esa-sceva/satcom-chunk-collection huggingface dataset:
# Experimentation Environments for Data Center Routing Protocols: A Comprehensive Review
## 3 Experimentation Environments
### Kathard  
Kathara [37; 38] is a network emulation system that accurately reproduces the behavior of a real system. It leverages upon Docker containers [39] to implement devices, allowing Kathara to run on mainstream Operating Systems such as Windows, Linux, and macOS. Moreover, using containers to emulate devices represents a lightweight alternative to standard virtualization solutions, which usually introduce costly overhead.  
With Docker containers, the management of different filesystem images is simplified, allowing devices to use different images in the same network scenario.  
This flexibility enables the network nodes to have different behaviors. For example, a node with the standard routing protocols provided by well-known network routing suites such as Quagga [40] or its industry-level branch Free Range Routing (FRR) [34; 41] may be accommodated, just by selecting the appropriate Docker image. Moreover, a node can be provided with a custom Docker image with all the routing protocols and network tools required. In addition, Kathara offers a set of pre-built images that can be used to run several services such as standard routing protocols, control-plane programmable network nodes, data-plane programmable switches, among others, which can be accessed at [42].  
The orchestration of emulations on Kathara is completely based on configurations. It integrates tools that automatize these configurations but does not offer any built-in programmability for this task.
## 3 Triple Passband Filter Architecture  
Figure 3a shows the overall layout of the triband bandpass filter, and Figure 3b shows its equivalent circuit model, which consists of a step impedance resonator with one end open-circuited and the other short-circuited. This type of configuration is also called \(\lambda\)/4 SIR with low and high impedance sections. The low impedance section \(Z_{1}\) is attached with the 50-ohm characteristic impedance input/output of the filter, and the high impedance section \(Z_{2}\) is parallel coupled with metallic via at the lower end for size compactness. Moreover, the low impedance is also coupled to the embedded L-shaped \(\lambda\)/2 uniform structure. The first two passbands at 1.57 GHz and 3.57 GHz are obtained due to the coupled \(\lambda\)/4 SIR for GPS and WiMAX wireless applications, and the third passband at 4.23 GHz is obtained due to the embedded coupled L-shaped structure for satellite communication. All the resonators are folded to introduce coupling and sharpness in the filter by exciting multiple transmission zeros between the passbands. There is a total of eight T.Z.s excited at different locations, i.e., 1.22, 1.42, 1.98, 3.18, 3.82, 3.98, 4.38, and 4.53 GHz. After simulating the proposed filter in HFSS software, it is fabricated on substrate RO-4350 using milling machine LPKF S63 ProtoLaser and tested on ZNB-20 VNA. The overall dimensions and substrate properties are listed in Table 1.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \(\mathbf{T_{1}}\) & \(\mathbf{17.61}\) & \(\mathbf{T_{2}}\) & \(\mathbf{20.88}\) & \(\mathbf{L_{1}}\) & \(\mathbf{20}\) \\ \hline \(\mathrm{L_{2}}\) & 9.5 & W & 1.35 & W\({}_{1}\) & 0.8 \\ \hline \(\mathrm{W_{2}}\) & 0.5 & \(\mathrm{L_{3}}\) & 6.6 & \
## 4 Results and Discussions
On the other hand, the FOM value of a homogeneously aligned LC phase shifter [11] is approximately 2.19 degree-dB\({}^{-1}\cdot\)V\({}^{-1}\). This is simply because of the very low transmittance of approximately 3% at 1 THz, even though the driving voltage is as low as 2.7 V\({}_{\text{RMS}}\). In addition, it is much easier to form a micron-level thickness of PEDOT: PSS layer on a large area substrate using the spin coating method. The fabrication cost, complexity, and transmittance loss can be reduced by employing the pristine form of PEDOT: PSS as a TCE film in comparison with doping with either DMSO or EG. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Phase Shifter** & \(\Delta\Phi_{\text{max}}\) & **Transmittance** & **L\({}_{\text{dB}}\)** & **V\({}_{\text{DFOM}}\)** & **FOM** \\  & **(degree)** & **(\%)** & **(dB)** & **(RMS)** & **(degree-dB\({}^{-1}\cdot\)V\({}^{-1}\))** \\ \hline MDA-250 (this work) & 38 & 73 & 1.36 & 5.2 & 5.37 \\ MDA-550 (this work) & 75 & 55 & 2.59 & 10.4 & 2.78 \\ Mixture-250 (this work) & 73 & 62 & 2.07 & 5.3 & 6.65 \\ DMSO-doped PEDOT: PSS [10] & 90 & 3 & 15.2 & 2.7 & 2.19 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Calculated figure of merit (FOM) for all types of PEDOT: PSS-based THz phase shifters at 1 THz. Dynamic responses of the devices are also of interest. The voltage-on and voltage-off times were measured by subjecting the device to a step-function-like voltage signal. The 20% to 80% rise time and fall time for an MDA-550 TN-LC phase shifter were found to be around 23 s and 120 s, respectively. The response time of the voltage-off state depended only on the material properties and cell thickness. The device based on Mixture 1825 is potentially advantageous due to its much higher birefringence. Nonetheless, the viscosity of such LCs is higher, namely, 311 [28] versus ~203 cP for MDA [30]. Thus, possible improvements using this approach remain to be seen. Because of the slow responses, the present device is not suitable for applications that require fast modulation. However, the device is appropriate for instrumentation or apparatuses that require, for example, tunable waveplates with fine tuning capabilities.
## 7 Use-Case Analysis
### Knowledge Sharing: Are There Role/Gender Barriers?
To assess the influence of roles, commenters and authors were partitioned in 8 categories, depending upon their gender \(g\) and role \(r\) (\(=\)\(\textit{executive},\textit{employees}\)). In this experiment, we tested all possible combinations of commenter and author. Each individual may belong to any of 8 categories: _female, male, executive, employee, female-executive, female-employee, male-executive, and male-employee_, for a total of \(8\times 8\) significance tests. For example,  
Figure 7: Gender homophily of commenters compared with prior probability of female and male commenters along the three years. we test if female executives have a strong tendency to comment threads authored by a female, or by an executive, or by a female executive. As before, the case for neutrality (see Hypothesis 1) is one in which the estimated conditional probability is not statistically different from the prior probability of a _C-commenter_, where \(C\) is one of the 8 categories. We here summarize only the relevant outcomes. In agreement with Reference [9], we noted that roles do influence social relations, but this is definitively more true for males. The main result emerging from this experiment is that males are influenced primarily by roles and then by gender: in fact, males executives mostly answer to male executives (Hypothesis 1 is accepted with \(p=1.555\times 10^{-9}\)) and male employees mostly answer to male employees (\(p=0.0004347\)), while they are anti-homophilous towards males with a different role and anti-heterophilous towards females, in general. Women instead are influenced primarily by gender since they show neutrality (see Hypothesis 1) towards females in different roles. Furthermore, female executives are more \"democratic\" than male executives, since they show neutrality towards employees (\(p=0.381\)), while male executives are strongly anti-heterophilous towards employees (\(p=4.006\times 10^{-8}\)).
## 9 Estimation of Mechanical Parameters Based on RQD Value
### Estimation of Compressive Strength Based on RQD Value
#### 9.2.2 Non-Linear Equations  
[PERSON] [58] assumed that the relation between the compressive strength of the rock mass and the intact rock (\(\sigma_{\rm cm}/\sigma_{\rm c}\)) and the deformation modulus of rock mass and intact rock (Em/Er) could be related approximately by the following equation [59; 60; 61]:  
\[\frac{\sigma_{cm}}{\sigma_{c}}=\left(\frac{Em}{Er}\right)^{q}=\left(\alpha_{ E}\right)^{q} \tag{43}\]  
The power q ranges from 0.5 to 1.0, with an average of 0.7 and most often falling between 0.61 and 0.74 [58]. [PERSON] believed that there was an approximate relationship between the unconfined compressive strength of the intact rock and the unconfined compressive strength of the rock mass.  
\[\frac{\sigma_{cm}}{\sigma_{c}}=\left(\alpha_{E}\right)^{0.7} \tag{44}\]  
Combining Equations (36), (43), and (44), [PERSON] [58] suggested the following empirical relation:  
\[\sigma_{\rm cm}/\sigma_{\rm c}=10^{0.013\rm RQD-1.34} \tag{45}\]  
The suggested equations by [PERSON] [57] and the AASHTO [47] are compared with the non-linear equation of [PERSON] [58] in Figure 24.  
