esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 The Active Vibration Isolation System  
### System Layout  
The tugboat, which was the platform for the active control system, is shown in Figure 1a. It is equipped with two six-cylinder, four-stroke 6135 diesel engines, at the stern. Each engine drives a generator which delivers 110 kW at 220 Volts alternating current. The mass of each diesel generator set is approximately 2000 kg. Each engine is attached to the hull by six BE-400 resilient isolators. The active control system was fitted to one of the generator sets to determine its potential in a practical marine environment. A photograph of the arrangement can be seen in Figure 1b. As there is limited space in the generator room, it was decided to use bespoke electrodynamic inertial shakers as the control actuators. Six inertial actuators were located as close as possible to each isolator, with each actuator being bolted to the lower frame of the generator set, above an isolator, via a very stiff steel plate. Three of the actuators fitted to one side of the engine are marked **A** in Figure 1b. Also shown in the figure is the optical tachometer to provide a reference signal for the active control system, which is marked **B**, the half-order generator (which is discussed later), which is marked **C**, and the forced ventilation system to cool the actuators, which is marked **D**. Six acceleration sensors (not shown in the figure) were placed on the bolt heads of the isolators to act as error sensors for the control system. These positions were chosen as they were as close as possible to the isolators through which the vibration is transmitted to the hull. To monitor the effectiveness of the vibration isolation system (both active and passive), six accelerometers were attached to the structure beneath each isolator. To give an indication of the radiated sound in the water, a hydrophone was suspended in the water near the boat.  
A schematic diagram and photograph of an inertial actuator can be seen in Figure 2. It is an EET-020 type inertial actuator made by Suzhou Dongling Tech. Each actuator has a diameter of 140 mm and a length of 224 mm. They each have a 6.5 kg inertial mass, and can generate 130 N force at their natural frequency of 10 Hz with about 10 W electrical power.  
Figure 1: Photographs of: (**a**) The tugboat, (**b**)Housing the generator whose vibrations were controlled using the inertial actuators marked A. The tachometer, marked B provided the reference signal for the active control system. The half order generator, marked C. The forced ventilation system, marked D to cool the actuators.
## 3 Numerical Model of the Data Center
### Solution Convergence  
The convergence of the solution is obtained through the relaxation of the variables, decreasing the abrupt variations during the iteration. It is considered that a solution converges when all the residuals reach the stopping criterion, \(\lambda=1\times 10^{-4}\) for all variables, except for the energy variable that assumes a stop criterion of \(\lambda=1\times 10^{-6}\). In order for a solution to be considered convergent. there are several aspects to consider, such as refinement of the computational mesh, established numerical methods, relaxation factors and the number of imposed iterations. There are cases where it is not possible to achieve the convergence established by the stopping criterion, because the residuals stabilize before this criterion, thus assuming that the solution obtained is the possible convergence for the numerical  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Magnitude**} & \multicolumn{3}{c}{**Area**} \\ \cline{2-4}  & **All Air Vents** & **Extraction** & **Smaller Extraction** \\ \hline Turbulence intensity & \(I_{t}=5\%\) & \(I_{t}=5\%\) & \(I_{t}=5\%\) \\ Hydraulic diameter & \(D_{h,adm}=0\) & \(D_{h,adm}=1\) & \(D_{h,adm}=0.736\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Boundary conditions imposed in the air vent and extraction areas.  
method under analysis. The relaxation factors have no influence on the numerical solution' they only change the time of the iterative process. Table 3 presents the various relaxation factors applied to the numerical methods, following the data provided in [46; 47; 48; 49].  
The iterative process of the various numerical models was defined for 1500 iterations, since the solution would not reach the stopping criteria, only if it could reach the possible convergence. The authors ran few scenarios with 7500 iterations, and no divergence was verified. However, due to the reason that these simulations took a high amount of time to compute, the authors decided to run the simulation up to 1500 iterations, since the variable residuals accomplished the possible convergence. Thus, this number of iterations was established to reduce the processing time of the simulation. Each case took about 14 h to complete all the iterations on a work station with two 3.47 GHz processors with 16 cores and with a memory of 48 GB of Random-access memory (RAM).
