esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
In this paper, a novel and efficient architectures for HPA non-linearity pre-distortion have been designed, optimized and implemented on FPGA. Then, it has been tested by using two types of HPA: TWTA and SSPA. To evaluate the performance of our implemented designs, three metrics were used: resource consumption, bit error rate and power spectral density. By using the proposed pre-distorters, the modulation constellation is not modified with respect to the original while respecting the demodulation slicer. In addition, a low consumption of resources is used, about 1% in case of TWTA, which makes it feasible to be implemented with the rest of ECMA-368 transmission chain on the same FPGA. In order to make sure that our implementations do not degrade the performance of the proposed wireless communication system standard, we carried out a bit error rate simulation and plotted the power spectral density. The results show that the system does not undergo any BER degradation for both TWTA and SSPA pre-distorters, with a negligible PSD regrowth. For future works, this proposal can be of interest in 5G and beyond communication systems.  
This work is developed in contribution of all authors including simulation, designing, implementation and validation. All authors have read and agreed to the published version of the manuscript.  
This work was partly funded by projects TERESA-ADA (TEC2017-90093-C3-2-R) (MINECO/AEI/FEDER, UE) and MFOC (Madrid Flight on Chip--Innovation Cooperative Projects Comunidad of Madrid--HUBS 2018/MadridFlightOnChip).  
Not applicable.  
The authors declare no conflict of interest. The funding sponsors had no role in the design of this research, in the analyses, interpretation data and decision to publish the results.
## 1 Introduction
While the concept of no-fly zones has been considered in the trajectory design in a few works [34], it has not been addressed in the context of obstacle-free navigations with motion constraints. Although obstacle-free path planning has been studied in the robotics community, the channel performance and specific trajectory optimization required for WCNs still need more consideration. Notably, _optimality_ and _autonomy_ are two key concepts. The _optimality_ of the UAV trajectory refers to an energy-efficient minimum path with optimal speed planning, which indicates that time is an important factor due to the movement of IVs that are under aerial LoS wireless services. Also, the designed trajectory should provide a path that maximizes channel performance and QoS. This makes a significant change compared with robotics literature in which the starting and goal points are known with no stress on the time. Also, the extra objectives make the conventional path planning more complicated with higher time complexity of nonconvex constraints. On the other hand, from the _autonomous navigation_ perspective, _real-time_ trajectory design is crucial for autonomous navigation, in which the UAV does not depend on external resources, such as memories, processors, and controllers for navigation. Therefore, regarding the size and cost of the overhead package, the onboard controller should be able to implement the optimization independently. This requires a linearized kinematic model with convexified objectives and constraints to develop a computationally tractable trajectory optimization problem.
## 3 Materials and Methods  
In this section, we describe the general framework and methodology of the research and the steps involved in this study. Figure 1 illustrates the workflow of our classification methodology.  
The pictorial representation of the experimental setup for the location category-based activity predictions and classification using RapidMiner include the following steps:  
1. Data collection and preprocessing: The first step is to collect the data and prepare it for the analysis. This involves cleaning the data, removing any duplicates, and encoding categorical variables. The data used in this research can be acquired using Weibo API.
2. Feature Selection and Data splitting: The data attributes used in this research are selected after verifying the variable significance using linear regression. The data are then split into training and testing sets. The training set is used to build the model, while the testing set is used to evaluate the performance of the model.
3. Model Training: Several machine-learning models are used to classify the location categories, including generalized linear model, deep learning, logistic regression, and gradient-boosted trees. Each model is trained and evaluated on the training and testing sets.
4. Model evaluation: The performance of each model is evaluated using metrics such as accuracy, precision, recall, F1-score, ROC curve, AUC, confusion matrices, and lift chart.
5. Model Comparisons: The best-performing model can be highlighted based on its overall performance and ability to accurately classify and predict the location categories.  
The experiments were performed on a computer with an Intel Core i7 processor, 16 GB of RAM, and a Nvidia GeForce GTX 1060 graphics card. The software used included RapidMiner 9.7, Python 3.8, and IBM SPSS. Further details of the dataset and methodology are provided in the following sections.
