esa-sceva/satcom-chunk-collection huggingface dataset:
# Stitching-Based Resolution Enhancement in Wavefront Phase Measurement of Silicon Wafer Surfaces
## 3 System Calibration
### Main Calibration  
The main calibration process involves precise adjustments of several system components to ensure accurate wavefront measurements. Key calibration steps are outlined below:  
* Main Lens Position: The distance between the sample and the main lens is critical for minimizing defocus. This affects the peak-to-valley values of the measured wavefront. As shown in Figure 4, the camera and sample must be positioned at the conjugate planes of the 4f relay system for optimal performance.
* Camera Position: The camera position is adjusted using the diffraction pattern of a calibration object, such as a thin wire or a patterned dice on a silicon wafer. The camera is moved back and forth until the diffraction pattern is minimized based on visual inspection.
* Calibration Mirror: The calibration mirror corrects system-induced wavefront aberrations, referred to as bias. Its tip and tilt angles are adjusted to create a quasi-circular image without vignetting. The camera is then shifted iteratively to intra- and extra-focal positions, where both images are fitted to ellipses to minimize ellipticity and vignetting.
* Flats: To reduce internal reflections, a servomotor-driven circular cover blocks the main lens, capturing images in intra- and extra-focal planes. These images are subtracted from all subsequent measurements to correct reflection artifacts.
* Camera Temperature Stability: Maintaining a stable camera sensor temperature ensures consistent readout noise between intra- and extra-focal images. Variations in signal-to-noise ratio (SNR) can cause incorrect defocus estimation. The camera's integrated PID control regulates sensor temperature, with future studies planned to evaluate SNR and temperature variations.
* LED Temperature Stability: A stable LED temperature prevents intensity fluctuations that could distort wavefront recovery. A PID-controlled Peltier plate regulates the LED temperature throughout the measurement process with less than 0.5 degrees variations.
* Room Temperature: Environmental temperature changes affect the lenses' focal lengths, causing defocus which worsens the low order frequencies of the recovered phase. To mitigate this, the system is housed in an ISO7 temperature-controlled clean room with 0.5-degree variations. Despite this controlled environment, temperature variations may still impact measurements.
* Camera Angle: The camera's angle must be accurately adjusted for proper image stitching. This parameter is important because of the nature of the stitching technique, which supposes that there is movement between consecutive measurements. This is achieved by using a calibration mask, as shown in Figures 6 and 7, ensuring seamless stitching between captured images.
## 7 Registration of MR Scans
### MR-PET Blur Removal Network
* By optimizing \(L_{perc}\), the generator is guided to produce images that not only minimize pixel-wise error but also preserve important perceptual qualities such as texture sharpness and realistic structural details, which are essential in medical imaging applications like MR-PET reconstruction. The pixel-wise loss \(L_{pix}\) is defined as an L1 distance:  
\[L_{pix}=\|I_{S}-\hat{I}_{S}\|_{1}. \tag{8}\]In this formulation of the pixel-wise loss,  
* \(L_{pix}\) represents the pixel-wise reconstruction loss between the ground truth sharp image \(I_{S}\) and the estimated deblurred image \(\hat{I}_{S}\). * The \(\ell_{1}\) norm (\(\|\cdot\|_{1}\)) computes the mean absolute error (MAE), summing the absolute differences between corresponding pixel intensities of \(I_{S}\) and \(\hat{I}_{S}\) across all pixels in the image. * The \(\ell_{1}\) loss is preferred over the \(\ell_{2}\) loss (mean squared error) in many image restoration tasks because it is less sensitive to outliers and tends to preserve image edges and fine details better, which is especially important in high-resolution MR-PET reconstruction. * By minimizing \(L_{pix}\), the generator is explicitly encouraged to produce images that closely match the true pixel intensities of the target image, providing a strong low-level supervision signal. * This loss complements the perceptual loss \(L_{perc}\), ensuring both pixel accuracy and perceptual fidelity are jointly optimized during training. This composite loss ensures that the generator produces restored images that are visually sharp, perceptually consistent, and faithful at the pixel level [49; 50]. The deblurred outputs \(\hat{I}_{S}\) are subsequently passed to the structured joint sparsity module [27; 31] and to the super-resolution generator \(G_{\theta}\). The removal of blur is essential to preserve the low-rank and joint sparse structure of the Hankel representations across MR and PET modalities. Without effective blur correction, the joint sparsity prior would be severely compromised, degrading the overall reconstruction quality. In experimental validation, the proposed blur removal network noticeably enhances the fidelity of the final super-resolved MR-PET images, particularly in high-frequency anatomical details and fine structural boundaries [11; 49; 50].
