esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods
### Theoretical Study
#### 3.2.3 Expected Impressions until First Click  
In the previous section, we have explored the probability of getting the first click and how \(FC\) impact it under the assumption that the next impression can be delivered. Following this, we calculate the expected number of impressions before the first click. This is relevant when choosing \(FC\) to guarantee an advertiser does not miss many clicks for choosing a \(FC\) too small.  
For this case we again consider both probabilities as constant (\(p_{I}=c_{1}\) and \(p_{C}=c_{2}\)). However, this time, we are interested in understanding the number of impressions required until the user reaches the first click. For this exercise, we will further assume that \(p_{I}\) is not only constant, but that the user will always receive a new impression (\(p_{I}=1\)).  
If we refer to X as the expected number of impressions, then we can formulate as follows:  
\[P(X\geq 1) =1\] \[P(X\geq 2) =(1-p_{C})\] \[P(X\geq 3) =(1-p_{C})(1-p_{C})\] \[P(X\geq 4) =(1-p_{C})(1-p_{C})(1-p_{C})\] \[P(X\geq n) =(1-p_{C})^{n-1}\]  
Therefore, we have that the expected number of impressions is  
\[E(X)=\sum_{n=1}^{\infty}(1-p_{C})^{n-1}=\frac{1}{1-1-p_{C}}=\frac{1}{p_{C}}\]  
For instance, based on this result, the number of impressions when \(p_{C}=0.9\) is \(1.11\) and for \(p_{C}=0.1\) is \(10\). Therefore, \(FC\) should be ideally higher than these values.
## 2 Double-Stranded DNA Recognition by Peptide Nucleic Acid (PNA)  
### Peptide Nucleic Acid (PNA)  
In 1991, Prof. [PERSON] and co-workers developed peptide nucleic acid (PNA; Figure 1) [16; 33; 34; 35; 36; 37; 38; 39; 40; 41]. PNA not only forms stable duplexes with single-stranded nucleic acids via Watson-Crick base pairing but also can directly recognize sequences in dsDNA through a unique DNA recognition called \"Invasion\" [42] (Figure 2). PNA is a synthetic DNA analog, and its backbone, \(N\)-(2-aminoethyl)glycine, consists of six atoms in the monomer unit like DNA. In PNA, the negatively charged sugar-phosphate backbone of DNA was substituted with an electrostatically neutral pseudo-peptide backbone. Consequently, the electrostatic repulsion between PNA and DNA is absent, and PNA/DNA duplexes are much more stable than those between negatively charged DNA strands [43]. In addition, the binding strength of PNAs to mismatched sequences is much lower than that to complementary strands, and PNAs have a higher sequence discrimination ability than DNA [44]. Furthermore, PNA is highly resistant to nucleases and proteases since PNA consists of an unnatural backbone different from DNA and peptide [45]. These characteristics make PNA promising for in-cell and in vivo applications targeting genomic DNA. Although there have been many reports of PNA analogs with modified backbones [46; 47; 48; 49; 50; 51] (Figure 3), this review focuses on \(N\)-(2-aminoethyl)glycine PNA (aegPNA), with an overview of its dsDNA recognition mode, invasion.  
Figure 1: Structures of DNA and PNA.  
Figure 2: The dsDNA recognition by PNA via double-duplex invasion.
## 3 Experimental Results and Discussion  
### Determination of EDF and SMF Lengths  
In this experiment, we test the influence of different lengths of EDF on the performance of the system laser, as shown in Figure 3. In Figure 3a, with 15 m EDF, the laser has the lasing power of 3.7 mW @400 mA; with 11 m EDF, the laser has the best pump slope efficient of 5 mW @400 mA. In Figure 3b, with 12 m EDF, the laser has the highest optical-signal-to-noise ratio (OSNR) of 59.86 dB, and the output power is 2.8 mW. To achieve a better single longitudinal mode state, 12 m EDF is selected in the subsequent experiments.  
