esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Numerical Example and Verification  
An example cycloid gear pair was considered to demonstrate the capabilities of the proposed model, and its basic parameters are listed in Table 2. For convenience, it was assumed that the cycloid gear and rollers are made of the same material, which is steel with Young's modulus E = 206 GPa and Poisson ratio \(v\) = 0.3. Note that the numbers of discrete elements \(p\) and \(q\) were selected for each contact zone based on the convergence time and the calculation accuracy. As the number of discrete elements increases, the computational time consumption increases proportionally, while the calculation accuracy tends to be stable, since the variations of the calculated results converge within an acceptable error. With this, the numbers of discrete elements were all determined to be 40 in this numerical example.  
Figure 8: Coordinate systems for loaded tooth contact analysis.
###### Abstract
Among the several solutions that can be found in the market, the data collection system used for the work presented in this paper is capable of performing the record and replay operations of GNSS signals at low cost. Preliminary experimental results on the use of the record and replay method for the GBPT performance assessment have been discussed in [9; 10]. This paper extends the analysis of [10], and it discusses the trade-off between the parameters and provides results that validate the method. Eventually, it highlights the strengths and weaknesses behind the use of such a method for the GBPT performance assessment in the case of different operational environments. The paper is organized as follows. After this introduction, the next section first recalls the concept of GBPT for road applications, and then it introduces the classical approaches used for its performance assessment. The following section is focused on the record and replay approach as an innovative method that can be used for the GBPT performance assessment: first an introduction about the SDR paradigm is presented, then moving to a discussion of the technical aspects and the use of such a method for the performance assessment. A comparison among the approaches that can be used for testing is presented together with the performance metrics. Obtained results from vehicular data collections and playback operations are then presented in different operational environments. Finally, the conclusions are provided.
## 2 The Building, Temporary Instrumentation and Methodology
### Signal Processing  
To perform the analysis correctly regardless of the method, it is important to verify that the input value is accurate. Many parameters can interfere in the data collection process, such as poorly installed instrumentation, as well as inconsistencies in the reception of data, for example, files that are in bad condition or confusion regarding the units of measurement. It must be made clear that these parameters depend entirely on the instrumentation. With technological advances, the instruments can correct all of these problems automatically, but taking these factors into account, in the case of this article, only one parameter is adjusted to be able to apply the two methods in question, namely the correction of the signal baseline.  
Baseline Correction  
This occurs when the registers, to perform the analysis, are displaced with respect to the origin. This is a common problem where, due to installation issues, the instruments are not perfectly level. This issue may also arise when the vertical component of an earthquake causes a rebound effect with the surface.  
Figure 3: Acceleration time histories recorded at the basement and the roof level of the building on 1 May 2010 (**Top**) and on 2 January 2011 (**Bottom**).  
Before the data can be correctly analyzed, they must be prepared via a convenient standardization procedure called a baseline correction, by which the sample values are transformed into a new set of values that have a sample average equal to zero [29].  
As previously mentioned, the different methodologies currently used for the identification of dynamic properties in structures are separated into parametric and nonparametric techniques. In the following sections, the procedures of both general methodologies are defined.
## 1 Introduction
A mixed pressure-displacement finite-element method and the perfectly matched layer technique were used as the boundary conditions [12]. An asymptotic scheme to approximate the trapped mode solutions to the time harmonic wave equation in a three-dimensional waveguide was developed. The WKB-type ansatz was first used to identify the solutions [13]. [PERSON] conducted zonal large-eddy simulations (ZLESs) to calculate the acoustic field. He also used a shear stress transport turbulence model to simulate the flow field. The wall-modeled LES formulation was applied to the cavity section to solve flow-acoustic resonant fields. Experimental results in other studies were compared with the ZLES results in order to validate the method [14]. [PERSON] used the feedback-loop closure principle to calculate the acoustic embedded trapped modes in 2D duct-cavity structures; two resonant modes that supported an embedded trapped mode were verified for the first time. The radiation loss of two coexisting standing waves in a cavity was discussed [15]. [PERSON] et al. developed a numerical model to determine the frequencies of the trapped modes near rigid obstacles placed in rigid waveguides in the presence of nonpotential flow, and experimental investigations were performed to validate the model [16]. However, despite the excellent work that was previously conducted, the interaction between the acoustic and flow fields has seldom been considered and researched. This interaction exists pervasively in the natural world and practical engineering problems, and it should be evaluated to more precisely capture the trapped mode. The present study investigates the computation of trapped modes (including embedded trapped modes) in a two-dimensional cavity-duct system via the Galbrun equation. Taking the internal nonpotential fluid field as an initial condition, the interference between the fluid and acoustic fields due to the appearance of a cavity is taken into consideration. Their relevant trapped modes were captured after the coupling of the acoustic and flow fields. In this paper, the physical model of a two-dimensional cavity-duct system is first proposed. Then, the governing equations for both the fluid and acoustic fields are described. For investigating the trapped mode in a 2D coordinate system, a coupled calculation method that combined computational fluid dynamics (CFD) and computational aeroacoustics (CAA) is proposed. The effect of the size of the cavity and the Mach number on trapped modes is also discussed.