# Image Transmission through Scattering Media Using Ptychographic Iterative Engine
###### Abstract  
Random scattering media prevent light information from directly transmitting through, them as the photons will deviate from their original propagation directions due to the inhomogeneity of the refractive index distribution in scattering media. Based on recent developed methods, light information transmission through scattering media is realized using a memory effect. However, the memory effect range limits it to a small field of view. To enlarge the field of view, in this article, we propose to use the ptychographic iterative engine to deliver information through scattering media. We experimentally demonstrate that the proposed method can deliver images beyond the memory effect range through the scattering layer with outstanding imaging performance.  
scattering theory; speckle; statistics optics; imaging through turbid media +
Footnote †: journal: Applied sciences
# Injury Incidence and Pattern in Elite Young Male and Female Trail Runners
## 3 Results
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**All**} & \multicolumn{2}{c}{**Male**} & \multicolumn{2}{c}{**Female**} & & \\  & **(\(n=95\))** & & **(\(n=70\))** & & **(\(n=25\))** & & \(p\)** & **ES (\(p\))** \\ \cline{2-7}  & \% & **(\(n\))** & \% & **(\(n\))** & \% & **(\(n\))** & & \\ \hline
**Moment** & & & & & & & & \\ Training & 40.0 & (38) & 40.0 & (28) & 40.0 & (10) & 1.000 & 0.000 \\ Competition & 33.7 & (32) & 32.9 & (23) & 36.0 & (9) & 0.775 & 0.029 \\ Indirect injuries & 2.1 & (2) & 1.4 & (1) & 4.0 & (1) & 0.459 & 0.079 \\ Not at all related & 24.2 & (23) & 25.7 & (18) & 20.0 & (5) & 0.567 & 0.059 \\ \hline
**Type** & & & & & & & \\ New & 57.0 \# & (55) & 60.0 \# & (42) & 52.0 \# & (13) & 0.487 & 0.071 \\ Recurrent & 29.5 \# & (28) & 31.4 & (22) & 24.0 & (6) & 0.484 & 0.072 \\ Exacerbation & 12.5 & (12) & 8.6 & (6) & 24.0 & (6) & 0.046 \({}^{*}\) & 0.205 \\ \hline
**Mode of onset** & & & & & & & \\ Acute sudden & 49.5 \# & (47) & 55.7 \# & (39) & 32.0 & (8) & 0.042 \({}^{*}\) & 0.209 \\ Repetitive sudden & 34.7 \# & (33) & 30.0 \# & (21) & 48.0 \# & (12) & 0.105 & 0.166 \\ Repetitive gradual & 15.8 & (15) & 14.3 & (10) & 20.0 & (5) & 0.501 & 0.069 \\ \hline
**Body side** & & & & & & & \\ Right & 60.0 \# & (57) & 62.9 \# & (44) & 52.0 & (13) & 0.342 & 0.098 \\ Left & 40.0 & (38) & 37.1 & (26) & 48.0 & (12) & 0.342 & 0.098 \\ \hline
**Time Loss** & & & & & & & \\
0 days & 7.4 & (7) & 1.4 & (1) & 24.0 & (6) & 0.001 \({}^{*}\) & 0.380 \\
1-3 days & 11.6 & (11) & 8.6 & (6) & 20.0 & (5) & 0.125 & 0.157 \\
4-7 days & 17.9 & (17) & 18.6 & (13) & 16.0 & (4) & 1.000 & 0.030 \\
8-21 days & 32.6 & (31) & 34.3 & (24) & 28.0 & (7) & 0.565 & 0.059 \\ \(>\)21 days & 24.2 & (23) & 30.0 & (21) & 8.0 & (2) & 0.031 \({}^{*}\) & 0.226 \\ All season & 6.3 & (6) & 7.1 & (5) & 4.0 & (1) & 1.000 & 0.057 \\ \hline \hline \end{tabular} \({}^{*}\) Significant differences between male and female trail runners (\(p<0.05\)); # significant differences between characteristics (\(p<0.05\)).  
\end{table} 
Table 2: Characteristics of injuries in male and female trail runners. in male runners the most notable diagnosis is a joint sprain (48.6%; \(p\) < 0.001; ES = 0.464) followed by tendinopathy (18.6%), injuries in female runners were more distributed among joint sprain (28.0%), tendinopathy (20.0%), muscle injury (12.0%), laceration (12.0%), and non-specific diagnoses (24.0%). Finally, it should be noted that 34 joint sprains out of 41 occurred in 35 male runners, which gives a 97.1% probability of sustaining a joint sprain. However, only 7 joint sprains were reported by 16 female runners, reducing the percentage to 43.8%. From the analysis of body region and diagnosis, it should be noted that 95.1% of joint sprains were in the ankles, while the second most common injury, tendinopathy (43.9%), was distributed between the knee, the ankles, and other body segments.
