esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experimental Data
### Design Day Revision  
According to the previously selected hourly change coefficient of dry bulb temperature \(\beta\), calculated temperature \(t_{wg}\) of summer air conditioning, and calculated daily average temperature \(t_{wp}\) of summer air conditioning, the formula is substituted to calculate the design daily hourly dry bulb temperature \(t_{sh}\) of Beijing and Shanghai, as shown in Tables 12 and 13.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Beijing** & **Cumulative Average Does** & **Cumulative Average Does** & **Cumulative Average Does** & **Cumulative Average Does** \\  & **Not Guarantee 10 h** & **Not Guarantee 50 h** & **Not Guarantee 100 h** \\ \hline
**Air conditioning** & & & & \\
**temperature calculation in** & 35.4 \({}^{\circ}\)C & 33.6 \({}^{\circ}\)C & 32.3 \({}^{\circ}\)C \\
**summer** & & & & \\ \hline
**Beijing** & **Averaging over years does** & **Averaging over years does** & **Averaging over years does** \\  & **not guarantee 1 d** & **not guarantee 5 d** & **not guarantee 10 d** \\ \hline
**Calculate the average daily** & & & & \\
**temperature of air** & 31.3 \({}^{\circ}\)C & 29.9 \({}^{\circ}\)C & 29.1 \({}^{\circ}\)C \\ conditioning in summer** & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 10: Selection results in Beijing.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Shanghai** & **Cumulative Average Does** & **Cumulative Average Does** & **Cumulative Average Does** & **Cumulative Average Does** \\  & **Not Guarantee 10 h** & **Not Guarantee 50 h** & **Not Guarantee 100 h** \\ \hline
**Air conditioning** & & & & \\
**temperature calculation in** & 38.1 \({}^{\circ}\)C & 35.9 \({}^{\circ}\)C & 34.8 \({}^{\circ}\)C \\
**summer** & & & & \\ \hline
**Shanghai** & **Averaging over years does** & **Averaging over years does** & **Averaging over years does** \\  & **not guarantee 1 d** & **not guarantee 5 d** & **not guarantee 10 d** \\ \hline
**Calculate the average daily** & & & & \\
**temperature of air** & 33.1 \({}^{\circ}\)C & 31.4 \({}^{\circ}\)C & 30.7 \({}^{\circ}\)C \\ conditioning in summer** & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 11: Selection results in Shanghai region.  
A comparison between the generated design day and the actual hourly temperature change is shown in Figure 11. As can be seen from the figure, the design day in the specification cannot effectively represent the hourly outdoor temperature change trend in summer, and the generated design day in Beijing and Shanghai has some changes in the value and occurrence time of the maximum temperature based on the original basis, and the predicted design day can represent the hourly temperature change in the future. In line with the effects of climate change, they can be better applied to the actual project requirements.
## 3 Results and Discussion  
For a comparison, seven classic hyperspectral anomaly detection algorithms, RX [3], local RX(LRX) [19], CRD [9], Whitening Special Correlation Filtering (WSCF) [20], Robust Principal Component Analysis Based on Reed-Xiaoli (RPCA-RX) [21], Low-Rank and Sparse Representation (LRASR) [22], and Kernel Isolation Forest-based hyperspectral anomaly Detection(KIFD) [23] are selected. The advantages and disadvantages of the algorithms of this paper are further analyzed by comparing the detection results of different algorithms. The experimental simulation was carried out on the Windows10 operating system with MATLAB R2018b software and i3 8300 processor (Intel, Santa Clara, CA, USA).
