esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology and Mathematical Formulations
### Computational Boundary Condition  
The accuracy of CFD simulations is influenced not only by grid resolution and numerical settings but also critically by the initial and boundary conditions. This study established boundary conditions based on measurements taken within the main ordination hall to ensure a realistic representation of the physical environment. Figure 6 provides details on the sizing of the computational domain, designed following standard guidelines and recommendations [26; 27; 28; 29]. The domain was set with an upstream length of 5D to account for the incoming flow, a downstream length of 10D to allow for full wake development, and a height of 6H for adequate vertical resolution. However, these dimensions were slightly adjusted based on the availability of computational resources or to address solution instability during simulations. This practical approach enhanced the reliability and applicability of the simulation results.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Boundary Surface**} & \multirow{2}{*}{**Boundary Condition Type**} & \multicolumn{2}{c}{**Case 1**} & \multirow{2}{*}{**Case 2**} & \multirow{2}{*}{**Case 3**} \\  & & & & \\ \hline \multirow{3}{*}{Inlet} & Velocity inlet & U\({}_{0}\) = 2.93 m/s & U\({}_{0}\) = 3.50 m/s & U\({}_{0}\) = 5.50 m/s \\  & Temperature & T\({}_{0}\) = 27.85 \({}^{\circ}\)C & T\({}_{0}\) = 27.85 \({}^{\circ}\)C & T\({}_{0}\) = 27.85 \({}^{\circ}\)C \\  & Relative humidity & \%RH = 66.0\% & \%RH = 66.0\% & \%RH = 66.0\% \\ \multirow{3}{*}{Outlet} & Pressure outlet & & & \\  & No-slip wall & & & \\  & Heat flux & q = 35 w/m\({}^{2}\) & q = 35 w/m\({}^{2}\) & q = 35 w/m\({}^{2}\) \\ \multirow{3}{*}{Roof} & Heat flux & q = 40 w/m\({}^{2}\) & q = 40 w/m\({}^{2}\) & q = 40 w/m\({}^{2}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Boundary conditions for each surface group.  
Figure 5: Measurement equipment and locations for onsite experiments.
## 3 Dynamic Modal Analysis (DMA) with Response Spectrum Technique  
In this section, as a first approach, the seismic response is evaluated by the linear dynamic modal analysis with response spectrum for segmental bridges with the two different schemes described above, namely the framed one and the continuous girder. The behaviors of these two typologies are investigated through the analysis of the responses of two testing bridges having the same geometrical dimensions and mechanical properties. The responses are compared to recognize which scheme is more sensitive to the seismic action and what the critical aspects are for the two schemes. All the analyses are carried out on a one-dimensional FE model of the structures. The Standard Complete Quadratic Combination (CQC) rule is applied for modal contribution combinations.  
The analyzed structural scheme consists of a three-span bridge with a total length of 245 m: the central span is 115 m long, the side ones are 65 m, and pier height is 49 m; the deck is composed of a one-cell prestressed concrete box girder of 9.50 m wide with web thickness of 0.4 m; the top slab has constant thickness of 0.25 m, while the bottom slab has variable thickness from 0.80 m on piers to 0.20 m at mid-span. The total height of the cross-section is variable too, from 7 m over the piers up to 2.2 m at the midspan and near the abutments. Materials are concrete grade C40/50 (characteristic cylinder strength \(f_{\text{ck}}\) = 40 MPa), mild reinforcement steel with characteristic yielding stress \(f_{\text{yk}}\) = 450 MPa, and prestressing steel of Dywidag bars with \(f_{\text{pyk}}\) = 1000 MPa. The pier section is a hollow section with dimensions of 450 \(\times\) 510 cm, a thickness of 0.40 m, and a total height of 43 m between the foundation and the deck, the pier cross-section has 0.8% longitudinal reinforcement and stirrups of diameter \(\phi\)14 mm with 4 legs and 0.20 m spacing, characterized by a M-N interaction  
Figure 1: (**a**) Cannavino bridge on the roadway between Paola and Cosenza, Italy, with clamped deck on piers and drop-in span; (**b**) Bridge on the A20 motorway in Sicily, Italy, with continuous box girder on piers.  
resistance domain in which, for the value of axial force due to permanent loads, 66.5 MN, the correspondent bending moment in the domain is 847 MNm. Soil structure interaction is neglected to highlight the structural behavior only. Thus, the piers are clamped at the base, and the abutment restraints will be particularized depending on the structural scheme analyzed. Furthermore, the hypothesis of equal seismic action at the foot of the piers was made, considering that there is a limited distance between them and the spatial variability of the motion has no influence.  
