esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 The Proposed Method
### CNN Predictor
#### 3.1.1 Preparation for Image Prediction  
The proper construction of the training set and target set is a crucial task for increasing the predictor's accuracy. In [18; 1], the cover image is divided into cross set and dot set, and each set is designed to predict each other. In [12], the cover image is divided into \(2\times 2\) blocks, and the pixels within each block are numbered in order as 1, 2, 3, and 4, and then grouped into four sets.  
In this paper, the general structure of the image is analyzed to divide the image into training set and target set according to the image structure. Although not all images follow this pattern, generally, the most important part in the image is located in the center of the image, while less important background elements are located towards the edges. Figure 2 presents GBVS saliency maps and GC saliency maps for 1000 photos, which is respecting the \"rule of thirds\" (GBVS and GC are algorithms for estimating saliency) in [19].  
Figure 1: Cover image divided into dot set image and cross set image.  
The \"rule of thirds\" is one of the important compositional principles used in photography and art. According to this principle, a virtual grid can be imagined by dividing the image into thirds horizontally and vertically, and position important elements or subjects at the intersections of the grid. This method is a way to obtain aesthetically attractive photos.  
The saliency map is a heat-map that visually represents the important areas in an image. It is widely used in various fields such as computer vision, image processing, and computer graphics. It is useful for tasks like object detection, image segmentation, and attention analysis.  
Based on this result, the cover image is divided into two sets: the inner square and the outer square. Assuming an image size of 512 by 512, the inner square set starts from the 4 pixels in the center of the image, (255, 255), (255, 256), (256, 255), and (256, 256), while the outer square starts from the 12 pixels surrounding the inner square. Furthermore, the 20 pixels surrounding the outer square are included in the inner square, and the 28 pixels surrounding the inner square are included in the outer square. This process is repeated until the pixels at the edges of the image are included in the outer square, completing the set composition as shown in Figure 3.  
In the divided image in Figure 3, the inner square is used as training data and the outer square is used as target data.
# Exploring ISAC: Information-Theoretic Insights
## 1 Introduction
### Notation  
In this survey, we shall use class notation for our mathematical expressions. For example, upper-case letters like \(X\) denote random quantities, while lower-case letters like \(x\) represent their deterministic realizations. Sets are represented using calligraphic font (e.g., \(\mathcal{X}\)). The \(n\)-tuples \((X_{1},\ldots,X_{n})\) and \((x_{1},\ldots,x_{n})\) are abbreviated as \(X^{n}\) and \(x^{n}\), respectively. Similarly, the \(n-t\)-tuples \((X_{t+1},\ldots,X_{n})\) and \((x_{t+1},\ldots,x_{n})\) are written as \(X^{n}_{t+1}\) and \(x^{n}_{t+1}\). _Independent and identically distributed_ is abbreviated as _i.i.d._, and _probability mass function_ as _pmf_. The conditional probability is written as \(P_{XY|UY}(x,y|u,v)\), and \(P_{X}(\cdot)\) represents the pmf of a finite random variable \(X\). The expectation of a random variable \(X\) is denoted by \(\mathbb{E}[X]\). \(\mathbb{R}\) and \(\mathbb{R}^{+}_{0}\) denote the sets of real numbers and non-negative real numbers, respectively. log typically represents the base-2 logarithm, accordingly information measures are measured in terms of _nats_. The operator \(\oplus\) typically indicates XOR (binary addition modulo 2).  
The \(\arg\min\) represents the set of minimizers of a function. \(\varlimsup\) and \(\varlimsup\) denote the limit superior and limit inferior as \(n\to\infty\). The operator \(f^{(n)}\) represents a transformation or operation on \(n\)-letter sequences. The term \(\operatorname{ess}\sup\) refers to the essential supremum in measure theory. Entropy, conditional entropy, and mutual information are denoted by \(H(\cdot)\), \(H(\cdot|\cdot)\), and \(I(\cdot\cdot)\), respectively. When the probability mass function (pmf) is not clear from the context, it is included as a subscript, e.g., \(H_{p}(\cdot)\). The Kullback-Leibler divergence between two pmfs is denoted by \(D(\cdot\|\cdot)\).
