esa-sceva/satcom-chunk-collection huggingface dataset:
# A DFB-SOA Based Optical Vector Network Analyzer for Characterization of Bandpass Optical Devices
## 1 Introduction
By utilizing a cascaded DFB-SOA with an ODUT, our proposed OVNA maintains the optical carrier, enabling the measurement of optical components with bandpass response characteristics. In this paper, the operating principle of the proposed DFB-SOA based OVNA is mathematically analyzed in detail. The experimental validation of our proposed OVNA is established through spectral analysis of an optical bandpass filter. The experimental results indicate that the proposed DFB-SOA based OVNA is capable of accurately assessing the ODUT utilizing a bandpass response, achieving a superior measurement resolution of 25 MHz. The dynamic range of the proposed system in relation to the driven current of the DFB-SOA is also investigated. Additionally, the detection range of our system can be expanded through the utilization of broadband optoelectronic devices. Furthermore, the OVNA we have introduced is highly suitable for integration into a microchip platform.
## 5 Clustering Analysis of the Selected Ground Motions Based on a K-Means Algorithm
### Pulse-like Ground Motion Clustering Analysis  
As shown in Section 5.1, the number of clusters, \(K\), is a crucial problem for the K-means clustering algorithm and directly impacts the final clustering effect. Therefore, an exploratory approach is employed to determine the reasonable number of clusters, \(K\), in this study. Firstly, according to the calculation procedure of the K-means clustering algorithm described in Section 5.1, the initial selected 100 pulse-like ground motions are clustered and analyzed, based on the cluster number \(K\) of 1, 2, \(\ldots\), 10, respectively, and the sum of the squared error value, \(D\), of each cluster number is calculated. Then, the curve of the relationship between the cluster number and the sum of squared errors is plotted, as shown in Figure 12. The optimal number of clusters for the K-means clustering algorithm is determined by identifying the cluster number corresponding to the point where the slope of the curve changes suddenly from large to small and thereafter changes gradually.  
As shown in Figure 12, the slope of the relationship curve decreases significantly once the number of clusters reaches 4, and the rate of change gradually slows down. Thus, the optimal cluster number, \(K\), is determined to be 4; following the analysis steps of the K-means clustering algorithm in Section 5.1, the initial selected pulse-like ground motions are clustered via a K-means clustering algorithm. The clustering analysis results are shown in Figure 13. Meanwhile, it can be seen from Figure 13 that the numbers of pulse-like ground motions in the first class, second class, third class, and fourth class are 20, 38, 39, and 3, respectively. Moreover, the dispersion between each ground motion in the fourth class is relatively large, while the ground motions of the fourth class are obviously far from the ground motions of the other three classes; therefore, it can be classified into one class. Furthermore, the ground motions in the first class, second class, and third class are clustered into cluster shapes, demonstrating the effectiveness of the classification results. It can thus be seen that utilizing the first three principal components of seismic _IMs_ as clustering indices and applying the K-means clustering algorithm for the clustering analysis of pulse-like ground motions can yield highly accurate classification results.
## 4 ANN Modeling
### ANN Model Initialization  
The ANN model must be initialized with appropriate hyperparameters before training, as these parameters significantly affect the model's performance. As shown in Figure 3, the training process begins with an initial set of hyperparameters. The training subset is used to train the model, while the validation data are employed for evaluation. The  
Figure 5: The relationship between the input features and the output feature.  
training process proceeds by adjusting the hyperparameters using a random search algorithm until the stopping criteria are met. Here, the stopping criteria achieved a higher \(R^{2}\) score with minimal MSE and MAE values. The optimization process is explored with the following parameters: number of hidden layers {2,3,4}, number of neurons in each hidden layer {5,10,15,20,25,30,40}, learning rate {0.1,0.05,0.01,0.001,0.0001}, regularization parameter {0.1,0.001,0.0001,0.00001}, epochs {500,1000,1500}, activation function {Identity,Logistic,Tanh,ReLU}, and solver {LBFGS,SGD,ADAM}. After extensively trying various combinations of hyperparameters, the optimal hyperparameters are listed in Table 1.  
