esa-sceva/satcom-chunk-collection huggingface dataset:
# Performance of Narrow Band Wide Area Networks with Gateway Diversity
## 3 System Model
### Path loss Model  
Figure 7: The interference model in the LPWAN module (illustration of procedures 4.2 and 4.3 in [16]).  
In this section, the path loss model is presented for the US 902-928 MHz ISM band. We define the path loss (in dB) including the shadow fading as follows:  
\[\text{PL(d,f)}=\text{PL}_{0}(\text{d}_{0},\text{f}_{0})+10\text{log}_{10}(\text{ d}/\text{d}_{0})^{n}+20\text{log}_{10}(\text{f}/\text{f}_{0})+\text{X(0,\sigma)},\text{[dB]} \tag{7}\]  
where \(\text{PL}_{0}(\text{d}_{0},\text{f}_{0})\) is the path loss at reference distance \(\text{d}_{0}\), and reference channel frequency, \(\text{f}_{0}\). The term \(n\) represents the path loss exponent, d is the distance between the GW and the EN, and f is the channel frequency. The term \(\text{X(0,\sigma)}\) (in dB) is a random variable that represents the large-scale shadow fading effects. Our field measurements show that it has a log-normal distribution and shows a standard deviation of \(\sigma=5\) dB ( Figure 8). Please note that these parameters depend on the environment that they are measured and hence can be different in different terrains or cities. Path loss is calculated based on the difference between the measured Received Signal Strength Indicator (RSSI) reported by the EN and the Transmit Power (TxP) at the GW, where TxP = 22 dBm. Both the EN and the GW were at 1.4 m from the ground. The path loss measurements versus the log-distance are presented in Figure 9, which are measured with an Evaluation Kit (EVK) with an SX-1262 chipset. The variation in the path loss around a given distance is due to shadow fading which changes versus time. With linear regression applied to the random path loss measurements, we estimate the mean path loss exponent to be n = 3.2, with 95% confidence bounds of [3.14, 3.2]. Additional HW losses of 14.5 dB and an environmental in-band interference level of 6 dB were added to the overall path loss based on field measurements. HW losses refer to the overall end-to-end losses caused by Tx and Rx antenna efficiencies, RF front-end losses, and chipset-to-chipset variations.  
Figure 8: RSSI samples in dBm and their histogram (samples shifted to center around 0).
# Synthesis and Characterization of Unsaturated Succin Acid Biobased Polyester Resins
## 3 Results and Discussion
### Characterisation of Unsaturated Resins  
#### 3.2.1 \({}^{1}\)H NMR  
The structure of the produced resins was examined by means of \({}^{1}\)H NMR. The collected spectra are presented in Figure 2. The \"a\" protons from the methylene groups of succinic acid were visible at 2.63 ppm. Following the ring opening of maleic anhydride, maleic acid was the main unsaturated acid incorporated into the resins' backbone, and the protons of the double bond appeared at 6.26 ppm, marked as \"d.\" Nevertheless, some isomerization from maleic to fumaric acid also took place, as the signal at 6.85 ppm clearlyshows (\"f\" protons, not shown in the inset scheme) [30]. An example of integration of the signals can be found in Figure S2. As the acid-to-diol ratio in the synthesis protocol was 1:1.25, free -OH terminal groups were also visible that were derived from monoeuserified diols. Free -OH groups of EG near the succinic acid appeared at 3.77 ppm--the protons marked \"b,\"--whereas those near the unsaturated acid appeared at 3.82 ppm--the protons marked \"c\" in Figure 2a. For the resins containing PEG200, a new signal appeared in the spectra. The signal came from protons marked \"g\" in the inset scheme, near the ether bond of PEG200, and appeared at 3.61 ppm. For the protons of the methyl groups near the ester bonds, the \"e\({}_{1}\)\" protons near the unsaturated acid appeared at 4.31 ppm and the \"e\({}_{2}\)\" protons near the succinic acid appeared at 4.2 ppm, shown in Figure 2b. Finally, the protons of free -OH groups, derived from monoeuserified PEG200, appeared at 3.71 ppm when close to the unsaturated acid and at 3.66 ppm when near succinic acid (protons \"b\" and \"c\" in Figure 2b, respectively).
