esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Various Sizes of Mountain Bike Tires
#### 3.1.1 Cross-Sectional Profiles  
Figure 4 shows side-by-side comparisons of measured, inflated tire profiles for four different knobby mountain bike tire sizes. All the tires were from the same manufacturer, and the three tires on the left are all the same tire model only in different sizes. The tire on the right is a different model. As described previously, as the tread pattern often contains alternating sets of knobs at regular intervals along the tire's circumference, these profiles overlay each of the individual knob arrangements onto one cross section. This was done to better understand and compare the cross-sectional shapes, that is, the effective toroid radius of each tire, although it does potentially limit the ability to visually decipher spacing between individual knobs, which can be read from the footprints instead.  
The tires shown increase in width from left to right. In terms of inflated outer radii (or diameters), the \(29\times 3\)\" is by far the largest, followed by the \(29\times 2.3\)\", which is very slightly larger than the \(26\times 4\)\" fat bike tire, and finally the \(27.5\times 2.8\)\" plus tire.  
The shape of these mountain bike tires' inflated profiles is obviously influenced by rim width, as can be seen in the curvature of the tires' sidewalls and the angle that the lower sidewalls assume toward their respective bead interfaces. Here, rim width and rim-width-relative-to-tire-width increase from left to right.  
Examining the treaded portion of each profile, intended to interact with the ground as the tire rolls and deforms and as the bicycle cambers and steers, several observations can be made. The \(29\times 2.3\)\" tire profile was rather flat, that is, having a large toroidal radius, with limited drop from the two center rows of knobs to the shoulder knobs. It is also worth noting that the knob widths were relatively small, and the shoulder knobs were almost equal in height and width but with considerable draft (or bracing) down toward the tire sidewall to presumably support the knob. The middle two profiles represent the \"plus\" tires with the \(27.5\times 2.8\)\" on the left and \(29\times 3\)\" on the right. These tires had a similar tread pattern to the \(29\times 2.3\)\", but both knob width and spacing increased as the tire size increased. It is uncertain if this tread scaling is for aesthetic or performance reasons. Notice the large gap between the center rows of knobs and shoulder knobs on the \(29\times 3\)\" tire. Notice also that the angle of the shoulder knob was steeper and that (neglecting deformation) the tire would need to roll farther for the tangent (ground line) to engage the shoulder knobs. The \(26\times 4\)\" fat bike tire on the right had a different tread pattern with seven rows of knobs as opposed to the four of the other tires, and included a center row.
## 3 Results and Discussion
The yields of the products and the degrees of polymerization (DP) of the graft chains were estimated by the precipitate weights and from the integrated ratios of the anomeric signals of the graft chains to the \(\gamma\)-methylene signals (-CH\({}_{2}\)C=O) of the PGA main-chain, respectively, which decreased with increasing the feed ratios of d-glucal to Glc-1-P. The decreases are attributed to less reactivity of the non-native monomer, D-glucal (actually 2 dGlc-1-P), than that of the native monomer, Glc-1-P. We also reported similar tendencies according to the d-glucal/Glc-1-P feed ratios on the thermostable GP-catalyzed modification of the P2D-amylose chains on glycogen and CMC [39, 41]. Powder XRD profiles of the amylose- and 2-deoxyamylose-grafted PGAs (entries 1 and 6 in Table 1) showed the same patterns as those of pure amylose and 2-deoxyamylose (at 2\(\theta\) = 16.9/22.5\({}^{\circ}\) and 16.5/18.8/21.1\({}^{\circ}\), Figure 4a,f, respectively). The XRD profiles of the P2D-amylose-grafted PGAs (runs 2-5) did not observe obvious diffraction peaks, as shown in Figure 4b-e, indicating their amorphous nature, probably owing to the hetero 2 dGlc/Glc unit structure with random sequence, as reported in our previous study on the thermostable GP-catalyzed copolymerization of d-glucal with Glc-1-P [38]. Because the amylose- and P2D-amylose-grafted PGAs (entries 1-5 in Table 1) produced cast films by evaporating their solutions in DMSO, we conducted water contact angle measurement of the resulting films to evaluate their hydrophobicity. Because the 2-deoxyamylose-grafted PGA (entry 6 in Table 1) did not produce a film owing to its highly  
Figure 4: XRD profiles of ((**a**), entry 1 in Table 1) amylose-, ((**b**–**e**), entries 2–5 in Table 1) P2D-amylose-, and ((**f**), entry 6 in Table 1) 2-deoxyamylose-grafted PGAs. crystalline nature, its hydrophobicity was not investigated; the similar phenomena were observed from the 2-deoxyamylase-modified glycogen and CMC [39, 41]. The value of the water contact angle of the amylose-grafted PGA film was 66.1\({}^{\circ}\), suggesting its hydrophilic nature (Figure 5a). While the water contact angle values above 100\({}^{\circ}\) were observed from all the P2D-amylase-grafted PGA films (Figure 5b-e). These results imply that the thermostable GP-catalyzed grafting of P2D-amylase chains achieved the efficient hydrophobization of the strong hydrophilic polypeptide, PGA.