## 2 Related Works  
### Anchor-Free Methods for Remote Sensing  
Traditional anchor-based object detection methods, which rely on predefined anchor boxes, have inherent limitations, including imbalanced sampling of positive and negative samples and significant computational complexity. To overcome those drawbacks, anchor-free methods have been proposed, eliminating predefined anchors and providing more flexibility and efficiency. Consequently, anchor-free methods have gained increasing attention, with numerous innovative approaches emerging in recent years. CornerNet [16], an early anchor-free method, detects objects by regressing their top-left and bottom-right corners, subsequently pairing them using a distance-based metric. CenterNet [17] improves upon this by introducing additional predictions of object center points, enhancing detection accuracy through geometric validation. ExtremeNet [18] further advances this concept by identifying four extreme points (top, bottom, left, right) alongside the center point and employing a center-aware grouping strategy for precise instance assembly. FCOS significantly simplifies detection by performing pixel-level bounding box regression and classification, achieving high accuracy and efficiency. RepPoints [19] employs deformable convolutions to dynamically select representative points, which subsequently form bounding boxes through geometric transformations. CentripetalNet [20] introduces a novel method for matching corners, establishing connections based on their positional relationships, which helps overcome the uncertainties seen in previous methods. CPNDet [21] identifies candidate bounding boxes by connecting keypoints and applies dual classifiers to enhance the accuracy and reduce false positives.
# Condensation Flow of Refrigerants Inside Mini and Microchannels: A Review
## 3 Condensation Flow of Refrigerants Inside Mini/Micro-Channels
### Predictive Models for Condensation Flow of Refrigerants Inside Mini/Microchannel  
In the majority of studies on condensation flow at the micro/mini-scale, R134a has been used as a working fluid. In the literature, many predictive correlations have been developed based on experimental studies for R134a condensation inside micro/minichannels. These studies have mainly focused on condensation mechanisms in single [(69)] and multiple rectangular cross-section microchannels [(73; 69; 70; 71; 72; 73)] and in single [(74; 75; 69)] and multiple microchannels with circular cross-section [(74; 73; 76; 77; 78)]. Similarly, predictive empirical correlations are available in the literature for other pure R11 [(79)], R12 [(70; 80)], R22 [(74; 75; 80)], R32 [(74; 80)], R123 [(79; 80)], R125 [(74)], R236 ea [(72; 74; 81)], R236 fa [(73; 80)], R245 fa [(69; 80)]. Experimental correlations have been produced for various microchannel geometries, hydraulic diameters, mass fluxes, and condensing temperatures for these refrigerants. Additionally, experimental studies have been conducted on the micro/mini-scale condensation mechanisms of R1234 ze [(73; 80)] and R1234 yf [(69)], which are from the HydroFluoroOlefin (HFO) refrigerant family that has been produced and used in recent years. Experimental correlations regarding the condensation flow in microchannels of refrigerant mixtures such as R404A [(80; 82)] and R410A [(72; 74; 75; 81; 83)], which are widely used in the refrigeration  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline  
industry along with pure refrigerants, are also available in the literature. Although a large number of correlations exist to predict two-phase heat transfer in microchannel flows, most of these correlations can be verified for relatively small numbers of working fluids and narrow ranges of geometric and flow parameters [31].
## 1 Introduction
For example, [PERSON] and [PERSON] [21] argue that VAR's integration into football broadcasting has transformed it into a public object of debate, particularly through social media platforms like Twitter, where emotionally invested fans question its legitimacy and narrative impact. They emphasize that VAR disrupts the flow of the game and introduces a layered mediatization that can alter the emotional spontaneity of the viewing experience. Similarly, [PERSON] and [PERSON] [22] frame VAR as a morally transformative innovation that promotes fairness but also acknowledge the tension between the demand for accuracy and football's traditional simplicity and authenticity. They note that critics perceive VAR as undermining the human element and aesthetic continuity that define the cultural identity of football [22; 23]. Despite these advances, many existing studies examining the impact of VAR are constrained by short observational periods--typically limited to a single season--which limits the ability to identify long-term patterns or behavioral adaptations associated with the technology. Moreover, there is a lack of comprehensive longitudinal research addressing how referee decisions and match dynamics evolve over multiple seasons following the implementation of VAR. These gaps hinder our understanding of whether the observed short-term effects are sustained or undergo modulation as referees, players, and teams become more accustomed to the system [18]. It is also unclear whether the initial trends observed post-implementation are sustained, intensified, or mitigated over time as referees, players, and teams adapt to the system. This research focused on the Turkish Super League to examine the long-term effects of VAR. There were several key reasons for this choice: (1) VAR was introduced relatively early (in the 2018-2019 season) compared to many other leagues, which allowed for an analysis over multiple seasons; and (2) the league is highly competitive and features a diverse mix of domestic and foreign players, making it ideal for exploring a wide range of tactical and behavioral dynamics. The aims of the present study were two-fold: (i) to compare match performance indicators such as yellow cards, penalties, fouls, and offsides between seasons with and without the implementation of VAR and (ii) to analyze how the implementation of VAR affects the same performance indicators in each following season. By examining multi-season data across a top-tier league, this research seeks to provide a deeper understanding of VAR's long-term implications for game conduct, regulatory consistency, and the evolution of match behavior in professional football. The hypotheses of this study are as follows:  
**H1:**: _The implementation of VAR leads to a decrease in the number of goals, yellow cards, fouls, and offsides, and an increase in the number of penalties and red cards._
**H2:**: _VAR continues to have a statistically significant impact on these match performance indicators in the seasons following its implementation._
# Influence of Hyperparameters in Deep Learning Models for Coffee Rust Detection
## 4 Results
### Evaluation by the Learning Rate (LR)  
In association with the optimizer, the learning rate affects the performance of the classifier. Then it is necessary to know which LR value for each of the optimizers gives the highest F1-score results. Furthermore, this analysis must be performed for each backbone, grouping the models by the triplet [backbone, optimizer, and learning rate], i.e., by 12 models for this study.  
