esa-sceva/satcom-chunk-collection huggingface dataset:
# Low-Complexity Chromatic Dispersion Equalization FIR Digital Filter for Coherent Receiver
## 3 Simulation and Analysis
### Result Simulation  
In this chapter, we will evaluate the impact of filter tap weight optimization and coefficient quantization on the performance of CDE in multiple formats compared with TD-CDE and FD-CDE, such as Equations (2) and (4). The FD-CDE adopts the method proposed in [7].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Value** & **Parameter** & **Value** \\ \hline Wavelength & 1550 nm & Chromatic dispersion coefficient & 16.5 ps/nm/km \\ Sequence length & 131,072 & Group velocity dispersion coefficient & 0.2 ps/km \\ Symbol rate & 28 Gbaud & Laser line width & 0.1 MHz \\ Modulation format & QPSK/16 QAM & Attenuation coefficient & 0.2 db/km \\ Roll-off factor & 0.25/1 & Signal power & 10 dbm \\ \hline \hline \end{tabular}
\end{table}
Table 1: System simulation parameter.  
Figure 4: Coherent optical communication system structure diagram.
# Energy-Efficient Spatial Query-Centric Geographic Routing Protocol in Wireless Sensor Networks
## 3 QTGrid Routing Protocol  
The proposed quadtree grid (QTGrid) routing protocol is introduced in five aspects, namely, cluster setup, the election of CH and parent CH (PCH), routing strategy, data aggregation, and spatial query.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Protocols** & **Energy Saving** & **Load Balancing** & **Spatial Query Efficiency** & **Large-Scale WSN** \\ \hline GeoGrid [26] & High & Low & Middle & Yes \\ GFTCRA [37] & Middle & Yes & Low & Yes \\ CEBCRA [16] & Middle & Yes & Middle & Yes \\ GCP [38] & Middle & Low & Low & Yes \\ EEGBR [39] & Middle & Low & Middle & No \\ GRCS [40] & Middle & Low & High & No \\ GEGR [41] & Middle & Low & Middle & No \\ MOCHs [42] & High & Middle & Middle & Yes \\ QTBDC [43] & High & Low & High & Yes \\ GCCR [20] & High & High & Middle & Yes \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of various clustering GR (Geographic routing) protocols.
## 2 Materials and Methods
### Analysis and Utilization of Data
#### 2.4.3 Model Estimation and Evaluation  
The most effective spectral indices, which showed a stronger correlation, were identified by generating isopotential plots of the coefficient of determination between the original spectral index and the first-order derivative spectral index with the nitrogen nutrient index. The inversion of the nitrogen nutrient index of winter wheat was then performed using the empirical modeling method. A regression model was developed to determine the appropriate vegetation index based on ground agronomic data. The selected function types included exponential, linear, logarithmic, quadratic, and power functions.  
Once the model was established, the reliability of the inversion model for winter wheat leaf nitrogen concentration was tested. The accuracy of the model was assessed using the following commonly used model evaluation parameters: the coefficient of determination (\(R^{2}\)), the relative error (RE), and the root mean square error (RMSE). A higher accuracy of the model is indicated by an \(R^{2}\) value closer to 1 and smaller values of RMSE and RE.  
The formula for calculating the evaluation indicators is as follows:  
\[R^{2}=\frac{SSR}{SST}=1-\frac{SSE}{SST} \tag{5}\]  
\[RMSE=\sqrt{\frac{\sum_{i=1}^{n}(y_{i}-\hat{y})^{2}}{n}} \tag{6}\]  
\[RE=\sqrt{\frac{1}{n}{\sum_{i=1}^{n}}\left(\frac{\hat{y}-y_{i}}{y_{i}}\right) \times 100\% \tag{7}\]  
where the total sum of squared deviations is \(TSS=\sum_{i=1}^{n}(y_{i}-\hat{y})^{2}\); the residual sum of squared deviations is estimated as \(SSE=\sum_{i=1}^{n}(y_{i}-\hat{y})^{2};y_{i}\) denotes the measured value of the NNI; \(\vec{y}\) denotes the mean value of the actual value; \(\hat{y}\) denotes the predicted value of the NNI; and n denotes number of samples.  