Besides the self-weight of the structure, a superimposed dead load of 3 kN/m\({}^{2}\) is considered, resulting in a total seismic weight of 37,127 kN. The elastic response spectrum corresponding to subsoil class \"B\" of Eurocode 8 [36], with a peak ground acceleration of 0.435 g, is considered for the seismic input characterization.
# Examining Pipe-Borehole Wall Contact and Pullback Loads for Horizontal Directional Drilling
## 3 Analytical Model for Calculating Pipe Pullback Loads
### Drilling Fluid Drag Force  
The ASTM model calculates the drilling fluid drag force \(T_{if}\) by considering a balance of the forces acting on the fluid annulus in the borehole due to the pressure difference and the lateral shear forces acting on the pipe and borehole walls [11]:  
\[T_{if}=\Delta P_{f}\frac{\pi}{2}\Big{(}R_{2}^{2}-R_{1}^{2}\Big{)}. \tag{29}\]  
The pressure difference \(\Delta P_{f}\) is estimated to be a constant value (70 kPa), which may cause errors in calculating fluid drag force because the related parameters differ for various HDD projects.  
This model calculates the drilling fluid drag force based on the ASTM model, but the pressure difference is not estimated to be a constant value. The pressure difference of the annulus is related to the annulus size, flow rate, and drilling fluid parameters. Previous studies have demonstrated that drilling fluid typically behaves as a non-Newtonian fluid with a yield point. The Bingham model effectively describes the rheological characteristics of drilling fluids [16]. Therefore, the Bingham model was used to describe the drilling fluid in this study. The pressure difference \(\Delta P_{f}\) required for the laminar flow of Bingham fluid in an eccentric annulus can be expressed as follows [42; 43]:  
\[\Delta P_{f}=C\Delta P_{fc}=C\Bigg{[}\frac{12\mu vS}{\left(R_{2}-R_{1}\right)^ {2}}+\frac{3\tau_{0}S}{R_{2}-R_{1}}\Bigg{]}, \tag{30}\]  
where \(\Delta P_{fc}\) represents the pressure difference in the concentric annulus; \(\mu\) denotes the dynamic viscosity of drilling fluid; \(v\) stands for the flow velocity of drilling fluid; \(\tau_{0}\) signifies the yield point of drilling fluid; \(S\) represents the length of the pipeline entering the borehole; and \(C\) denotes the correction factor, which is related to the eccentricity \(e\) and the overcut ratio \(R_{2}/R_{1}\).  
The correction factor \(C\) can be calculated by the following formula [43]:  
\[C=1-0.072e\bigg{(}\frac{R_{1}}{R_{2}}\bigg{)}^{0.8454}-1.5e^{2}\bigg{(}\frac{R_{1} }{R_{2}}\bigg{)}^{0.1852}+0.96e^{3}\bigg{(}\frac{R_{1}}{R_{2}}\bigg{)}^{0.2527}. \tag{31}\]  
A schematic of the eccentric annulus is presented in Figure 9. Eccentricity \(e\) is defined as follows:  
\[e=\frac{\lambda}{R_{2}-R_{1}}, \tag{32}\]  
where \(\lambda\) is the distance between the centers of the pipe and the borehole.  
The drilling fluid drag force \(T_{if}\) can be obtained by substituting Equation (30) into Equation (29):  
\[T_{if}=\Delta P_{f}\frac{\pi}{2}\Big{(}R_{2}^{2}-R_{1}^{2}\Big{)}=C\pi\bigg{(} \frac{6\mu vS}{R_{2}-R_{1}}+\frac{3\tau_{0}S}{2}\bigg{)}(R_{2}+R_{1}). \tag{33}\]  
The calculation of the drilling fluid drag force in this model considers the effects of the annulus size, flow rate, drilling fluid parameters, and eccentricity.
# Energy Efficient Resource Allocation for M2M Devices in LTE/LTE-A
## 2 Overview and Related Works
### M2M Communication  
M2M communication describes the exchange of data between smart devices and remote servers. These intelligent machines integrate computing capabilities that enable them to capture data around them and share it with other devices without the need for human interaction. The characteristics of the M2M network are quite different from the H2H network [6; 7]. The M2M network is composed of a large number of machines. According to Machina Research [8], the number of these intelligent devices will reach 27 billion in 2024. Besides, in M2M technology, the traffic is mainly in the uplink side, from the M2M device to the base station, contrarily of H2H communication, where the traffic is almost all at the downlink side. In addition, the majority of M2M devices are used to send only a few packets with minimal data.  
