analysis (PCA) was used. However, the algorithm was validated only on intensive care unit patients with little movement. Secondly, the implementation of MA reduction algorithms in wearable health monitoring devices during intense physical exercises is a challenging task owing to the highly dynamic nature of limb/trunk activities. Existing MA removal methods are ineffective in such cases. The reasons are as follows:  
* The methods are too reliant on expertly tuned multiple parameters and heuristic thresholds, which prevents generalization [102].
* The commonly used acceleration signal in MA removal is not a direct MA source but is rather a superposition of movement of a body part and the gravitational acceleration [103]. Therefore, the recorded acceleration signals cannot be directly translated in to the movement of limbs during exercises.  
A potential remedy is the improvement of the sensors themselves. For example, a heart sensor with strong adhesion and good conformity as reported in [104; 105] or an improvement of electrode design for impedance measurements by adding a soft layer that reduces the effect of skin stretching [106].
## 4 Radiative Transfer in Coupled Atmosphere-Water (Including Snow/ice) Systems
### Radiative Transfer Equation--Unpolarized Radiation
#### 4.1.2 The Interface between the Two Slabs--Calm (Flat) Water Surface  
When a beam of light is incident upon a plane interface between two slabs of different refractive indices, one fraction of the incident light will be reflected and another fraction will be transmitted or refracted. For unpolarized light incident upon the interface between the two slabs, the Fresnel reflectance \(\rho_{\text{F}}\) is given by \[\rho_{\rm F}=\frac{1}{2}(\rho_{\perp}+\rho_{\parallel}) \tag{121}\]  
where \(\rho_{\perp}\) is the reflectance for light polarized with the electric field perpendicular to the plane of incidence, and \(\rho_{\parallel}\) is the reflectance for light polarized with the electric field parallel to the plane of incidence [3; 14; 73; 74]. Thus, one finds  
\[\rho_{\rm F}=\frac{1}{2}\left[\left|\frac{\mu_{1}-m_{\rm rat}\mu_{t}}{\mu_{1}+ m_{\rm rat}\mu_{2}}\right|^{2}+\left|\frac{\mu_{2}-m_{\rm rat}\mu_{1}}{\mu_{2}+m_{ \rm rat}\mu_{1}}\right|^{2}\right] \tag{122}\]  
where \(\mu_{1}\equiv\mu_{\rm air}=\cos\theta_{1}\), \(\theta_{1}\) being the angle of incidence, \(\mu_{2}\equiv\mu_{\rm con}=\cos\theta_{2}\), \(\theta_{2}\) being the angle of refraction determined by Snell's law (\(\bar{m}_{\rm r1}\sin\theta_{1}=\bar{m}_{\rm r2}\,\sin\theta_{2}\)), and \(m_{\rm rat}=\bar{m}_{c,2}/\bar{m}_{c,1}\). Similarly, the Fresnel transmittance becomes  
\[\mathcal{T}_{\rm F}=2m_{\rm rel}\mu_{t}\mu_{t}\left[\left|\frac{1}{\mu_{i}+m_{ \rm rat}\mu_{t}}\right|^{2}+\left|\frac{1}{\mu_{t}+m_{\rm rat}\mu_{i}}\right|^ {2}\right] \tag{123}\]  
where \(m_{\rm rel}=\bar{m}_{\rm r2}/\bar{m}_{\rm r1}\).
# Two-Step Multiuser Equalization for Hybrid mmWave Massive MIMO GFDM Systems
## 4 Receiver Design
### Hybrid Analog-Digital Equalizer
#### 4.3.3 Digital Part of the Hybrid Equalizer  
In the digital part, we consider the ZF detector, with the aim of cancelling the interference neglected in the analog part to avoid the inversion of the high-dimension matrices. Therefore, the interference is only explicitly taken into account in the digital part, where the needed matriaxial inversions are computationally much less demanding. Therefore, we assume that the digital equalizer is given by  
\[\overline{\mathbf{W}}_{d,l}=\bigg{(}\mathbf{H}_{\alpha l,l}^{H}\mathbf{H}_{ \alpha l,l}\bigg{)}^{-1}\mathbf{H}_{\alpha l,l}^{H}, \tag{34}\]  
where \(\mathbf{H}_{\alpha l,l}=\left(\mathbf{W}_{a}\right)^{H}\mathbf{H}_{l}\).