Furthermore, this study randomly allocated 67% of the data as the modeling dataset and 33% as the validation dataset. These datasets were derived from agronomic parameters and remotely sensed data collected during various fertility periods. The purpose was to analyze and compare the relationship between the spectral data and agronomic parameters and to develop the most effective inversion model.
# Automatic Detection Method of Sewer Pipe Defects Using Deep Learning Techniques
## 5 Sewer Defect Detection System  
At present, there are many use cases for applying deep-learning-based detection technology in the real world [6,31], which demonstrate the practical implementability of the proposed method.  
In this section, we propose a potential sewer defect detection system whose workflow is shown in Figure 14. Firstly, the CCTV robot collects the video data of the sewer pipes. Secondly, these videos will be fed directly into the system for the detection of specific defects. Finally, the system will output the final inspection results and give them to professional technicians for processing. The output of the system contains two parts, the labeled video and the defect frames extracted from the video. Figure 15 shows the labeled video and the extracted defect frames which come from an actual inspection project of an underground pipeline in Shanghai. It can be seen that in the labeled video, different defects are marked with bounding boxes of different colors, which makes it easy for technicians to review.  
Defect detection is usually divided into two steps: fieldwork and office work. In fieldwork, technicians use professional detection equipment to obtain video data of sewer and save it to storage devices for office personnel to view. In office work, experts watch the collected sewer videos in detail and manually record information about each defect. The proposed system is mainly used for office work inspection, so only a personal computer that can run the system is required and no hardware equipment is required. Of course, the better the performance of the computer, the faster the detection speed of the system, which is far higher than the speed of manual defect detection.  
When the detection performance of the system is good enough, technicians can directly evaluate the state of the sewer pipe by watching the labeled video. The defects annotated using bounding boxes allow the technician to spend less time and effort in reviewing them. Moreover, the defect frames output by the system allows technicians to produce inspection  
Figure 14: The workflow of sewer defect detection system.  
Figure 15: System output results, the red box is stagger, the purple box is root. (**a**) labeled video and (**b**) defect frames.  
reports without manually capturing and labeling images from the video. As shown in Figure 13c, the red arrow is manually annotated by technicians, which also takes a lot of time. Overall, this system can significantly speed up the detection efficiency of sewer defects, reduce the work intensity of technicians and has high accuracy, which provides a very important application value.
## 3 Code Reuse via Principal Component Analysis
This is achieved since NOMA grouping takes into account not only the desired MS signal and channel conditions, but interference minimization to the other MS of the same NG as well. ```
Step 1: Set \(ng\gets 1\), \(U_{o}\leftarrow\{1:K\}\), \(U_{ng}\)\(\leftarrow\{\ \}\) Step 2: while (\(ng<NG_{m}\)) \((u_{o},u_{1})\leftarrow\underset{(k,k^{\prime})\in I_{ho}}{\operatorname{argmax}} \Bigg{\{}\frac{\prod\limits_{i=1}^{L}|\tilde{\mathbf{c}}_{u_{i}}(l)|}{\tilde{ \mathbf{c}}_{u}^{k}\sum\limits_{i=1}^{L}\sum\limits_{j=1}^{L}\sum\limits_{j= 1}^{L}\tilde{\mathbf{c}}_{u}(l)^{2}}\Bigg{\}}\) \(U_{ng}\gets U_{ng}\cup\{u_{o},u_{1}\}\), \(U_{o}\leftarrow U_{o}\cup\{u_{o},u_{1}\}\) \(ng\gets ng+1\), \(U_{ng}\)\(\leftarrow\) [ ] end Step 3: SIC \(ng\gets 1\)  while (\(ng<NG_{m}\)) \(\{u_{o},u_{1}\}\gets U_{ng},j\gets 1\)  Decode the \(s\)th symbol of user \(u_{o}\): \(z_{u_{o},s}\leftarrow\underset{y\in C}{\operatorname{argmin}}\sum\limits_{l =1}^{L}\zeta_{u_{i},l,s}-y\Bigg{\}}\) while (\(j<L\)) \(\zeta_{u_{1},l,s}\leftarrow\zeta_{u_{1},l,s}-\mathbf{r}_{u_{1},l}\sum\limits_ {l=1}^{L}\mathbf{H}_{u_{1},l^{\prime}}\mathbf{t}_{u_{o}}\big{(}\rho_{u_{1},u_{ o},u_{1}-l^{\prime}}\hat{b}_{u_{o},s-1}+\overline{\rho}_{u_{1},u_{o},l-l^{ \prime}}\hat{b}_{u_{o},s}\big{)}\sqrt{\overline{\
u_{u_{o}}}}\) \(j\gets j+1\) end \(z_{u_{1},s}\leftarrow\underset{y\in C}{\operatorname{argmin}}\sum\limits_{l =1}^{L}\zeta_{u_{1},l,s}-y\Bigg{\}}\) \(ng\gets ng+1\) end