The majority of M2M applications can be divided into two categories [5]:  
* Event-driven applications: when an event occurs in the MTC device, the latter has to establish a connection with the base station, called evolved NodeB (eNB), to send messages to the server. These applications are mainly real-time applications.
* Time-driven applications: MTC devices send their data on a regular basis. The transmitting interval can vary from a few milliseconds to several minutes or hours. These applications are delay tolerant. Most M2M devices are included in this category.
## 3 Results of the Algorithm
### Example 1  
The first example is taken from [11]. The nonlinear system is given by  
\[f\left(x\right) = \left(\begin{array}{c}-x_{2}+x_{1}\left(1-x_{1}^{2}-x_{2}^{2} \right)\\ x_{1}+x_{2}\left(1-x_{1}^{2}-x_{2}^{2}\right)\\ -x_{3}\left(x_{1}^{2}+x_{2}^{2}\right)\end{array}\right) \tag{26}\] \[h\left(x\right) = x_{1}^{2}+x_{2}^{2}+x_{3}^{2}.\]  
According to [11], this system is non observable. To verify the algorithm presented in the previous section, the observability is examined using the presented method. Therefore, the system is analyzed on the set  
\[D_{x}=\left(\begin{array}{cc}\left[-1&1\right]\\ \left[-1&1\right]\\ \left[-1&1\right]\end{array}\right).\]  
After 16 days, 5 h, 18 min and 30 s no globally observable interval could be identified. During this time, 75,000 iterations have been executed. The long computing time and small amount of iterations implies that the system (26) is not observable. The small amount of iterations also implies that the lists \(\mathcal{L}_{i}\) contain several intervals. Hence, numerous intervals are indistinguishable from one another.  
Additionally to the statement that the system is non observable, the presented method is capable of providing the set of states that are indistinguishable from a specific initial state. This is exemplarily demonstrated in Figure 2. All blue intervals are indistinguishable from the initial state  
\[x^{I}=\begin{pmatrix}\left[-0.5&-0.4375\right]\\ \left[-0.4375&-0.375\right]\\ \left[\begin{array}{cc}0.3125&0.375\end{array}\right]\end{pmatrix}, \tag{27}\]  
which is located inside the blue torus of indistinguishable intervals. The shape of a torus results from the quadratic terms in the measurement equation \(h(x)\) and the recurring terms within the system equations \(f(x)\). Additionally, overestimation expands the shape. The figure illustrates an example after 10,000 iterations. After further iterations, the structure of indistinguishable intervals keeps its shape, however, the torus is enclosed more sharply.  
The information about the set of states that are indistinguishable from another state can be used during the design phase of a plant. Information can be derived on how to adapt the measurement equation to make the system observable. In the provided example, the shape of a torus suggests that the quadratic terms in the measurement equation \(h(x)\) should be avoided. Other methods are not able to provide this type of information. For example, in [11] only non-observability could be identified.
# Sound Synthesis of Objects Swinging through Air Using Physical Models +
## 3 Method
### Aeolian Tone
#### 3.2.1 Tone Frequency  
[PERSON] (1878) defined a useful relationship between the tone frequency \(f_{l}\), air speed \(u(t)\) and cylinder diameter \(d\), Equation (1). The variable \(S_{t}\) is known as the Strouhal number.  
\[S_{t}=\frac{f_{l}\,d}{u(t)} \tag{1}\]  
Rearranging to isolate the tone frequency gives:  
\[f_{l}=\frac{S_{t}u(t)}{d} \tag{2}\]  
As air flows around a cylinder, vortices are shed, causing a fluctuating lift force normal to the flow dominated by the fundamental frequency, \(f_{l}\). Simultaneously, a side axial fluctuating drag force is present with frequency \(f_{d}\), twice that of the lift frequency. It was noted in [11] that, \"The amplitude of the fluctuating lift is approximately ten times greater than that of the fluctuating drag\".  
