esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 IIr-Ofdm
### IIR-OFDM Transmitter  
In order to resist the ISI caused by the loop signals of the IIR channel, IIR-OFDM is based on the guard interval designed for the IIR channel to maintain the circular convolutional structure of the transmitted and received signals. We consider the guard interval for the corresponding transmitted signal \(s_{v}(k)\) to be \(\overline{s}_{v}(k)\); the guard interval vector \(\overline{\mathbf{s}}_{v}=[\overline{s}_{v}(0),\cdots,\overline{s}_{v}(l_{g}- 1)]^{\mathrm{T}}\) can be expressed as  
\[\overline{\mathbf{s}}_{v}=\overline{\mathbf{A}}_{1}\mathbf{y}_{v}+\overline{ \mathbf{A}}_{2}\mathbf{y}_{v-1} \tag{13}\]  
where the length of the guard interval is the same as the number of loops, signal vector \(\mathbf{y}_{v}=[y_{v}(0),\cdots,y_{v}(l_{g}-1)]^{\mathrm{T}}\).  
\(y_{v}(k)\) can be expressed as  
\[y_{v}(k)=\frac{1}{\sqrt{N}}\sum_{n=0}^{N-1}\frac{d_{n,\rho}}{A_{n}}e^{\rho 2\pi(k+N-l_{g})n/N} \tag{14}\]where \(A_{\pi}\) are the \(N\)-point DFT of \(a(i)\), which can be expressed as  
\[A_{n}=\sum_{i=0}^{N-1}a(i)e^{-j2\pi in/N} \tag{15}\]  
Then, the guard interval generation matrix \(\overline{\mathbf{A}_{1}}\) and \(\overline{\mathbf{A}_{2}}\) can be expressed as  
\[\overline{\mathbf{A}_{1}}=\sum_{i=0}^{l_{g}-1}a(i)\Omega^{i}=\begin{bmatrix}a (0)&&\\ \vdots&\ddots&\\ a(l_{g}-1)&\cdots&a(0)\end{bmatrix}, \tag{16}\]  
\[\overline{\mathbf{A}_{2}}=\sum_{i=0}^{l_{g}-1}a(l_{g}-i)\Omega^{-i}=\begin{bmatrix} a(l_{g})&\cdots&a(1)\\ &\ddots&\vdots\\ &&a(l_{g})\end{bmatrix}, \tag{17}\]  
with  
\[\Omega=\begin{bmatrix}0&\cdots&0&0\\ 1&\ddots&0&0\\ \vdots&\ddots&\ddots&\vdots\\ 0&\cdots&1&0\end{bmatrix}_{(l_{g}-1)\times(l_{g}-1)}, \tag{18}\]  
note that \(\Omega^{0}\) is the identity matrix.  
The transmitted signal is inserted into the guard interval to ensure it is free from ISI at the receiver. After inserting the guard interval, the transmitter signal can be expressed as  
\[\mathbf{\hat{s}}_{v}=[\mathbf{s}_{v}(0),\cdots,\mathbf{s}_{v}(l_{g}-1),s_{v}( 0)\cdots,s_{v}(N-1)]^{\mathrm{T}}. \tag{19}\]
# A Performance Evaluation of In-Memory Databases Operations in Session Initiation Protocol
## 2 Session Initiation Protocol  
SIP is a signaling protocol defined and adopted by the SIP Working Group, under the umbrella of the Internet Engineering Task Force (IETF). The protocol was published with the first issue of RFC 3261 [15] toward the status of a proposed standard. SIP operates jointly with SDP (Session Description Protocol) [16], which is responsible for exchanging multimedia parameters such as IP version, port numbers, and voice codecs for RTP (Real-Time Transport Protocol) streams [17]. In general, the SIP server is commonly used to register new contact users, trigger and control multimedia sessions, modify ongoing calls, re-establish calls, and terminate calls [18].  
One physical SIP server potentially consists of multi-logical servers on the same machine, each of which is reasonable for certain tasks. The main SIP elements involved in one SIP server device are below:  
* Proxy Server:It is a network element that is responsible for routing incoming requests from a user agent and forwarding it to the next hop. SIP proxies route requests/responses to/from other entities based on URI.
* Registrar Server: It handles registration requests coming from user agents. When a new user tries to join the SIP network, the registrar server stores the user ID and password, URI, and location of the user in a database (database types explained in the next section). The registrar server aims to authenticate clients within their network.
* Redirect Server: It requests and looks up the destination possible addresses in the location database, which was originally created by the registrar server. The main aim of the redirect server is to provide updated routing information to the proxy server. The redirect server stores such information in its associated database.