## Data and Methods
### Other Data
#### 2.2.3 Sentinel 1a/b  
**Data source:** The Technical University of Denmark (DTU). The 3-day Sentinel 1a/b mosaic product (1 km) of the Arctic and Antarctic regions were accessed from the website: [[http://www.seaice.dk/](http://www.seaice.dk/)]([http://www.seaice.dk/](http://www.seaice.dk/)). The single scene of the Sentinel 1a/b (.jpg) could also be downloaded from the website with a spatial resolution of between 100 m and 1 km.
## 4 Experimental Evaluation
The antenna array was designed so that the rotated scan can be collected without any mechanical movement, reducing the overall patient scanning time to about 30 s. For example, antennas \(\mathbf{s}_{1}\), \(\mathbf{s}_{2}\), \(\mathbf{s}_{3}\) are in a concentric ring offset from each other by \(36^{\circ}\), so the response for channel \(S_{\mathbf{s}_{1},\mathbf{s}_{2}}\) is given by  
\[S_{\mathbf{s}_{1},\mathbf{s}_{2}}=S^{\prime}_{\mathbf{s}_{1},\mathbf{s}_{2}}- S^{\prime}_{\mathbf{s}_{2},\mathbf{s}_{3}} \tag{8}\]  
where \(S^{\prime}_{\mathbf{s},\mathbf{s}^{\prime}}\) are the unprocessed backscattered reflections recorded at \(\mathbf{s}^{\prime}\) after transmitting on \(\mathbf{s}\). Images were generated using the Delay-and-Sum beamformer [4; 6; 7; 47; 48; 49; 50; 51; 52; 53; 54; 55; 56; 57]:  
\[I_{\epsilon^{\prime}_{r}}(\mathbf{r})=\sum_{0}^{T}\left(\sum_{\epsilon}^{N_{c }}S_{c}\left(t-\tau_{c}(\mathbf{r},\epsilon^{\prime}_{r})\right)\right)^{2} \tag{9}\]  
\(S_{c}\) is the time-domain response as described above, \(N_{c}\) is the number of multistatic channels and \(T\), the window-length, is 330 ps, the length of the excitation pulse in the time-domain. \(\tau_{c}(\mathbf{r},\epsilon^{\prime}_{r})\), the propagation delay for each channel, is estimated based on the effective average dielectric properties \(\epsilon^{\prime}_{r}\), which is the parameter estimated in the parameter search algorithm. Forty-nine images, ranging from \(\epsilon_{r}=1\) to \(\epsilon_{r}=25\) were reconstructed (\(\mathcal{I}\)) and the best-case image was selected using the metrics described in Section 2.
## 5 Iron, ROS and Iron Chelators That Can Cause DNA Damage in Cancer
### Iron Chelators and ROS
#### 5.1.1 Mimosine and Analogues  
Mimosine (\(\beta\)-N-3-hydroxy-4-pyridine-a-amino-propionic acid) is a natural iron chelator and was initially used to synchronise cells in the G1/S phase of the cell cycle. It has been shown to complex iron and indirectly inhibit RnR, first demonstrated in viral DNA [171]. Due to its inhibition of RnR and its antiproliferative effect, the molecular mechanisms were studied on U937 leukaemia cells and promyelocytic HL-60 cell lines. Here, mimosine induced ROS, resulting in apoptosis [172]. This was confirmed using the powerful antioxidant N-acetyl cysteine (NAC), which inhibited ROS-induced apoptosis [173]. When NAC, a glutathione precursor, was used, no superoxide was produced. [PERSON] et al. theorised that as mimosine reduced glutathione, NAC reversed the apoptotic effect of Mimosine [173]. Furthermore, mimosine's ability to induce ROS was due to the inhibition of catalase [173]. Mimosine had previously been shown to induce DNA breaks; the study by [PERSON] et al. theorised that ROS induction causes this [173; 174]. In another study, mimosine was tested against the cell proliferation of the C6 glioma cell line. The study found 12.6% apoptosis in  
Figure 5: The potential therapeutic effect of redox-active iron chelators in cancer increasing ROS resulting in DNA damage while reducing the size of the labile iron pool in theory and thus iron available for Fe-S biogenesis and RnR (ribonucleotide reductase) synthesis. The iron chelator Dp44 mt for example also upregulates NRDG1 and other cell signalling pathways.  