# Monocular Depth Estimation Using Res-UNet with an Attention Model
## 4 Results and Discussion
### Ablation Study  
#### 4.5.1 Hyperparameters and Training  
Hyperparameters--such as dropout, learning rate, optimizers, and batch size--searches were conducted to a considerable extent. During experimentation, small tweaks in hyperparameters were performed to check the model performance. For example, we used a 10% rate of dropout during training to ascertain the training behavior which resulted in better model convergence. Increasing the dropout from 10% also reduced the performance of our network. Similarly, trial and error were used to determine the learning rate and batch size. During trials, we started from standard learning rates such as 0.01, 0.001, and 0.0001. After certain epochs, we found that a learning rate of 0.001 performs better than the other learning rates and the model started to learn the features with fewer errors. In addition to that, an ADAM optimizer was used during training, because Res-Net performed better with an ADAM optimizer as compared to an SGD optimizer. The weight decay and the alpha and beta values of the optimizer were used as the default. Only the baseline network was subjected to the hyperparameter search. The base network was taken as an experimental model for the best fit parameters search. Due to the complex nature of our model, we checked for small changes during the hyperparameter search but, even with small iterations, we achieved quality results. We developed a simple attention Res-UNet for depth estimation in which we used simple model hyperparameters. We tested several more hyperparameters within a close range of the optimum values, but the results did not improve further.
## 1 Introduction
### Difference between Spoken Term Detection and Query-by-Example Spoken Term Detection  
It should be noted that STD and QBe STD tasks are quite similar. Both tasks aim to retrieve the speech files that contain the query of interest along with the appropriate timestamps. They only differ in the input format. Whereas, for STD, the input is text, for QBe STD it is speech. Although it is true that an STD system can be used to build a QBe STD system by decoding the acoustic query with an ASR system and then performing an text-based STD search, both tasks are fully-independent.
# Effect of Different Acoustic Parameters on NOx Emissions of Partially Premixed Flame
## Materials and Methods
### Experimental Conditions  
The primary equivalence ratios (\(\Phi\)) of 2 and 3 were selected for comparison because the partially premixed flame would not flash back or blow off under acoustic excitation at these equivalence ratios. Methane and air flow rates were controlled by the mass flow controller ([PERSON], SY-93) with precision of approximately 1%. Experimental conditions are summarized in Table 2.  
The mean velocities of premixed fuel in the combustor were 0.393 m/s, 0.5895 m/s, and 0.426 m/s corresponding to case 1, case 2, and case 3, respectively. The average velocity of coflow air was 0.00964 m/s; therefore, the influence of the coflow air on the flame structures and flame surface wrinkling could be ignored. The Reynolds number ranged from 422.1 to 663.1, which belonged to laminar combustion without acoustic excitation. For laminar flame, the relationship between NOx and the flow field induced by acoustic excitation was more clearly. Comparison of NOx emission characteristics with equivalence ratios and Reynolds numbers were conducted.  
The ranges of acoustic frequency and acoustic amplitude were selected between 0-400 Hz and 0-1400 Pa. Firstly, pulsed combustors tend to operate at low frequencies, usually around 100 Hz [17]. Secondly, based on the experimental results of the premixed flame under acoustic excitation, [PERSON] [35] found the flame shape changed significantly within the frequency range of 100 Hz to 220 Hz. In addition, the flame structure also changed significantly following an increase in the amplitude. Furthermore, the acoustic response of the speaker had a good behavior between 0-400 Hz and 0-1400 Pa. The maximum of acoustic amplitude was 1400 Pa, and pressure amplitude \(\mathrm{p}^{\prime}_{n}\) could be converted to velocity amplitude \(\mathrm{u}^{\prime}_{n}\) by conversion relations [36; 37]:  
\[\mathrm{p}^{\prime}_{n}/\mathrm{u}^{\prime}_{n}=\rho_{m}\mathrm{c}. \tag{3}\]  
Herein \(n\) is acoustic mode; \(\rho_{m}\) is density (=0.946 kg/m\({}^{3}\)); \(c\) is sonic speed (=387.23 m/s). As a result, the maximum of acoustic velocity amplitude was 3.8218 m/s.