# Efficient FPGA Implementation of Automatic Nuclei Detection in Histopathology Images
## 2 Materials and Methods
### Mean-Shift Clustering
The _Merger_ module scans through the _Identified nuclei_ column. If there exists a previously generated Nuclei \(C_{l}(l\
eq k)\) whose distance to the current convergent point \(C_{k}\) is smaller than a threshold (e.g., 16 pixels), then \(C_{k}\) should merge with \(C_{l}\). The value for the merged nuclei \(C_{l}\) is changed to the mean coordinate of \(C_{l}\) and \(C_{k}\). In _cluster number_ column, if \(L(s_{i})=l\), the _maximum votes_ value for corresponding candidates are changed to \(V_{M}(S_{i})=V_{M}(S_{i})+V_{C}(S_{i})\). If there are no _Identified nuclei_ within the threshold distance to \(C_{k}\), then \(C_{k}\) becomes a new Nuclei and added to _Identified nuclei_ column. Finally, the comparison between _current round votes_ and _maximum votes_ are done. For a candidate \(S_{i}\in C_{k}\) if \(V_{M}(S_{i})<V_{C}(S_{i})\) then \(V_{M}(S_{i})\) is changed to \(V_{C}(S_{i})\) and \(L(S_{i})\) to \(k\) (indicating that candidate \(S_{i}\) belongs to the new cluster \(C_{k}\)). Example data format can be seen in Table 1.  
After the operations in the _Merger_ module finished, the _Flag operator_ module scans through the _Visited-flags table_ to check whether there are any unvisited candidates. If there are unvisited candidates, the _Iterator_ is enabled again to generate a new cluster, otherwise, the _Clustering_ module is disabled, and the MS clustering is done. The mean coordinate of the candidates belonging to one cluster (corresponding to a nucleus) is considered as the seed coordinate of the detected nuclei.
# Exploration of Multi-Scale Reconstruction Framework in Dam Deformation Prediction
## 4 Conclusions  
Based on the strong volatility and non-linear deformation monitoring data of the dam, combined with the data decomposition technology of VMD and the LSTM-RF multi-scale prediction model, this research develops a hybrid framework called VMD-LSTM-RF. This article innovatively combines signal decomposition technology with deep learning, and uses the advantages of LSTM and RF to capture and reconstruct different frequencies after decomposition, which perfectly realizes the accurate prediction of dam deformation. Through the analysis and comparison of two measuring points, the proposed model can more accurately predict the deformation of the dam, and has a more obvious improvement  
Figure 12: Comparison of total prediction results of measuring point EX2. ((**a**) prediction results; (**b**) prediction residuals; (**c**) boxplot of residuals).  
effect on the basis of the existing model. The MAE improvement of the measuring point EX5 can be increased by 51.0% at least. The highest can reach 72.9%; the measuring point EX2 is even as high as 662.4%, which also shows that the proposed model can better achieve the expected goal and the following conclusions are drawn:  
1. The components obtained by VMD decomposition of the original sequence of dam deformation can effectively solve the impact of strong volatility deformation data on the prediction results. In the decomposition process, the instantaneous frequency average method can be used to determine the best decomposition modulus.
2. Building a multi-dimensional and multi-scale model for high-frequency components and low-frequency components; establishing a prediction model and superimpose the prediction results of each component, which can effectively excavate the change law of dam deformation, accurately capture the change trend of deformation, and greatly improve the prediction performance.
3. Case analysis shows that the model has good robustness and excellent prediction accuracy in different areas of the dam, which plays an important role in the long-term safe operation and management of RCC dams.  
Data curation, E.C. and R.Y.; Formal analysis, R.Y. and C.S.; Methodology, E.C. and R.Y.; Writing--original draft, R.Y. and S.H.; Writing--review and editing, C.S., R.Y.; Supervision, H.Z. and S.H. All authors have read and agreed to the published version of the manuscript.  
This study was funded by the National Natural Science Foundation of China (grant no. 51579089).  
Not applicable.  
The authors declare no conflict of interest.
## 3 FM-ACSK System's Description
### Chaos Generator and Synchronization
Initial slave chaos generator's state conditions are shown in Table 2.  
In the ideal communication system, \(R_{in}=R_{out}\), but in a practical system, input chaotic signal \(R_{in}\) is a sum of output signal \(R_{out}\) and a combination of distortions \(\eta\) from communication system's modulation, filters and transmission channel noise:  
\[R_{in}=R_{out}+\eta\, \tag{5}\]  
where \(\eta\) describes all distortions between master and slave chaos generators, which is shown as a dotted line in Figure 2.  