## Appendix A Data Acquisition System Details  
The data acquisition system (DAS) is based on PCI-DAS4020/12 by MCCDAQ, on-board of a desktop computer, and controlled by a LABVIEW script.  
The DAS samples the IF voltage signal (\(S_{0}\) in Figure 3) and the oscillator reference voltage signal (\(S_{1}\) in Figure 3) simultaneously, using two independent 12-bit precision analog-to-digital-converters (ADC), acquiring \(10^{7}\) samples, every 10 s for cloud measurements or 3 s during the drone experiment, with a sample-rate of 20 MHz, and with a range of \(\pm 1\) V.  
The digital output is transferred to random-access-memory (RAM) and then stored into a binary file inside a Hard Disk Drive (HDD). The RAM capacity limits the maximum amount of samples captured at once and the HDD writing speed limits the continuous acquisition of data at higher sampling rates.  
It was noted that, when the acquisition system is used at the maximum sampling rate (20 MHz), noise spikes can appear randomly. These noise spikes can cause a loss of approximately 10% of the estimated profiles.
## 3 Results and Discussion
### Analysis of Volatile Compounds of Embryo Rice During Storage
#### 3.2.4 Correlation Analysis Between Volatile Compounds and Basic Indexes  
Figure 5A shows the correlation analysis of lipid metabolism and oxidation with volatile compounds. The volatile compounds were positively correlated with fatty acids, malondialdehydehehyde, lipase, and FRAP, and negatively correlated with catalase and DPPH, especially aldehydes and acids. In the storage of embryo rice, acids can create an environment conducive to the action of lipase, accelerate the hydrolysis of fat and increase fatty acids, and also participate in the oxidation reaction to increase malondialdehydehehyde. Aldehydes cause fat oxidation, which not only generates more fatty acids and malondialdehydehehyde, but also promotes more lipase synthesis, so acids and aldehydes are positively correlated with the changes in the three. Acids can promote the formation of antioxidant substances to increase the FRAP value. Although aldehydes often promote oxidation, special aldehydes also have antioxidant properties, so they are positively correlated with FRAP. The accumulation of acids and aldehydes may inhibit the activity of CAT, consume free radicals themselves, and reduce DPPH free radicals, so there is a negative correlation with the changes in CAT and DPPH in the storage of embryo rice.  
Figure 5B shows the correlation between texture, taste value, and color and the main volatile compounds analyzed by the Mantel test. A thicker line represents a larger R-value, which indicates a stronger correlation between two matrices. The lighter the color, the smaller the \(p\)-value, indicating a more significant correlation. In general, the compounds that were most correlated with texture, taste value, and color difference in MT analysis were classified as hydrocarbons, ketones, and aldehydes. The influence of volatile compounds on the color difference is strongest, followed by texture and taste value. The main reason for the decline in texture is that with the extension in storage time, the relative content of hydrocarbons decreases, which leads to changes in hardness and viscosity. The reaction of ketones and aldehydes with protein, starch, and other components affected the retention and distribution of water and made the texture of the rice loose. The reaction of hydrocarbons, ketones, and aldehydes with the aroma components in the rice reduces or removes the original aroma substances, reduces the aroma quality, and leads to the decline in taste value. The Browning reaction and oxidation of volatile compounds during storage resulted in a decrease in the color difference of the remaining embryo rice.