When the SMF lengths are 30-, 70-, 100-, 120-, 200-, and 280 m, respectively, the corresponding OSNR is as shown in Figure 4a. Since the reflectivity of the FBG is the same, the variation of OSNR variation is slight. When the pump current is 400 mA, the output of the proposed laser is 2.8 mW, and an OSNR at multiple-longitudinal modes is 59.86 dB. At this point, there are many side modes in the output spectrum. The transverse mode affects the distribution of the field intensity when the laser propagates and determines the laser spatial coherence. In comparison, the longitudinal mode affects the laser linewidth and determines the time coherence of the laser. By adjusting the VOA near the FRM from a multiple-longitudinal modes state to single-longitudinal modes state, the optical power is attenuated from 2.8 mW to 1.4 mW. Nevertheless, the OSNR still maintains 59.86 dB. The SLM laser output in 0 to 1 GHz spectrum range is obtained after fine-tuning, only the 80 MHz frequency is observed as shown in Figure 4b.  
Next, the impact of SMF with different lengths on laser linewidth is measured and compared. As shown in Figure 5a, when the length of SMF is 30-, 70-, 100-, 120-, 200-, and 280 m, the corresponding laser linewidths are 8.668-, 10.087-, 6.184-, 4.176-, 6.080- and 5.816 kHz, respectively. From Figure 5b, it is found that the smallest linewidth of optical laser is obtained when the SMF length is 120 m. The corresponding laser linewidth is 4.176 kHz.  
Figure 3: Effects of (**a**) output power against current, (**b**) OSNR against current in various EDF lengths.
# Economic Superiority of PIP Slip Joint Compared to Conventional Tubular Joints
## 2 Methodology
### Net Present Value  
The NPV analysis of joint types in offshore wind structures, as per Figure 6, reveals significant insights into their long-term financial viability, as depicted in the bar chart comparing grout joints, bolted flange joints, and PIP slip joints. The PIP slip joint demonstrates the highest NPV at EUR 39.559 M, surpassing grout joints (EUR 39.013 M) by 1.40% and bolted flange joints (39.051 EUR M) by 1.30%, indicating its superior profitability over the project's lifetime. This advantage can be attributed to PIP slip joint's streamlined installation process and enhanced durability, which minimize operational costs and extend service life, thereby maximizing discounted cash flows. In contrast, the negligible 0.10% difference between grout joints and bolted flange joints suggests comparable financial performance, with the latter holding a marginal edge. These findings, consistent with PIP slip joint's leadership in other financial metrics, such as LCOE, IRR, and ROI, underscore its potential to optimize the economic performance of offshore wind farms, offering a compelling case for its adoption in future projects aiming to balance cost-efficiency with long-term returns.
## 2 Structure of the Photovoltaic System
### DC-DC Boost Power Electronic Converter  
The DC-DC boost PEC transfers energy from the PVP to the load, its objective is to generate a voltage at the output \(V_{out}\), greater than the voltage of the PVP \(V_{in}\), which is related to the duty cycle \(D\) of the power switch \(Q\). The converter diagram shown in Figure 3, comprises the input and output voltages (\(V_{in}\) and \(V_{out}\)), an inductor \(L\), a resistor inductor \(r\), a capacitor \(C\), a fast recovery diode \(S\), a power switch \(Q\) and a load resistor \(R_{L}\). The mathematical model of this converter [56] is presented in Equation (5).  
\[\left[\begin{array}{c}\frac{d\dot{i}_{L}(t)}{dt}\\ \frac{dV_{out}}{dt}\end{array}\right]=\left[\begin{array}{cc}-\frac{f}{L}&- \frac{1-D}{L}\\ \frac{1-D}{C}&-\frac{1}{R+C}\end{array}\right]\left[\begin{array}{c}i_{L}(t) \\ V_{out}(t)\end{array}\right]+\left[\begin{array}{c}\frac{1}{L}\\ 0\end{array}\right]V_{in}(t) \tag{5}\]  
Figure 3: DC-DC boost power electronic convertor (PEC) diagram.