# Effects of Simulated Nitrogen Deposition on the Bacterial Community of Urban Green Spaces
## 4 Discussion
### Temporal Trends and Potential Consequences of N Deposition  
The present study focused on the delicate areas of the urban greens in the Chinese atmospheric fallout is consistent with previous ones that showed the decline of soil bacterial biodiversity under N enrichment [40; 59; 60; 61]. Considering the three observation periods separately, the short-term (3 months) N deposition did not change the composition of soil bacterial diversity and communities (Figures 1, 2 and 4). The response of soil bacterial alpha diversity and community composition to N deposition was gradually confirmed after N addition for 6 months and 12 months (Figures 1, 2 and 4). Results indicate that high deposition (100 and 150 kg N ha\({}^{-2}\) y\({}^{-1}\)) caused the critical changes in soil bacterial of urban green space. Experiments that use a high rate of N deposition may be useful for assessing the long-term effects of chronic low rates of N deposition [62]. The results of this study indicate that the higher deposition or accumulation of N in the urban green space deeply affects patterns of bacterial diversity and community structure. The bacterial response is affected by the amount of added N as well as by the duration of the treatment. Overall, nitrogen (N) addition had a negative impact on bacterial richness and diversity in the urban green space in both dose- and time-dependent fashions.  
At finer taxonomy level, it is worth reporting that one of the genera that markedly increased, as N was added, was _Mizugakibucter_, a genus known to be a denitrified, which is expected as a consequence of the added nitrate and the oxidation of ammonium. On the opposite side, the main decline regarded the _Nitrosomonadales_ order, which is consistent with the fact that nitrifiers are made superfluous by our addition of N forms, which are already oxidized, as nitrate is. We must not underestimate that N deposition causes changes in the soil environment because it does change bacterial composition at finer taxonomic levels other than phylum, and most of all that it heavily impacts on overall taxa diversity and richness. If N deposition keeps increasing in the future, the decline of bacterial diversity may become dramatically worse. Soil microbial diversity plays a central role in ecosystem processes by driving the Earth surface's biogeochemical cycles [63, 64], and therefore any changes in soil bacteria by N deposition might affect not only urban ecosystem processes and functions, but eventually the global ones.
## 1 Introduction
Once injected, PRP transforms into a platelet-rich gel (PRG), which initiates the release of several bioactive molecules, including GFs and anti-inflammatory/regulatory cytokines. This process creates an immunomodulatory hub that attracts nearby stem cells and promotes regenerative mechanisms, ultimately aiding in the repair and healing of damaged tissue [14]. The primary objective of this study was to systematically review the existing scientific literature regarding the clinical efficacy of PRP in the treatment of horses with clinical or experimentally induced tendon and ligament injuries. Specifically, this review aimed to answer several key questions: (1) What adverse effects have been reported with intralesional PRP injections in horses, and what safety profile can be established from the available studies? (2) What types of PRP are most commonly used to treat horses with tendon and ligament injuries? (3) What are the optimal concentrations of platelets and leukocytes in PRP for the treatment of these soft tissue musculoskeletal conditions? (4) Should PRP be activated prior to intralesional injection, and, if so, which platelet activator is most effective? (5) What is the recommended dosing regimen for PRP in the treatment of equine tenodesmic injuries? (6) What are the clinical outcomes of horses with tenodesmic injuries treated with PRP in the short term (1-3 months), medium term (3.1-11.9 months), and long term (1 year or more)? And, (7) Is the combined use of PRP with mesenchymal stem cells or bone marrow aspirates a better therapy for the treatment of tendon and ligament injuries in horses than the use of PRP alone?
# A Stochastic Bulk Damage Model Based on Mohr-Coulomb Failure Criterion for Dynamic Rock Fracture
## 2 Formulation
### Properties of the Damage Model
#### 2.3.1 Damage Force and Effective Stress  
Equations (5) and (8) determine under what strain (elastic stress) conditions damage initiates and how the effective stress evolves as \(D\) tends to unity. A common approach in continuum damage mechanics is to break the elastic stress tensor into its spectral positive and negative parts, and to express \(D_{f}\) and \(\sigma_{\text{eff}}\) as,  
Figure 3: The effect of brittleness exponent \(a\) on the rate of damage evolution.  
\[D_{f}(\mathbf{\sigma}) =D_{f}(\mathbf{\sigma}_{+}) \tag{9a}\] \[\mathbf{\sigma}_{\text{eff}} =(1-D)\mathbf{\sigma}_{+}+\mathbf{\sigma}_{-} \tag{9b}\]  
We note that alternative expressions exist where instead of \(\sigma\), the spectral decomposition of strain is considered [21; 49; 50; 51]; however, due to the use of \(\mathbf{\sigma}\) in (5) and (8), the form (9) is preferred for the discussion in this section.  