# Interpretation of Bridge Health Monitoring Data from Satellite InSAR Technology
###### Abstract
The number of scientific publications focusing on InSAR-based SHM of civil infrastructure is increasing exponentially, as well as the special sessions on this topic in international conferences. Most current research works can be divided into two groups. Group 1 addresses the estimation of displacement time series of PSs identified on the civil infrastructure, focusing on the InSAR data processing technique and the mathematical aspects of the algorithms exploited, without giving any interpretation of data from the structural standpoint (e.g., kinematic behaviour of the structure, structural response to loads, identification of abnormal variation in extracted time series possibly related to structural damages or evolving degradation). Group 2 addresses the interpretation of the PS displacement time series, paying little or no attention to measurement accuracy or the possibility that some patterns visible in the time series were related to the interpretation model used in the data analysis. In the given context, this paper aims to cover the gap by presenting (i) an entire MT-InSAR data processing specifically performed for road bridges in operational conditions to extract displacement time series of PSs; (ii) the engineering interpretation of the millimetric ground and bridge displacements observed while being aware of the process followed to obtain such results; and (iii) a study of the correlation between the displacements of the bridge and environmental phenomena (temperature and river water flow) to further improve the engineering interpretation of InSAR results. Specifically, our analysis is based on a real-life case study: the A22 Po River Bridge of the Italian A22 Highway, a strategic, prestressed concrete bridge along the European route E45, which crosses the wider and longer Italian river, the Po River. Our study exploits a dataset of 109 X-Band HIMAGE mode Stripmap images acquired in the descending geometry by the Italian COSMO-SkyMed mission from 2014 to 2021. We performed the MT-InSAR data processing with the software SarProZ/\(\copyright\) ([[https://www.sarproz.com/](https://www.sarproz.com/)]([https://www.sarproz.com/](https://www.sarproz.com/))), version 'SARPROZ 08-September-2022 16:32:33'. In detail, Section 2 introduces the A22 Po River Bridge, the case study area, and the dataset of SAR images. Section 3 describes the methods used to process SAR images through MT-InSAR, calculate the vertical and horizontal components of PSs displacements from the InSAR results, and study the correlation between the bridge displacements over time and the environmental load it experienced. Section 4 reports and discusses our study's results, from the structural interpretation of the MT-InSAR displacements to the correlation analysis between bridge displacements and environmental loads. Conclusions are eventually drawn in Section 5.
# Combined Application of Filter Cake and Macadamia Husk
## 3 Results and Discussions
### Effect of Soil Amendment on Soil Fertility
MHC-only and IF-only treatments had the lowest influence on dry matter partitioning to the root when compared with other treatments and the control in both cultivars (Figure 2). The higher partitioning of the dry matter to the root in filter cake-related treatments may have been due to early initiation and faster growth of the storage roots [42]. Phosphorus, which is abundantly present in filter cake, has been implicated in promoting early root growth, stimulating tillering, and increasing water use efficiency [44]. The application of organic compost has been reported to favor the partitioning of dry matter to the root of sweet potatoes [41; 42]. Effect of Soil Amendment on the Mineral Content in the Leaves and Storage Roots of 199062.1 Cultivar of Orange-Flesled Sweet Potato  
Combined application of filter cake and macadamia husk compost (FC+MHC) and other treatments investigated altered the nutrient content in the leaves and storage root of the 199062.1 cultivar of orange-fleshed sweet potato. It has been reported that the application of soil amendments alters the mineral content in the leaves and storage roots of plants [45]. However, it must be noted that the changes that occurred in the nutrient composition of the test plants were more prominent in the leaves when compared to the changes that occurred in the storage roots. Although there were changes in the concentration of N in the leaves and storage roots in all the treatments, significant changes occurred only in the macadamia husk compost-related treatments. For instance, N concentration significantly increased 1.19-, 1.22-, and 1.25-folds in MHC, MHC+IF, and FC+MHC, respectively (Table 5). Also, in the storage roots of the test cultivars, N was significantly higher than the control in the FC+MHC treatment (Table 5). The increases in N concentration of the macadamia husk-related treatments may be attributable to the higher contents of N in macadamia husks (Table 4). Figure 2: Effect of combined application of filter cake, macadamia husk compost, and other treatments on the dry matter partitioning of Beauregard and 199062.1 cultivars of orange-fleshed sweet potato. Each bar represents the average of each treatment. Bars along the same row with different letters/symbols for each cultivar are significantly (\(p\) < 0.05) different.