Similarly, the deformation modulus of the rock mass can be transformed to the following form:  
\[\sigma_{\rm cm}/\sigma_{\rm c}=0.0457\rm{e}^{0.0299 RQD} \tag{46}\]
# Methods for Determining Polycyclic Aromatic Hydrocarbons (PAHs) in Milk: A Review
###### Abstract
Detecting these compounds--both qualitatively and quantitatively--is challenging, requiring the development of more efficient and rapid methods [25]. Enhanced detection capabilities would enable more precise control of PAH levels throughout the production process. Milk is a complex matrix. Hydrophobic PAHs tend to accumulate in fats, primarily 'encased' within casein micelle membranes, making their extraction for analysis more complicated than in many other food matrices. Analytical methods for identifying PAHs, which typically rely on chromatography, demand rigorous sample clean-up to prevent the adulteration of results. The purification step may involve the more traditional liquid-liquid extraction or the more novel solid-phase or HeadSpace extraction. The most commonly used methods are liquid chromatograph with a fluorescence detector and gas chromatograph with a mass spectrometer [26]. This review will compile information about PAHs in milk and dairy products, their health implications, and relevant legislation. It focuses specifically on the presence of PAHs in milk and dairy products, chromatographic methods for extracting and isolating them, and instrumental techniques for their detection and quantification. In the first section, \"Polycyclic Aromatic Hydrocarbons\", we provide a concise overview of PAHs, characterizing these molecules in physicochemical terms. We then discuss their impact on human health and pathways of food contamination. Next, we address legislative issues concerning PAHs in the context of milk. The second part of the review, \"PAHs Detection Methods in Milk and Dairy Products\", focuses on extraction techniques, followed by chromatographic methods used for analyzing PAHs in milk.
# Current-Signal-Based Fault Diagnosis of Railway Point Machines Using Machine Learning
## 3 Results  
### Results of Current-Data Measurements  
The electric current of the RPM equipment was measured under three conditions: (1) normal operating conditions, (2) warning conditions with old grease, and (3) failure conditions when moving to the end position. Figure 6 shows the results of the current datasets obtained from each RPM equipment classification condition. The number of data samples obtained for conditions 1, 2, and 3 was 128, 145, and 153. The number of data samples slightly followed the balanced dataset scenario, which has several advantages like improving model performance, preventing bias, and enhancing generalization [29]. The collected data are illustrated in Figure 6. The time-series current signals of condition 1 and condition 2 were identical. The only difference in the visual reading was that condition 1 took less time to advance the RPM toward the end position compared to condition 2.  
To gain a better understanding of the obtained current data, we compared the three types of current data for conditions 1, 2, and 3 of the RPM equipment using average data, as shown in Figure 7. There was a big difference between the three types of data in terms of data patterns and lengths (data dimension). Therefore, the use of time-series feature extraction is useful for dimensional reduction [30]. Dimensional reduction makes the dataset used as input for ML modeling have the same data dimensions, meaning the same number of variables or features.  
Figure 5: Machine learning modeling process. Green arrows indicate the validation process, whereas white arrows indicate the final training and testing processes on the blind test set [28].
## 3 Results
### Ventilation Rate
#### 3.1.3 Assessment of the Perfect Mix Hypothesis  
The perfect mix hypothesis was evaluated by comparing daily CO\({}_{2}\) measurements at the front (main measurement point) and at the back (potential stagnation zone) of the classroom. A typical day is shown in Figure 8, with a slower CO\({}_{2}\) concentration dynamic at the back of the classroom.  
Figure 6: Monthly ACH results. (**a**) shows mean ACH results computed from all the box model adjustments. (**b**) shows results computed from daily time-weighted ACH averages. Results for both occupation shifts are included, as well as the minimum ventilation rate indicated by the ASHRAE 62.1 standard and WHO’s pandemic recommendation.  
