esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Numerical Simulations  
In the set of numerical simulations, the stochastic fluctuations are set to be \(f_{m}(\xi)=\sin(\log(100)m\xi)/(m+1)^{\sigma}\), and the KL-expansions are truncated at \(M=5\). The external loading is assumed to constant, \(l(x)=1\), and acting on the \(w\)-component. The simulations are divided into four groups with initial working hypothesis:  
* Effect of decay of parameters--as \(\sigma\) increases, the observed amplitudes should decrease.
* Effect of local thickness--As \(f_{b}(\mathbf{x})\to f^{\omega}(\mathbf{x})\), the local changes in curvature start dominating. In other words, aggregate quantities of interest such as norms of displacement fields should start increasing.
* Localised variation--If the variation is removed from the fixed boundaries, the layer effects are significantly reduced.
* Higher Fourier modes--This should primarily affect the deterministic part if the stochastic setup is kept constant.  
The fifth and final section after the simulations concerns the statistics of the error. Since the error estimate has the same representation as the actual finite element solution, exactly the same procedures can be applied to it. Indeed, it is possible to consider the reliability of the simulations by considering the expectation of the error and even its variance. This is of interest particularly when there is an a priori understanding of the underlying results that is contradicted by the simulations.  
The nominal domain is \(\Omega=[-1,1]\times[f_{b},1]\) in all cases. Loading is a transverse unit traction load acting on the top surface with the angular wave number \(K=2\). The Poisson number \(\
u=1/3\). Since the Young's modulus is assumed to be constant, it has been set to a unit value \(E=1\), and hence, all displacements are given in scaled units that should be interpreted only in relative terms. Simulation data are tabulated in Table 1, and the specific parametrisations of various simulation sets are given in Table 2.  
Finally, in all simulation sets, the parameter runs from low (blue) to high (purple).  
\begin{table}
\begin{tabular}{l l} \hline FEM & Simulations: \(p=4\), auxiliary space: \(p=6\) (interior), \(p=5\) (edges). \\ Mesh & Nodes: 63, edges: 102, interiors: 40. \\ DOF & Approximation: 2187, auxiliary space: 2226. \\ Loading & Transverse unit traction load acting on the top surface (\(K=2\)). \\ Stochastic Dimension & \(M=5\), full tensor basis, 243 collocation points. \\ Filters & (boundary) \(N=4\), \(\gamma=1/4\), \(\phi=\pi/4\), and \(y=\pm 1\), \\  & (interior) \(N=10\), \(\gamma=2/3\), \(\phi=\pi/4\), and \(y=0\). \\ \hline \end{tabular}
\end{table}
Table 1: Simulation setup.  
Figure 5: Monte Carlo convergence of the \(L^{2}\)-error of the transverse deflection. The solid line (upper bound) indicates the theoretical convergence speed of \(\sim\)1/\(\sqrt{N}\), where \(N\) is the number of simulations.
# Adaptive Energy Balanced Routing Strategy for Wireless Reschargeable Sensor Networks
## 5 Implementation Guidelines and Complexity Analysis  
Here, we give simple implementation guidelines of our algorithm. As a data aggregation and processing center, sink node has sufficient energy and computing capacity. To lessen the burden of WCV and nodes, some tasks with high computing demands (such as charging plan formulation) can be processed in a centralized computing infrastructure by sink node. As for the routing selection, it is easy for each node to locally execute this process.  
In the system, nodes send data packets containing the sensing data and their energy information to sink node, then sink node calculates the positions of charging points and all the \(M_{v}\), nodes set charged by WCV on \(r_{v}\). In the worst case, the positions of charging points can be calculated according to \(d\) and \(l\) in \(l\times\left(L/2d\right)^{2}\) steps. Moreover, considering that there will always be only \(nc\) different kinds of charging points distribution, the total complexity of the calculation of charging positions is \(O(1)\). To determine the \(M_{v}\) and calculate the average consumption \(aveE_{v}\), there will be totally \((L/2d)n\) steps, so the time complexity is \(O(n)\).  