## 4 Discussion  
Creating 3D geovisualization of a geosite requires the proper acquisition of primary data images using a UAS. More specifically, for the 3D mapping of a geosite with sharp and irregular slopes such as the Voulgaris gorge, the above research shows that the coverage rate of 80% front and 60% side does not sufficiently cover the vertical sides of the area even when the flight altitude of UAS is low (60 m). None of the three flight altitudes, 60 m, 80 m, and 100 m, with this percentage of overlap managed to fully render the geological structure in the areas where negative slopes prevail. The images collected from the flights made at the respective altitudes (60 m, 80 m, and 100 m) with a coverage rate of 90% front lap and 70% side overlap produced 3D point clouds with a greater number of points on the steep surfaces, unsuitable for later creating the 3D geovisualization of the gorge. However, a significant absence of points was observed in the 3D point clouds created from the 60 m and 100 m flights. The collection of images for the creation of panoramas, although executed at three different altitudes, did not affect the results. The differences between the panoramas were minimal as the camera's lineup for capturing images was 360\({}^{\circ}\). The use of panoramas as high dynamic range images was crucial for photorealistic geovisualization. It had been proven to help smooth out sharp and intense shadows and, more specifically, in areas with intense geomorphology and steep slopes where the shading effect intensifies. Transferring the 3D model of the geosite to a virtual reality environment was a challenge due to the gorge's geometry, making the user's information complex and confusing. Using the panorama as environmental lights for lighting the 3D model of the geosite helped in simulating the natural environment in the virtual space. When the 3D model was adequately illuminated, the virtual space became more user friendly and realistic.  
The proposed methodology presented some limitations: (a) flight duration, (b) geomorphology of the study area, and (c) computer power. The total time of data collection lasted 2 h, resulting in capturing different shadows in the images. The shades then affected both the processing of the data and the texture rendered in the 3D models. Another difficulty encountered in this research was the geomorphology of the geosite, which made the study area approachable only from the south side. The available processing power capabilities significantly affected the data processing time, and the limitations imposed by the software used did not allow full utilization of the results. Nevertheless, the constant evolution of geovisualization means should have enabled full utilization of the 3D cartographic information.
## 5 Simulation Verification
### Steady State Error Elimination  
On the basis of the previous analysis, although the traditional virtual impedance control strategy can enhance the transmission capacity of the system, it causes the steady-state error of DC voltage. Figure 17 shows the comparison of two virtual impedance control strategies. The length of the DC cable is 50 km. At 2 s, the steady-state power command value is set from 500 MW to \(-\)500 MW.  
The DC voltage in the blue line represents the traditional virtual impedance, shown as the yellow frame in Figure 5, and the controller parameters are \(k_{p\text{r}dc}=15\), \(k_{i\text{n}dc}=100\), and \(R_{eq}=2\)\(\Omega\). The DC voltage in the red line represents the proposed control strategy, with \(k_{p\text{r}dc}=15\), \(k_{i\text{n}dc}=100\), \(R_{eq}=2\)\(\Omega\), and \(k_{i}=10\). Under the two control strategies, the DC voltage fluctuates in a short time and a small range only, and the active power can steadily step down. The DC voltage shown by the red line is close to the instruction value of 250 kV, and the steady-state error of the blue line is approximately 5 kV in both power flow directions, which are consistent with the theoretical analysis. The simulation  
Figure 16: DC-link voltage and active power of the VSC–HVDC system (**a**) under the proposed control strategy and traditional control strategy [13] and (**b**) under the proposed control strategy with different virtual impedance parameters.  
results show that the improved virtual impedance control method can improve the stability of the DC system and eliminate the steady-state error of the DC voltage caused by the virtual impedance.
# Mobility Management of Unmanned Aerial Vehicles in Ultra-Dense Heterogeneous Networks
## 3 Mobility with UAV Technology
### Handover in UAV Networks  
HO performance is a crucial indicator of the UAV network performance since it possesses a level of network flexibility. UAV coverage fluctuates based on transmission power and altitude. Researchers are investigating seamless HO to provide ground users with dependable HO. Traditional cellular networks and HO in UAV networks are not similar. To deliver ongoing services to mobile users, an intelligent HO approach has been devised. Effective solutions based on enhanced software have been provided to achieve quick HO in UAV networks. Knowing the quantitative expression of the likelihood of HO can aid in the construction of system gridlines. Small stochastic geometric models in UAV networks can also be created for evaluating mobility performance. By simulating UAV movement with a random mobility model, the statistical aspects of the channel gain can be examined [53; 54; 55].
# A Review of Deep Learning Based Speech Synthesis
## 2 An Overview of Speech Synthesis
### Traditional Speech Synthesis Technology
#### 2.3.2 Parametric Speech Synthesis  
The parametric speech synthesis refers to the method that uses digital signal processing technologies to synthesize speech from text. In this method, it considers the human vocal process as a simulation that uses a source of glottal state to excite a time-varying digital filter which characterizes the resonance characteristics of the channel. The source can be a periodic pulse sequence that is used to represent the vocal cord vibration of the voiced speech, or a random white noise to indicate undefined unvoiced speech. By adjusting the parameters of the filter, it can synthesize various types of speeches [15]. Typical methods include vocal organ parametric synthesis [23], formant parametric synthesis [24], HMM-based speech synthesis [25], and deep neural network (DNN)-based speech synthesis [26; 27].