these cells compared to the control after 24 h at 250 \(\upmu\)M [175]. Their findings exhibited an increase in ROS generation in the mitochondria and led to downstream upregulation of Jun-N-terminal protein kinase (JNK) and p38, which both play roles in apoptosis induced by stress [176]. This provided the first evidence that ROS generated after immosine treatment was produced in the mitochondria, which differs from [PERSON]'s findings [173]. More recently, immosine has been shown to inactivate aconitase when complexed to ferrous iron. This ensues in the autooxidation of Fe\({}^{2+}\) and result in the formation of 8-hydroxy-2\({}^{\prime}\)-deoxyguanosine in Calf thymus DNA [177]. However, reports of immosine's cytotoxic side effects have resulted in its limited use and the generation of prodrugs based on immosine's structure [178]. Mainly a drug termed L-SK4, a methylated analogue of L-mimosine, compound 22. L-SK4 produced a four-fold induction of ROS after 24 h compared to the untreated control (100 \(\upmu\)M) in A375 cells. They then go on to show that both the intrinsic and extrinsic pathways of apoptosis were activated suggesting that this was in response to ROS induction by L-SK4. Later studies showed that the ROS induction by L-SK4 resulted in a significant increase in sphingolipids which can act as messengers for both apoptotic pathways [179].
## 3 Proposed Methodology
### Transfer Learning Process  
One of the main applications of transfer learning is feature extraction [28]. In the feature extraction approach, the output from one or more than one layer in the pretrained CNN is used as the input feature vector for the classification phase [29]. Since the deeper layers extract the higher-level features, the layer right before the classification phase can be an appropriate choice for feature extraction [30].  
A typical CNN structure consists of two parts; (1) convolutional layers, composed by a stack of the convolutional and the pooling layers to extract the features from the image-based input. (2) a classification part including a set of fully-connected (FC) layers followed by an activation function, like Soft-Max, to classify the images using the extracted features [11]. In the transfer learning process, the classification part can be replaced by a new classifier that fits the application in hand and the model can be tuned using one of the following strategies [11]:  
* Training the entire dataset: The pretrained CNN can be trained from scratch using a new dataset. Therefore, a large dataset and lots of computational power are required.
* Training some layers and leaving the others frozen: As the lower layers extract the general features while higher layers represent the most specific features, it can be decided how many layers need to be retrained depending on the application. For a small dataset with a large number of parameters, it is efficient to leave more layers frozen. Because the frozen layers are kept unchanged during the training process to avoid overfitting. On the other hand, for a large dataset with a small number of parameters, training more layers would be reasonable to the new task, since overfitting is not an issue.
* Freezing the convolutional part: In this scenario, the convolutional part can be kept unchanged, and its output can be fed to a new classifier. In other words, the pretrained model is considered as a fixed feature extraction basis, which is beneficial in case of having a small dataset and suffering from a lack of computational power. Notably, in this study, we have applied this strategy.  
Figure 3: Scalogram representation of target classes: (**a**) SoI, (**b**) SoI+CWI, (**c**) SoI+MCWI, and (**d**) SoI+CI.  
Notably, the first two strategies highly depend on the learning rate, which defines how much the weights of a network can be adjusted. A small value learning rate can be chosen over a high-value to reduce the risk of losing previous knowledge [11].
# Creating a Remote Choir Performance Recording Based on an Ambisonic Approach
## 4 Realization of Remote Music Recording  
There are several important references to audio realization in the context of both concert streaming and the possibility of immersive audio reproduction, i.e., inducing the viewer/listener to be \"immersed in sound\" [67; 68; 69]. Among them is a chapter by [PERSON], [PERSON], and [PERSON], entitled \"The MPEG-H 3D Audio standard and its applications in digital television\" [70]. The authors point out that the immersive audio transmission uses the MPEG-H Audio standard based on Higher-Order Ambisonics (HOA) as the optimal input to the 3D audio codec. The ambisonic technique is designed to record and later reproduce the whole sound field using sound objects. This requires transmitting information about the object's position in space and time in the form of a meta-description. The meta-description enables the correct rendering of objects on the playback side [70].  
In this Section, the implementation stage of a concert recording is to be presented, from the preparation stage to the postproduction of the recording.