# Diversity and Functional Evolution of Terpene Synthases in Rosaceae
## 2 Results
### Ka/Ks Ratios of TPS Family Members in Rosaceae  
In addition, based on the phylogenetic relationship in Figure 3, we assumed TPSs pairs that derived from recent duplication as paralogs. There were two types of paralogs: one is the \"between-species paralog\" and the other is \"within-species paralog\" [22]. In this study, we only used the latter type of paralogs. As a result, a total of 82 TPS paralogs from recent duplication were found in the six Rosaceae species (_P. persica, P. mume, M. domestica, P. betulifolia, F. vesca, R. chinensis_). To explore the selection pressure in the evolution of TPSs, the Ka/Ks values were calculated for the six Rosaceae species (Table S3); a Ka/Ks value of less than one implies purifying selection, Ka/Ks = 1 represents neutral selection, and Ka/Ks > 1 indicates positive selection. The results showed that the Ka/Ks values of TPS paralogs were mostly less than one, suggesting that these genes evolved under purifying selection. There were five gene paralogs with Ka/Ks values greater than one, including three pairs of the TPS-a clade (Ro.chi-TPS18/Ro.chi-TPS20, Ma.dom-TPS14/Ma.dom-TPS13, and Ma.dom-TPS6/Ma.dom-TPS8), one pair from the TPS-b clade (Fr.ves-TPS11/Fr.ves-TPS9), and one pair from the TPS-c clade (Ma.dom-TPS29/Ma.dom-TPS30), which indicates that they were  
Figure 5: Synteny analysis of TPSs among Rosaceae species. Grey lines in the background indicate the collinear blocks between different genomes, while the yellow lines highlight the syntenic TPS gene pairs. The chromosome is indicated by different colored boxes and labeled by Pr.pLo (_P. persica_), Pr. Mir (_P. mira_), Ma.dom (_M. domestica_), Fr.ves (_F. vesca_), Ro.chi (_R. chinensis_). Collinear relationships between _P. persica-P. mira_, _P. persica-M. domestica_, _P. persica-F. vesca, P. persica-R. chinensis_, _F. vesca-R. chinensis_ are shown in (**A**–**E**), respectively.  
evolved under positive selection. Based on Ks values, the divergence time was calculated, which showed that five gene paralogs were diverged less than 5.5 Mya, especially for Ro.chi-TPS18 and Ro.chi-TPS20, and their divergence time was estimated to be around 0.55 Mya, indicating these gene paralogs were diverged recently. We observed that the divergence time varied among different clades. The divergence time of paralogs from TPS-e were all more than 11.4 Mya, which is higher than other clades, indicating the TPS-e clade diverged relatively auction.
# A Non-Invasive Fetal QRS Complex Detection Method Based on a Multi-Feature Fusion Neural Network
###### Abstract
This study introduces a non-invasive fetal QRS complex recognition method based on a multi-feature fusion neural network. Initially, we designed a signal entropy data quality detection algorithm based on blind source separation to ensure the selected maternal ECG signals meet quality requirements. Subsequently, the signals were subjected to data preprocessing operations such as denoising and normalization to prepare for further analysis. Following this, the maternal ECG signals were segmented using a sliding window technique and computed across four modalities: time domain, frequency domain, time-frequency domain, and nonlinear characteristic values. Ultimately, we proposed three types of deep neural networks that integrate multiple features to identify fetal QRS complexes rapidly. The overall algorithmic workflow of this topic is illustrated in Figure 1.  
Figure 1: The overall algorithmic workflow. The method proposed in this paper has four main advantages. First, traditional methods require at least four channels to separate fetal ECG signals from maternal ECG signals, which can be uncomfortable for pregnant women in clinical applications and have significant limitations. The method used in this paper does not require the separation of maternal and fetal ECG signals. It can directly detect fetal heart QRS complexes from a single-channel fetal ECG signal obtained from the abdomen of pregnant women. Second, fetal ECG waveforms can intuitively display the fetal cardiac activity status, containing valuable information for doctors to diagnose and treat. Traditional methods can only calculate the fetal heart rate and fetal heart rate variability, whereas the technique proposed in this paper can extract complete fetal ECG waveforms with apparent features. This allows for the precise prenatal monitoring of fetal health based on morphological information from the fetal ECG. Third, this paper explores lightweight networks with lower storage and computation requirements. By using a window moving method, it is possible to quickly label the position and shape of the fetal heart, achieving the real-time monitoring of the fetal ECG. Fourth, including multimodal data provides the deep learning model with more prosperous and adequate information. The model's overall performance is improved by leveraging the complementarity of information across multiple modes.
