esa-sceva/satcom-chunk-collection huggingface dataset:
## Material and Methods
### Pseudovirion Production and Transduction  
HIV-based pseudotype viral particles were generated as described previously [43]. In brief, 90% HEK293T cells were cotransfected in 6-well plates with 1 \(\upmu\)g total DNA using polyethylenimine (PEI) (Sigma-Aldrich, San Luis, MS, USA) according to manufacturer's instructions. DNA ratios were 1 part transfer plasmid:1 part packaging plasmid:4 parts envelope plasmid for reporter pseudotype virus generation or 7:7:1 for transgene delivery. Cell supernatants were collected at 48 and 72 h post-transfection and passed through a 0.45 \(\upmu\)m pore filter. Viral stocks were stored at 4 \({}^{\circ}\)C for short time (less than 7 days) or aliquoted and frozen at \(-80\)\({}^{\circ}\)C for long-term storage.  
For transduction assays, viral supernatants were first mixed with polybrene (H9268-5G, Sigma-Aldrich, San Luis, MS, USA) at a final concentration of 4 \(\upmu\)g/mL and 500 \(\upmu\)L or 1 mL was added to 30-50% confluent target cells (80-90% for heterokaryon transductions) in 12 or 6-well plate format, respectively, for 6 h. Seventy-two hours post transduction, cells were lysed for luciferase quantification or processed for fluorescence detection. For virus-cell specificity studies, GFP-based pseudoparticles were titrated by limiting serial dilution and subsequent transduction of the susceptible cell line Huh-7.5. Transducing units per ml (TU/mL) were calculated as [((% infected cells/100) * # transduced cells) / virus volume (mL)] by FACS analysis. For pharmacological inhibition studies, target cells were preincubated with drugs in DMSO or DMSO alone for 1 h prior to addition of pseudoparticles and further incubation.  
Luciferase-based GP-driven entry was measured as previously described [48]. For filovirus GP-driven susceptibility cell screening, nonspecific (NoEnvpp), EBOV (EBOVpp), or MARV (MARVpp) GP-mediated entry relative light units (RLU) values were normalized to positive control VSV GP-driven (VSV-Gpp) values for cross-experimental comparison [(EBOVpp or MARVpp RLUs / VSV-Gpp RLUs) \(\times\) 100] and displayed as luciferase activity arbitrary units. EBOV species susceptibility evaluation is expressed as absolute RLU values in 100 \(\upmu\)l cell lysate. For fluorescence reporter expression, target cells were quantified by FACS and shown as the % positive cells from total.
## 4 Proof of Concept Implementation
### Model Management  
We implement the model management process in the Eclipse platform [35] since we apply the model-driven approach. We use the technical environment Eclipse Modeling Framework (EMF) as it is adapted to modeling, metamodeling, and model transformation.  
The choice of the transformation language is based on specific criteria for our approach. Indeed, the tool must be integrated into the EMF environment to be easily used with the modeling and metamodeling tools. Thus, we used the QVT operational mapping often called QVTo language [11]. We have two types of transformations: the direct transformation and the indirect transformation.  
* **Direct transformation**  
Direct transformation allows implementing the abstraction phase. The process is made up of several operations. First, we create the metamodels based on the Ecore metamodeling language. The next step is to create models as instances of the source metamodels that represent the instantiation of source models in the EMF platform. Then, we use the QVT plugin to implement the transformation rules. Finally, we execute the QVT script to provide the abstracted models in an XML Metadata Interchange (XMI) format.  
* **Indirect transformation**  
This mechanism allows the model concretization activity that represents the opposite process of the abstraction phase. We conduct the same operations as represented in the direct transformation. However, we use the updated graphs as instances of the source metamodel (i.e., digraph metamodel). Then, we use the QVT plugin to implement the transformation rules. The execution of the QVT script provides the updated models.