# Advanced Strategies for Mitigating Particulate Matter Generations in Poultry Houses
## 6 Particulate Matter Emission Mitigating Strategies
### Housing Systems and Cleaning  
Particulate matter emission differs according to housing types. Dust concentrations in caged buildings are influenced by cage design and rearing practices, but dust levels in floor housing are determined by litter management, hen age, temperature, and humidity control [41,78]. The air quality in CF houses is generally worse than in caged houses. Usually, PM concentration is higher in CF houses than in other housing types because of litter accumulation and hen activities on the floor [24,40]. In the floor housing system, an average concentration of respirable ambient dust of 0.37 mg/m\({}^{3}\) was detected, which was more significant than average values in the caged system (0.13 mg/m\({}^{3}\)). Similarly, [PERSON] et al. (2013) also found the highest concentration of dust (1.19 mg/m\({}^{3}\)) in AV housing [41]. The researchers evaluated PM levels in three-layer houses (AV, CC, and EC) and discovered that the daily mean PM\({}_{10}\) level in AV was about six to nine times higher compared to CC and EC [40]. The emission rates for PM\({}_{10}\) and PM\({}_{2.5}\) were highest because of increased activities on the littered floor [40]. However, in CC and EC, PM emissions were similar, accounting for 16% of AV PM\({}_{10}\) ER and 10-20% of AV PM\({}_{2.5}\) ER, respectively. Thus, caged hen housing shows the lowest dust concentration, almost four to five times lower than floor-raised AV housing systems [111]. As a result, the type of housing and the amount of litter significantly impacted air quality. According to [PERSON] et al. (1999), PM concentration inside the farm were significantly higher during scraper cleaning and feed distribution [84]. As a result, reducing PM generation and emissions is critical for maintaining the health and well-being of laying hens and caregivers while also increasing the environmental stewardship production operation. Thus, housing types and cleaning procedures are vital in controlling PM emissions.  
Figure 3: Overview of the PM emission mitigating strategy used in poultry housing.
# Guaranteeing QoS for NOMA-Enabled URLLC Based on \(\kappa\)-\(\mu\) Shadowed Fading Model
###### Abstract  
Sixth-generation (6G) wireless communication scenarios are complex and diverse. Small-scale fading is a key part of wireless channels and its impact on performance in scenarios with time sensitivity and 6G ultrareliable and low latency communications (URLLC) quality-of-service requirements cannot be ignored. Therefore, it is necessary to accurately characterize small-scale fading when designing wireless communication systems. In this paper, we derive approximate closed form expressions for the probability density function, cumulative distribution function and moment-generating function of the postprocessing signal-to-noise ratio following the zero-forcing detector in a cell-free massive multiple-input multiple-output (CF mMIMO) system. CF mMIMO system is a nonorthogonal multiple access (NOMA) system that enables users to share all channel uses and can ensure the fairness of the communication quality experienced by different users. Our key contributions include the extension of the \(\kappa\)-\(\mu\) shadowed fading model to a CF mMIMO system and the proposal of theoretical tools (the derived closed-form expression) to improve its mathematical tractability. By exploiting the statistical characterizations of the arrival and service processes, another important contribution is the exploitation of the upper bound of the queuing delay violation probability (UB-QDVP) over the Mellin transforms of the arrival and service processes in the proposed CF mMIMO system under the \(\kappa\)-\(\mu\) shadowed fading model. Corroborated by extensive simulations, our analyses validate that the CF mMIMO system outperforms the orthogonal multiple access and power-domain NOMA systems and reveal the relationships among different small-scale fading types, energy efficiency, delay and the UB-QDVP, as well as the accuracy and effectiveness of the proposed theoretical tools based on the \(\kappa\)-\(\mu\) shadowed fading model.  