The slave chaos generator's output signal \(R^{\prime}\) is formed similarly as in the master chaos generator, but using an absolute value of recreated piecewise linear function \(g^{\prime}\):  
\[R^{\prime}=k_{1}p^{\prime}_{1}+k_{2}p^{\prime}_{2}+k_{3}p^{\prime}_{3}+k_{4}p^ {\prime}_{4}+\left|g^{\prime}(p_{1},p_{3})\right|. \tag{6}\]  
Chaotic synchronization is established when master and slave state variables are equal, taking into account signal transmission delay. Piecewise linear function \(g(p_{1},p_{3})\) values, according to (2), are always positive. The lack of chaotic synchronization can lead to the recovery of negative values from the function \(g^{\prime}\), which is incorrect. Thus, the absolute value of the function \(g^{\prime}(p_{1},p_{3})\) is used to estimate the synchronization error. To evaluate the synchronization error, the slave chaos generator's output signal \(R^{\prime}\) is subtracted from its input \(R_{in}\):  
\[e=R^{\prime}-R_{in}\, \tag{7}\]  
where \(e\) is the synchronization error. This synchronization error is inconsistent due to the fact that the piecewise linear function at some moments is equal to zero. To ensure consistency in the estimation of the synchronization error, the root mean square (RMS) of it is calculated.
## 4 Experimental Results
### Experimental Study 2
#### 4.2.2 Results Obtained Using the VGG16 Model  
The VGG16 model architecture accepts the image input size as 224 \(\times\) 224 \(\times\) 3 by default. Therefore, the dimensions and channels of all images in the dataset were resized from 50 \(\times\) 50 \(\times\) 3 to 224 \(\times\) 224 \(\times\) 3. In model training, the previous layers were initialized with weights from the ImageNet dataset to maintain the pre-trained weights, and only the last layers were trained. Subsequently, new layers were added by accessing the output layer of the model. The sigmoid activation function is used in the last output layer. The model parameters were set as follows: BCE loss function, Adam optimizer, a learning rate of 0.001, 50 epochs, and a batch size of eight. The model trained for 50 epochs achieved an accuracy of 86.60%, a precision of 75%, a recall of 42.86%, and an F1 score of 54.54%. The model also yielded an accuracy rate of 90.18% on the validation dataset. Figure 10 depicts the accuracy and loss function graph of the VGG16 model for experimental study 2.  
Figure 9: **(a)** Accuracy and **(b)** loss function graph of custom-built CNN model within the scope of experimental study 2.
# Performance Comparison of NB-Fi, Sigfox, and LoRaWAN
## 3 An Overview of NB-Fi, Sigfox, and LoRaWAN
### Comparison of Nominal Parameters  
Table 1 lists the nominal parameters of NB-Fi, Sigfox, and LoRaWAN, which can be used to compare the considered technologies. While the considered technologies operate in the same frequency band, have the same topology, and have a similar coverage range, there are many differences between them, such as the payload size, bitrates, receiver sensitivities, etc. However, despite their differences, NB-Fi, Sigfox, and LoRaWAN networks can be configured to provide the same functionality to the user using comparable time-frequency resources, which raises the question of which technology to choose when  
Figure 9: Class A transmission procedure in LoRaWAN.  
deploying a network. To answer this question, we need to evaluate the PLR, PER, and delays for networks with many operating devices. Therefore, we consider the scenarios described in Section 4.
# Indoor Wavelet OFDM VLC-MIMO System: Performance Evaluation
###### Abstract  
Both light emitting diode (LED) characteristics for illumination and communication simultaneously have made visible light communication-orthogonal frequency division multiplexing (VLC-OFDM) a strong competitive to radio frequency (RF). In this juncture, to improve signal to noise ratio (SNR) and coverage contour, the wavelet-OFDM is suggested for indoor VLC systems. In this paper, a wavelet VLC-OFDM is proposed for imaging multiple-input multiple-output (MIMO) systems. The proposed wavelet-OFDM is exploited for a hybrid space-frequency domain pre-equalization technique instead of the traditional fast Fourier transform (FFT)-OFDM technique. The Meyer filter is selected and employed in the proposed technique. A comparable achievement is elaborated for several numbers of channels to achieve the enhanced performance in terms of bit rate and coverage contour. In addition, a useful comparison is executed between our wavelet VLC-OFDM and the traditional FFT-OFDM for a hybrid space-frequency domain pre-equalization technique. The simulation results emphasize the superiority point of wavelet VLC-OFDM MIMO system by improving the coverage contour by \(\sim\)20% over the traditional OFDM at a \(10^{-3}\) bit error rate (BER) target. Hence, the proposed technique can be potentially executed in indoor VLC-MIMO systems.  