###### Abstract  
Subtochanterior nonunion is especially challenging. Extramedullary fixation using fixed-angle devices with a closing lateral wedge osteotomy is the standard surgical procedure for restoring the hip's normal alignment and achieving bone union. However, this procedure is technically demanding and relies on devices that facilitate bone union in a non-dynamic manner, along with the limitations that this type of fixation entails, especially in this highly biomechanically stressed area. This paper aims to describe a novel surgical technique to heal subtrochanterior nonunion based on dynamic fixation performed through an over-valgus correction of the hip and fixed with a dynamic hip screw (DHS). Between March 2022 and July 2023, five patients diagnosed with nonunion of a subtrochanterior fracture were operated on by a single surgeon and followed prospectively. The average age of the patients was 64 (range: 34-85). The mean duration of surgery was 112 min (range: 63-153). The femoral neck angle before surgery was 120\({}^{\circ}\) (range: 110\({}^{\circ}\)-122\({}^{\circ}\)) and 147\({}^{\circ}\) (range: 142\({}^{\circ}\)-150\({}^{\circ}\)) after surgery. The mean leg length increased by 8 mm (range: 6-12). The Harris hip score improved from 38 points (range: 30-44) to 90 points (range: 88-96), corresponding to excellent or good results. All patients healed without major complications and were asymptomatic after 12 months of follow-up. In conclusion, over-valgus correction using a DHS is a novel technique that seems helpful for subtrochanterior nonunion. It allows for dynamic fixation, offering the advantages of dynamic fixation, especially in this high-stress area. It is also performed without osteotomy, making it a less demanding technique than the current methods described.  
subtochanterior curs nonunion; pseudoarthrosis; dynamic fixation; dynamic hip screw; valgus correction Article
# Ventricular Fibrillation Prediction and Detection: A Comprehensive Review of Modern Techniques
## 2 Modern ECG Signal Processing Techniques for Detection and Prediction of Ventricular Fibrillation
### Neural Networks and Deep Learning Algorithms  
Neural networks are an advanced class of machine learning algorithms inspired by the structure and function of the human brain. They provide a flexible and robust approach to ECG signal processing capable of modeling complex relationships and identifying subtle patterns in the data. In the context of ventricular fibrillation (VF) detection and prediction, neural networks are particularly useful due to their ability to extract and learn relevant features from ECG signals.  
We will discuss two main types of neural networks used very often in ECG signal analysis, namely convolutional neural networks (CNNs) and recurrent neural networks (RNNs), with an emphasis on specific architectures, such as Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU).  
In [23], the authors classify and compare deep learning (DL) architectures used in ECG arrhythmia detection in the period 2017-2023, architectures that showed superior performance. Thus, in [23], different DL models, such as convolutional neural networks (CNNs), multilayer perceptrons (MLPs), transformers, and recurrent neural networks (RNNs), are analyzed, and a summary of their effectiveness is provided. In [24], the authors provide a comprehensive overview of recent advances in prediction models and the implementation of AI and ML in the prediction and management of sudden cardiac arrest.  
Current risk stratification for ventricular arrhythmias relies on static measurements that fail to adequately capture the dynamic interactions between arrhythmic substrate and triggers over time. To address this, in [25], a dynamic machine learning (ML) model and neural network were employed, which extracted features from longitudinally collected electrocardiograms (ECGs). These features were then used to predict the risk of malignant ventricular arrhythmias.