```  
**Algorithm 2** Non-orthogonal multiple access (NOMA) user grouping and decoding algorithm.
## 3 Sensing Per Crop  
Studies are being carried out for each fruit and vegetable around the world, depending on the importance of crop yields per country. It is impossible to write a review that covers all these researches so the main fruits and vegetables around the world would be covered, including apple, grapes, avocado, mango, citric, tomato and potato (Table 1). The latter botanically is not a fruit or a vegetable, but developed countries consume potatoes in huge quantities, so a lot of funding is available to detect diseases and increase its quality.  
\begin{table}
\begin{tabular}{l l l} \hline Fruit or vegetable & Pathogen common name & Pathogen scientific name \\ \hline Mango and avocado & Anthracnose & _Colletotrichum_ \\  & & _gloeosporoides_ \\  & stem-end rot & _Lasiodiglodia theebromae_ \\ Potato & Fusarium wilt & _Fusarium oxysporum_ \\  & Late blight & _Phytophthora infestans_ \\ Tomato & Fusarium wilt & _Fusarium. oxysporum_ \\  & Rhizous rot & _Rhizopus stolonifer_ \\  & Downy mildew & _Pseudoperonospora cubensis_ \\ Apple & Apple scab & _Venturia inaequalis_ \\  & Fire blight & _Erwinia amylovora_ \\  & Black rot & _Physalospora obtuse_ \\ Citrus & Black spot & _Guignardia citricarpa_ \\  & Gummosis & _Phytophthora parasitica_ \\  & Trizteza & _Toxoptera citricidus_ \\  & Sooty canker & _Hendersonula toruloidea_ \\ Grapes & Downy mildew & _Plasmopara vitcola_ \\  & Bunch rot & _Botrytis cinerea_ \\ \hline \end{tabular}
\end{table}
Table 1: Pathogens attacking selected fruits and vegetables.
# Towards Flexible Distribution Systems: Future Adaptive Management Schemes
## 3 Future Adaptive Control and Management Methods
### Frequency Adaptive DER Unit PU-Droops: Stage 2-3  
As also presented in Table 3 and [81], the DER unit \(PU\)-droop settings could be frequency adaptive (Figure 6) at evolution stages 2-3 (Table 1). During larger frequency deviations, frequency adaptive \(PU\)-droops could enable larger PV and BESS power system frequency support as well as maximize smaller LV network-connected demand (loads) participation to frequency control [81]. On the other hand, the implementation could also be smoother without exact frequency and voltage limits for the activation of the frequency adaptive functionality, as shown in Figure 6b. A real-time frequency adaptive \(PU\)-droop for PV and BESS units (Figure 6b) was proposed in [81] when frequency deviation is between \(\pm\)1.0 Hz from the nominal frequency \(f_{\mathrm{n}}\). If frequency deviation is larger, then similar settings will be used as if the deviation would be 1.0 Hz from \(f_{\mathrm{n}}\). In Figure 6b, equations for X\({}_{1}\), X\({}_{2}\), X\({}_{3}\), and X\({}_{4}\)  
\[f=\text{measured frequency (Hz)}\]  
\[f_{\mathrm{n}}=\text{nominal frequency (Hz)}\]  
\[U_{\mathrm{min\_ini}}=\text{chosen initial minimum voltage set-point value when}f=f_{\mathrm{n}}\text{ (pu)}\]  
\[U_{\mathrm{max\_ini}}=\text{chosen initial maximum voltage set-point value when}f=f_{\mathrm{n}}\text{ (pu)}\]  
\[P_{\mathrm{n}}=\text{nominal active power (MW)}\]does not require knowledge about minimum and maximum voltages in the corresponding distribution network (LV and MV). Therefore, this approach could be further improved in the future in stage 2 with the real-time measurements-based data in order to achieve even more fine-tuned and optimized OLTC set values as well as compatible settings for the real-time adaptive \(QU\)-droops of the DER units (Section 3.1).  