visible light communication (VLC); wavelet-OFDM; orthogonal frequency division multiplexing (OFDM); imaging receiver (ImR); imaging angle diversity receiver (ImADR); pre-equalization; [PERSON] wavelet; [PERSON] wavelet +
Footnote †: journal: _Journal of the Royal Society Interface_  
1
## 2 Basic Principles of RNS Arithmetic
### Determining the Integer Value from Residue Representation  
In RNS arithmetic, reconstructing the integer \(X\) from its residue code \((\chi_{1},\chi_{2},\ldots,\chi_{k})\) is required for performing non-modular operations. This reconstruction relies on the so-called positional characteristics of the residue code.  
There are two primary approaches to constructing RNS arithmetic based on these characteristics: (1) rank-based computation and (2) mixed-radix representation [7; 8; 9].  
According to the Chinese Remainder Theorem [29; 31], for a given RNS defined by the set \(\{m_{1},m_{2},\ldots,m_{k}\}\) of pairwise relatively prime moduli, the integer \(X\) and its residue code \((\chi_{1},\chi_{2},\ldots,\chi_{k})\) are related by the following equation:  
\[X=\sum_{i=1}^{k}M_{i,k}\chi_{i,k}-\rho_{k}(X)M_{k}, \tag{1}\]  
where \(M_{i,k}=M_{k}/m_{i}\), and \(\chi_{i,k}=\big{|}M_{i,k}^{-1}\chi_{i}\big{|}_{m_{i}}\) is a normalized residue modulo \(m_{i}\) for \(i=1,2,\ldots,k\), with \(\big{|}Y^{-1}\big{|}_{m}\) denoting the multiplicative inverse of \(Y\) modulo \(m\).  
The integer \(\rho_{k}(X)\) and Equation (1) define the _rank_ and _rank form_ of \(X\), respectively [2; 3]. Equation (1) establishes an isomorphic mapping between \(X\in\mathbb{Z}_{M_{k}}\) and its residue code \((\chi_{1},\chi_{2},\ldots,\chi_{k})\in\mathbb{Z}_{m_{1}}\times\mathbb{Z}_{m_{2 }}\times\cdots\times\mathbb{Z}_{m_{k}}\). In this context, computing the rank \(\rho_{k}(X)\) enables the realization of this isomorphic mapping.
## 7 Significant Computational Characteristics of the Proposed Model
### Cross-Section Stiffness Matrix--Voronoi Diagram of Control Points  
For the computation of the stiffness matrices of the control cross-sections of an element, a Voronoi diagram of the stress-control points is used. Based on the definition of a Voronoi diagram, the basic idea is that the stiffness of the cross-section area around a particular control point can reliably defined by the Voronoi area of this point. This technique led to rapid convergence of the model's algorithm, significantly reducing the number of iterations per load step, as resulted from thorough numerical investigations on this theme. For this reason, the use of Voronoi diagrams in the topic of the computation of the cross-section stiffness matrix is considered a very important innovation of the present model.
## 2 Fault Diagnosis of Rolling Bearing Based on VMD and ANN
### Fault Diagnosis Experiment and Analysis of Rolling Bearings  
To verify the effectiveness of the proposed rolling bearing fault diagnosis method based on VMD and an ANN, this paper designed test and analysis procedures for the rolling bearing fault, as shown by Figure 7. Among others, the Case Western Reserve University (CWRU) bearing vibration signals was taken as the test data and test bed. The test procedures for the experiment mainly included data acquisition, data preprocessing, fault feature extraction and fault diagnosis.  
The test bed for the rolling bearing damage was mainly composed of an electric motor, rolling bearings, accelerometers, torque transducer and dynamometer. The damage was located at the drive end bearing. The sampling frequency and spindle speed were 48 kHz and 1796 r/min, respectively. The accelerometers were mounted at the 12 o'clock position at the drive end of the motor housing. The fault vibration data were acquired through the accelerometers and stored in a MATLAB data format.  