* Location Server: is another logical SIP server that is responsible for providing information about the possible destination to proxy and redirect servers. Destination user potentially updates their addresses regularly when the cases of the clients are in a mobility network or IP version translation (IPv6/IPv4). The location server stores all client's addresses in a database.  
We can notice from above that all types of SIP servers are connected to local/remote SQL databases such as MySQL, MariaDB, Postgre, Berkeley, Oracle, Unix odbc, and many other names [19]. SIP core or modules can utilize the SQL services through the SQL interface. For instance, the Avopps module is an SQL interface that offers a direct query towards the SQL database.  
In practice, SQL databases in all Internet applications, especially the real-time service, are considered to be an expensive operation, which made database hit disk penalty an existing issue in SIP technology; thus, NoSQL databases are found to make SIP life easier.
# Markov Chain Model-Based Optimal Cluster Heads Selection for Wireless Sensor Networks
## 2 Related Work and Background
### Clustering
#### 2.1.3 Centralized vs. Distributed Clustering  
There are many types of center-based clustering like [27], k-means clustering [16], k-median [23], genetic-based algorithm [25], and schemes of affinity propagation [14; 31]. In center-based clustering, there exists a node that is full of resources and supervises all the clustering process. This class of clustering strategy has no scalability, which makes it unsuitable for the large scale WSNs. The other category, which is distributed clustering, is discussed in [17; 19], this class of clustering is more scalable as compared to center-based clustering. However, this strategy is associated with a trade off; it involves additional signaling. [PERSON] et al. proposed a clustering routing algorithm which is based on the Artificial Bee Colony (ABC) algorithm [9]. The ABC's CHs selection process is centralized. The CHs selection procedure of ABC is controlled by the BS because the hardware of the BS is more reliable as compared to the sensor node and CH. The CHs are selected on the basis of the distance of the node from the BS. The lesser the distance of the node from the BS, the greater the probability of selection as the CH. However, the nodes in [27] are selected as CH if either their energy is lesser or greater. In this way, the nodes with lesser power resources run out of battery very quickly making the network unstable.
## 3 Results and Discussions
### Three-Point Bending of a Beam with Three Circular Holes
#### 3.1.4 Case IV  
The dimensions of this particular specimen match those of the specimen in case III, except that it does not contain any holes. Figure 16 displays the predicted crack growth path predicted by ANSYS, as well as the distribution of von Mises stress. This predicted path is then compared to the path obtained from ABAQUS, which also considers von Mises stress and utilizes a mesh-independent computational algorithm [43]. The comparison reveals good agreement between the two predicted paths.  
Figure 16: Von [PERSON] stress distribution for case IV: (**a**) present study; (**b**) ABAQUS with a mesh-independent computational algorithm [43].  
Figure 15: Comparison for the second mode of SIF for case III [46].  
It is significant to emphasize that the absence of internal holes in the specimen contributes to higher von Mises stress levels compared to the same specimen with holes, as shown in Figures 6, 8, 13 and 16. This difference can be attributed to the fact that the presence of holes acts as stress concentrators, causing localized stress intensification. Conversely, the absence of holes enables a more uniform distribution of stress, leading to higher overall von Mises stress levels in the hole-free specimen.
## 4 Model Verification  
An experiment on the rock cutting process is conducted to verify the correctness of the numerical model. Figure 5 reveals the experiment apparatus for rock cutting, which consists of a tool-holder, triaxial forces sensors, a data acquisition system, a rock sample and 3-RDE cutters. The planning blade is mounted on the tool-holder, and the sensors are equipped in the middle of planning blade. The 3-RDE cutter is fixed below the sensors. Strain gauges (BE120-5 AA with an accuracy of 0.05 degree) are attached to the planning blade, and are bridged as sensors. The cutting forces are represented by the voltage signal collected by the data acquisition system. And according to the following formula, the cutting forces can be decoupled.  
\[\left\{\begin{array}{l}F_{n}=163.09u_{n}+4.22u_{h}-1.32u_{r}\\ F_{h}=-24.38u_{n}+8.74u_{h}+1.16u_{r}\\ F_{r}=-3.43u_{n}+0.12u_{h}+20.65u_{r}\end{array}\right. \tag{7}\]  
where \(u_{n}\), \(u_{h}\), \(u_{r}\) are the axial, tangential and radial voltages of the sensors output, respectively.  