## 2 Literature Review
### Review of Empirical Models for Extinction and Backscattering
#### 2.3.1 Empirical Models for Fog  
Fog can occur for a variety of reasons, depending on the geographical conditions. Under continental conditions, fog can occur mostly in spring and autumn, when during clear and windless nights, warm humid air radiates its energy into the upper atmosphere, thereby cooling down and reaching the condensation point of water, causing small water droplets to form in the air. This type of fog is called, quite intuitively, radiation fog. Another mechanism of fog is referred to as advection fog, occurring mostly in coastal areas, where warm, humid air from the sea gets pushed over cold land masses by wind. As the air cools down, the carried water begins to condensate [18].  
Several empirical models have been proposed for fog. [PERSON] et al. [19] proposed expressions for the extinction coefficient \(\alpha\) models based on the visibility \(V\) [m], for both advection and radiation fog mechanisms, as shown in Table 1. However, they also stated that the mechanisms of fog generation are very dynamic and affected by many other factors, so in reality, the actual particle size distributions may vary considerably. A more generic model was proposed by [PERSON] et al. [20], based on improving the earlier model from [PERSON] et al. [21]; we refer to this as the Kim-Kruse model for a visibility range below 500 m, which is especially relevant for automotive lidar applications.  
Figure 3: Comparison of the scattering anisotropy of water droplets and spherical SiO\({}_{2}\) particles.  
Comparative analysis of the three models, summarized in Figure 4, shows that they are very similar. However, the model for radiation yields higher extinction coefficients for a set visibility, and can therefore be considered as a worst-case scenario.  
In relation to the backscattering coefficient \(\beta\), only one source could be identified in the literature, defined by [9], as shown in Table 1.
## 3 Results and Discussion
### SWSS Fabrication: Inverted Pyramid  
For the first demonstration of SWSS from our designed lithography process, we decided to fabricate an inverted pyramid Si surface structure which is well known for its highly effective light trapping when it is built in an optimal subwavelength scale [7]. In this paper, we successfully demonstrated the effectiveness of our lithography process and the results are shown in Figure 6. With our process, we fabricated a \(\sim\)1.1 \(\upmu\)m scale inverted pyramid with \(\sim\)1.57 \(\upmu\)m period in a hexagonal array. (Note: Optically, this is not an optimal SWSS scale and density, but is intended to show the capability to control the size and density of fabricated SWSS.) To provide a stable masking layer on the Si surface for the KOH etching process, Cr metal layer has been chosen since it provides a reasonably robust material nature in both BOE and KOH solutions. In addition, Cr is a relatively cheap material that meets our intention to develop a low cost lithography process in this article. For Cr mask layer deposition, an e-beam evaporation system has been used and deposited a 50 nm thick Cr layer. The sample with Cr layer was, then, dipped in BOE (10:1) solution for SS layer etch. The SS removed Si sample was subsequently immersed in KOH (1%) solution for anisotropic etching of Si inverted  
Figure 5: (**a–f**) Schematic illustration of ultra-violet ozone (UVO) treatment effect on SS removal after RIE. (Green-line indicates deposited Cr layer, and red-line for SiC\({}_{x}\)F\({}_{y}\) passivating layer formed during RIE process, scale-bar = 1 \(\upmu\)m).  
pyramid structure. The KOH solution was prepared after Si saturation and 4% IPA addition to provide moderate Si etching rate and enhanced etching uniformity for fabricated structures. After 2-min KOH etching at 85 \({}^{\circ}\)C, well dimension-defined Si inverted pyramids were produced as shown in Figure 6b-d. In Figure 6a, the diameter of patterned circle was \(\sim\)1.1 \(\upmu\)m, and the lateral dimension of fabricated Si inverted pyramid is also approximately 1.1 \(\upmu\)m, as shown in Figure 6b. Furthermore, we also successfully confirmed that our SWSS fabrication process using a solvent-controlled SS lithography technique can produce highly uniform Si inverted pyramid over a large surface area in a desired dimension and density as shown in Figure 6c-e. In the study from [PERSON] et al., the optimized dimension of SWSS can produce a greatly enhanced absorption close to the Lambertian limit in the visible and near IR regions [7]. Therefore, future dimension and period optimization for SWSS will be able to produce broadband absorption enhancement for thin c-Si solar cell applications.