# Prediction of Arrival Time of Vessels Considering Future Weather Conditions
## 3 Proposed Method
### (B) Voyage Speed Calculation
The definition of the conditional probability can be used to rewrite this expression using four terms. \[p(\text{Heading, HTSGW, PERPW, WindPower}|v)=p(\text{Heading}|v)\times p(\text{HTSGW}|v)*p(\text{PERPW}|v)\times p(\text{ WindPower}|v) \tag{4}\]  
\(p(\text{Heading}|v)\) is the conditional probability for the bow direction, \(p(HTSGW|v)\) is the conditional probability for the wave height, \(p(\text{PERPW}|v)\) is the conditional probability for the wind speed. The number of data can be obtained by calculating the conditional probability for each of these parameters. Thus, the likelihood function can be calculated using Equation (4). By splitting up the probability distribution as shown in Equation (4), even when there is  
Figure 8: Using Bayesian learning to estimate the voyage speed distribution. only a small amount of data, Bayesian learning can estimate more accurately than other methods such as support vector machine. The conditional probability of each parameter is calculated as follows. Figure 7 shows an example where the bow direction of the prediction target vessel is 60\({}^{\circ}\). The percentage of vessels found in B1 with a bow direction of 60\({}^{\circ}\) is calculated for each voyage speed, where an example of this process is shown in Figure 8 for a voyage speed of 10 knots. The same procedure is followed for the other parameters, and the likelihood function is calculated. In B3, the time required for travel from the current location to the destination port is calculated using the distance between sites and the voyage speed at each transit site. The time of arrival at the destination port is subsequently calculated. In Figure 7, the voyage speed at site A is 15 knots, and the distance between sites A and B is 42 km. Therefore, the time required to travel from A to B is calculated as 42 km \(\div\) 15 knots = 1.5 h. This process is repeated for all sites, and the time required to reach the destination port is calculated as 1.5 + 1.5 + 2 + 1.5 = 6.5 h. Adding this required time to the departure time of 11:00 results in a calculated time of arrival at the destination port of 17:30. Note that the great-circle distance between two sites is used in this study.
## 2 Signal Model
### Probabilistic Signal Model  
Consider signal DOA \(\theta\) as a random variable with a probability density function (PDF) \(f(\theta)\). In [18; 19], it is assumed that coarse knowledge of \(f(\theta)\) is available. In this case,  
Figure 1: Block diagram of an antenna array without performing compression [18].  
according to the law of the total probability, the PDF of the compressed measurement vector \(\mathbf{y}\) is expressed as  
\[f(\mathbf{y})=\mathbb{E}_{\theta}\{f(\mathbf{y}|\theta)\}=\int_{\theta\in\Theta}f(\mathbf{y}| \theta)f(\theta)\mathrm{d}\theta, \tag{4}\]  
where \(\Theta\) is the angular region of the observations. We discretize the PDF \(f(\theta)\) into \(K\) angular bins with an equal width of \(\Delta\bar{\theta}\) so that the probability of the \(k\)th angular bin is approximated as probability mass function \(p_{k}\approx f(\bar{\theta}_{k})\Delta\bar{\theta}\) with \(\sum_{k\in\mathcal{K}}p_{k}=1\), where \(\bar{\theta}_{k}\) is the nominal angle of the \(k\)th angular bin and \(\mathcal{K}=\{1,2,\cdots,K\}\). As a result, the PDF of \(\mathbf{y}\) can be reformulated as  
\[f(\mathbf{y})\approx\sum_{k\in\mathcal{K}}p_{k}f(\mathbf{y}|\bar{\theta}_{k}). \tag{5}\]  
That is, the PDF of \(\mathbf{y}\) is approximated as a Gaussian mixture distribution consisting of \(K\) zero-mean Gaussian distributions \(\mathbf{y}|\bar{\theta}_{k}\). Considering a signal \(s(t)\) impinging from the \(k\)th angular bin with a nominal DOA \(\bar{\theta}_{k}\), the compressed measurement vector is given as  
\[\mathbf{y}|_{\theta=\bar{\theta}_{k}}(t)=\mathbf{\Phi}(\mathbf{a}(\bar{\theta}_{k})s(t)+ \mathbf{n}(t)), \tag{6}\]  
and the corresponding conditional PDF is  
\[f(\mathbf{y}|\bar{\theta}_{k})=\frac{1}{\pi^{M}|\mathbf{C}_{\mathbf{y}\mathbf{y}|\bar{\theta} _{k}}|}e^{-y^{\mathrm{H}}\mathbf{C}_{\mathbf{y}\mathbf{y}\mathbf{y}}^{-1}\mathbf{y}}, \tag{7}\]  
where  
\[\mathbf{C}_{\mathbf{y}\mathbf{y}|\bar{\theta}_{k}}=\mathbf{\Phi}(\sigma_{s}^{2}\mathbf{a}(\bar{ \theta}_{k})\mathbf{a}^{\mathrm{H}}(\bar{\theta}_{k})+\sigma_{n}^{2}\mathbf{I})\mathbf{ \Phi}^{\mathrm{H}} \tag{8}\]  
is the covariance matrix of the compressed measurement vector \(\mathbf{y}|_{\theta=\bar{\theta}_{k}}(t)\) and \(\sigma_{s}^{2}\) is the signal power. Additionally, define \(\mathbf{C}_{\mathbf{y}\mathbf{y}}=\mathbf{\Phi}\mathbf{A}(\theta)\mathbf{SA}(\theta)^{\mathrm{H}}\mathbf{ \Phi}^{\mathrm{H}}\), as the covariance matrix of the compressed measurement with \(\mathbf{S}=\mathrm{diag}([\sigma_{s}^{2},\sigma_{\zeta}^{2},\cdots])\) is the source covariance matrix.