Keywords:cell-free massive multiple-input multiple-output (CF mMIMO); \(\kappa\)-\(\mu\) shadowed fading model; statistical delay quality-of-service (QoS); ultrareliable and low latency communications (URLLC) +
Footnote †: journal: _Sensors_**2022**, 22, 5279. [[https://doi.org/10.3390/s22145279](https://doi.org/10.3390/s22145279)]([https://doi.org/10.3390/s22145279](https://doi.org/10.3390/s22145279))
# Effects of Invasive Watermilfoil on Primary Production in Littoral Zones of North-Temperate Lakes
## 1 Introduction
IWM can change the physical structures of macrophytes assemblages through altering abundances of native macrophytes [5; 28]. Relative to other macrophytes, IWM does not necessarily produce more biomass, yet it can grow vertically faster and earlier than many other species [3], which allows it to shade other macrophytes and reduce their growth. Shoots of IWM  
Figure 1: Interactions among littoral primary producers (bold) and their resources (italics) in lakes. Arrows represent the directions of interactions but do not differentiate between positive or negative effects. have whorls of finely dissected leaves along elongated stems that have a high surface area to biomass ratio relative to other macrophyte species [29; 30]. Along with high surface areas, when growing near the surface, these shoots may provide an abundant habitat with high light availability for epiphytes (but see [31]). Canopy-forming macrophytes can also decrease production by primary producers like benthic periphyton at the bottom of the water column [32; 33]. Therefore, IWM presence may lead to shading of phytoplankton and benthic periphyton growing under IWM canopies, yet offer epiphytes growing attached to IWM canopies better access to light high in the water column. Because macrophytes and epiphytes can disproportionately contribute to whole-like primary production, changes in the distribution of primary producers caused by IWM could have important consequences for energy availability and flow in lake ecosystems and food webs. The aim of this study was to determine if IWM alters standing crops and rates of primary production by different groups of littoral primary producers. To address this question, we conducted a comparative study between plots where IWM was abundant compared to those where IWM was absent or sparse in littoral zones of six north-temperate lakes in Michigan. We measured standing crops of primary producers and their rates of primary production and respiration using bottle incubations. We also measured rates of primary production and respiration at the whole-plot scale in the littoral zone using open-water metabolism. We then applied a mass balance approach to determine the relative contributions of macrophytes, epiphytes, phytoplankton, and benthic periphyton to whole-plot primary production. This study was designed to test the following hypotheses in plots where IWM was abundant vs. those where it was absent or sparse: (1) macrophyte and epiphyte standing crops will be higher due to higher biomass and physical structure of IWM, (2) whole-plot gross primary production and ecosystem respiration rates will be higher due to higher standing crops or production of macrophytes and epiphytes, and (3) macrophytes and epiphytes will make larger contributions to whole-plot gross primary production.
## 3 Results  
Table 4 (Appendix C) summarizes the single scores of all evaluation tests for the exemplarily tested stereo-to-multichannel upmix algorithms, the used weighting factors and the resulting overall evaluation score. The higher a score, the better the test result of the tested stereo-to-multichannel upmix according to the defined criteria. Zero is the worst, one the best evaluation score. With the help of weighting factors, significance of single tests can be adjusted (see Appendix D). Figure 14 illustrates the evaluation scores according to Table 4. All in all, upmix 1(a) has the best overall evaluation score by far, upmix 1(b) the second best. Upmix 2(b) has the worst overall evaluation score, upmix 2(a) the second worst. Note that two commercial upmix algorithms in two different modes were used to demonstrate the functional principle of the proposed evaluation test and to illustrate how possible results can be visualized. The aim of this paper was not to compare existing upmix algorithms but to introduce an objective evaluation test to gain the possibility of objective comparison. So, an overall evaluation of an upmix algorithm with the proposed evaluation test is appropriate in comparison with other upmix algorithms as references. Therefore, Figure 14 provides an appropriate graphical overview for the comparative evaluation of different upmix algorithms. Note that corresponding results of the single evaluation tests were presented in the end of each section.  