The rock sample was Wusheng sandstone, whose mechanical parameters are shown in Table 1. The rock sample is fixed and the cutting depth is 1.5 mm. During the test, the cutter moves linearly with a speed 0.3 m/s. A total of five sets of tests were carried out at the back-take angle of \(5^{\circ}\), \(10^{\circ}\), \(15^{\circ}\), \(20^{\circ}\), \(25^{\circ}\). To avoid random errors and improve the accuracy of the experiment, each set of tests was repeated 3 times. The sampling frequency is 1000 Hz. For the convenience of analysis and comparison, the average value of axial force and tangential force, as well as MSE, are adopted to compare the experimental and numerical results, as shown in Figure 6. It can be observed that the cutting forces and MSE in simulation and experiment all gradually increase with the increase of the back-take angle. In addition, the tangential force is greater than the axial force. That is, numerical results are basically consistent with that of the experiment in terms of values and variation laws. However, the experimental results are slightly smaller than that of the simulation, which may be due to rock heterogeneity.  
Figure 5: Experimental apparatus.
# Analysis of Dust Detection Algorithms Based on FY-4A Satellite Data
## 6 Conclusions  
FY-4A/AGRI provides valuable data sources for studying dust over China, especially where ground-based observations are often not available. In this study, four dust detection algorithms applied to FY-4A/AGRI data were evaluated to examine the effectiveness of these algorithms in detecting the extent of dust over China. These algorithms have good agreement in detecting the spatial distribution of dust. BTD can detect dust events in the Chinese region effectively and accurately if the threshold is well set. NDDI still has a  
\begin{table}
\begin{tabular}{l c c c c c} \hline
**BTD\_IDDI** & **YY** & **YN** & **NY** & **POCD (\%)** & **POFD (\%)** \\ \hline
02:00 UTC & 428 & 478 & 554 & 47.24\% & 56.42\% \\
04:00 UTC & 509 & 297 & 691 & 63.15\% & 57.58\% \\
06:00 UTC & 476 & 287 & 817 & 62.39\% & 63.19\% \\
08:00 UTC & 286 & 316 & 259 & 47.51\% & 47.52\% \\ Average & & & & 55.07\% & 56.17\% \\ \hline \end{tabular}
\end{table}
Table 7: Statistical metrics for the comparison between the BTD superimposed IDDI dust identification and the PM10 observations.  
large area of misclassification, even though the thresholds were set according to different regions and times of the day. IDDI and DST had similar performances in dust detection during the daytime. Validation of the dust detection results for the four methods using PM10 concentrations showed that BTD had the best detection performance with an average POCD of 56.15%, followed by those for IDDI, DST, and NDDI, which were 48.22%, 46.75%, and 39.39%, respectively. Additionally, a low POFD for the combined algorithm of BTD and IDDI indicates its potential to accurately detect dust events. The validation of the dust detection algorithm remains a challenge. Future work will focus on two areas: first, to focus on dust detection at night to enable continuous dust detection during the day and night, and second, to investigate if the machine learning approach to dust detection can overcome the threshold limitations of empirical-based algorithms.  
Conceptualization, L.Y., L.S. and Y.C.; Methodology, L.Y., L.S., X.H, C.Y. and Z.F.; Software, L.Y., Y.C. and C.Y.; Validation, L.Y., L.S. and Y.C.; Formal analysis, L.Y., L.S. and X.H.; Writing--original draft preparation, L.Y., L.S. and Y.C.; Writing--review and editing, L.Y., L.S., Y.C., C.Y. and Z.F.; Funding acquisition, L.S. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Science and Technology Department of Ningxia under grant number 2022 AAC03122.  
Not applicable.  
PM10 concentration data were supplied by the National Environmental Monitoring Station of China. The authors would also like to thank the National Satellite Center of the China Meteorological Administration for providing the FY-4A-related data free of charge.  
The authors declare no conflict of interest.