## 3 System Model
### Structure of JQS and DQS  
JQS, as shown in Figure 1, is one of the straightforward scheduler structures to manage the multiple CCs. Each user is assigned a separate buffer to store the user data and the buffer is organized in a first-in first-out (FIFO) manner. We denote the buffer of the user \(k\) with \(Q_{k,su}\). The resource pool is composed of RBs which belong to the CCs. JQS combines the multiple carriers together as one carrier to allocate RBs. This is the same as the scheduling in the traditional single-carrier systems. Considering that the bandwidth of the SA system is wide, the JQS-based implementation can lead to increased complexity. Therefore, the weakness of the JQS is its high complexity. However, the JQS maximizes the spectral efficiency and achieves the saturated resource utilization. In terms of performance, the JQS is the optimal scheduler structure for SA.  
The structure of the DQS is shown in Figure 2. In each CC, there is a serving queue for each user. The resource pool is composed of RBs which belong to the CC that are controlled by RB assignment scheduler. In the DQS, each user has one traffic queue on each CC. Obviously, the DQS needs two level scheduling. The first level is CC selection, which is in charge of the user allocation between the CCs. The second level is RB assignment, which carries out the assignment of RBs in each CC. DQS is the simplest scheduler structure for the SA systems, but it cannot fully utilize the resource in the SA systems and is inefficient especially when the packets are large and sparse.  
The performance of the DQS is found to be inferior than that of the JQS in terms of spectral efficiency and resource utilization. The average burst rate supported in the DQS is always lower than that of the JQS, and the gain in average data rate of the JQS over the DQS decreases with the number of users per sector due to a decrease in difference in frequency selective gains.
## 2 Measurements and Quality Control Processes
### Daily Data
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multicolumn{2}{l}{\(L_{thr}\) (MJ/m\({}^{2}\))} & & & & & & & & \\ \hline
**Month** & **N** & **Mean** & **StDev** & **CV(\%)** & **Min** & **Q1** & **Median** & **Q3** & **Max** \\ \hline
1 & 58 & 24.7 & 3.3 & 13.4 & 17.4 & 22.5 & 24.8 & 26.8 & 31.2 \\
2 & 56 & 24.2 & 3.0 & 12.3 & 17.7 & 22.2 & 24.4 & 26.5 & 29.5 \\
3 & 62 & 25.0 & 2.3 & 9.0 & 20.9 & 22.9 & 24.9 & 26.6 & 31.1 \\