## 5 Performance Analysis  
We considered several topologies to evaluate the performances of our proposed architectures for caching mechanisms and our main topologies contain 35 nodes. These nodes represent the content providers, routers, end-users, and other proximity devices, and their identification starts from node number zero to thirty-four. In our topological environment, each node has all the capabilities like a CCN-enabled device can do. We enabled all the nodes as a CCN node and they can generate interest packets, they can work as a router, and they can work as a content provider. These nodes are consideredas a \(7\times 5\) matrix in a two-dimensional space. The distance between two nodes is 40 m and a node is connected to all the neighbor nodes. Each row has five nodes and is considered as the one hop of the devices and there are total seven hops in our topologies. The node zero to four are located at a six-hop distance from the end-users. Similarly, the other nodes are located to the other hops consecutively from the end-user devices. Any node of any hop can work as a content provider or consumer or router. Following these characteristics, we had simulated several scenarios where the content providers' and consumers' distance is at about five hops. We will describe the simulated results based on the scenarios that we have used for simulations.  
We simulated and evaluated our proposed mechanisms in the Network Simulator-3 (NS-3) environment. Direct code execution (DCE) [38] was used to apply the CCNx application [38] in the NS3 environment. The CCNx application was used to apply the CCNx functionalities at each device in the simulation environment and the different modules of the CCNx application were used in the C++ environment to apply the clustering concept in the simulated network environment. Table 2 parameters were used during the simulation.
## 4 Discussion
For our method, after the convolution filtering of the H-scan, the R channel is essentially composed of more reflected signals of relatively large scatterers (large layer scattering), and the power Doppler results of which suggest that the R channel contains more tissue vibration information and high-frequency noise. The H-Doppler can directly give power Doppler images with high CNR by the subtraction of R channel data from B channel data. One of the most important findings in this paper is that the loss of small blood flow signals in deep tissue caused by the global SVD filtering can be recovered to a certain extent through BWL-SVD processing. The H-Doppler method based on BWL-SVD filtering not only improves the Doppler image quality but, also, gives more abundant images of fine blood flow with the noise subtraction process block by block. The comparison results of Doppler images with different methods are summarized as Table 2. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Global SVD Filtering**} & \multicolumn{2}{c}{**BWL-SVD Filtering**} \\ \cline{2-5}  & **UCDI** & **H-Doppler** & **UCDI** & **H-Doppler** \\ \hline Low-order SVT & 22 & 22 & 22–31 & 22–31 \\ High-order SVT & 232 & / & 232–264 & / \\ CNR (white) & 15.5 dB & 19.6 dB & 20.1 dB & 23.2 dB \\ CNR (yellow) & 10.1 dB & 16.7 dB & 19.7 dB & 24.7 dB \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Summary of contrast-to-noise-ratio (CNR) measurements between ultrafast compound Doppler imaging (UCDI) and H-Doppler imaging. SVT: singular value thresholding and BWL-SVD: block-wise local-singular value decomposition.
## 4 Fingerprinting-Based Positioning with Spatial Side Information
### Discretized Area Fractions with Tiles
\begin{table}
\begin{tabular}{l l} \hline \hline \multicolumn{1}{c}{**Parameter/Variable**} & **Description** \\ \hline \(N_{SS}\) & Number of sub-tiles per side of the tile \\  & (Number of sensors per side of the 2D sensor array) \\ \(N_{ST}\) & Total number of sub-tiles in a tile \(=N_{SS}\)2 \\  & (Total number of sensors in the 2D sensor array) \\ \(N_{T}\) & Total number of tiles to cover the whole positioning area \\ \(S_{S}\) & Side length of sub-tile \\ \(S_{T}\) & Side length of tile \(=N_{SS}\times S_{S}\) \\  & (Side length of the 2D sensor array) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Parameters and variables for tiles (2D sensor array). sufficient sampling of the positioning area in the sense that the training is carried out at the discrete positions delimited by the fixed tiles, whereas the testing is carried out at random positions that may occur in between the discretized positions used for the training. However, we recall that our primary goal is to learn whether the use of spatial side information produces a gain over the case of not using such side information. It is not relevant to our scope whether the use of side information with an arrangement of tiles with or without overlapping produces an optimal result. Within our scope, it is enough that we can observe that there is some gain with the proposed approach using non-overlapping tiles. From another perspective, the discretization with the tiles helps us retain consistency for comparing the different pattern-matching methods considered, in the sense that comparisons of the outputs are performed for the same inputs. One of the pattern-matching methods is based on classification, in which we need to discretize the positioning area into distinctive classes. Therefore, we base our study on the use of discretized fractions of the positioning area in the form of non-overlapping tiles. A comparison against cases with overlapping tiles, or any other arrangement using side information, is deferred to future work.