## 3 Experiments  
In this section, we address two main topics: (i) the exploitation of advanced RS techniques to evaluate the impacts of global climate change and anthropogenic activities on the coast of large river delta regions and (ii) the study of the interactions between the ocean and the coast using RS methods and the evaluation of their implications in response to projected climate change. We summarize the main findings of some recent case studies for selected areas, including major river deltas in China and Eastern Asia, the Po River Delta and the Venice Lagoon in Italy, the St. Petersburg Bay in Russia, the Nile Delta in Egypt, the Mississippi River Delta in the U.S., and the Amazon River in Brazil (see Figure 5). Of great concern is that most major river deltas are sinking at a larger rate than SLR [253], which significantly increases the impact of relative sea level rise. Many deltas are extremely sensitive and vulnerable to the cascading effects and increased risks of global climate change, local ground subsidence, and intensive human activities. The acceleration in global mean sea level, increasing frequencies of extreme weather events, ground subsidence, declining upstream sediment fluxes, and decreasing wetland areas impact the integrity of the delta regions of the world. Recently, the work of [126] has systematically assessed the changing risk profiles of 48 coastal deltas and found that most coastal deltas are at an elevated risk level. Here, we emphasize how RS technologies are valuable instruments for detecting, monitoring, and quantifying risk conditions and the potential impacts on land and population centers. The following sub-sections address specific natural and/or anthropogenic phenomena that affect coastal regions, including the risks due to coastal erosion, coastal flooding and extreme meteorological events, sea level rise, and storm surge. Next, we will focus on selected delta river zones and provide a brief overview of recent investigations performed in these regions.
# Investigation of the Influence of Splitter Blades on the Resonance Conditions of Impellers
## 4 Validation and Discussion
### Harmonic Response Calculation  
During the harmonic response analysis, the form of the excitation is that of Equation (19). The amplitudes of the exciting forces are taken as 1 N and 0.8 N for the main blades and splitter blades, respectively. The structural damping in the harmonic response calculation is set as 0.005. Due to the effect of aliasing, the excitation order only takes from EO1 to EO17 [23].
# Survey for Soil Sensing with IOT and Traditional Systems
## 4 Inference Method and Evaluation Metrics
### Method for Spectral Information Analysis
#### 4.1.4 Least Squares Support Vector Regressions  
Support Vector Machines is a supervised learning method commonly used for classification tasks, such as pattern recognition [80], and regression tasks, such as data analysis and result prediction [81, 82]. SVM operates based on statistical theory and maps input data to a higher-dimensional feature space [82]. However, the high computational burden associated with SVM limits its applicability in certain scenarios. To address this, the LS-SVR was introduced for regression tasks. LS-SVR simplifies the quadratic programming problem of SVM into a linear system, which can be efficiently solved using iterative methods [83]. The LS-SVR model for function estimation can be expressed as shown in Equation (15) [83].  
\[f(x)=\sum_{i=1}^{N}\alpha_{i}K(x,x_{i})+b \tag{15}\]  
\(X\) represents the input of spectral information collected by the photodiode and \(f(x)\) is the target concentration level of macronutrient. The Lagrange multipliers, denoted as \(\alpha_{i}\), represent the support values, while the bias term is denoted as \(b\). The kernel function \(K(x,x_{i})\) plays a crucial role in SVM, and there are various options for kernel selection, such as the linear kernel, polynomial kernel, and radial basis function kernel (RBF kernel).  
The equations for these three kernels are shown in Equations (16)-(18) [83]  
\[K(x,x_{i})=\langle x\,\ x_{i}\rangle \tag{16}\]  
\[K(x,x_{i})=(\langle x\,\ x_{i}\rangle+p)^{d} \tag{17}\]  
\[K(x,x_{i})=\exp(\frac{-\|x-x_{i}\|^{2}}{2\sigma^{2}}) \tag{18}\]  
\(\langle\cdot\,\ \cdot\rangle\) denotes the dot product, \(p\) is the bias term, and \(d\) is the index for the polynomial kernel, \(\sigma^{2}\) is the bandwidth of RBF kernel. Using Grid Search [84] to optimize the parameters of LS-SVR not only ensures prediction accuracy, but also significantly reduces training time. However, in the study by [36], the application of a nonlinear support vector machine method did not yield particularly favorable results in terms of the Root-Mean-Square-Error (RMSE) on the cross-validation set when compared to the performance of PLSR.
# Semantic Segmentation of Aerial Imagery Using U-Net with Self-Attention and Separable Convolutions
## 2 Literature Review
### CNN-Based Segmentation of High-Resolution Aerial Images with Pyramid Pooling  
In the landscape of image segmentation, ([PERSON] et al., 2018) innovatively devised a robust end-to-end methodology in 2018. Their comprehensive approach was aimed at semantically segmenting high-resolution aerial images and demonstrated a strategic fusion of a convolutional neural network (CNN) structure with a pyramid pooling phase. This thoughtful integration allowed for the extraction of feature maps across diverse scales, contributing to an enhanced and nuanced understanding of the semantic content embedded in the high-resolution aerial imagery [18].