If adaptive OLTC control at stages 2-3 (Table 5) will be increasingly frequency deviation-dependent during larger frequency deviations, then it needs to be also taken into account how these different adaptive OLTC functionalities affect each other and the number of available flexibility services.
###### Abstract  
This study designed a wide-beamwidth pixelated dielectric resonator antenna (DRA) combined with a low-pass filter (LPF) to suppress harmonics. The DRA was designed to create a wide-beam pattern with a pixelated structure. The pixelated DRA was optimized by a genetic-learning particle swarm optimization algorithm. To prevent significant higher-mode radiation and harmonics from occurring in the DRA, an LPF was included in its feeding line. The filter had a seventh-order Chebyshev design, and a hybrid step-impedance filter was proposed by modifying the step-impedance filter for use in narrow spaces behind the ground.  
pixelated antenna; dielectric resonator; low-pass filter; filtenna; wide-beamwidth
## 3 Uncertainty Modeling Techniques
### Hybrid Probabilistic-Possibilistic Methods  
Hybrid probabilistic-possibilistic methods have emerged as powerful tools for addressing uncertainties in power systems, particularly in scenarios where both exogenous and endogenous uncertainties play significant roles. In [46], a hybrid framework is introduced to evaluate the capacity credit of demand response (DR) in smart grids by combining probabilistic and possibilistic models to capture uncertainties from both physical and human factors. This approach enables a more comprehensive assessment of DR's reliability potential, factoring in variables like load participation and recovery. In [47], a hybrid method is employed to manage the risk of high-impact, low-probability (HILP) events in power system self-scheduling. Probabilistic models capture the stochastic nature of wind power, while possibilistic models address forced outages due to extreme events. This allows for a more resilient scheduling strategy that accounts for both common and rare events. In [48], a hybrid probabilistic-possibilistic flexibility-based unit commitment model incorporates Z-numbers to manage the uncertainty of DR resources alongside supply-side uncertainties. This method optimizes system flexibility and reduces operational costs. Furthermore, [49] combines probabilistic approaches with IGDT for energy management in active distribution networks, balancing uncertainties in load, renewable generation, and grid prices while improving operational efficiency. Reference [50] presents a hybrid scenario-based/IGDT method for optimizing multi-carrier energy systems, effectively addressing uncertainties in both renewable energy sources and electricity prices. These applications demonstrate the versatility of hybrid probabilistic-possibilistic methods in power systems, enabling more reliable and resilient operations under varying uncertainty conditions.
# Perspectives on the Structural Health Monitoring of Bridges by Synthetic Aperture Radar
## 2 Methodology  
In this section the analytical formulation of the proposed micro-motion estimation technique is described. The SAR synthesizes the electromagnetic image through a \"side looking\" acquisition, according to the observation geometry shown in Figure 1, where:  
* \(r\) is the zero-Doppler distance (constant);
* \(R\) is the slant-range;
* \(R_{0}\) is the reference range at \(t=0\);
* \(d_{a}\) is the physical antenna aperture length;
* \(V\) is the platform velocity;
* \(d\) is the distance between two range acquisitions;
* \(L_{sa}\) is the total synthetic aperture length;
* \(t\) is the acquisition time variable;
* \(T\) is the observation duration;
* \(t=0\) and \(t=T\) are the start and stop time acquisition respectively;
* \(L=\frac{\lambda r}{L}\) is the azimuth electromagnetic footprint width;
* \(\theta\) is the incidence angle of the electromagnetic radiation pattern;
* \(V\) is the platform velocity.  