As an example, Figure 6 shows the consolidated results for the VGG19 backbone, with the three optimizers, and for each of the learning rates. In each case, the box plot shows the average of the 48 cases, with their first and third quartiles, as well as the outliers. For the Nadam optimizer, when LR = \(1\times 10^{-6}\) (the lowest of the LRs selected in this study), the F1-score obtained with the 48 models has less dispersion among them compared with the results for LR = \(1\times 10^{-4}\) and LR = \(1\times 10^{-5}\). However, the highest F1-score for this backbone and optimizer is obtained for LR = \(1\times 10^{-5}\) (intermediate value of the LRs in this study). For the SGD optimizer, the results are the opposite of Nadam. In this case, the worst performance is obtained for the lowest LR (i.e., LR = \(1\times 10^{-6}\)), while the best results are obtained for the highest LR (i.e., LR = \(1\times 10^{-4}\)). Finally, for RMSprop, the greatest dispersion is obtained in the models that use the intermediate LR value (i.e., LR = \(1\times 10^{-5}\)), but at the same time, it is with this value that the best F1-score is obtained.  
Figure 6: Evaluation results for VGG19 model by learning rate and Optimizer, in terms of F1-score.  
In the second part of the analysis of this hyperparameter, the results are grouped by LR value for each backbone, regardless of the optimizer used. The purpose is to see if one of the three LR values in the study generally gives better results than its competitors. The result is shown in Figure 7, where each bar represents 36 models.  
According to the results obtained, for six of the eight backbones in this study, the average results are higher when using LR = \(1\times 10^{-4}\) (the higher of this study), with significant differences for some backbones. For example, for InceptionResNetV2, F1-score = 77.2% when LR = \(1\times 10^{-4}\) and drops to F1-score = 57.9% when LR = \(1\times 10^{-6}\). Another point to consider in Figure 7 is the slight increase of the F1-score value, in relation to the value of the learning rate for six architectures except ResNet50, which presents a decreasing trend, and for Xception architecture, where the highest value was obtained for the intermediate value (LR = \(1\times 10^{-5}\)).  
In summary, this hyperparameter has a moderate impact on model performance (with variations of up to 20% among them).
# An Overview of Material Extrusion Troubleshooting
## 2 General Strategies to Achieve High Quality Parts
### Additional Structures  
A key factor that can help improve the print quality of ME parts is to implement features such as raft, brim and skirt (Figure 4). The use of rafts and brims help to stabilize the printed part with a small surface area or footprint to improve adhesion and to reduce warping and delamination. A raft is made up of a predetermined layer of material with an adjustable infill percentage. It is first printed with a specific layer depth and with an optimal separation distance between the sitting model to aid part removal when the object cools. It can also be used to compensate for small inaccuracies in the build platform or for warped bed surfaces, scratches or dents [55]. The disadvantage of using a raft is that the model may be difficult to remove if the settings are not properly chosen. The lower layers may also have a different finishing result as compared to parts produced from printing directly onto the platform. A raft also consumes slightly more material and thus generates additional waste. As a result,  
Figure 3: Types of infill pattern from the Slic3r PE software.  
the brim feature is often preferred [55]. A skirt consists of several layers of an outline that is offset away from the model. It is mainly used to ready or prime the extruder, and to also enable a final check for bed levelling issues and the quality of the filament being extruded before the print process commences. A brim feature is similar to a skirt, but it has a connected offset that is outward from the perimeter of the model. The brim can be used to prime the extruder as well as to enlarge the bottom surface area and hold down the edges of the model to improve the build platform adhesion. In the case of warping, the corners of the model are less likely to curl up or deform because the brim is attached to it. This is particularly useful when using materials that have a large shrinkage factor such as ABS or with semi crystalline polymers (e.g., PA12). The brim width is determined by the number of skirts that are built around the model [56].  