# Algorithmic Modified Denavit-Hartenberg Modeling for Robotic Manipulators Using Line Geometry
## 3 Line Geometry
### Line Geometry Block, \(\mathcal{LG}(L_{1}(\hat{z}_{1},v_{1}),L_{2}(\hat{z}_{2},v_{2}))\)  
The method to express how two adjacent lines (or joint axes) are located each other can be classified into four cases: collinear, distant parallel, intersected, and skewed. To begin with, criterion 1 is defined to check whether two lines are parallel or not, which is performed using the two direction vectors:  
\[\|\hat{z}_{1}\times\hat{z}_{2}\|=0\rightarrow\|\hat{z}_{1}\times\hat{z}_{2} \|<\sin(\epsilon_{\theta}), \tag{28}\]  
where \(\epsilon_{\theta}\in\mathbb{R}^{+}\) denotes a small positive constant with units of radians, introduced to handle numerical issues when comparing angular values. If criterion 1 of Equation (28) is satisfied, the two lines are parallel to each other. The parallel lines can be further classified into two cases: collinear (parallel) and distant (parallel). For this purpose, criterion 2-1 is defined as the distance between the closest points from the origin of the coordinate frame to the lines using Equation (11) as follows:  
\[\|p_{1}^{\perp}-p_{2}^{\perp}\|=0\rightarrow\|p_{1}^{\perp}-p_{2}^{\perp}\|< \epsilon_{d}, \tag{29}\]where  
\[p_{1}^{\perp} =\hat{z}_{1}\times v_{1}=\hat{z}_{1}\times(p_{1}\times\hat{z}_{1})\] \[p_{2}^{\perp} =\hat{z}_{2}\times v_{2}=\hat{z}_{2}\times(p_{2}\times\hat{z}_{2}).\]  
where \(\epsilon_{d}\in\mathbb{R}^{+}\) is a small positive threshold with units of meters, introduced to ensure numerical robustness when comparing distances between points. If criterion 2-1 is satisfied, the two lines are collinear (parallel); otherwise, they are distant (parallel). These parallel cases are summarized in Figure 6.  
On the other hand, if criterion 1 of Equation (28) is not satisfied, the two lines are not parallel to each other. The non-parallel lines can be further classified into two cases: intersected and skewed. For this purpose, criterion 2-2 is defined as the existence of the intersection point, which is conducted using the reciprocal product (16):  
\[|L_{1}*L_{2}|=0\rightarrow|L_{1}*L_{2}|<\epsilon_{L}. \tag{30}\]  
where \(\epsilon_{L}\in\mathbb{R}^{+}\) is a small positive threshold used to evaluate the reciprocal product. Note that unlike \(\epsilon_{\theta}\) (in radians) and \(\epsilon_{d}\) (in meters), the reciprocal product is inherently dimensionless. If criterion 2-2 of Equation (30) is satisfied, two lines are intersected at one point, otherwise, they are skewed.  
The line geometry block \(\mathcal{LG}(L_{1},L_{2})\) to be designed in this section receives two lines (\(L_{1},L_{2}\)) as inputs, selects one from the four cases using the criteria, and yields five parameters (\(\alpha,a,\hat{n},t_{1},t_{2}\)) as outputs, where it is noted that two lines as the inputs consist of the direction vectors \(\hat{z}_{1},\hat{z}_{2}\) of joint axes and the points \(p_{1},p_{2}\) on joint axes as follows:  
\[L_{1}(\hat{z}_{1},v_{1}=p_{1}\times\hat{z}_{1})\qquad\qquad\text{ and }\qquad\qquad L_{1}(\hat{z}_{2},v_{2}=p_{2}\times\hat{z}_{2}). \tag{31}\]
# Influence of Region on Sensory and Chemical Profiles of Pennsylvania Gruner Veltliner Wines
## 2 Materials and Methods
### Chemical Analysis
#### 2.4.3 Phenolic Analysis of Wine  
Individual phenolics were quantified using high performance liquid chromatography (HPLC) with a method adapted from [PERSON] and Elias [26]. A Shimadzu HPLC (Shimadzu, Torrance, CA, USA) equipped with LC-10 ADvp pumps, a reverse phase Eclipse Plus C18 column (4.6 \(\times\) 250 mm, 5 mm; Agilent Technologies), SPD-M10 Avp controller, DGU-14A degasser, SCL-10 Avp UV-DAD detector, SIL-20 AC HT autosampler, and Brinkman CH-30 column heater with an Eppendorf TC-45 temperature controller was used for analysis. The binary mobile phase system consisted of 1% phosphoric acid in water (A) and methanol (B). Samples were centrifuged at 13,500\(\times\)\(g\) for 5 min (Microfuge 16, Beckman Coulter, Indianapolis, IN, USA) and 200 \(\upmu\)L of sample was transferred into 11 mm glass HPLC vials (VWR, Radnor, PA, USA) with 250 \(\upmu\)L reduced volume vial inserts (6 \(\times\) 31 mm; MicroSolv Technology Corporation, Leland, NC, USA). Injection volume was 20 \(\upmu\)L, flow rate was 0.7 mL per minute, and column temperature was 30 \({}^{\circ}\)C. Phenolicswere eluted using the following sequence: 0-5 min, 5% B; 7 min, 15% B; 10 min, 17.5% B; 22 min, 19.75% B; 26-33 min, 50% B; 34-35 min, 85% B, and re-equilibration at 5% B for 7 min before the next injection. For phenolic analysis, gallic acid, (+)-catechin, and (\(-\))-epicatechin were quantified in wine samples at 210 nm (280 nm served as the qualifier wavelength), based on previous work in identifying phenolic compounds in white wine and juice [27]. External 8-point calibration curves were established between 0 and 10 mg/L.