Rock fracture is often under compressive stress state. The shortcomings of (9) can be illustrated by referring to Figure 1. First, as can be seen a large difference between the principal stresses \(\sigma_{1}\) and \(\sigma_{2}\) corresponds to a large enough shear stress \(\tau\) that can initiate damage evolution; see for example the stress state A. However, if (9a) is used, \(D_{f}\) (and damage) remain zero, since \(\mathbf{\sigma}_{+}=\mathbf{0}\). Second, even if damage could evolve by an equation other than (9a), the stress would not degrade using (9b); that is, \(\mathbf{\sigma}_{\text{eff}}=\mathbf{\sigma}_{-}=\mathbf{\sigma}\) at \(D=1\). In contrast, stress state A induces a \(D_{f}=0.5\); cf. (5). Moreover, \(D_{f}\) is sensitive to the hydrostatic stress. For example, for the same maximum shear \(\tau\) and higher compressive \(\sigma_{ave}\), no damage occurs for the stress state B. Finally, through damage evolution, \(\mathbf{\sigma}_{\text{eff}}\) tends to the hydrostatic compressive stress \(\langle\mathbf{\sigma}_{h}\rangle_{-}\) as \(D\to 1\). This can be seen for stress state A and \(D=0.5\). In damage reaches unity, the effective stress state will correspond to the point \(\sigma_{ave}\) in the figure.  
The two sets of equations for \(D_{f}\) and \(\mathbf{\sigma}_{\text{eff}}\) predict a similar response for tensile dominant loading, i.e., when \(\sigma_{ave}>0\); while there are some differences in the details of damage evolution, in both cases \(\mathbf{\sigma}_{\text{eff}}\to\mathbf{0}\) as strain (proportionally) increases. There are, however, some differences in the failure damage state, \(\mathbf{\sigma}_{\text{eff}}(D=1)\), for pure shear and compressive dominant mixed loading (\(\sigma_{2}<0<\sigma_{1}\) and \(|\sigma_{2}|>\sigma_{1}\)). In short, the proposed damage model based on the Mohr-Coulomb failure criterion is more appropriate for rock fracture, especially when compressive mode failure is concerned.
## 4 Inventory and Analysis
### Framework for Inventory and Potential Assessment Methods  
We sorted out the process of roof space inventory and agricultural production potential assessment in the previous text, summarized the entire process, and established the following concise method framework diagram (Figure 4).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \multirow{2}{*}{**Building Types**} & \multicolumn{2}{c}{**Roof**} & \multirow{2}{*}{**Suitability**} & \multirow{2}{*}{**Usable Area(ha)**} & \multirow{2}{*}{**Suggested Planting Method**} & \multirow{2}{*}{**Annual Production (kg)**} \\ \cline{2-2} \cline{6-6}  & **(m\({}^{2}\))** & & & & & \\ \hline \multirow{3}{*}{Other types of buildings} & 500 \(\leq\) S & 23.9 & Level 1 & 20.2 & Facility cultivation & \(0.30\times 10^{7}\) \\ \cline{2-6}  & 500 \(<\) S \(<\) 5000 & 94.4 & Level 2 & 80.0 & Facility cultivation & \(1.20\times 10^{7}\) \\ \cline{2-6}  & 15 \(\leq\) S \(\leq\) 500 & 11.7 & Level 3 & 9.9 & Soil cultivation & \(0.05\times 10^{7}\) \\ \hline \multirow{3}{*}{Residential buildings} & 500 \(<\) S \(<\)5000 & 167.7 & Level 2 & 142.0 & Soil cultivation & \(0.68\times 10^{7}\) \\ \cline{2-6}  & 15 \(\leq\) S \(\leq\) 500 & 85.6 & Level 3 & 72.5 & Soil cultivation & \(0.35\times 10^{7}\) \\ \hline \multirow{3}{*}{Total} & \multirow{3}{*}{383.3} & \multirow{3}{*}{324.6} & \multirow{3}{*}{7.95 kg/m\({}^{2}\)} & \multirow{3}{*}{
# Study of the Effect of Exploiting 3D Semantic Segmentation in LiDAR Odometry
## 3 Methodology
### Data  
Since it is one of the most popular datasets in autonomous vehicles research, the KITTI dataset [7] is the data source selected for this study. More specifically, the odometry benchmark was used, providing input LiDAR point clouds from a Velodyne HDL-64E sensor and the ego-vehicle ground-truth poses. In addition to its popularity, another reason to select the KITTI dataset is the recently published SemanticKITTI dataset [2]. This dataset is based on the original odometry sequences from KITTI and provides point-wise 3D semantic information directly annotated in the point clouds. The annotations include label categories such as vegetation, ground and traffic signs, among many others. Additionally, the semantic labels also divide static and dynamic road agents, i.e., vehicles, pedestrians and cyclists.  