The nonlinear activation function, Rectified Linear Unit (ReLU), is used, defined as \(f(x)=\max(0,x)\), where x is the input to the neuron. Information flows from the input layer to the output layer through the intermediate hidden layers.  
We used the Limited Memory Broyden-Fletcher-Goldfarb-Shanno (LBFGS) algorithm as the iterative solver. LBFGS optimizes the loss function by adjusting model parameters, including weights and biases. This solver is recognized for its ability to achieve faster convergence and deliver improved results, especially with smaller datasets.
## 4 Result and Discussion  
### The Description of the Datasets  
#### 4.1.1 Case Study 1  
The data are from the Konstruktions-und Antriebstechnik (KAt), in the school of Mechanical Engineering at University Paderborn [28]. The bearing test rig is shown in Figure 5a (II). The modular test rig consisted of an electric motor, a torque-measurement shaft, a rolling bearing test module, a flywheel, and a load motor. The bearings 6203--with different types of damage caused by an accelerated lifetime test on the apparatus, as shown in Figure 5a (I)--were mounted in the bearing test module (as shown in Figure 5a (II)) to generate the experimental data. The severity of the damage was described by the percentage of length relative to pitch circumference. The three levels for 6203 are shown in Table 2[28].  
The information of the test bearings with real damages caused by the accelerated lifetime test is shown in Table 3. The rotational speed of the drive system, the radial force onto the test bearing, and the load torque in the drive train were the main operating parameters. The parameters were defined as shown in Table 4. All three parameters were kept constant for the time of each measurement.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Damage Level** & **Percentage Values** & **Limits for Bearing 6203** \\ \hline
1 & 0–2\% & \(\leq\) 2 mm \\
2 & 2–5\% & \(>\) 2 mm \\
3 & 5–15\% & \(>\) 4.5 mm \\ \hline \hline \end{tabular}
\end{table}
Table 2: The damage levels to determine the severity of damage.  
Figure 5: The bearing test rig. (**a**) The test rig of Konstruktions-und Antriebstechnik (Kat); (**b**) The test rig of Dynamic and Identification Research Group (DIRG): (I) apparatus for accelerated life time test to obtain the damaged bearings; (II) modular test rig to obtain the vibration signals under different working conditions: (1) an electric motor; (2) a torque-measurement shaft; (3) a rolling bearing test module; (4) a flywheel; and (5) a load motor.
## 3 Results and Discussion
### Characterization  
#### 3.2.1 FTIR Analysis  
The FTIR spectrum in the range of 400-4000 cm\({}^{-1}\) for the H-TTAPA resin is presented in Figure 3. The band observed at 1625 cm\({}^{-1}\) was matched to the of _v_(C=O) stretching vibrations of carboxylic acid; the peak at 2922 cm\({}^{-1}\) was ascribed to C-H stretching vibrations; the peak at 3481 cm\({}^{-1}\) was matched to _v_(N-H) stretching vibrations; the broad peak at 3280 cm\({}^{-1}\) was matched to _v_(O-H) bonds; the peak at 1043 cm\({}^{-1}\) was matched to the C-O stretching frequency; the medium peak at 1405 cm\({}^{-1}\) was attributed to O-H bending vibrations; and the peak at 1312 cm\({}^{-1}\) was interpreted as C-H bending vibrations. These FTIR results were in agreement with those of the previous research [48; 49; 50; 51; 52; 53]. These peaks suggested that the presence of functional groups in the H-TTAPA resin may facilitate the adsorption of metal ions in water onto the surface of the resin.