All the above parameters are related to the stripmap SAR acquisition that is adopted in this work. The SAR data belonging to the electromagnetic image are formed through the focusing process that  
Figure 1: SAR geometry.  
involves the application of a two-dimensional matched filter acting in the range direction and in the azimuth direction. The radar transmits the following complex signal  
\[s_{t}(t)=\exp\left\{J\left(\omega t+\frac{\mu t^{2}}{2}\right)\right\}\text{ rect}\left(\frac{t}{\tau}\right), \tag{1}\]  
where \(\omega=2\pi f_{0}\) with \(f_{0}\) the SAR central frequency, \(\tau\) is the pulse duration, and \(\mu\) is the chirp rate.  
The single look complex (SLC) signal resulting from compression is given by [16]:  
\[\begin{split}& s_{SLC}(k,x)=2N\tau\exp\left[-J\frac{4\pi}{\lambda }r\right]\text{sinc}\left[\pi B_{c_{r}}\left(k-\frac{2R}{c}\right)\right]\text{ sinc}\left[\pi B_{c_{D}}x\right]\\ &\text{for }n=kt,\;k=\left\{0,1,\ldots,N-1\right\},\;x=\left\{0,1, \ldots,M-1\right\},\;\text{with }N,M\;\in\mathbb{N}.\end{split} \tag{2}\]  
Equation (2) represents the focused SAR signal generated by the back-scattered electromagnetic energy of a pointing target supposed to be stationary. The terms \(B_{c_{r}}\), and \(B_{c_{D}}=\frac{4 Nd}{\lambda r}\) are the total chirp and Doppler bandwidths respectively. The total synthetic aperture is equal to \(L_{sa}=2 Nd\) and the azimuth resolution \(\delta_{D}\approx\frac{1}{k_{c_{D}}}=\frac{\lambda R}{2L_{sa}}\). In (2) the \(\frac{2R}{c}\) parameter identifies the position in range where the maximum of the sinc function is positioned, while in azimuth it is centered around \"zero\". In the case where the peak of the sinc function has a nozero coordinate along the azimuth dimension, Equation (2) can be recast as:  
\[\begin{split}& s_{SLC}(k,x)=2N\tau\exp\left[-J\frac{4\pi r}{ \lambda}\right]\text{sinc}\left[\pi B_{c_{r}}\left(k-L_{c_{g}}\right)\right] \text{sinc}\left[\pi B_{c_{D}}\left(x-L_{D_{h}}\right)\right]\\ &\text{for }L_{c_{g}},\;L_{D_{h}}\;\in\mathbb{N}.\end{split} \tag{3}\]
# Bearing Fault Diagnosis Using a Vector-Based Convolutional Fuzzy Neural Network
## 2 Conventional CNN
### Convolutional Layer  
A convolutional layer [19] utilizes a sliding-window mechanism to perform the dot product operation with the convolution kernel. The convolution results are then calculated by a sigmoid function to obtain feature maps. The convolutional operation [20] can be expressed as follows:  
\[O_{r,c}^{l}=s\left(\sum_{k=1}^{n}\sum_{i=1}^{k_{h}}\sum_{j=1}^{k_{w}}l_{(r-k_{ h}+i,c-k_{w}+j)}^{k}\times W_{ij}^{kl}+b^{l}\right) \tag{1}\]  
\[s(x)=\frac{1}{1+e^{-x}} \tag{2}\]  
where \(O_{r,c}^{l}\) represents the \(l\)th feature map; \(c\) and \(r\) are the column and row of the feature map, respectively; \(n\) denotes the number of input channels; \(k_{w}\) and \(k_{h}\) are the width and height, respectively, of a kernel; \(W_{i,j}^{k}\) is the weight of the \(i\)th row and \(j\)th column convolution kernel in the \(k\)th channel; \(l_{i,j}^{k}\) is the input of the \(i\)th row and \(j\)th column in the \(k\)th channel;  
Figure 1: Architecture of LeNet-5.  