It was shown in [8] and confirmed in [12] that aeroacoustic sounds in low flow speed situations could be modelled by the summation of compact sound sources, namely monopoles, dipoles and quadrupoles. An acoustic monopole can be described as a pulsating sphere, much smaller than the acoustic wavelength. A dipole is equivalent to two monopoles separated by a small distance, but of opposite phase. Quadrupoles are two dipoles separated by a small distance with opposite phases. A longitudinal quadrupole has the dipole axes in the same line, while a lateral quadrupole can be considered as four monopoles at the corners of a rectangle [13]. Aeolian tones can be represented by  
Figure 1: A simplified taxonomy of aeroacoustic sounds.  
dipole sources, one for the lift fundamental frequency and one for the drag; each source can include a number of harmonics.  
The turbulence around the cylinder affects the frequency and the bandwidth of the tone produced. A measure of this turbulence is given by a dimensionless variable, the Reynolds number \(R_{e}\), given by the relationship in Equation (3).  
\[R_{e}=\frac{\rho_{air}\,d\,u(t)}{\mu_{air}} \tag{3}\]  
where \(\rho_{air}\) and \(\mu_{air}\) are the density and viscosity of air, respectively. An experimental study of the relationship between the Strouhal number and the Reynolds number was performed in [14], giving the following equation:  
\[S_{t}=\lambda+\frac{\tau}{\sqrt{R_{e}}} \tag{4}\]  
where \(\lambda\) and \(\tau\) are constants and given in Table 1 of [14] (additional values were calculated in [10]). The different values represent the turbulence regions of the flow, starting at laminar up to sub-critical. With the Strouhal number obtained, diameter and air speed known, we can apply them to Equation (2) and obtain the fundamental frequency, \(f_{l}\), of the Aeolian tone, generated by the lift force.
## Data and Method
### Research Methods
#### 2.2.4 Seasonal Autoregressive Integrated Moving Average (SARIMA) Forecast Analysis  
The autoregressive integrated moving average model (ARIMA) may be helpful for time series prediction. It has been proposed by [PERSON] and [PERSON] in 1976 [30; 31]. As an improved form of the ARIMA model, the SARIMA model is used for periodic time series and performs the seasonal difference on the basis of the ARIMA model [32]. The SARIMA model is defined as:  
\[\mbox{SARIMA}=(\mbox{q},\ \mbox{d},\ \mbox{q})\times(\mbox{P},\ \mbox{D},\ \mbox{Q})_{ \mbox{s}} \tag{9}\]  
where p, d and q are the autoregressive, differencing and moving average orders in the non-seasonal part of the model; the P, D and Q are the values of autoregressive, differencingand moving average orders in the seasonal part of model [33]. These term are determined by an autocorrelation function (ACF) and a partial autocorrelation function (PACF) [34]. Finally the s is the length of seasonality.  
The identification of the SARIMA model follows:  
1. To judge the stationarity of a sequence, this paper uses the Dickey-Fuller (DF) unit root test to judge whether the sequence is stationarity.
2. If the sequence is non stationary, it is processed by difference, eliminating the fluctuation of the sequence to make the data tend to be stationary, and extracting the effective information in the sequence.
3. Order the model. In this paper, the autocorrelation, partial correlation and criterion functions are used.
4. Test the model, including residual DF unit root test, residual Ljung-Box Q (LBQ) test and residual white noise test. If there are insignificant parameters, it is necessary to eliminate them and readjust the model structure [35; 36]. The white noise test ensures that the model can fully extract the relevant information of the sequence.
# Voxel-Based Digital Twin Framework for Earthwork Construction
## 3 Methodology
### Digital Twin Update  
The proposed framework is continuously evolving as new sensing data emerges into the system. This framework uses a voxel-to-voxel comparison to monitor the volumetric information and track productivity progress and material tracking. When new data is acquired with cameras or laser scanning, it is processed into a point cloud [48]. The TIN surface is generated from the point cloud and extruded relative to the ground surface to create a solid model. The solid is voxelized according to our previous method [25].  
The as-planned voxels achieved by voxelization of the design surface and as-built voxels, which are obtained from the sensing data, are spatially registered by aligning their global coordinates. Since, in the context of this research application, both as-planned and as-built models are created in the same coordinate system, coordinate transformation is not required. Consequently, each voxel in the as-planned model is compared with the as-built voxels using centroid proximity. Centroid proximity ensures that both models are spatially aligned.  