## 6 Challenges and Future Research Directions
### Detection Speed and Computational Overhead  
Some detection methods employing entropy and statistical methods have fast detection speeds and low computational overhead. However, in most cases, the accuracy result is not sufficient for practical applications. In addition, the high false alarm rates associated with these methods limit their application in real-world scenarios. Nevertheless, machine learning and deep learning have improved detection accuracy but suffer from longer processing times. Deep learning uses more layers to achieve better performance, thus increasing the processing time. Balancing the trade-off between accuracy and detection speed remains a future research problem.
# Progressive Multi-Scale Fusion Network for Light Field Super-Resolution
## 3 Progressive Multi-Scale Fusion Network
### Residual Receptive Field Block (ResRFB)  
Extracting and using discriminative features with rich context information is meaningful to reconstructing HR images with more details. Inspired by [25], we propose the ResRFB to enlarge the receptive field and extract hierarchical features from each LF SAIs. As shown in Figure 4, the RFB consists of the convolutions of different kernel parameters and the dilated convolutions of different dilated rates, which can imitate the human receptive field and increase the diversity of convolution. In the feature extraction part, two RFBs are used with a residual connection. Compared with the atrous spatial pyramid pooling (ASPP) used in [15; 16], this block has superior discriminative representation and robustness.  
As shown in Figure 2a, we first put the SAIs into a \(1\times 1\) convolution to extract the initial features. Then, these features are fed into cascaded ResRFBs and ResBlocks, whose structures are shown in Figure 2b,c. The ResRFB is constructed from two identical RFBs and applied with the shortcut design, whose parameter is set to 0.1. For each RFB, it has two branches to obtain the hierarchical features, as illustrated in Figure 4. Then, a ReLU activation is used after each ResRFB. For each ResBlock, it consists of two \(3\times 3\) convolutions and a ReLU activation. In summary, multi-scale features of \(L_{\text{LR}}\) are extracted by using these blocks. The effectiveness of ResRFB is discussed in Section 4.4.  
Figure 2: Architecture of the proposed PMFN. The overall network composes of three parts: feature extraction, feature fusion, and reconstruction. The input of our network is LR SAIs. The reference view is randomly selected from these SAIs, and the remaining images are auxiliary view images. The output is an SR reference-view image.
# Diffuse Optical Tomography Using Bayesian Filtering in the Human Brain
## 2 Material and Methods  
### Data Acquisition in MRI  
Functional magnetic resonance images were acquired from a 3T Signa Excite HD scanner (General Electric). T1-weighted volume was acquired for precise anatomical localization (TR = 6 ms, TE = 1 ms, flip angle = 12\({}^{\circ}\), matrix size = 256 \(\times\) 256 pixels, \(98\times 98\) mm in plane resolution, spacing between slices = 1 mm, slice thickness = 1 mm, inter-slice gap = 0). The anatomical slices covered the whole brain and they were acquired parallel to the anterior-posterior commissure. T2-weighted echo-planar imaging (EPI) volumes were acquired in 36 axial slices that covered the whole head, with the following parameters: field of view 25.6 mm, slice thickness 4 mm, inter-slice gap 1 mm, \(64\times 64\) matrix, flip angle 90\({}^{\circ}\), repetition time (TR) = 2 s, echo time (TE) =22.1 ms.
## 2 An ODE-Based Model for OORS
### Performance Metrics  
We define the offloading efficiency \(\epsilon(t)\) at the end of the time interval \([0,t]\) as the ratio of the total number of content items received through opportunistic communicationsto the total number of content items delivered. Utilising \(\Omega_{o}(t)\) and \(\Omega_{c}(t)\), we can easily derive that  
\[\epsilon(t)=\frac{\Omega_{o}(t)}{\Omega_{o}(t)+\Omega_{c}(t)}. \tag{7}\]  
By using (7), we can compute the average offloading efficiency as follows: \(\epsilon=\lim_{t\to\infty}\epsilon(t)\).  