## 4 Experimental Results
### Entropy of Images  
An image of high randomness is desirable for communication as the pixels in it are indistinguishable in nature. The purpose of using an encryption algorithm is to generate an encrypted image from which no informative patterns about the pixels can be inferred [60]. Hence, the benefit of using an encryption algorithm is the production of an image with maximum pixel randomness [61]. This can be measured using a quantitative measuring metric denoted entropy. Entropy is a statistical measure of randomness that can be used to characterize the texture of the input image [62]. Here, as the pixel values extend up to 256, the maximum entropy values should be near 8. The entropy of the encrypted images is greater than the entropy of the original images. The entropy of an image \(E_{I}\) can be calculated as:  
\[E_{I}=\sum_{n=0}^{p_{k}-1}P(n)\log_{2}\frac{1}{P(n)}, \tag{13}\]  
where \(Pk\) denotes the peak intensity level and \(P(n)\) gives the probability of the occurrence of the pixel \(n\). The entropy values of the original image, encrypted image, and the information  
Figure 5: Intermediate outputs generated during the experiments: Figure (**a**–**d**): Original images, Figure (**e**–**h**): Encrypted images, Figure (**i**–**l**): Images after the embedding process, and Figure (**m**–**p**): Recovered images.  
embedded image for the images of baboon, airplane, boat, and peppers are presented in Table 4.
# User-BS Selection Strategy Optimization with RSSI-Based Reliability in 5G Wireless Networks
## 4 Wireless BS Selection-Strategy Optimization Based on Identified Distribution
### Unconstrained Optimization  
The original problem can be regarded to be an optimal allocation problem, \(N\) processes correspond to the connection with \(N\) BSs, and the allocation object corresponds to the probability of connecting to each BS in selection strategies.  
We can define  
\[F_{k}(I)=\min_{s\in\mathcal{S}}\sum_{i=1}^{k}\iint s_{i}(x)R_{BS}(i)\log_{10}d _{i}(x)f(x)dx,\]with \(\sum_{i=1}^{k}s_{i}(x)=I\). Next, we need to assign the connection probability of \(\sum_{i=1}^{k}s_{i}(x)=I\) to \(k+1\) BSs, and the total probability assigned to the first \(k\) BSs is \(I-s_{k+1}(x)\). According to the optimization principle, we have:  
\[\begin{split} F_{k+1}(I)=&\min_{s\in\mathcal{S}} \sum_{i=1}^{k+1}\iint s_{i}(x)R_{BS}(i)\log_{10}d_{i}(x)f(x)dx\\ =&\min_{s_{k+1}}[F_{k}(I-s_{k+1}(x))\\ &+\iint s_{k+1}(x)R_{BS}(k+1)\log_{10}d_{k+1}(x)f(x)dx].\end{split} \tag{29}\]  
Since the selection strategies were noncontinuous and unguided, we only needed to ensure that the selection strategy was optimal at each point to ensure that the overall selection strategy was optimal. Therefore, we could transform the original overall optimization (29) into optimizations on input node.  
Given user location \(x_{l}\), when the SEN and BS capacity constraints were not considered, we could first calculate distance \(d_{i}\) from the user to each BS \(i\). According to dynamic programming, \(\sum_{i=1}^{k}s_{i}(x_{l})=I\) was divided, and the following recurrence formula was obtained:  
\[\begin{split} F_{k+1}(I)=&\min_{s\in\mathcal{S}} \sum_{i=1}^{k+1}s_{i}(x_{l})R_{BS}(i)\log_{10}d_{i}(x_{l})\\ =&\min_{s_{k+1}}[F_{k}(I-s_{k+1}(x_{l}))\\ &+s_{k+1}(x_{l})R_{BS}(k+1)\log_{10}d_{k+1}(x_{l})].\end{split} \tag{30}\]
## 6 Training Algorithm with Multi-Dimensional Aligned Q-Fusion
### Aligned Q-Fusion  
As proposed in the previous subsection, the multi-dimensional Q-network determines the phase green times for two rings independently. This makes it more challenging to ensure that two concurrent lagging phases end simultaneously, as required by DRBPS. In other words, it raises the challenge of how to ensure that the agent selects the same sub-action for each of the two dimensions in such a situation.  