Then, the sink node sends the charging scheduling to WCV for the charging cycle. Considering the worst case where each node can be charged by \((L/d)\) charging points, the \(\hat{E_{i}}\) can be gained after \((L/d)\) times computation. In this case, the complexity to calculate \(\hat{E_{i}}\) for \(n\) nodes is \(O(n)\).  
In the routing part, node \(i\) chooses the next hop from the forward neighbor nodes set \(NF(i)\). In the worst case, the number of nodes in \(NF(i)\) is \(n-1\). Then, node \(i\) chooses \(j(j\in NF(i))\) with the minimum value \(U(i,j)\) as its next hop node. For all sensor nodes in the network, the operation needs to be repeated \(n\) times, so that the time complexity is \(O(n^{2})\).  
As a result, we conclude that the worst computing time complexity of the whole algorithm is \(O(n^{2})\).
###### Abstract  
The primary aim of this was to assess the effectiveness of stretching for improving shoulder range of motion (ROM) in overhead athletes with glenohumeral internal rotation deficit (GIRD). The secondary aims were to compare whether the combination of stretching plus manual therapy was more effective than stretching in isolation and if any stretching technique was superior to others. A systematic review and a meta-analysis were designed. The MEDLINE, PEDro, Cochrane Library, and Web of Science databases were searched. Clinical trials investigating the effects of stretching in isolation or combined with other manual therapy techniques on ROM and pain intensity in athletes with GIRD were included. The PEDro scale was used to assess the methodological quality of the studies, and the certainty of evidence was assessed using the GRADE tool. Two independent assessors extracted data through a standardized form. The random-effects models were applied. Sixteen randomized controlled trials were included in this systematic review with a meta-analysis, with a methodological quality ranging from high to low. The stretching techniques in isolation showed statistically significant improvements in internal rotation and horizontal adduction ROM. Adding glenohumeral dorsal gliding to a stretching protocol showed better improvement in internal rotation ROM than stretching in isolation. Stretching techniques with manual stabilization showed better benefits than self-stretching techniques. A very low certainty of evidence suggests that stretching produces statistically significant changes for improving ROM in patients with glenohumeral internal rotation deficit. The combination with glenohumeral dorsal gliding seems to produce better improvements.  
athletes; glenohumeral internal rotation deficit; stretching; range of motion; pain
# Lyapunov-Guided Energy Scheduling and Computation Offloading for Solar-Powered WSN
## 7 Conclusions  
This paper investigates the optimal partial offloading decision and resource allocation problem for solar-powered MEC networks. Considering the randomness of solar arrival, channel state information, and task data arrival, we construct a stochastic optimization model for partial task offloading decisions and resource allocation with respect to maximizing network long-term energy efficiency. We then propose a joint computational offloading and resource allocation algorithm based on Lyapunov optimization to achieve long-term energy efficiency maximized under unknown prior knowledge of energy arrival, channel conditions, and task arrival. Simulations verify that the network energy efficiency and average queue backlog in the proposed algorithm follow the tradeoff of \([\mathcal{O}(V),\mathcal{O}(1/V)]\). Further, the impact of network size and bandwidth on system energy efficiency is demonstrated. In practice, given the nonlinear characteristics of renewable energy harvest and the obvious distribution characteristics of different types of energy, it is more practical to study the hybrid energy provision MEC system under nonlinear conversion. Meanwhile, HAP broadcasts power can be extended by using MIMO technology to reduce interference and improve energy utilization. Moreover, together with the improvement of network intelligence, the deployment of IoT terminals gradually becomes intensive, and the massive terminal access not only puts forward higher requirements on network energy and computation offload, but also results in serious spectrum resource shortages, which makes it valuable to study the renewable energy provision MEC system based on NOMA protocols instead of TDMA.  