The average content latency \(\overline{W}(t)\) experienced by nodes within the time interval \([0,t]\) is modelled using the expected waiting time \(E[\widehat{W}_{c}]\), given by expression (6). We recall that \(E[\widehat{W}_{c}]\) is a function of \(\mathcal{I}(t)\), the solution of the system (2). Since the average request rate \(\lambda_{0}\) is constant, we can average \(E[\widehat{W}_{c}]\) over time and directly derive the following expression:  
\[\overline{W}(t)=\frac{\int_{0}^{t}E[\widehat{W}_{c}(T^{*}(\tau))]d\tau}{t}. \tag{8}\]  
In our system model, the average time \(T_{N}\) required to generate \(N+1\) content requests can be derived as \(T_{N}=\frac{N+1}{\lambda_{0}}\). Then, we approximate the average content latency experienced by users as \(\overline{W}(T_{N})\).  
Finally, we derive the average number of content copies in the system, denoted by \(\overline{C}\). This value corresponds to the time-averaged number of 'infected' nodes, expressed as  
\[\overline{C}=\frac{\int_{0}^{T_{E}}\mathcal{I}(\tau)d\tau}{T_{E}}\, \tag{9}\]  
where \(T_{E}=T_{N}+\overline{W}(T_{N})+T_{s}\) represents the approximate average duration for all nodes to receive and then stop sharing the requested content. The approximation used in Equation (9) is grounded in the assumption that, beyond the time \(T_{N}\), all nodes have requested the content, on average. The last request will be satisfied after an average \(\overline{W}(T_{N})\) time. Subsequently, all copies of the content are deleted within the sharing timeout. This formulation implicitly assumes that the evolution of the \(\mathcal{I}(t)\) function predominantly occurs within the interval \([0,T_{E}]\).
## 3 Optimizing In-Orbit SAR Operational Parameters for Altitude Variation
### PRF Optimization
#### 3.2.1 Selection of Constant PRF  
This method uses the PRF diamond diagram shown in Figure 14. The final PRF is selected within the white clear regions in the PRF diamond diagram. In each subfigure of Figure 14, the green and blue areas represent Tx and nadir interference regions, respectively. The two areas are drawn to be overlaid each other for three altitudes: the minimum, nominal, and maximum altitudes. The white area represents the allocable PRF range. As shown in Figure 14, the allocable range becomes narrower for the increasing incidence angle, and no allocable range exists for SW#8. As a result, this method has limitation to be applied only for the lower incidence angle range.  
Figure 13: Examples of RAR performances for altitude variation, respectively, when (**a**) incidence angle and (**b**) look angle at center of swath are used for the reference angle in SW#5, and when (**c**) incidence angle and (**d**) look angle at center of swath are used for the reference angel in SW#8.  
If the altitude variation is small and the allocable PRF range exists simultaneously, an alternative method is to apply the constant RPF only for low look angles of Figure 13(a). This method is easy to apply for the in-orbit operation, but the SAR performance is degraded as the actual altitude starts to deviate from the predicted altitude.
## 3 Results and Discussion
### Microstructural Characterization of As-Extruded, 37% Rolled and 68% Rolled Samples
Plots of the grain size distribution for each rolling condition taken from longitudinal sections indicated relatively small changes in average grain diameter, as shown in Figure 6. Although the rolling process has visibly changed the grain structure, the average grain size remains approximately the same, beginning at 5.15 \(\pm\) 3.39 \(\upmu\)m in the ShAPE extruded material, to 5.27 \(\pm\) 3.53 \(\upmu\)m after 68% rolling reduction. Notably, this excludes the regions of the rolled specimens where obtaining a diffraction pattern was not possible. It was also noticed that while the average grain diameters are the same with respect to rolling extent, the maximum grain size, \(X_{\text{max}}\), after hot rolling had increased. From Figure 6, the maximum grain sizes grew from \(\sim\)19 \(\upmu\)m to 33.6 and 26.8 \(\upmu\)m after 37% and 68% hot-rolling, respectively. Figure 5: EBSD orientation (IFF Y) maps and pole figures of the ShAPE extruded and flattened and the 37% hot rolled ZK60 specimens in the transverse section. Figure 6: The grain size distributions of ShAPE extruded, 37% hot-rolled, and 68% hot-rolled ZK60 specimens, as taken from EBSD scans on longitudinal sections.