For the proposed evaluation test several assumptions about stereo-to-multichannel upmix algorithms were made. Since upmix algorithms are also based on assumptions, the evaluation test will measure how well the assumptions made here were met. Furthermore, a self-contained evaluation of a single upmix algorithm should focus on panning test, direct signal test and volume test. That is because the effects of the correlation of the surround channels (phase test) are perceived subjective. In addition, the impacts of lateral energy fraction and interaural cross-correlation (perception test) on perceived spati  
Figure 14: Evaluation scores of the upmix algorithms—graphical overview.
# Improving Energy Efficiency on SDN Control-Plane Using Multi-Core Controllers
## 3 Materials and Methods
### Multi-Core and Single-Core  
SDN controllers are composed of software and a hardware platform. Most of the controllers on the market are developed using multipurpose languages and are available for many operating systems. Similar to any application, they may take advantage of the multiprocessing capacity of those platforms. Since most operating systems are developed for different tasks, it is possible to optimize the hardware/software to a specific job such as the one executed by the SDN controller.  
s [PERSON]'s law [24] divides problem workloads into two fractions: the part that is infinitely parallelizable \(P\) and a fraction \(S=1-P\) that is strictly serial, thus the performance \(R\) obtained by adding new parallel capabilities is limited by the fraction \(S\) as defined by  
\[R=R_{S}*\frac{S+P}{S+\frac{P}{M}} \tag{7}\]  
where \(R_{S}\) is the is the performance of a computer that can only execute the problem in a totally sequential manner, \(M\) is the number of parallel units of execution (cores or processors).  
[PERSON]'s law assumes a fixed problem size which for SDN controllers is not the case since the load a controller receives is related to network load that changes along the time. For this type of problems, where the size of parallel fraction grows as more resources are available, [PERSON]'s law may be applied [25]. It states that larger problems would benefit from more processors if the parallel fraction of those problems also grow accordingly, leading to a more optimistic speedup on parallel systems; this has been proven true by the emergency and prevalence of multicore processors on the market.  
On multicore hardware, it is possible to take advantage of parallelism to decrease processing time, i.e., deliver faster results when compared to single-core processors. However, it is also possible to reduce energy consumption by reducing frequency of operation of each core such that, when compared to a single core (with higher frequency), the results are delivered in the same time [26]. For the SDN controller, this is equivalent to comparing a faster (higher frequency of operation) single-core controller and a slower (lower frequency of operation) multicore controller; both could handle packets keeping latency and throughput nearly the same, but saving energy on lowering frequencies. This solution to improve energy efficiency is especially useful on SDN controllers because the deadlines for delivering results (packet handling or throughput) do not need to be as fast as possible but may lie within a range of pre-defined values, commonly described on a Service Level Agreement (SLA).
# Dose Estimation by Geant4-Based Simulations for Cone-Beam CT Applications: A Systematic Review
## 2 Methodology
### Inclusion and Exclusion Criteria  
Research studies based on publications estimating radiation dose using Geant4 in different CBCT applications published in international peer-reviewed journals were included. Reviews, technical notes, conference proceedings, reports, and articles were considered.  
The exclusion criteria were:  
* not medical use;
* papers that do not focus on CBCT;
* studies that do not focus on humans; and
* papers that do not estimate the dose using Geant4-based toolkits.
# Small Satellite Tools for High-Resolution Infrared Fire Monitoring
## 4 The BIROS Pre-Processing Unit  
The FireBIRD concept also includes testing of on-board fire detection and transmission of these compact \"fire messages\" via a commercially proven orbital communications system to potential users of such information. An \"OrbCom Modem\", one of the secondary BIROS payloads, was used for this purpose.  
To obtain these \"fire messages\", the raw data from the MIR, TIR, NIR, and VIS (red) bands of the multi-spectral camera system have to be\" computed\" on board of BIROS in a data pre-processing unit (PPU). However, the tasks of the PPU go well beyond this on-board generation of \"fire messages\", because it is also used, for all BIROS payloads:  
* to receive commands and software uploads transmitted from the FireBIRD ground station for telemetry and command (T/C) to the satellite during its overpass;
* to supervise the payload status and functioning based on the collection of house keeping (HK) data;
* to store all the generated scientific and HK data; and
* to transfer these data to the BIROS transmitters during the satellite's overpass over the FireBIRD ground station.