## 3 Results and Discussion
### Ablation Study
#### 3.6.3 Overall Strategies

Table 4: Ablation experiments of Focaler-shape-IoU on the SBDS dataset. strawberry, which not only underscored the baseline model's inadequacy in handling overlapping targets but also indicated its low sensitivity in recognizing strawberries at different stages of ripeness. However, our model effectively addressed the limitations of the baseline model by introducing multiple key strategies. Specifically, we enhanced the model's feature extraction capability by integrating PCIB, significantly improved the detection of small strawberries by introducing MS-FPN, and with the help of Focaler-shape-IoU technology, increased the recognition rate of difficult strawberry samples in overlapping situations. The integrated application of these techniques enabled our model to excel in handling occlusions, recognizing small targets, and distinguishing overlapping targets, overcoming the deficiencies of the baseline model in these complex scenarios.
# Analysis and Experimental Validation of Efficient Coded OFDM for an Impulsive Noise Environment
## 2 System Overview
### Description  
The proposed physical layer [13] consists of a C-OFDM-based RC code concatenated with a CC scheme, as depicted in Figure 1. RC coding has been thoroughly studied in the literature in coding theory, but used now in communication systems to mitigate impulsive noise. Since the noise present in power substations appears as burst errors, this kind of scheme is well suited. However, it has been demonstrated that the background noise considered as Gaussian noise is also present in this environment. In order to cancel the isolated noise, the use of a CC becomes essential.
# Error Performance of Amplitude Shift Keying-Type Asymmetric Quantum Communication Systems
## 4 Derivation of Analytical Expression for the Error Probability of the Quasi-Bell State
### Derivation of Analytical Expression  
The eigenvalues \(\{\tau_{i}^{(+)}\}\cup\{\tau_{i}^{(-)}\}\) of \(U^{\prime}\) (i.e., \(U\)) that were obtained from the analysis above can be substituted into Equation (22). Here, it can be confirmed that \(\tau_{1}^{(+)},\tau_{1}^{(-)}>0\), and thus the error probability \(P_{\text{e}}^{\text{(Max)}}\) of the AQC system when using the maximum quasi-Bell state is finally given as follows:  
\[P_{\text{e}}^{\text{(Max)}} =\frac{1}{2}\Bigg{\{}1-\frac{1}{2}\Bigg{(}\sqrt{{\Lambda_{\text{ M}}^{(+)}}^{2}-4\Xi_{\text{M}}^{(+)}}-\Lambda_{\text{M}}^{(+)}\Bigg{)}-\frac{1}{2} \Bigg{(}\sqrt{{\Lambda_{\text{M}}^{(-)}}^{2}-4\Xi_{\text{M}}^{(-)}}-\Lambda_{ \text{M}}^{(-)}\Bigg{)}\Bigg{\}}, \tag{63}\] \[\Lambda_{\text{M}}^{(\pm)} =-\frac{(L_{0}\pm 1)(a\pi\mp 1)+(1\pm L_{1})(1\mp d\kappa)}{2(\kappa^{ 2}-1)},\] (64) \[\Xi_{\text{M}}^{(\pm)} =-\frac{(L_{0}\pm 1)(L_{1}\pm 1)\{(ax\mp 1)(d\kappa\mp 1)-(b\mp c \kappa)^{2}\}}{4(\kappa^{2}-1)^{2}}. \tag{65}\]  
The error probability \(P_{\text{e}}^{\text{(NonMax)}}\) for the AQC system when using the non-maximum quasi-Bell state can also be obtained using steps \(1\) to \(4\) as per the case for the maximum quasi-Bell state. As a result, the corresponding probability is given by:  
\[P_{\text{e}}^{\text{(NonMax)}} =\frac{1}{2}\Bigg{\{}1-\frac{1}{2}\Bigg{(}\sqrt{{\Lambda_{\text{ N}}^{(+)}}^{2}-4\Xi_{\text{N}}^{(+)}}-\Lambda_{\text{N}}^{(+)}\Bigg{)}-\frac{1}{2} \Bigg{(}\sqrt{{\Lambda_{\text{N}}^{(-)}}^{2}-4\Xi_{\text{N}}^{(-)}}-\Lambda_{ \text{N}}^{(-)}\Bigg{)}\Bigg{\}}, \tag{66}\] \[\Lambda_{\text{N}}^{(\pm)} =-\frac{(L_{0}\mp 1)(a\pi\mp 1)-(1\mp L_{1})(1\mp d\kappa)}{2(\kappa^{ 2}+1)},\] (67) \[\Xi_{\text{N}}^{(\pm)} =-\frac{(L_{0}\mp 1)(L_{1}\mp 1)\{(a\pi\mp 1)(d\kappa\mp 1)-(b \mp c\kappa)^{2}\}}{4(\kappa^{2}+1)^{2}}, \tag{68}\]  