# Analysis of Power Allocation for NOMA-Based D2D Communications Using GADIA
## 5 Proposed Algorithm
### Energy Model  
In this section, we analyze the energy efficiency (EE) of NOMA systems. Considering the downlink, the total power consumed \(P_{tot}\) by the BS can be written as:  
\[P_{tot}=P_{s}+P_{T} \tag{18}\]  
In (18), \(P_{T}\) is the power consumed by the signal transmission, and \(P_{s}\) is the power consumed due to other hardware and signal processing operations. Therefore, the total transmission power can be expressed as the sum of the energy consumed by the signal transmission power and the energy consumed. EE can be defined as the ratio of the throughput over the total power. Mathematically speaking, it is given as [42].  
\[EE=\frac{R_{t}}{P_{tot}} \tag{19}\]
# Removal of Organic UV Filters Using Enzymes in Spent Mushroom Composts from Fungicultures
## 3 Results  
### Performances of UV Filter Removal Using Enzyme Extracts of SMCs  
As shown in Figure 3, the order of the UV filter removal rates of enzyme extracts from the four SMCs was _P. djamor_ > _P. ostreatus_ > _P. eryngii_ > _A. polytricha. P. djamor_ exhibited the highest efficiency of UV filter removal. The order of the UV filter removal efficiency was EHMC > EHS > HMS. The enzymes of _A. polytricha_ were ineffective in EHS and HMS degradation. In contrast, EHMC can be removed by all of the four SMC enzyme extracts very fast. These results indicate that the enzyme associated with EHS and HMS removal is absent or at a very low level in the SMC enzyme extracts of _A. polytricha._ In contrast, the enzymes involved in the EHMC removal were present in all SMCs.  
The lacase activities and pH values of the SMC enzyme extracts at the start and end of the batch experiments are shown in Figure 4. The lacase activities in the enzyme extracts from _A. polytricha_ were much lower than those of the other three fungi (Figure 4A). The pH values of all SMC enzyme extracts increased after the batch experiments (Figure 4B).  
Figure 3: Removal of three UV filters (2 ppm) by enzyme extracts of the spent mushroom compost of four mushrooms ((**A**) _P. eryngii_, (**B**) _P. djamor_, (**C**) _P. ostreatus_ and (**D**) _A. polytricha_). Data from triplicate experiments are presented as the mean \(\pm\) SE. EHMC: ethylhexyl methoxycinnamate, EHS: 2-ethylhexyl salicate, HMS: homosalate.
# Frequency-Separated Attention Network for Image Super-Resolution
## 4 Experiments
### Comparison with State-of-the-Art Methods
#### 4.3.3 Comparison with Other Methods on Real-World Images  
Our model is designed specifically for the restoration of low-resolution images that have been downsampled using bicubic interpolation. In real-world scenarios, images may exhibit noise, sensor damage, compression artifacts, and other imperfections. As shown in Figure 13, the processed images still retain some level of blurriness and may not exhibit significant visual differences compared to other methods. However, we conducted tests on the RealSR [55] and DrealSR [56] real-world datasets, evaluating the PSNR and SSIM metrics for a magnification factor of 4. As demonstrated in Table 5, our network performs slightly better than other methods.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Dataset**} & **DRealSR** & **RealSR** \\  & **PSNR/SSIM** & **PSNR/SSIM** \\ \hline Bicubic & 30.78/0.8468 & 27.30/0.7557 \\ SRCNN & 30.88/0.8490 & 27.65/0.7711 \\ FSRCNN & 30.79/0.8473 & 27.65/0.7692 \\ CRAN & 30.82/0.8474 & 27.66/0.7700 \\ VDSR & 30.82/0.8458 & 27.52/0.7554 \\ IMDN & 30.80/0.8489 & 27.66/0.7717 \\ Ours & **31.47/0.8580** & **27.77/0.7739** \\ \hline \hline \end{tabular}
\end{table}
Table 5: The test results on benchmark datasets for real-world super-resolution at a magnification factor of \(\times 4\).  
Figure 12: The graph in (**a**) illustrates the comparison between image restoration quality and network parameter count, while that in (**b**) depicts the comparison between image restoration quality and network computational burden.
## 3 Relative Importance Measure Based on Distance Distribution and Multi-index Fusion  
To fully measure the impact of network structure information on the relative importance of nodes, this study proposes a relatively important node mining method based on distance distribution and multi-index fusion, i.e., the DDMF method. In this section, we first introduce the problem definition in complex networks and use a specific example to explain what is the distance distribution. Then three indicators of cosine similarity, Euclidean distance, and relative entropy are described in detail. Finally, we discuss how to calculate the relative importance score of a node based on multi-index fusion.