Conceptualization, [PERSON] and [PERSON]; methodology, [PERSON] and [PERSON]; validation, [PERSON]; formal analysis, [PERSON]; writing--original draft preparation, [PERSON] and [PERSON]; writing--review and editing, [PERSON], [PERSON] and [PERSON]. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Key Research and Development Program (No. 2022 YFB2402900).  
**Institutional Review Board Statement:** Not applicable.  
Figure 6: Queue backlog vs. control parameters V.
## 4 Result
### Landslide Detection Based on Multi-Direction Phase Gradient Stacking
Conversely, landslides D25-D27 evade detection due to compression within a few pixels in the orbital ascent data, a situation mirrored in the SBAS results [16; 40]. A total of 39 landslides with deformation can be detected in this region by applying the ascending and descending data through the method in this paper, and their specific information is shown in Table 2. The two largest landslides, the A/D1 Suoertou landslide and A/D11 Zhongpai landslide, both have an area of more than 3 km\({}^{2}\), and it can be seen that these landslides are mainly distributed at an elevation of about 2000 m. The highest landslide is D22, which is located at 3000 m above sea level. The detected landslides are mainly shallow landslides, with some debris flow landslides but no landslides with sudden deformations such as rock falls, mainly because the InSAR technology detects creeping deformations, and landslides with deformations that are too large cannot be detected by the phase information [10].
# An Operations Chain Model for Automatic Assessment of Operation Procedure for Equipment Operators
## 2 Operations Chain Model
### Operations Chain Model for a Task  
Each operation task corresponds to an operations chain. To obtain the operations chain for a specific task, the target cascading method can be utilized to decompose the task into basic operations and to analyze their interrelationships. In general, the decomposition of a task is implemented by professionals related to manipulating the equipment. According to the task requirements, the function modules are decomposed first, and then each module is further decomposed into basic operations, as shown in Figure 3. A specific task can be decomposed into multiple functional modules with a fixed sequent relationship, which will be nodes in the chain. In each node, there may be three types of function modules, as shown in Figure 3: (1) direct decomposition into one or more basic operations, such as function modules 1, 21, 22, and 23; (2) combinations of basic operations and function modules, such as function module 2; (3) combinations of function modules, such as function module n. Eventually, all functional modules will be decomposed into basic operations. During decomposing, the relationships among functional modules or basic operations will be determined and recorded. The recorded relationships will be applied to build the tree. It should be noted that the basic operations corresponding to each function module can overlap, meaning that one basic operation can correspond to multiple function modules.  
The top-level function modules in Figure 3 correspond to the operation nodes in the operations chain model in Figure 2. Several nodes are combined sequentially to form the operations chain. The basic operations that comprise the top-level function modules form an operation tree based on the logical relationships of \"AND\", \"OR\", and \"MEX\". The operations chain model of an operation task includes all the basic operations, and their combinations required to accomplish the task. Some operations may be crucial in a task. Crucial operations will be determined by professionals according to specific tasks, for example, one can be defined as a crucial operation if it must be completed in a task. Crucial operations should be defined as critical steps and explicitly indicated in the operations chain. Crucial operations must be given special attention when calculating scores.  
Figure 3: Operation task decomposition schematic.
## 4 Classification Models
### Full Description Models  
Since the classification models developed require supervised learning, it was essential to have a number of perfectly labeled descriptions to build the models. Therefore, avoiding unsupervised techniques, 99 textual descriptions were read and labeled manually. Within the set of 99 events, 33 had presence of witnesses, and 66 did not. These descriptions were evenly divided into two sets, the training and the test set. Two types of feature extraction techniques were applied to the descriptions: TF-IDF and BoW, from the sklearn feature-extraction package [30], and a binary model was applied to the labels with the LabelBinarizer module of the sklearn preprocessing library. After fitting and computing the models several times, it was found that the most efficient parameters for feature extraction were the minimum and maximum df at 0 and 1 and the number of n-grams limited to 1.  