where  
\[a =\text{e}^{-2R_{0}|a|^{2}}, b =\text{e}^{\sqrt{R_{0}R_{1}}|a|^{2}-\frac{1}{2}|a|^{2}(R_{0}+R_{1})}, c =\text{e}^{-\sqrt{R_{0}R_{1}}|a|^{2}-\frac{1}{2}|a|^{2}(R_{0}+R_{1})},\] \[d =\text{e}^{-2R_{1}|a|^{2}}, \kappa =\text{e}^{-2|a|^{2}}, L_{0} =\text{e}^{-2(1-R_{0})|a|^{2}}, L_{1} =\text{e}^{-2(1-R_{1})|a|^{2}}. \tag{69}\]
# SACRB-MAC: A High-Capacity MAC Protocol for Cognitive Radio Sensor Networks in Smart Grid
## 2 SACRB-MAC Framework
### Protocol Description  
#### 2.3.1 MAC Frame Structure  
In SACRB-MAC, a MAC frame comprises two parts, _i.e._, the spectrum sensing slot (\(T_{ss}\)) and the transmission slot (\(T_{pt}\)). In the spectrum sensing slot, the CR sensor checks the status of each channel in the spectrum aggregation list. The CR user will suspend the channels occupied by the PUs and access the available channels for transmission. We note that, in realistic conditions, PUs may be interfered with due to imperfect spectrum sensing. We use the _Interference Ratio_ (IR) to quantify the interference. IR is defined as the expected ratio of PU transmission interrupted by CR users [10]. The IR for the \(i\)-th channel can be represented by:  
\[R_{I}^{j,i}=P_{busy}^{j,i}\left(1-P_{d}^{j,i}\right)+P_{idle}^{j,i}\left(1-P_{ f}^{j,i}\right)+e^{-\mu T_{pr}}\left(P_{f}^{i}-P_{d}^{i}\right) \tag{5}\]  
where \(\mu=\max(\mu_{ON}^{i},\mu_{OFF}^{i})\).  
It is assumed that the CR nodes can use the transmission time excellently, such that the throughput of the CR network is maximized to an interference constraint, namely \(R_{I}^{i}\leq R_{max}^{i}\), where \(R_{max}^{i}\) denotes the maximum tolerable interference ratio on the \(i\)-th channel. Therefore, the transmission time on the \(i\)-th channel can be given by:  
\[T^{i}=\mu^{-1}\left[\ln P_{idle}^{j,i}-\ln\left(P_{idle}^{j,i}P_{d}^{\prime}+P_ {busy}^{j,i}(1-P_{d}^{\prime})-R_{max}^{i}\right)+\ln(2P_{d}^{\prime}-1)\right] \tag{6}\]  
where \(P_{d}^{\prime}\) is the detection probability at the tolerable SNR lower limit, which is specified by the regulator.
## 2 Materials and Methods
### Detection of Vegetation Regeneration Areas  
Previously, the fire severity index of NBR and vegetation index NDVI were mainly used to extract the vegetation recovery area. We performed the analysis using additional EVI and SAVI indices to reduce distortion due to light scattering, soil, and trees. The difference images after four months and immediately after the fire were extracted using three vegetation indices. Moreover, we calculated the correct reproduction boundary by performing cluster analysis of the extracted images. Finally, we extracted the final regeneration area (e.g., the extracted regions from cluster analysis on NDVI and the extracted regions from the fire severity index of the dNBR) by overlapping the regeneration area derived from the cluster analysis and the enhanced growth area derived by dNBR. The proposed method is robust to environmental effects using multispectral data and reliable by overlapping with dNBR.
# CaAIS: Cellular Automata-Based Artificial Immune System for Dynamic Environments
## 6 Experimental Study
### Experiments
#### 6.3.2 Effect of Varying the Number of Neighborhood Sizes  
Other experiments investigated the effects of several neighbor cells in CA. This experiment avoids large neighborhood structures to avoid additional computational challenges and a long run time. Therefore, the numbers of neighbor cells are studied from one to five for the effects of cell neighborhood sizes. The effects of the different cell neighborhood sizes are summarized in Figure 7.  