4 & 60 & 26.3 & 2.3 & 8.7 & 22.0 & 24.5 & 26.8 & 27.9 & 32.1 \\
5 & 53 & 28.3 & 2.1 & 7.5 & 24.8 & 26.4 & 28.1 & 29.7 & 34.1 \\
6 & 30 & 31.7 & 1.7 & 5.5 & 29.2 & 30.3 & 31.2 & 33.0 & 35.0 \\
7 & 31 & 33.4 & 0.7 & 2.0 & 32.5 & 32.9 & 33.4 & 33.9 & 35.1 \\
8 & 31 & 33.4 & 0.9 & 2.8 & 31.7 & 32.5 & 33.3 & 34.2 & 35.2 \\
9 & 30 & 31.0 & 1.8 & 5.8 & 27.3 & 29.9 & 31.4 & 32.5 & 33.4 \\
10 & 31 & 29.3 & 1.1 & 3.6 & 27.3 & 28.5 & 29.3 & 29.9 & 31.4 \\
11 & 51 & 25.8 & 2.0 & 7.7 & 22.9 & 24.6 & 25.4 & 27.4 & 32.0 \\
12 & 61 & 25.4 & 2.5 & 9.8 & 20.2 & 23.7 & 25.1 & 26.8 & 31.4 \\ \hline \multicolumn{2}{l}{All} & 554 & 27.3 & 3.8 & 13.8 & 17.4 & 24.7 & 27.1 & 29.9 & 35.2 \\ \hline \multicolumn{2}{l}{\(L_{sp}\) (MJ/m\({}^{2}\))} & & & & & & & & \\ \hline
**Month** & **N** & **Mean** & **StDev** & **CV(\%)** & **Min** & **Q1** & **Median** & **Q3** & **Max** \\ \hline
1 & 58 & 30.6 & 1.3 & 4.1 & 26.3 & 29.8 & 30.6 & 31.6 & 33.2 \\
2 & 56 & 32.1 & 1.8 & 5.5 & 28.6 & 31.0 & 31.9 & 33.1 & 35.7 \\
3 & 62 & 33.7 & 1.4 & 4.2 & 30.9 & 32.7 & 33.8 & 34.6 &  
Figure 14: Cumulative density function (CDF) of the daily downward (\(L_{dtn}\)) and upward (\(L_{up}\)) longwave irradiation (MJ m\({}^{-2}\)) at Athalassa. Figure 13: Boxplots of the daily (**a**) net shortwave (\(R_{ns}\)), (**b**) net longwave (\(R_{nt}\) ), and (**c**) net allwave irradiation (\(R_{nt}\) ) (MJ/m\({}^{2}\)).
### Overexpression of TaPS in Arabidopsis  
To overexpress _TaPS_ in plants, _TaPS_ was subcloned into pCAMBIA3301 under control of the 35S promoter and subsequently transformed into _Agrobacteriumium tumefaciens_ GV3101. _A. thaliana_ Col-0 was used for Agrobacteriumium transformation through flower dipping. Transgenic plants were screened with spraying of 100 mg L\({}^{-1}\) glufosinate until T3 generation. Overexpression plants were obtained and two independent lines were randomly chosen for further analysis. The positive T3 plants were analyzed by RT-PCR for _TaPS_ gene expression. The amplified fragments were verified by sequencing. The above-ground tissues of T3 plants were ground as fine powders in liquid N\({}_{2}\) for terpene extraction with hexane as described above, ready for GC-MS analysis. \(\beta\)-patchoulene quantification was conducted as above.
_Applied_  
_sciences_  
_HPLC and TEM Examinations_  
_MABA [PERSON] 1, [PERSON] 1, [PERSON] 2,*_  
**and [PERSON] 3,4**  
1 Conservation Department, Faculty of Archaeology, Cairo University, Giza 12613, Egypt; [PERSON]ESS] (M.M.A.M.); [PERSON]IL_ADDRESS] (S.A.E.-K.M.H.)