Subsequently, different classification models were built to check which one is the most efficient for the given database. These models are: logistic regression, naive Bayes multinomial, support vector machines (SVMs), k-nearest neighbors and an SGD classifier. The evaluation of the models was based on the metrics related to the \"with witness\" category. Considering the purpose of Hollaback!'s awareness campaign, the goal of this first automatic analysis was to conduct a \"pre-selection\" of descriptions with bystanders in order to avoid the marketing staff reading thousands of stories. Having said this, the most important metric is the sensitivity of the true class (in our case, the true positive, real descriptions with a witness predicted as true).  
In order to conduct a more precise comparison, all the metrics of the exposed models are collected in Figure 17. These metrics are calculated based on the confusion matrices of each model. The acronyms ST and CT refer to the categories \"with witness\" or \"without witness\". Although the table collects information from both categories, the one that will be taken into account is \"with witness\". The models evaluated are logistic regression (LR), multinomial naive bayes (NB), a support vector machine (SVM), stochastic gradient descendant (SGD) and k-nearest neighbors (KNNs) as some of them had been tested before in different fields, returning good results [13].  
All models have an accuracy of around 70%. Moreover, except for the logistic regression and naive Bayes, they also have an accuracy of around 63% for the model with a control. This means that 63% of the time, an item drawn from the class with a control will be well classified. It can also be seen that most models have a low sensitivity for detecting tokens, the highest being the SVM for BoW with 47%. This would mean that by having a witness description, the algorithm has a 47% chance of categorizing it as such.
# Hybrid Deep Learning-Based Epidemic Prediction Framework of COVID-19: South Korea Case
## 1 Introduction  
Human mobility plays a crucial role in the temporal and spatial spread of infectious diseases. During the past few decades, researchers have extensively investigated how human mobility affects the propagation of disease. The travel of individuals carrying the virus among communities is one of the main causes of infectious diseases. In the mathematical modeling of disease transmission, the compartment model [1] is popular among epidemiologists to divide the population into classes labeled as susceptible, infected, and recovered. Meta-population models [2] are a type of spatial models that investigate interactions and movements among different subpopulations of the same species across time and space. As a virus is transferred between heterogeneous hosts and the environment, the meta-population model could be extended to serve as a more appropriate modeling framework for capturing the dynamics of epidemic propagation. Although several research studies [3] consider human mobility in epidemic modeling, it is difficult to capture mobility parameters. Real-time estimation of these parameters affecting the transmission of infectious diseases is important for accurate prediction of the epidemiological pattern of an infectious disease.  
This study proposes a new hybrid deep learning (DL) approach for estimating COVID-19 transmission patterns in South Korea. The proposed framework combines deep learning with the susceptible-exposed-infected-recovered (SEIR) meta-population model. To show its effectiveness, the hybrid deep learning framework is compared with the long short-term memory (LSTM) model and the general deep neural network (DNN) model to forecast epidemic patterns in South Korea based on the same dataset. The dataset used in this study was provided by the Korea Centers for Disease Control and Prevention [4], which is an organization under the South Korean Ministry of Welfare and Health. The detailed framework and procedures are provided in the following sections.  
The following section provides a review of the existing research studies and literature related to COVID-19 transmission and prediction using deep learning. Section 3 proposes the hybrid deep learning framework based on meta-population model. The numerical analysis of the proposed framework and the performance analysis are provided, including the comparison with other methods, in Section 4. Finally, Section 5 presents the concluding remarks and the future work.
## 4 Experimental Analysis
### Experiment Results  
Finally, the recognition accuracy and operation resource loss are shown in Table 3. In crack recognition, the accuracy of the recognition algorithm can be evaluated by comparing the SSIM values of the recognition results with the reference image (i.e., the actual crack image). The closer the SSIM value is to 1, the more similar the recognition result is to the manually labeled image--that is, the higher the accuracy of crack recognition.  