## 3 Results and Discussion
### Training Results by ANN Structure
For cases 6 and 7, the number of hidden-layer nodes differed by a factor of 2, and the number of hidden layers was 2. As a result, the training times differed by a factor of about 4.7. From these results, it can be seen that the ANN training time is proportional to the number of nodes in the hidden layer(s), but the effect is larger when the number of hidden layers is two. In general, the number of hidden layers and nodes is the most influential factor in ANN training [34]. However, in this study, it can be confirmed that the learning ability of the ANN is not substantially  
Figure 9: Training results by ANN structure. affected by the number of hidden layers and nodes. In other words, it is considered that, despite the differences among the network structures, fairly high accuracy is shown for all of the ANN structures.
# Unlicensed Spectrum Allocation for LTE and Wi-Fi Coexistence with HAP
## Appendix A  
\(P(i\ col)\) is the probability of \(i\) collision. After the \(i\)-th collision, Wi-Fi stations can transmit the packets successfully at the \((i+1)\)-th retransmission. Therefore, \(P(i\ col)\) can be expressed as the probability of \(i\) collision multiplied by the probability of no collision. That is, it is the probability of the successful transmission.  
\[P(i\ col)=\Big{(}1-(1-\tau)^{N_{STA}-1}\Big{)}^{i}(1-\tau)^{N_{STA}-1} \tag{1}\]  
where \(\tau\) is the probability of a Wi-Fi station transmitting packets. It can be obtained using the Markov chain [29].  
\(P(j\ slots\ |\ i\ col)\) represents the probability that the sum-up numbers of back-off slots are equal to \(j\) when there are \(i\) collisions [22].  
\[P(j\ slots\ |\ i\ col)=P(\sum_{k=0}^{i}unif(0,CW_{k}-1)=j) \tag{2}\]  
where \(\text{unif}(0,CW_{k}-1)\) stands for a discrete random variable uniformly distributed in the range \(\{0,\ 1,.,CW_{k}-1\ \}\), and \(CW_{k}\) represents the size of the contention window for the \(k\)-th back-off.  
\(P(d<D\mid i\ col,\ j\ slots)\) is the probability of \(d<D\) with \(i\) collisions and \(j\) back-off slots [11].  
\[P(d<D\mid i\ col,\ j\ slots)=\begin{cases}0.5+0.5\text{-erf}\left(\frac{D-m_{ij}}{ \sqrt{2}\sigma_{ij}}\right),\ \frac{D-m_{ij}}{\sigma_{ij}}\geq 0\\ 0.5\text{-erfc}\left(\frac{D-m_{ij}}{\sqrt{2}\sigma_{ij}}\right),\ \frac{D-m_{ij}}{ \sigma_{ij}}<0\end{cases} \tag{10}\]  
where \(m_{ij}\) is the sum of the average duration of all slot times in \(d_{ij}\), as shown below:  
\[m_{ij}=jm_{n}+iT_{c}+T_{s} \tag{11}\]  
where \(m_{n}\) is the average duration within which the Wi-Fi station does not transmit a packet, \(T_{c}\) is the duration of a collision and \(T_{s}\) is the duration of a successful transmission.  
With the assumption of independence between different slot times, the standard deviation \(\sigma_{ij}\) can be computed according to (11).  
\[\sigma_{ij}^{2}=j\delta_{n}{}^{2} \tag{12}\]  
where \(\sigma_{n}\) is the standard deviation of the duration within which the Wi-Fi station does not transmit packets.