## 6 Measuring EMI Shielding Effectiveness (EMI SE) of Porous Polymer Nanocomposites  
The most widely used instrument to measure the EMI SE of polymeric nanocomposite shields is placing the test samples inside the waveguide of a Vector Network Analyzer (VNA), where one can measure both the magnitude and phase of the EM signals. However, by using a Scalar Network Analyzer (SNA), one can only measure the magnitude of the signals and cannot measure the complex permittivity and permeability of test specimens. Hence, VNA is the most preferred instrument to elucidate the shielding performance. The incoming and transmitted EM waves from a two-port VNA can be expressed in terms of scattering parameters (viz. S\({}_{11}\), S\({}_{21}\), S\({}_{22}\), S\({}_{21}\)), where S\({}_{11}\) and S\({}_{21}\) are forward reflection and transmission coefficients, and S\({}_{22}\) and S\({}_{21}\) are reverse reflection and transmission coefficients. As per the EMI shielding theory, the relation between power coefficients of reflectance (R), absorbance (A) and transmittance (T) is expressed as [100]:  
\[\mathrm{A}+\mathrm{R}+\mathrm{T}=1 \tag{15}\]  
where  
\[\mathrm{T}=|\mathrm{S}_{12}|^{2}=|\mathrm{S}_{21}|^{2} \tag{16}\]  
\[\mathrm{R}=|\mathrm{S}_{11}|^{2}=|\mathrm{S}_{22}|^{2} \tag{17}\]  
The EMI SE can be calculated using the following equations [100]:  
\[\mathrm{SE}_{\mathrm{R}}=-10\,\log(1-\mathrm{R}) \tag{18}\]  
\[\mathrm{SE}_{\mathrm{A}}=-10\,\log\biggl{(}\frac{\mathrm{T}}{(1-\mathrm{R})} \biggr{)} \tag{19}\]  
\[\mathrm{SE}_{\mathrm{Total}}=\,\mathrm{SE}_{\mathrm{A}}+\,\mathrm{SE}_{\mathrm{ R}}+\mathrm{SE}_{\mathrm{MR}}=-10\,\log\mathrm{T} \tag{20}\]  
Herein, SE\({}_{\mathrm{MR}}\) is the shielding due to multiple internal scatterings which can be neglected when SE\({}_{\mathrm{A}}\geq\) 10 dB [101].
# The Domain Mismatch Problem in the Broadcast Speaker Attribution Task
## 4 Methodology
### Hybrid Solution  
The next alternative, inspired by [11], presents a new approach that simultaneously performs diarization and speaker attribution using the enrollment audio as anchors. For this purpose, we consider a modification of the algorithm proposed in [36] by merging the clustering and identity assignment blocks. The system, whose functionality is represented in Figure 7, starts estimating the embeddings obtained from the inferred segments. Regarding the new algorithm, it is a statistical Maximum A Posteriori (MAP) solution to estimate the set of labels \(\Theta^{\prime}=\Theta_{entroll}\cup\Theta\) that best explains the set of embeddings \(\Phi^{\prime}=\Phi_{entroll}\cup\Phi\), i.e., the union of enrollment and evaluation embeddings, assuming the labels \(\Theta_{entroll}\) for the enrollment subset are fixed. This enrollment information generates a set of anchor clusters for the speakers of interest. Thus, the algorithm iteratively assigns the embeddings from the audio under evaluation to the anchor clusters or new clusters created on the fly for speakers out of the group of people of interest. The flowchart for the presented solution is shown in Figure 8. From the evaluation audio \(\Omega\), a stream of features is extracted and used for segmentation (VAD and SCPD blocks), extracting one embedding per estimated segment (red box). Similarly, enrollment audios are processed to obtain their embeddings in the yellow box. Finally, both sets of embeddings are fed into the new hybrid clustering and identity assignment block (green box) to obtain the final labels.  
Figure 6: Flowchart of the indirect assignment approach. Red and yellow boxes, respectively, stand for embedding extraction pipelines for the evaluation audio \(\Omega\) (online) and enrollment audios \(\Omega_{entroll}\). The green box means a diarization system, which clusters the evaluation embeddings \(\Phi\) to obtain diarization labels \(\Theta_{DIAR}\). The obtained embeddings (\(\Phi\) and \(\Phi_{entroll}\)) as well as the estimated labels \(\Theta_{DIAR}\) are taken into account in the identity assignment block.
# Predicting Output Power for Nearshore Wave Energy Harvesting
## 1 Introduction
### A Summary of Wave Energy Conversion  
Wave energy conversion is realized with the aid of a wave energy converter (WEC), a device that captures the energy inherent in the motion of waves and transforms it into electricity. WECs can be categorized according to their location (relative to the shoreline), their operational principle and their directional characteristics. Based on its distance from the shoreline, a WEC is categorized as either onshore, nearshore or offshore, with potential energy generation and difficulty of installation increasing in direct proportion to distance from the shore.  