According to Table 3, it is obvious that the recognition accuracy of the combined method of region growth and regression analysis is approximately 7% higher than that of the traditional region growth algorithm, and its calculation cost is not significantly increased. Compared with the U-Net method, the recognition accuracy of the combined method is decreased by 5.96%, but its CPU resource usage is only half of that of the deep learning method, and GPU resources are not used. A comparison of the methods' recognition of cracks in pictures of typical concrete structures is shown in Figure 4.  
Figure 4: Display of test comparison results.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Accuracy** & **Time Consumption** & **CPU Occupancy** & **GPU Occupancy** \\ \hline Region growth & 81.30\% & 0.43 s/photo & 2.4\% & 0\% \\ Region growth + regression & 88.29\% & 1.21 s/photo & 4.6\% & 0\% \\ U-Net & 94.25\% & 2.37 s/photo & 8\% & 49.1\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Experimental results.  
As we can see in Figure 5, the combination algorithm identifies smaller cracks in A2 compared to the region growing algorithm in A1, while the over-detection of cracks is avoided in B2, unlike B1. Our combination algorithm has a performance similar to U-Net's in A3 and B3 when detecting small cracks and interfering textures.
###### Abstract  
Hydrocarbons can have very harmful effects on organisms and the environment, and conventional techniques for their removal are expensive and require the use of chemicals and long-term actions. _Trichoderma_ is an ascomycete genus known to be active on different recalcitrant substrates, since it can produce a set of nonspecific extracellular enzymes generally involved in the degradation of lignin. However, the literature concerning the use of _Trichoderma_ to degrade hydrocarbons is still limited. In this work we aimed to investigate the ability of _Trichoderma_ to exploit used engine oil as its sole carbon source for prospective bioremediation of contaminated substrates. Four different strains belonging to _Trichoderma_ asperellum_ and _Trichoderma_ _harzianum_ species were tested. The fungi were inoculated in direct contact with used engine oil, and after 45 days the samples were analyzed by gas chromatography/mass spectrometry (GC/MS). The results showed that all strains (except _Trichoderma_ asperellum_ F1020) significantly changed the oil composition, decreasing the aromatic fraction in favor of the aliphatic one. _T. harzianum_ F26, especially, showed a significant reduction of the BTEX (benzene, toluene, ethylbenzene, and the three xylene isomers) and alkylbenzenes fraction and an increase in short-chain aliphatics C\({}_{1}\)-C\({}_{20}\). Enzymatic tests for lacase and peroxidase were also carried out, demonstrating that every strain seems to express a different mode of action.  
fungi; contaminated substrates; hydrocarbons bioremediation; enzymes +
Footnote †: journal: Applied Soft. 2020, 10, 3152
## 4 Discussion
### Addition of Carbon Source to Enhance The Denitrification Process  
The low removal of nitrate after the addition of an external carbon source proved that aeration was the limiting factor of the denitrification process. The additional influential factors of nitrate removal are the acidic pH values (between 3.9 and 4.3) in wastewater of treatments A, B, and C fluctuated. Reported cases have shown that acidic conditions apart from influencing nitrification rates, can also inhibit denitrification [36; 37]. However, it is possible that the added glucose solution was aerobically biodegradation before it could be used by denitrifying bacteria available in the containers. Furthermore,it could be possible that the glucose solution prepared with a 3:1 COD/TN ratio was not enough to provide the necessary carbon source to promote a complete denitrification process. [PERSON] et al. [35] in their study tested the effectiveness of recirculating the effluent, the addition of glucose, and the use of iron activated carbon stimulated with a micro electrolysis process, to decrease nitrate and ammonium concentrations. The use of an effluent with high nitrate concentrations and a controlled addition of organic content would promote the denitrification process. More effective conditions for denitrification were encountered in the non-aerated IBC. The pH values between 7.8 and 8.7, anaerobic conditions and possibly, the sufficient carbon sources, favoured the denitrification process [36; 38].