Figure 7: Results obtained from the weather station on the roof of the school. (**a**) shows monthly temperature boxplots. (**b**) shows monthly relative humidity boxplots.  
The CO\({}_{2}\) concentration difference between the front and the back of the classroom was computed for all available simultaneous measurements. For about two thirds of the measurements the concentration at the back of the classroom was higher than at the front. The absolute differences third quartile was of 81 ppm, with a median of 39 ppm. These differences were deemed acceptable given the monitor's accuracy [37]. Nevertheless, when expressing the results in the percentage form indicated by the ASTM D6249 and E741 standards, it is evident that the classroom does not comply with the perfect mix hypothesis in terms of these standards (less than 10%). These results are summarised in Figure 9.  
Taking into account the monitors' accuracy (\(\pm\)(20 ppm + 5%)), the perfect mix hypothesis is considered acceptable. This is also supported by the ventilation rate results of the controlled tests, which were relatively insensible to monitor position. Nonetheless, the classroom does not comply with ASTM standards' requirement for perfect mix. These could be too exigent for the application at hand, particularly considering the relatively low concentrations measured in the classroom, which rarely surpass 1500 ppm. Further evaluation of the perfect mix hypothesis might be needed. Its applicability should not only be analysed in terms of CO\({}_{2}\) concentrations, but also considering its impact on ventilation rate and risk results.
## 2 Materials and Methods
### Evaluation of Spatial Representativeness  
The spatial representativeness was evaluated based on the comparisons of the EVI and FTVT between the footprint source areas and the series of target areas. For the vegetation type representativeness, the FTVT of the EC measurements was calculated as the fraction of the target vegetation type in the land cover map and was further weighted by monthly footprint climatology to evaluate the degree to which the EC measurements are related to the target vegetation. As the target vegetation type in this study is a coniferous plantation, a higher FTVT means better representativeness in relation to the coniferous plantation. Then, the seasonal variation and spatial distribution along different wind directions of FTVT were analyzed.  
For vegetation indices representativeness, the EVI sensor location bias (\(EVI_{bias}\)) was calculated to evaluate to what spatial scale the EC flux measurements can be upscaled. The \(EVI_{bias}\) was defined as to what extent the footprint-weighted EVI in the source area matches with the averaged EVI in a certain spatial scope of interest:  
\[EVI_{bias}=\frac{EVI_{ footprint}-EVI_{target}}{EVI_{target}}\times 100\% \tag{4}\]  
where \(EVI_{ footprint}\) is the footprint-weighted EVI of the source area. \(EVI_{target}\) is calculated from the average value of the EVI over a target-size circle.
# Mitigating the Impact of Noise on Nonlinear Frequency Division Multiplexing +
## 6 System Performance
The bExp-modulation performs better than NIS with standard FNFT, I-FNFT, and DF-FNFT detections, but worse than NIS with DF-BNFT detection. Unfortunately, the DF-BNFT detection strategy is specifically tailored for the NIS modulation and cannot be directly applied to the bExp-modulation, for which the causality property in Section 4.1 does not hold. Eventually, the power-tilt strategy proposed in Section 5.3 can further improve the performance of NIS with DF-BNFT, with a gain of 2.7 dB at rate efficiency \(\eta=34\%\), and an overall gain of almost 8.3 dB over the standard NFDM system (NIS+FNFT). Remarkably, the NFDM system with DF-BNFT detection and NIS modulation with power tilt outperforms the linearly-modulated system with EDC for rate efficiency less than approximately 30%. However, even the performance of this system--the best among all the NFDM systems considered in this work--eventually decays for higher rate efficiencies, becoming non-competitive with a conventional linearly modulated system.
# Multiple-Parallel Morphological Anti-Aliasing Algorithm Implemented in FPGA
## 4 Results and Comparisons
### Anti-Aliasing Performance  
In this paper, we choose a black-and-white image with a width and height of 100 as the input of our algorithm. It contains a triangle with three vertices: (40, 10), (10, 40), and (90, 25). The anti-aliasing performances achieved by our algorithm and other algorithms are shown in Figure 13.  