In this study, the semantic information provided by the SemanticKITTI dataset was exploited to filter the original point clouds from the KITTI odometry benchmark.
## 3 Methods
### Data Processing Flow
#### 3.4.3 Event Exporting and Feature Extraction  
Following the signal-filtering process, the EMG signal still consisted of all events of FMA movement tasks, including the relaxation period. Separating task-related amplitude from a relaxed state is essential to avoid undesirable data deployment in the feature extraction process and machine learning algorithms, such as the relax-related signal. In the following step, feature extraction was conducted on each exported event separately. As a result, only EMG signals corresponding to task-related events were extracted. An illustration of the event-exporting and feature extraction process is depicted in Figure 4.  
Many features have been introduced for recognition purposes, with time- and frequency-domain features mainly employed due to their simplicity, low computational costs, and promising recognition results. In the previous experiment, seven time-domain features and one frequency-domain feature were employed [8]. The extracted features were mean absolute value (MAV), variance (VAR), root mean square (RMS), waveform length (WL), slope sign change (SSC), zero crossing (ZC), Willison amplitude (WAMP), and mean power frequency (MPF). Many studies have utilized these features to identify the amplitude, complexity, and frequency characteristics of EMG signals [32; 33; 34; 35]. However, in the case of impairment-level recognition, the amplitude-related features can marginally differ in both between and within participants. Therefore, emphasizing the complexity-related features provides distinct information between impairment levels.  
Several studies have introduced features that depict the complexity of EMG signals. [PERSON] et al. and [PERSON] et al. utilized the skewness feature to provide complexity information from EMG signals [36; 37]. Skewness measures the asymmetry of a variable within a distribution. Zero skewness signifies a symmetric distribution, while positive skewness indicates a right-skewed distribution and vice-versa [36]. Another promising feature that provides complexity information for EMG signal is Shannon entropy. This feature describes a signal's irregularity, complexity, and unpredictability characteristics [38].  
Furthermore, the inherent non-stationary nature of EMG signals potentially provides vital feedback with respect to the entropy feature [39]. Typically, an increased value of Shannon entropy of an EMG signal represents good muscle condition, with many muscle motor units contributing to muscle contraction and a high-amplitude and randomized EMG signal. On the contrary, an impaired muscle makes it difficult to capture an EMG signal, resulting in a low value of Shannon entropy. In this regard, this study employed several features to focus more on the complex characteristics of EMG signals, including 5 time-domain features and 1 frequency-domain feature: waveform length (WL), zero crossing (ZC), mean absolute value (MAV), skewness, Shannon entropy, and mean power frequency (MPF).  
Figure 4: Event-exporting and feature extraction process.
# A Comprehensive Hybrid Deep Learning Approach for Accurate Status Predicting of Hydropower Units
## 5 Experimental Results and Discussion  
All the experiments in this study were conducted using Python v3.8.18, with TensorFlow 2.10.0 serving as the primary deep learning framework. To accelerate the training process, we employed an NVIDIA GeForce RTX 4060 GPU with 16 GB of memory (NVIDIA, Santa Clara, CA, USA). The Adam optimizer, based on stochastic gradient descent, was used to update the model parameters. Furthermore, the data preprocessing, model training, and analysis were carried out using key Python libraries, such as pandas (version 2.0.3) for data manipulation and matplotlib (version 3.7.5) for result visualization.