According to Figure 7, it can be observed that the cell neighborhood size has been increased until size two has relatively improved. However, no more than three to five values will be enhanced, and OE will be increased. Indeed, increasing the cell neighborhood size causes more complexity, and the advantage of local search deteriorates during the changing environment.  
Figure 6: Offline error for various numbers of initial \(Ab\) population size.  
Figure 7: Effect of varying cell neighborhood sizes.
# A Blockchain-Based Auction Framework for Location-Aware Services
## 3 The Proposed Blockchain-Based Auction Framework
### Auction
#### 3.2.3 Settlement  
Algorithm 2 shows the various steps of the auction settlement process. Once the auction is finalized, the settlement process will be initiated to determine the auction winner and to process the payment. The bidder with the cheapest offer will be declared the winner, and their offer details will be forwarded to the buyer (lines 1-3). If the buyer rejects the proposed winning offer, the collected fees from the buyer will be transferred to the bidder's account. All other fees collected from the participating bidders will also be refunded to their accounts. If the buyer accepts the winning offer, the location-related information will be exchanged between the buyer and the seller. The nature of the exchanged information is determined based on the service type. For instance, when the service type is the provision of a storage area, an apartment, or goods transport, the buyer and bidder will exchange contact information, including addresses and phone numbers. When the requested service is a parking space, the car specifications and parking location are the only shared information. Furthermore, the payment for the service will be deducted from the buyer's wallet based on specific criteria. For instance, a user can specify a monthly payment strategy for the service.
# Chinese Geomagnetic Reference Field 2020 by the Revised Surface Spline Method
## 2 Materials and Methods
### Source and Processing of the Geomagnetic Data  
We collected three-component geomagnetic data for 2832 repeat stations, 1447 of them measured in 2019 and 1385 of them measured in 2020. The mean separation between two adjacent stations is about 70 km in most areas of China and is about 150 km in Tibet, China. All of the stations are located in a weak magnetic interference environment (e.g., far from man-made magnetic sources and with lower magnetic field gradients (less than 5 nT/m)). The measured geomagnetic elements are the geomagnetic declination (D), the inclination (I), and the total intensity (F). A GSM-19T proton magnetometer with a sensitivity of 0.05 nT and an accuracy of 0.2 nT was used to measure F. A CTM-DI magnetometer was used to measure D and I with a sensitivity of 0.1\({}^{\prime}\) and an accuracy of 0.2\({}^{\prime}\)[35]. A PM-100 GNSS system was used to measure the longitudes, latitudes, and elevations of stations and also provide the direction of geographical north, which is needed to determine the geomagnetic declination [36]. All geomagnetic elements (F, D, and I) of each station were measured in 6 sets. In each set, F was measure 10 times, D was measure 4 times, and I was measure 4 times to avoid operational mistakes.  
All geomagnetic data of the three elements were calculated to 00:00-03:00 Beijing time on 1 January 2020 after reduction of the diurnal and secular variations. The reference data used in the reduction of the diurnal variations were the minute mean values of the nearest geomagnetic observatory to repeat stations [37]. The reference data used in the reduction of the secular variation were from a 6-order natural orthogonal component (NOC) model of the geomagnetic secular variation in China [38; 39].  
Then, we averaged the results of the same repeat stations in 2019 and 2020 with the preceding data, processing to further reduce the error and removing some data that were too close. In all, 1120 repeat stations remained for building the Chinese geomagnetic reference field. The spatial distribution of these repeat stations is shown in Figure 1. We also collected geomagnetic data at 45 geomagnetic observatories and at 118 stations in neighboring countries and calculated geomagnetic data by the enhanced magnetic model [40] for 419 positions. The spatial distribution of all four parts of these positions is shown in Figure 1.