\(b\) is the bias; and \(s(x)\) is the sigmoid activation function. In a deep network, the gradient of the sigmoid function becomes smaller as \(|x|\) increases. Therefore, in vector-CFNN, the rectified linear unit (ReLU) function is used in place of the sigmoid function. The ReLU function can be expressed as follows:  
\[R(x)=max(0,x) \tag{3}\]
## 4 Conclusions  
The addition of 4 wt.% Ti into the Al10.8 Si10 Cu filler alloy decreased the solidus temperature from 522.3 to 510.5 \({}^{\circ}\)C and the liquidus temperature from 570.0 to 549.2 \({}^{\circ}\)C. The main microstructures of the Al10.8 Si10 Cu filler alloy consisted of Al-rich and Si-rich solid solution phases and Al\({}_{2}\)Cu intermetallic compounds. The Al10 Si10 Cu4 Ti and Al10 Si10 Cu0.1 RE filler alloys contained Al-rich and Si-rich solid solution phases and Al\({}_{2}\)Cu and Al\({}_{5}\)Si\({}_{12}\)Ti\({}_{7}\) intermetallic compounds. The Ti addition in the Al10.8 Si10 Cu filler alloys reduced the Al\({}_{2}\)Cu particle size. AlLa intermetallic compounds formed in the Al10 Si10 Cu4 Ti0.1 RE filler metal. Due to the large number of intermetallic compounds and Si-rich particles in the filler alloys, the hardness values of all the filler alloys were higher than that of the AA6061 substrate. The Ti addition in the Al10.8 Si10 Cu filler alloys increased the hardness due to Al\({}_{5}\)Si\({}_{12}\)Ti\({}_{7}\) precipitation hardening and Al\({}_{2}\)Cu grain refinement. When the trace rare-earth elements were added to the Al10.8 Si10 Cu4 Ti filler alloy, the hardness values increased further. The average joint shear strengths of the AA6061/AA6061 joints bonded with the Al10.8 Si10 Cu, Al10 Si10 Cu4 Ti and Al10 Si10 Cu4 Ti0.1 RE filler alloys were determined to be 39.8, 47.8 and 57.3 MPa, and the highest joint shear strengths were 46.5, 51.5 and 61.1 MPa, respectively. The Al10.8 Si10 Cu filler alloy without titanium and rare-earth elements cannot be used to join AA6061 and alumina. The shear strength of the AA6061/alumina joint brazed with the Al10 Si10 Cu4 Ti0.1 RE filler alloy was significantly higher than that of the AA6061/alumina joint brazed with the Al10 Si10 Cu4 Ti filler alloy. The highest shear strengths of the AA6061/alumina joints brazed with Al10 Si10 Cu4 Ti and Al10 Si10 Cu4 TiO.1 RE filler alloys were 11.7 and 19.2 MPa, respectively.  
Conceptualization, Y.-K.S., S.-Y.C., L.-C.T., T.-H.C. and G.-Z.Z.; Data curation, Y.-K.S., L.-C.T. and G.-Z.Z.; Investigation, Y.-K.S., S.-Y.C., L.-C.T., G.-Z.Z. and C.-Y.Y.; Methodology, L.-C.T. and T.-H.C.; Project administration, S.-Y.C. and T.-H.C.; Validation, Y.-K.S., S.-Y.C., G.-Z.Z. and C.-Y.; Writing--original draft, Y.-K.S. and S.-Y.C.; Writing--review & editing, Y.-K.S., L.-C.T. and T.-H.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Ministry of Science and Technology grant number MOST 109-2221-E-224-006.  
Not applicable.  
The authors declare no conflict of interest.