esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Effect of Surface Material and Nutrient Composition on Biofilm Formation and eGFP Expression
#### 3.2.1 Biofilm Cell Analysis  
_E. coli_ biofilms formed on the three different materials exhibited a similar behavior (Figure 4a-c). In general, the total cell count in TB and M9 ZB media increased from day 1 to day 5 and then remained constant, whereas in the LB medium, the cell count remained constant throughout the assay. Additionally, TB was the culture medium that most promoted biofilm development at all sampling points (\(p<0.05\)), resulting in biofilms with, on average, 1.32 and 1.73 Log higher cell counts compared to those grown in LB or M9 ZB media, respectively. TB is a rich medium commonly used for high-density growth [44] and, as shown in a previous study, is superior to LB for achieving higher cell densities [5; 9]. In turn, biofilms developed in the LB medium showed, on average, a slightly higher cell number (0.41 Log cells-cm\({}^{-2}\)) compared to those grown in the M9 ZB medium.  
Biofilms grown in TB medium displayed, on average, a higher number of culturable cells than those developed in LB and M9 ZB media (1.14 and 1.12 Log higher cells, respectively; \(p<0.05\); Figure 4d-f). There were no considerable differences in the number of biofilm culturable cells for LB and M9 ZB media. Biofilms formed on SST material reached the highest number of culturable cells between days 3 and 5, with 9.26, 7.81, and 7.79 Log cells-cm\({}^{-2}\) for TB, LB, and M9 ZB media, respectively (Figure 4d). On PVC material, biofilms grown in TB and M9 ZB media maintained a constant number of culturable cells throughout the assays (on average 8.63 and 7.81 Log cells-cm\({}^{-2}\), respectively), while biofilms developed in LB medium showed a decrease in culturability by day 7, achieving a minimum of 7.07 Log cells-cm\({}^{-2}\) (Figure 4e). In turn, on SIL material, the number of culturable cells in the TB medium increased up to day 7 (9.22 Log cells-cm\({}^{-2}\); Figure 4f). In contrast, biofilms developed in LB and M9 ZB media kept the number of culturable cells relatively constant (on average 7.66 and 7.59 Log cells-cm\({}^{-2}\), respectively).
## 4 Experiments
### Experiment Setup
#### 4.1.3 Experiments Details  
We randomly divided the UCAS-AOD [32] dataset into a training set and testing set according to a ratio of 7:3. To ensure that the target size conformed to the definition of the absolute and relative scales of the small target, all images padded with zeros were resized to 800 x 800. The target size is shown in Table 1. Small targets were defined on an absolute scale by MSCOCO [34], a large publicly available universal dataset, which considers targets with pixels less than \(32\times 32\) to be small targets. In addition, There small targets can be defined the on a relative scale, in this case, a target for which the ratio of the target width to the image width and that of the target height to the image height are both less than 0.1.  
From the analysis of Figure 6, it can be seen that the number of instances of cars accounts for more than 50% in the samples meeting the two definitions of absolute scale and relative scale. To a certain extent, the improvement of the detection accuracy of vehicle samples by the network reflects the superiority of the network on the detection task for small targets.  
To ensure the repeatability of the experimen, we set the initial seed to 0. We used Stochastic Gradient Descent (SGD) to optimize the training, with the weight decay set to 0.0005 and the momentum to 0.937. The initial learning rate was set to 0.01 and attenuated according to the cosine annealing algorithm. We warmed up the training process for the first three iterations and trained the model without pre-trained weights for 300 iterations. For data augmentation, we used random image translation, scaling, left and right flipping, and HSV enhancement to make the model more robust to images obtained from different environments. We implemented the model on Pytorch 1.10.2, and all experiments were completed on a PC with one RTX 3090 GPU and an Intel(R) Core(TM) i7-8700 CPU @3.20 GHz. Unless otherwise stated, the aforementioned training details are used in the experiments.  