We propose \"aligned Q-fusion\" to address this challenge. Let \(a_{t,i}^{d,e}\) denote a specific choice of sub-action \(a_{t,i}^{d}\) for dimension \(d\), and \(Q_{\theta}^{d,e}(S_{t},a_{t,i}^{d,e})\) denote its value, where \(e\in\{0,1,\ldots,E-1\}\) is the ordinal number of this choice in its dimension. Then, \(Q_{\theta}^{d,e}(S_{t},a_{t,i}^{d,e})\) by definition is the agent's estimate of the future rewards, conditioned on extending \(e\) seconds of green time for the current phase in the \(d\)-th ring.  
We first align the sub-actions and their values for different dimensions like in Figure 6. If current phases are lagging, we calculate the \"fused\" value \(FQ_{t,i}^{*,e}\) for each aligned sub-action \(a_{t,i}^{*,e}\). It equals the mean of the values of sub-actions with the same ordinals across all dimensions:  
\[FQ_{t,i}^{*,e}\left(S_{t},a_{t,i}^{*,e}\right)=\frac{1}{D}\sum_{d=1}^{D}Q_{ \theta}^{d,e}\Big{(}S_{t},a_{t,i}^{d,e}\Big{)}. \tag{15}\]  
The resulting \(FQ_{t,i}^{*,e}(S_{t},a_{t,i}^{*,e})\) can represent the average expected returns on extending the same green time for two concurrent phases. We then take the aligned sub-action \(sa_{t,i}^{*}\) that maximizes this fused value, as follows:  
\[sa_{t,i}^{*}=\arg\max_{a_{t,i}^{*,e}}\Big{[}FQ_{t,i}^{*,e}\Big{(}S_{t},a_{t,i} ^{*,e}\Big{)}\Big{]}. \tag{16}\]  
For lagging phases, we make \(sa_{t,i}^{*}\), shared by all sub-action dimensions (i.e., the two rings). Thus, the action becomes \(A_{t,i}=\langle sa_{t,i}^{*},sa_{t,i}^{*}\rangle\). Essentially, \(sa_{t,i}^{*}\) attempts to seek the best compromise between two lagging phases, upon the condition that DRBPS requires them to end simultaneously. For leading phases, we still take the best sub-action in each dimension independently as usual. In summary, the selected sub-action per dimension is as follows:  
\[\begin{cases}a_{t,i}^{1}=a_{t,i}^{2}=sa_{t,i}^{*},\\ \qquad\text{if current phases are lagging;}\\ a_{t,i}^{k}=\arg\max_{a_{t,i}^{k,e}}\Big{[}Q_{\theta}^{k}\Big{(}S_{t},a_{t,i }^{k,e}\Big{)}\Big{]}\text{ for }k\in\{1,2\}\,,\\ \qquad\text{if current phases are leading.}\end{cases} \tag{17}\]  
The above Equation (17) in effect rewrites Equation (14) by embedding the aligned Q-fusion into it.
# New Landslide Disaster Monitoring System: Case Study of Pingding Village
## 2 Material and Methods
### Analysis of the Causes of Collapse in Pingding  
The southwestern hills of Pingding belong to the Toukoshan Formation; the poor cementing of the hills' conglomerate and sandstone tissue raises the possibility of collapse due to washouts. As a result of the frequent headward erosion in the Linbeiken area (Pingding River), the platform has shrunk in the southeastern direction, leaving only the Pingding area behind. Enveloped in serious erosion, the entire  
Figure 2: Pingding in Taiwan: (**a**) Pingding in the township of Linnei; (**b**) Pingding in Taiwan; (**c**) satellite image of Pingding.  
mountain area collapses easily when rainfall occurs. The proposed cause of this quick downward flow of the seepage water in the above soil stratum is because of the excellent permeability of the red soil and gravel strata on the surface. The seepage water, however, gets stuck here when confronted with gray sandy mud strata, which are poor in permeability and move horizontally. The underground water finally causes a collapse due to the decompression effect following seepage through the soil from the side slope surface. As the water content in the mud stratum increases, the mud stratum's ability decreases its shear resistance strength. These are key factors that contribute to the damage caused to the side of slopes, and can be judged from the locations and types of previous collapses. Generally, large-scale types of mass collapse are the main factors causing damage, while collapse locations are influenced by the load of slope tops, the water content of local soil (rock) strata, and the gradient.