# Design and Implementation of a Six-Degrees-of-Freedom Underwater Remotely Operated Vehicle
## 2 Mathematical Model
### Kinetic Equations
#### 2.4.2 Coriolis and Centripetal Matrix  
The Coriolis and centripetal matrix refers to the sum of a rigid-body Coriolis and the ROV's centripetal matrix \(C_{RB}(v)\) and an added-mass Coriolis and centripetal matrix \(C_{A}\) (Equations (9)-(11)).  
\[C(v)=C_{RB}(v)+C_{A}(v)\in R^{6\times 6} \tag{9}\]\[C_{RB}(v)=\left[\begin{array}{cccc}0&0&0&0\\ 0&0&0\\ 0&0&0\\ -m(y_{G}q+z_{G}r)&m(y_{G}q+w)&m(z_{G}p-v)\\ m(x_{G}q-w)&-m(z_{G}r+x_{G}p)&m(z_{G}q+u)\\ m(x_{G}r+v)&m(y_{G}r-u)&-m(x_{G}p+y_{G}q)\\ \end{array}\right] \tag{10}\] \[m(y_{G}q+z_{G}r) -m(x_{G}q-w) -m(x_{G}r+v)\] \[-m(y_{G}q+w) \ m(z_{G}r+x_{G}p) -m(y_{G}r-u)\] \[-m(z_{G}p-v) -m(z_{G}q+u) \ m(x_{G}p+y_{G}q)\] \[-m(z_{G}p-v) -m(z_{G}q+u) \ m(x_{G}p+y_{G}q)\] \[0 -l_{yz}q-l_{xz}p+l_{zz}r \ l_{yz}r+l_{xy}p-l_{yy}q\] \[l_{yz}q+l_{xz}p-l_{zz}r 0 -l_{xz}r-l_{xy}q+l_{xx}p\] \[-l_{yz}r-l_{xy}p+l_{yy}q \ l_{xz}r+l_{xy}q-l_{xx}p 0\]  
\[C_{A}(v)=-\left[\begin{array}{cccc}0&0&0&-Z_{\dot{w}}w&Y_{\dot{v}}v\\ 0&0&0&Z_{\dot{w}}w&0&-\dot{X}_{\dot{u}}u\\ 0&0&0&-\dot{Y}_{\dot{v}}v&X_{\dot{u}}u&0\\ 0&-Z_{\dot{w}}w&Y_{\dot{v}}v&0&-N_{\dot{r}}r&M_{\dot{q}}q\\ Z_{\dot{w}}w&0&-X_{\dot{u}}u&N_{\dot{r}}r&0&-\dot{K}_{\dot{p}}p\\ -Y_{\dot{v}}v&X_{\dot{u}}u&0&-M_{\dot{q}}q&K_{\dot{p}}p&0\end{array}\right] \tag{11}\]
# Natural Frequency Response of FG-CNT Coupled Curved Beams in Thermal Conditions
## 1 Introduction
It is worth mentioning that there is only one boundary condition that must be satisfied at each boundary point of a second-order differential equation describing a Timoshenko beam. For this reason, in a GDQ method, the discrete boundary conditions can be placed at the boundary points. Starting with the literature overview, the vibrational behavior of curve coupled beams seems to be an interesting and relevant topic of structural engineering, possibly introducing a beneficial and efficient procedure for investigating their dynamic behavior, especially for nanomaterial reinforcements. This corresponds to the main purpose of the present work, which considers the effect of temperature on the frequency response of the selected systems, involving thermal-dependent mechanical properties for each phase. Notably, our novelty lies in the exploration of temperature-dependent (TD) mechanical properties, specifically focusing on poly methyl methacrylate (PMMA) strengthened with single-walled CNTs (SWCNTs). The unique aspect is the application of the basic rule of mixture to define equivalent mechanical properties for nanocomposites with TD characteristics. The equivalent mechanical properties of the composite material reinforced with CNTs are determined using a simple rule of mixture, where the governing equation of the beam is obtained by means of the Hamiltonian principle, which is then solved in discrete form based on the GDQ method. A validation study is performed against the reference literature to show the correctness and accuracy of the proposed procedure, where a systematic investigation examines the sensitivity of the frequency response to different input parameters, for both thermal-dependent and independent mechanical parameters of the composite material. Our analysis extends beyond the existing literature by comprehensively assessing the vibrational behavior of the structure under these varied conditions.