2 Forestry and Wood Technology Department, Faculty of Agriculture (EL-Shatby), Alexandria University, Alexandria 21545, Egypt
3 Botany and Microbiology Department, College of Science, King Saud University, P.O. Box 2455, Riyadh 11451, Saudi Arabia; [EMAIL_ADDRESS]
4 Timber Trees Research Department, Sababia Horticulture Research Station, Horticulture Research Institute, Agriculture Research Center, Alexandria 21526, Egypt
5 Correspondence: [EMAIL_ADDRESS]  
Received: 13 March 2020; Accepted: 17 April 2020; Published: 22 April 2020  
**Abstract:** In the present study, _Acacia saligna_ (Labill.) H.L.Wendl. wood blocks with dimensions of \(0.5\times 1\times 2\) cm were inoculated with five molds (_Aspergillus niger, A. flavus, Alternaria tenuissima, Fusarium culmorum_, and _Trichoderma harzianum_) and the changes in the organic acids (oxalic, citric, tartaric, succinic, glutararic, acetic, propionic, and butyric) of powdered wood were analyzed by HPLC. The effects of the five inoculated fungi on the alterations to the wood cell wall ultrastructures were examined by TEM. The wood became more acidic as it was inoculated with the studied fungi. From the HPLC analysis, the oxalic acid (293.34 \(\mathrm{\SIUnitSymbolMicro g}\)/g o.d.) in the _A. saligna, A. tenuissima_ (167.33 \(\mathrm{\SIUnitSymbolMicro g}\)/g o.d.), and _T. harzianum_ (245.01 \(\mathrm{\SIUnitSymbolMicro g}\)/g o.d.) wood decreased, but it increased in the _A. flavus_ (362.08 \(\mathrm{\SIUnitSymbolMicro g}\)/g o.d.), _A. niger_ (1202.53 \(\mathrm{\SIUnitSymbolMicro g}\)/g o.d.), and _F. culmorum_ (431.85 \(\mathrm{\SIUnitSymbolMicro g}\)/g o.d.) inoculated wood. Citric acid was observed in the wood inoculated with _A. flavus_ (110 \(\mathrm{\SIUnitSymbolMicro g}\)/g o.d) and _A. niger_ (2499.63 \(\mathrm{\SIUnitSymbolMicro g}\)/g o.d). Tartaric (1150.98 \(\mathrm{\SIUnitSymbolMicro g}\)/g o.d), acetic (2.04 \(\mathrm{\SIUnitSymbolMicro g}\)/g o.d), and propionic (1.79 \(\mathrm{\SIUnitSymbolMicro g}\)/g o.d) acids were found in the wood inoculated with _A. niger_. Butyric acid was found in small amounts. A loss of wood substances appeared as the electron-lucent increased in the middle lamella and the layers of the secondary wall. Within the secondary cell wall regions, checks and splits were also noted, which resulted from the effects of the acids on the carbohydrates, according to the fungus type and the acids. In conclusion, increasing the amount of organic acids in the wood samples through inoculation with fungi results in more degradations in the wood, especially in the wood inoculated with _A. niger_.  
**Keywords:**_Acacia saligna_ wood; organic acid; wood cell walls; TEM; HPLC; molds
# Efficient Load Balancing for Blockchain-Based Healthcare System in Smart Cities
## 4 Proposed Scheme
### Proposed Algorithm for Load Balancing Node
To achieve this goal, we modified the well-known greedy algorithm by incorporating the longest job first (LJF) algorithm to achieve the efficient distribution of load on fog mining machines. The proposed scheme is described below by providing an example. Suppose there are 3 mining nodes called \(X_{1}\), \(X_{2}\), and \(X_{3}\) with uniform computing capacity and 7 different sizes data blocks D1, D2, D3, D4, D5, D6, D7 with the varying load as 6, 8, 4, 10, 2, 1, and 6 respectively. The proposed algorithm allows the load balancing node to rearrange all these data blocks according to their sizes such as [\"D4\", 10], [\"D2\", 8] [\"D1\", 6], [\"D7\", 6], [\"D3\", 4], [\"D5\", 2], [\"D6\", 1]. The proposed algorithm allows the load balancer to check the load on each mining node and assign these jobs according to the mining machine load in such a way that the data block is forwarded to that mining node that holds less load. The distribution of all these 7 data blocks on three mining nodes is shown in Figure 3.  
The maximum load assigned to a mining node is 13 in this example, which is assigned to the \(X_{1}\) machine. However, the load on \(X_{2}\) and \(X_{3}\) are 12 each. A complete algorithm for load distribution is shown in Algorithm 1. Figure 3: Example of the proposed algorithm technique. ```
1 Task execution policy Input \(Datablocks=N\) Jobs= job sizes \((D1,D2,D3\ldots Dn)\), number of fog mining nodes \((X_{1},X_{2},X_{3}\ldots X_{n})\)
2\(Load(Xi)=\) Current load on \(Xi\)
3 Sort the data blocks in descending order according to their job sizes for\(i=1\) to \(N\)do
4 Allocate \(Di\) to the fog mining node \(j\) with minimum load such that its \(D_{lim}\) is not exceeded \(Load(Xj)=Load(Xj)+Di\)
5 end for
```  
**Algorithm 1**Proposed algorithm for load balancing