After the as-planned and as-built voxel correspondence, the as-planned voxel status is updated according to the presence of as-built voxels at the same location within the digital twin environment. The status of the as-planned voxels is changed from planned to excavated when as-built voxels are found at a given coordinate. After the status is changed, the excavated voxels are hidden in the visualization to show the as-built status of earthwork construction. Additionally, the volume of the excavated voxels is used for volumetric quantification, productivity calculation, and monitoring. Moreover, the material information, along with other semantics, is extracted from the excavated voxels for material tracking. The voxel-to-voxel comparison not only provides productivity and progress information but also material information, which is significant for project managers in assessing what kinds of materials have been excavated since these materials can be used for filling or other activities. It allows a fine-grained representation of construction progress, ensuring that the digital twin evolves accurately according to the current state of the construction.
# Raman Spectroscopy of Individual Cervical Exfoliated Cells in Premalignant and Malignant Lesions
## 4 Discussion
It should be highlighted that this study was carried out on individual cells of patient samples, and the Raman measurements were taken directly from the cytoplasmic and nuclear cellular compartments, which allowed to define spectral differences corresponding to the components and processes in healthy cervical cells and those cells with abnormalities due to viruses. Finally, this approach allows performing the test on cervical exfoliation samples and obtaining the Raman spectra without destroying the sample and without complicated preparation processes. There are some limitations in this study. The quality of the cervical sample is critical to conduct proper Raman measurements, and this partially depends on the trained personnel that obtains the smear. The presence of blood in the specimen is a major cause of errors in the spectral analysis. Bleeding was observed during the obtention of a cancerous sample but also in non-malignant conditions. Spectra of abnormal specimens show heme and fibrin features, and this can lead to false interpretations [19]. To solve this problem, samples with little or no evidence of blood were treated with H\({}_{2}\)O\({}_{2}\), and samples with the macroscopic presence of blood were treated with red blood corpuscles lysis buffer in an attempt to eliminate the risk of misinterpretations in the spectral analysis. Another limitation is the sample size. It is necessary to increase the number of samples for each study group to obtain a better comparison and thus to be able to establish a fingerprint for healthy cells and for different degrees of cervical lesions.
## 6 Network Architectures for IEC61850-90-5 Multicast over Public WAN
### P2M Architecture with Separate GRE Tunnels  
As discussed earlier, multicast communication of R-SV/R-GOOSE over public WAN requires tunneling multicast traffic across the IP network. GRE tunnels can be used for this purpose in a P2M architecture (Figure 17) which enables multicast communication of R-SV/R-GOOSE from Substation 1 to Substation 2 and 3. GRE tunnels can be established over either IP or IPsec, although IPsec is the preferred choice because it creates a secure communication path (state-of-the-art encryption and authentication) for any transmitted messages.  
As can be seen, the above-mentioned architecture uses separate GRE tunnels in the routers between the IP-multicast sender and each IP-multicast receiver. Although this architecture provides P2M communication, it is not a scalable solution since the new GRE tunnel must be configured in FHR and LHR for every new receiver added to the system. To address this scalability issue, multipoint GRE (mGRE) within Dynamic Multipoint VPN (DMVPN) [43] can be used.
## 3 Results and Discussion
As expected from the above analysis, the MB sunlight degradation rate of the non-treated Au/TiO\({}_{2}\) was improved in comparison to that of bared TiO\({}_{2}\) (Figure 9). Even weakly attached AuNPs already increased the degradation rate by a factor of 4 from 0.01 min\({}^{-1}\) with TiO\({}_{2}\) to 0.04 min\({}^{-1}\), with non-treated Au/TiO\({}_{2}\) supporting the electron-hole separation and absorption spectra broadening. This effect is maximized in the treated Au/TiO\({}_{2}\) as the degradation rate reaches the extremely high value of 0.13 min\({}^{-1}\). This value is far better than the MB sunlight degradation rate in the order of only 0.022 min\({}^{-1}\) obtained with the AuNPs doped TiO\({}_{2}\) prepared by the chemical method, as reported by [PERSON] et al. [22].
# Iterative Camera Calibration Method Based on Concentric Circle Grids
## 4 Discussion  
Concentric circle targets are widely used in camera calibration due to their high detection accuracy. To improve the calibration accuracy, traditional methods mainly focus on the high-precision location of the imaged center. However, the presence of position deviation in a concentric circle caused by lens distortion was commonly ignored by previous researchers. In the proposed method, the position deviation is calculated to refine the image points iteratively thus improving the calibration accuracy. Compared with previous research, the proposed method greatly improves camera calibration accuracy and can provide more accurate camera calibration results for computer vision tasks.