It can be seen from Figure 13 that there are a large number of obvious jagged patterns at the triangle edge of the original image.  
Though MSAA4x can achieve a very good anti-aliasing performance at the boundaries of the image [13, 23], it can only achieve very limited color gradients, because MSAA4x corrects the color by calculating the coverage of four sampling points in each pixel, so it can only achieve four color gradients: \(\times\)0, \(\times\)0.25, \(\times\)0.5, and \(\times\)0.75. Though we can increase the color gradients of MSAA by increasing the number of sampling points in each pixel (such as increasing the number of sampling points in each pixel to eight to achieve eight color gradients: \(\times\)0, \(\times\)0.125, \(\times\)0.25, \(\times\)0.375, \(\times\)0.5, \(\times\)0.625, \(\times\)0.75, and \(\times\)0.875), it also increases the hardware complexity when calculating the coverage of each sampling point and the memory size used to store the color of each sampling point.  
Although MLAA seems to achieve more color gradients and a better anti-aliasing performance than MSAA4x, the difference between the color gradients is too large, and it achieves a very poor anti-aliasing performance for aliasing patterns of length 1 [22, 25].  
Figure 12: Flow diagram of the module Calculate_Aliasing_Color.  
Our algorithm improves the two disadvantages of MLAA by optimizing the weight coefficient of color blending between pixels. It not only reduces the difference between the color gradients but also improves the processing performance of the aliasing patterns with a length of 1. It achieves a better anti-aliasing performance than MSAA4x and MLAA.
# How to Create and 3D Print a Model of the Skull and Orbit for Craniofacial Surgeons
## 3 Steps
### Preparing the Model for Printing and Sending It to the Printer (Slicing) Using Ultimaker Cura  
Video 3 [[[https://youtu.be/50n2](https://youtu.be/50n2) agH7_2M]([https://youtu.be/50n2](https://youtu.be/50n2) agH7_2M)] demonstrates the slicing and printing process using Ultimaker Cura. To send a 3D model to a filament or resin printer, it needs to be opened in a \"slicer\" software (not to be confused with 3D Slicer). This software is usually supplied by the printer manufacturer. This software automatically adds supports  
Figure 1: The three main steps and their associated software required to produce an in-house 3-dimensional printed craniofacial model: 3D Slicer v4.11 (Slicer community, [[http://www.slicer.org](http://www.slicer.org)]([http://www.slicer.org](http://www.slicer.org))). Autodesk MeshMixer v3.5.474 (Autodesk, San Rafael, CA, USA). Slicer for many filament printers: Ultimaker Cura v4.13.1 (Utrecht, Utrecht, The Netherlands).  
for printing, configures printing parameters, and sends the necessary instructions to the printer to print the model.  
If the model is required in the operating room, it can be wrapped in a sterile plastic bag. Some resin printing materials are certified for sterilization and do not require sterile bag wrapping for usage in the operating field.  
The two most common types of 3D printers are filament and resin printers. Filament printers come at a wide range of prices and work by depositing thin layers of plastic in order to form a shape. Generally speaking, the technology used for filament printers is more mature. A variety of plastic filaments exist to create models of different colors, weight, texture, and biomechanical properties. PLA filaments have been shown to the be adequate, reliable, and inexpensive for use in CMF model printing [1,2]. One can expect a relatively high printing reliability [1] with automatically defined settings on prosumer grade printers. An adequate printer usually costs between USD 3000 and 10,000. Although cheaper printers exist, the considerable amount of time required for the assembly and manual calibration of the printer, to find by trial-and-error optimal printing parameters, as well as their high print failure rate make them less than ideal for busy clinicians starting with 3D printing.  
At this time, resin printing also appears to be a very promising technology as it often allows for faster printing of complex CMF models. However, resin printing is still a very messy process. It involves manipulating liquid resin, cleaning models with alcohol, and curing the model with UV light. Sometimes, models also need to be \"cooked\" at a specific temperature to fully cure. This takes a considerable amount of time for a clinician, as well as a large working space with adequate ventilation and protective equipment.