In terms of operational principle, WECs are divided into four types [1]: oscillating water columns, overtopping devices, submerged pressure differential devices and oscillating wave surge converters. Oscillating water columns use waves to compress and expand air to rotate an air turbine that produces electricity. Overtopping devices capture the movement of the tides and waves and coverts it into potential energy by lifting the water to a higher-level reservoir, where it is used to drive a hydro-pump to produce electricity [7]. Submerged pressure differential devices are submerged point absorbers that utilize the pressure differential above the device between wave crests and troughs. Oscillating wave surge converters are composed of a hinged deflector positioned perpendicular to the wave direction that moves back and forth, exploiting the horizontal particle velocity of the wave.  
On the basis of directional characteristics, WECs can be categorized as attenuators, point absorbers and terminators. Attenuators lie parallel to the predominant wave direction and ride the waves. Point absorber devices absorb wave energy in all possible directions through the wave's multi-directional movement. Terminator WECs absorb the wave energy in one direction only [8].  
In addition to integrating renewable energy systems in the main power grid, maximizing the yield from these systems is important. Since the performance of such systems is influenced by non-human factors, such as environmental conditions, accurate prediction of output power is essential in determining the optimal location, size and configuration of the power plants. This study focuses on developing a prediction model for nearshore WECs that can help to determine both the optimal location and the optimal shape of the buoy.
## 3 Results and Discussion
### Astaxanthin and Fatty Acid Production
Compared to the control group, melatonin improved the lipid content of _H. pluvialis_ LUGU by 49.5% under photoautotrophic conditions [32], and salicylic acid increased the total fatty acid content _of_ _H. pluvialis_ 192.80 by 36.0% under photoheterotrophic conditions [30]. The combination of melatonin and nitrogen deficiency improved the lipid content in \(M\). sp. QLY-1 by 1.22-fold [33]. Indole-3-propionic acid promoted growth and lipid productivity in _C. pyrenoidosis_ FACHB5 and _Scenedesmus quadrica_ FACHB506 [34]. This suggests that the effects  
Figure 5: The contents and production of astaxanthin (**a**,**b**) and total fatty acid (**c**,**d**) of _H. pluvialis_ after 30 d photosynthetic cultivation under different _rac_-GR24 concentrations (2.0–8.0 uM). Asterisk symbol (*) represents the significant differences (_p_ < 0.05). of phytohormones on algal lipids are highly dependent on species and culture conditions, which may be an interesting topic for further research. Although the complex metabolic networks have not been clearly elucidated, the biosynthesis of fatty acids and neutral lipids in _H. pluvialis_ is believed to be closely related to the biosynthesis and storage of astaxanthin [5,6]. To understand the different accumulation patterns between astaxanthin and total fatty acids observed in this study (Figure 5), detailed genetic and biochemical investigations are required. The effect of higher _rac_-GR24 concentration above 8 uM on astaxanthin and lipid biosynthesis may be also the subject of further investigation. _H. pluvialis_ is regarded as a fatty acid-rich algal species available for commercial use in food and nutritional products [1]. PUFA have gained considerable public recognition in terms of their therapeutic potential, such as reduced risk of cardiovascular disease. Table 1 shows the fatty acid profiles of mature _H. pluvialis_ cyst cells after the 30 d cultivation with different _rac_-GR24 concentrations (2.0-8.0 uM). Overall, the addition of _rac_-GR24 increased the composition of palmitoleic (C16:1), \(\gamma\)-linolenic (C18:3n2c), and linolenic (C18:3n3c) acids in the total fatty acids, whereas it decreased the ratio of palmitic (C16:0) and oleic (C16:1) acids. Accordingly, the PUFA content (41.5-44.6%) of the _rac_-GR24-treated _H. pluvialis_ cells was slightly higher than that of the control (40.4%). It should be noted that the total fatty acid production was significantly improved by \(\sim\)60% (Figure 5), although the change in cellular PUFA content was small. Similarly, [PERSON] et al. [32] reported that the combination of melatonin and CaCl\({}_{2}\) promoted the PUFA ratio among the total fatty acids in _H. pluvialis_ LUGU. [PERSON] et al. [35] observed the promotion of PUFA biosynthesis in _C. mexicana_ GU732420 and _S. obliquus_ HM103382 in the presence of the phytohormone indole-3-acetic acid.