# Energy Efficiency Analysis of MIMO Wideband RF Front-End Receivers
###### Abstract  
Energy-efficiency is crucial for modern radio-frequency (RF) receivers dedicated to Internet of Things applications. Energy-efficiency enhancements could be achieved by lowering the power consumption of integrated circuits, using antenna diversity or even with an association of both strategies. This paper compares two wideband RF front-end architectures, based on conventional low-noise amplifiers (LNA) and low-noise transconductance amplifiers (LNTA) with N-path filters, operating with three transmission schemes: single antenna, antenna selection and singular value decomposition beamforming. Our results show that the energy-efficiency behavior varies depending on the required communication link conditions, distance between nodes and metrics from the front-end receivers. For short-range scenarios, LNA presents the best performance in terms of energy-efficiency mainly due to its very low power consumption. With the increasing of the communication distance, the very low noise figure provided by N-path LNA-based architectures outperforms the power consumption issue, yielding higher energy-efficiency for all transmission schemes. In addition, the selected front-end architecture depends on the number of active antennas at the receiver. Hence, we can observe that low noise figure is more important with a few active antennas at the receiver, while low power consumption becomes more important when the number of active RF chains at the receiver increases.  
wideband RF front-end receiver; LNA; LNTA; energy efficiency +
Footnote †: journal: Journal of the Acoustical Society of America
# Spitzer Resurrector Mission: Advantages for Space Weather Research and Operations
## 3 Mission Design
### Programmatics  
#### 3.5.1 Operational Readiness of Spitzer  
At the end of the mission (EOM), Spitzer was fully capable of continuing the Warm Mission [5], with substantial power and propellant margins and a healthy IRAC instrument. The spacecraft is anticipated to be fully operational upon the arrival of the Resurrector in health, power margin, available propellant, thermal stability, and IRAC functionality. Of the two thruster strings, String 2 was nominal. Inertial Reference Unit 1 was slightly degraded but functional, Unit 2 was nominal. The 3.6 \(\upmu\)m and 4.5 \(\upmu\)m channels of the IRAC instrument used during the Warm Mission were nominal. The transponder was set to carrier-only mode. The spacecraft was left on Side B of the avionics after a solar event, with Side A in reserve. All four Reaction Wheel Assemblies (RWAs) were fully operational.  
At EOM, Spitzer was placed into indefinite safe mode in a two-hour rotation about the sunline, pointing its sunshield and solar cells directly normal to the Sun. In this configuration, the only nominal influences on the spacecraft that would affect the need to desaturate the RWAs are solar radiation pressure and precession of the sunline during an orbit, both minor in magnitude. At EOM, nitrogen propellant reserves were 6.674 kgremaining of an original 15.59 kg load (42%). Power generation at EOM was 12.2 A at 26.3 V for 345 W, with a total of 2015 battery cycles out of a minimum 25,000-lifetime capacity (90% remaining). Expected degradation in power generation by the time the Resurrector arrives places the generated current at 11.9 A (336 W). The Warm Mission is estimated to need 11 A (311 W) for nominal thermal zone profiles, yielding a margin of approximately 8% before taking any power-saving steps for an additional 10% margin.  
Power generation is one limiting factor for the lifespan of the resurrected Spitzer. Current models show an expected reduction of 0.0377 A/year, giving Spitzer an additional 24 years of operations before power-saving measures such as turning off heaters in the spacecraft bus would be required. Unexpected incidents like CME damage and micrometeoroid impacts would decrease this timespan.  
Another limiting factor is the propellant necessary to desaturate the PCS during nominal operations. In later years, sophisticated planning for pointing the spacecraft in ways that remove momentum inputs from prior turns was adopted to considerably reduce the rate of propellant usage. Assuming a similar conservative approach, the telescope can be expected to operate for an additional ten years or more.