After attaining the original data on the rolling bearing faults, the data preprocessing method, as shown in Figures 1 and 2, were applied to split the original data of each fault into 800 fault data, with each fault data containing 128 data points. Furthermore, the VMD and discrete Fourier transform methods were used to attain the normalized energy of the fault data. The normalized energy and fault label were taken as the input of the ANN to train the neural network. Finally, the training process was performed to obtain the prediction ANN model of the rolling bearing fault types.
## 5 Discussion  
Non-vibrational rock splitting method is effectively being used in the construction industry for tunneling and various other purposes. Borehole spacing and borehole depth are some of the important parameters involved in this method that can remarkably affect the excavation process. The borehole spacing values typically used in the field are based only on the UCS values of rock and the modulus ratio of rock is not considered at all. Using two-dimensional DEM model in this study, feasible borehole spacing values are presented according to the combined effect of UCS and modulus ratio. Although a wide range of UCS and modulus ratio were used for the numerical analysis, but the results show a clear trend in which the borehole spacing values depend on both the UCS and modulus ratio of rock. Based on the results obtained in this study, it is proposed that the guidelines suggesting the maximum borehole spacing values in different kinds of rock should be based not only on the UCS but also the modulus ratio of rock. While some important factors like pre-existing joints and fractures, and in-situ stresses were not considered, yet the findings of this study can still be useful for the industry to optimize the rock-splitting process. The existing literature on this topic does not cover the important parameters like borehole spacing, borehole depth and modulus ratio concept, so the results produced in this study can be utilized as a base to investigate various other parameters involved in the non-vibrational rock splitting method.
## 4 Results and Discussion
### Correlation between the Acoustic Emission and Deformation Characteristics  
AE is an important physical index that reflects the evolution of rock deformation. To study the characteristics of AE and rock deformation evolution during the deformation localization stage, the relationships between the relative displacement rate of the DLB and the AE counts were analyzed. It should be clarified that the AE data we used are based on the acoustic signals of sensor 2, as this sensor records a relatively rich amount of data throughout the entire loading process. For one sensor's data, one signal source corresponds to one event [55], and the number of events can be determined based on the density of the curve. The AE counts can reflect the intensity of the AE signal source, so we combined the AE counts with deformation field data for analysis to explore the evolution mechanism of rock deformation localization.  
Figure 9 plots the curves of the AE counts, the relative displacement rate, and stress vs. time. At 5.96 \(\times\) 10\({}^{3}\) s, the deformation localization was initiated, and the AE counts increased from 6.10 \(\times\) 10\({}^{3}\) s to 6.21 \(\times\) 10\({}^{3}\) s. The sliding rate of DLB1 increased gradually, and the AE counts increased with the sliding rate. From 6.21 \(\times\) 10\({}^{3}\) s to 6.24 \(\times\) 10\({}^{3}\) s, which corresponded to the first stress drop, the sliding rate was large. For each moment with a sudden increase in the sliding rate, a sharp increase in the AE counts was also observed during this period. At 6.24 \(\times\) 10\({}^{3}\) s, both the sliding rate and opening rate increased sharply, and the AE counts were also high. After the loading time of 6.24 \(\times\) 10\({}^{3}\) s, the sliding rate became 0, and the opening rate was about 1.5 \(\times\) 10\({}^{-3}\) mm/s; the single AE signal had a high counts value. At 6.33 \(\times\) 10\({}^{3}\) s, the sliding rate and opening rate increased suddenly, which corresponded to the post-peak stress drop. At this moment, the AE counts for a single AE signal reached its maximum. The above correlations among the different characteristics indicate that the sliding rate and opening rate greatly influenced the AE counts.  
Figure 8: Correlation between the relative displacement rate and the length or width of the DLB.
# Rainfall Estimation from Tropospheric Attenuation Affecting Satellite Links
###### Abstract  
A novel methodology for estimating rainfall rate from satellite signals is presented. The proposed inversion algorithm yields rain rate estimates by making opportunistic use of the downlink signal and exploiting local ancillary meteorological information (0 \({}^{\circ}\)C isotherm height and monthly connectivity index), which can be extracted on a Global basis from Numerical Weather Prediction (NWP) products. The methodology includes different expressions to take the different impact of stratiform and convective rain events on the link into due account. The model accuracy in predicting the rain rate is assessed (and compared to the one of other models), both on a statistical and on an instantaneous basis, by exploiting a full year of data collected in Milan, in the framework of the Alphasat Aldo Paraboni propagation experiment.  
Keywords:rain rate estimation; tropospheric attenuation; satellite communications; radio propagation +
Footnote †: journal: Information