An example of how a brim can be used to keep a part attached to the build platform during the printing of PA12 on a glass surface is shown in Figure 5. PA12 is a semi crystalline polymer which shrinks significantly when it cools down. The shrinkage is strong enough to cause the part detaching from the build platform when only using a skirt (Figure 5A). If a brim is used, the part can be printed more successfully (Figure 5B).
# Comparison of Basic Notch Filters for Semiconductor Optical Amplifier Pattern Effect Mitigation
## 6 ODI vs. MRR Qualitative Comparison
### Multi-wavelength operation:  
Both filters can mitigate the SOA pattern effect induced on wavelength-division-multiplexed (WDM) data inputs, for all channels simultaneously [87]. The specific potential is enabled by the periodic spectral response of these filters and can be exploited provided that the center wavelengths of the different input data are spaced apart by integer multiples of the FSR [21; 58]. In this case, the amplitude equalization can be realized by means of a single such optical notch filter, which is attractive from an economical point of view, since the cost of using the device can be shared among the multiple served data inputs and remain affordable with the increase of channel count [62].  
Furthermore, the two types of filters can be discriminated, with regard to their efficiency, to enhance the amplitude uniformity of multiple SOA amplified data-carrying channels, by the amount of the achieved FSR, which is compared against the wavelength separation between adjacent channels in real deployed WDM communication systems. Thus, for the ODI, the FSR that corresponds to a relative delay of \(\sim\)10 ps between its two arms is almost the standardized dense WDM grid spacing of 0.8 nm at around a 1550-nm carrier wavelength. On the contrary, the MRR \(\upmu\)m radial size results in an FSR that is an order of magnitude higher and accordingly more suitable for coarse WDM purposes. Another option is to cascade several MRRs of different radii along the same waveguide bus [88]. This allows one to accommodate more data wavelengths impaired by the SOA pattern effect and with a more dense spacing than for a single MRR [33], at the expense of a larger footprint and a more complex configuration. On the other hand, in WDM systems of some given interchannel spacing and spectral width, a single MRR can support numerous channels in a much more flexible manner than a single ODI. In fact, this capability critically depends on one filter's finesse, which, as previously mentioned, can be flexibly adjusted for the MRR, whereas it is very low and fixed for the ODI. Furthermore, the sharper spectral selectivity of the MRR allows the latter to better handle variations in the channel positions and compensate for possible misalignments against notches. This further allows one to use uncooled, i.e., non-temperature-controlled, single-mode lasers, which is an attractive feature from a cost and operational perspective [8]. In contrast, these functional requirements can be met with the ODI only if multiple ODI stages are concatenated [52], which, along with the aforementioned inevitable compromises associated with the employment of chains of filters, comes at the additional cost of extra losses.
# Construction of Nighttime Cloud Layer Height and Classification of Cloud Types
## 4 Results and Discussion
### Nighttime Cloud Classification  
The scene construction algorithm provides a possible method to identify cloud type in the swath near the track. Figure 9 shows the fractional distribution of the eight types of clouds identified by the NSRM method for the same tested dataset as in Section 4.1. The figure presents the distribution in terms of all cloud layers (all), the uppermost cloud layer (top), and the single layers (single). The most frequently occurring cloud type was Ci near the equatorial region and low level Sc from middle to higher latitudes. Cu clouds also contributed a significant proportion.  
To test the reliability of the cloud distribution calculated from the RXS-expand results, again we used the reconstruction method. Figure 10 compares the distribution of each cloud type summarized from the successfully reconstructed profiles with dead zone set to 100 km with the original profiles on a one-to one base. In this case, the pixels that failed to find matching donors were not taken into account. The figure illustrates that most clouds--both high and low, optically thin and thick ones--were well matched in numbers by the reconstructed profiles. There was a major difference between the original and the reconstructed DC clouds, which could partly be explained by the scarcity of this cloud type causing it to be more difficult to find an accurate match.  
Figure 8: Comparison of CTH inferred from the retrieved cross section (RXS)-expand results up to 400 km on both sides of the ground track and collocated MODIS retrievals. The gray box indicates a mismatch caused by attributing a donor with high thin cloud layer on top of the low cloud layers detected by MODIS.