# Efficient and Robust Arabic Automotive Speech Command Recognition System
## 4 Experiment Results and Discussion
### Dataset Collection Procedure
Figure 6 represents a comparison of recordings of the same command by one contributor in clean and noisy environments. The \(x\)-axis represents time, while the \(y\)-axis shows the intensity of the audio signal. In a clean environment, the signal remains stable, indicating minimal background noise. In contrast, the noisy environment shows fluctuating intensity levels due to background noise, affecting the clarity of the recorded command. Since phones have recently been equipped with more capable microphones, most of the recordings used phone microphones. Out of 14 contributors, two audio recordings used laptop microphones. To ensure a smooth and stress-free recording process for contributors, each contributor received a list of 20 commands, clear instructions, and an example recording to follow. Generally, a driver's voice is impacted by many factors, such as fatigue and stress. To capture these factors, we instructed the contributors to read the provided list 10 times in a row (recording round). As shown in Figure 7, due to fatigue or boredom during the process of recording, we noticed a change in voice tone and pith, which simulates the status of a driver's voice. This process enhances the dataset's representativity. In each recording round, the contributor reads the 20 commands consecutively with a 2 s pause in between. After each round, the file is saved. This process is repeated 10 times by each contributor. The average time spent collecting the data from each contributor was about 3 h. As a result of this process, we have collected 10 records from each contributor. Each record is -5 min long and contains 20 commands with 2 s pauses in between. Following this, we divided each record into smaller audio segments, each lasting 2 s and containing only one command. To ensure accurate splitting, we manually divided the audio segments using Audacity ([[https://www.audacityteam.org/](https://www.audacityteam.org/)]([https://www.audacityteam.org/](https://www.audacityteam.org/))), free and open-source software for audio processing. Subsequently, each audio segment was saved in a way format, labeled, and categorized  
Figure 6: Comparison of command recordings in clean and noisy environments. (**a**) Spectrogram of Command 1, Contributor 1 in a clean environment. (**b**) Spectrogram of Command 1, Contributor 1 in noisy environment. into one of the 20 classes. The audio label follows this format: Contributor-ID_Repetition-number_Command-number.wav (e.g., contributor_2_Repetition_9_command_1_.wav).
# Probabilistic Condition Monitoring of Azimuth Thrusters Based on Acceleration Measurements
## 2 Materials and Methods
### Condition Monitoring Algorithm  
The algorithm consists of separate identification and monitoring procedures, which are illustrated in the flowcharts in Figure 2. The algorithm uses acceleration signals from selected measurement positions with the associated sampling rate and rotational speed of the shaft. The parameters used by the algorithm include user-defined and system-specific parameters and parameters identified from the training data. The parameters are summarized in Table 2 and explored more in the following sections.  
Sections 2.2.1-2.2.7 clarify the different processing stages of the algorithm. Section 2.2.1 introduces the quality control of acceleration signals. The challenges of feature selection and the choices made in this study are discussed in Section 2.2.2. Section 2.2.3 focuses on the residual calculation based on the linear regression models. Section 2.2.4 introduces the method for the selection of shaft speed based on the checks of normal distribution by using the automated procedure. Section 2.2.5 introduces the multicollinearity check, which is shown in Figure 2 as the only decision block in the system identification part. The probabilistic model whose parameters are identified in the model identification process is discussed in Section 2.2.6. Finally, the probabilistic monitoring with squared Mahalanobis distance in the inference process is presented in Section 2.2.7. The inference process estimates if the system is operating in the expected condition or in a changed condition, based on the model defined in the identification stage.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**BPFI** & **BPFO** & **BSF** & **FTF** & **GMF** \\ \hline
10.8435 & 8.1565 & 3.3667 & 0.4293 & 13 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Kinematic frequencies (Hz) related to 60 rpm shaft speed.