###### Abstract  
With advantages such as short and long transmission ranges, D2D communication, low latency, and high node density, the 5G communication standard is a strong contender for smart healthcare. Smart healthcare networks based on 5G are expected to have heterogeneous energy and mobility, requiring them to adapt to the connected environment. As a result, in 5G-based smart healthcare, building a routing protocol that optimizes energy consumption, reduces transmission delay, and extends network lifetime remains a challenge. This paper presents a clustering-based routing protocol to improve the Quality of services (QoS) and energy optimization in 5G-based smart healthcare. QoS and energy optimization are achieved by selecting an energy-efficient clustering head (CH) with the help of game theory (GT) and best multipath route selection with reinforcement learning (RL). The cluster head selection is modeled as a clustering game with a mixed strategy considering various attributes to find equilibrium conditions. The parameters such as distance between nodes, the distance between nodes and base station, the remaining energy and speed of mobility of the nodes were used for cluster head (CH) selection probability. An energy-efficient multipath routing based on reinforcement learning (RL) having (Q-learning) is proposed. The simulation result shows that our proposed clustering-based routing approach improves the QoS and energy optimization compared to existing approaches. The average performances of the proposed schemes CRP-GR and CRP-G are 78% and 71%, respectively, while the existing schemes, such as FBCFP, TEEN and LEACH have average performances of 63%, 48% and 35% accordingly.  
clustering; routing; game theory; reinforcement learning; smart healthcare; 5G and IoT +
Footnote †: journal: Applied Sciences  
Article  
An Intelligent Clustering-Based Routing Protocol (CRP-GR) for 5G-Based Smart Healthcare Using Game Theory and Reinforcement Learning  
[PERSON]  
Department of Computing and Information Systems, Sunway University, Petaling Java 47500, Malaysia; [EMAIL_ADDRESS] (M.A.S.); [EMAIL_ADDRESS] (K.I.A.);  
[EMAIL_ADDRESS] (A.M.)  
10 October 2021
# Study of the Effect of Exploiting 3D Semantic Segmentation in LiDAR Odometry
## 5 Conclusions  
An in-depth analysis of the use of 3D semantic knowledge to improve the performance of LiDAR odometry was carried out. The multiple experimental results confirm the effectiveness of preprocessing the raw data before feeding it to the localization algorithm. In particular, our study proved the convenience of filtering out those points belonging to dynamic objects, including pedestrians and the ground area, whereas far-off points proved useful to establish meaningful correspondences. Furthermore, it was also shown that the type of driving environment has a decisive impact on the suitability of each filtering approach. Hence, in some specific scenarios (e.g., highways), the results show that it is possible to drastically downsample the input data, thus lowering the computational requirements while improving the performance of the algorithm. This improvement is produced by reducing the occurrence of spurious correspondences in the matching process.  
The analysis was performed on a very representative LiDAR SLAM approach to avoid loss of generalization; nonetheless, future work will focus on the analysis of different alternatives to further confirm the generality of the conclusions drawn from the study. Likewise, although the KITTI dataset presents a decent variety of environments, the use of additional datasets complementing its properties (e.g., featuring heavily crowded environments) is also being considered in order to strengthen some of the outcomes from the analysis. However, beyond the particularities of the study, this work serves to demonstrate that the benefits of exploiting 3D semantic information to preprocess raw LiDAR data well justify further research on this topic.  
Conceptualization, F.M.M., C.G. and F.G.; methodology, validation, formal analysis and writing, F.M.M. and C.G.; supervision, J.M.A. and F.G.; and project administration, J.M.A. and F.G. All authors have read and agreed to the published version of the manuscript.  
Figure 5: Distance obtained from ground points and distance from object points after vehicle translation.  
Funding Research was supported by the Spanish Government through the CICYT projects (TRA2016-78886-C3-1-R and RTI2018-096036-B-C21) and the Comunidad de Madrid through SEGVAUTO-4.0-CM (P2018/EMT-4362) and PEAVAUTO-CM-UC3M. Conflicts of Interest The authors declare no conflict of interest.
## 4 Feature Parameter Extraction
### Reflection Coefficient  
The reflection coefficient is defined as R = \(Ar/Ai\), where \(Ar\) is the maximum value of the reflected wave spectrum, and \(Ai\) is the maximum amplitude of the direct wave spectrum to explore crack size information in the frequency domain. Figure 10 shows a 3D map with the angle as the \(X\)-axis, depth as the \(Y\)-axis, and reflection coefficient as the \(Z\)-axis.  
Figure 10 shows that when the angle increases, the reflection coefficient values have a gradual upward trend, and the reflection coefficient values gradually increase when the depth increases. There is a linear correlation between the reflection coefficient and the geometric information of cracks. The reflection coefficient can be a class feature used by the SVM model to obtain class marks.