## 2 Materials and Methods
### Data Sources  
The Landsat 8 OLI_TIRS 30 m digital satellite image from 11 May 2019 was selected as the data source. Based on the ENVI software platform, we used high-resolution (15 m) single-band image and low-resolution (30 m) multi-spectral image band fusion to obtain a 15 m resolution satellite image, through the combination of 30 m DEM digital elevation data and related survey data; then, we classified the remote-sensing image into five types of land use: forest land, waters, grassland, cultivated land and construction land. Finally, we established the land use status in the Taishan area (Table 2, Figure 2). Additionally, through on-the-spot investigation, we learned about the restoration project pilot sites of various ecological restoration types in the Taishan area, such as the Ningyang County coal  
Figure 1: Location of the life community of mountains–rivers–forests–farmlands–lakes–grasslands in the Taishan area.  
mining subsidence treatment projects, the damaged mountain restoration on both sides of the Taifei First-Level Road and Provincial Road 330 Line, the mine ecological environment restoration in Ningyang County, the trilateral greening of the Dongping County section of the Dawen River, the construction of the Wenhe National Wetland Park and the construction of ecological protection forests in the old lake town of Dongping Lake.
# Coexistence of 5G/6G and Digital Terrestrial Television Networks
## 2 Networks Re-Planning
Additionally, correction factors for the carrier-to-noise ratio (C/N) as per [24] were incorporated to calculate Emin:  
* A--correction factor related to BER,
* B--correction factor associated with the pilot,
* C--correction factor related to the estimation of the real channel,
* D--correction factor related to the level of secondary noise of the receiver \(-\)33 dBc (value dependent on C/N'Rice determined in accordance with Equation (1)). \[\text{C/N}^{\prime}\text{{}_{Rice}}=\text{C/N}_{\text{Gauss-raw}}+\Delta_{ \text{Rice}}+\text{A}+\text{B}+\text{C} \tag{1}\]  
\[\text{C/N}_{\text{Rice}}=\text{C/N}^{\prime}\text{{}_{Rice}}+\text{D} \tag{2}\]  
These coefficients were taken into account according to Equations (1) and (2). The values of coefficients A, B, C, and D used are presented in Table 1.  
The protection ratio (PR) was determined in accordance with the guidelines outlined in [24], with its value set to the corrected and rounded resultant carrier-to-noise ratio (C/N) for a specific operating mode, as detailed in Table 1.  
In the analysis, the following additional conditions for fixed reception were applied: reception at a height of 10 m above ground level, utilizing a directional antenna characterized by its polarization and directional discrimination, with parameters as described in GE06 [11]. For the SFN, the k-LNM signal analysis method [25] was employed, with a coefficient k set to 0.6, along with a detailed examination of both the network gain and network  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \(\mathbf{E_{min}}\) & **Protection Ratio** & **C/N\({}_{\text{Gauss-raw}}\)** & \(\mathbf{\Delta_{Rice}}\) & **A** & **B** & **C** & **D** \\ \([\text{dB}\text{\text{\text{\text{\text{\text{\text{\text{\text{\text{\text@  
interference. Standard and practical methods of synchronizing the receiver to either the strongest (E\({}_{\text{max}}\)) or first (T\({}_{\text{min}}\)) signal in the SFN were utilized, which are commonly employed in most receivers. The SFN networks operating on the same channel in different parts of the country were considered fully synchronized, implying that the relative delay values between transmitters within the same SFN channel across the country should be appropriately set and controlled. The nominal range was defined by conditions that allow for stationary reception using external antennas, achieving a useful signal level that surpasses the required minimum electric field strength value at a height of 10 m above ground level. Interfering fields resulting from other planned DVB-T2 stations of the remaining multiplexes were considered for interference management. When calculating the population coverage, data on the population of communes were used, based on the statistics provided by the Central Statistical Office [27].