During post-processing, we applied a confidence threshold of 0.001, indicating that only prediction boxes with a confidence score exceeding 0.001 were included in the sorting process. Moreover, an IOU threshold of 0.6 was set, which meant that if the IOU value between a prediction box with lower confidence and one with higher confidence was greater than 0.6, the prediction box with lower confidence was discarded.
## 4 Results and Discussion
### Performance of Throughput  
Figure 10 depicts the results of the throughput performance of conventional and proposed procedures. The proposed ECTSA provided better throughput results than the CEELBRP [24], EECRP-PSO [25], CLPDM-SI [26], and HFFGSO [28]. The access point obtains additional network packets, since the ECTSA has a lengthy network lifetime. Table 9 shows that the suggested ECTSA obtained a high throughput of 0.91 Mbps, compared to the existing CEELBRP [24], EECRP-PSO [25], CLPDM-SI [26], and HFFGSO [28].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Node Count**} & \multicolumn{3}{c}{**PLR**} \\  & **CEELBRP [24]** & **EECRP-PSO [25]** & **Proposed ECTSA** \\ \hline
50 & 0.62 & 0.57 & 0.32 \\
75 & 0.42 & 0.36 & 0.19 \\
100 & 0.24 & 0.11 & 0.07 \\
125 & 0.17 & 0.1 & 0.06 \\
150 & 0.09 & 0.07 & 0.02 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Performance of Packet Loss Rate.  
Figure 9: Analysis of PLR.
# Adaptive Energy Balanced Routing Strategy for Wireless Reschargeable Sensor Networks
## 6 Simulation and Evaluation  
In order to verify the effectiveness of the algorithm, we used MATLAB to analyze the performance of the algorithm through the average values of fifty simulation results. The parameter settings are shown in Table 1. The impacts of network size \(n\) and charging duration \(T_{c}\) on network lifetime, network balance and average hop count were analyzed, and two other algorithms were compared. Network balance can be seen as the variance of residual energy of nodes when the first node die, which evaluates the charging performance in energy balancing. Average hop count means the hop that each node takes to transit packets to sink node, and it can signify the efficiency of routing part.  
\begin{table}
\begin{tabular}{c c} \hline
**Definition** & **Value** \\ \hline Diameter of simulation area (\(L\)) & 100 m \\ Distance of different charging path (\(d\)) & 5 m \\ Number of charging points of \(r_{v}\) ( \(\left\{S_{v}\right\}\)) & \(4v,5v,6v,7v,8v\) \\ Network sizes (\(n\)) & 200, 250, 300, 350, \(\ldots,1000\) \\ Maximum charging range (\(R_{c}\)) & 5 m \\ Maximum communication range (\(R\)) & 30 m \\ Packet sizes & 1024 bits \\ Nodal original energy (\(Es\)) & 0.5 J \\ Sink node & (50,50) \\ Data rate & 4096 bit/round \\ \hline \end{tabular}
\end{table}
Table 1: Simulation Parameters.
# Multiple Thermal Parameter Inversion for Concrete Dams Using an Integrated Surrogate Model
###### Abstract  
An efficient and accurate method for concrete thermal parameter inversion is essential to guarantee the reliable and prompt thermal analysis results of dams. Traditional inversion methods either suffer from low analysis efficiency or are limited in accuracy. Thus, this paper presents a method for multiple thermal parameter inversion based on an integrated surrogate model (ISM) and the Jaya algorithm. This method replaces finite element analysis with an ISM incorporating three machine learning algorithms, Kriging, support vector regression (SVR), and radial basis function (RBF), to describe the mapping relationship between thermal parameters and structure temperature responses. The input datasets for model training and testing are generated by a uniform design approach. Subsequently, a simple and efficient global optimization algorithm, Jaya, is used to identify the thermal parameters by minimizing the error between calculated and monitored temperatures. The effectiveness and practicality of this method are verified by applying monitored data of two strength grades of concrete in a dam. The verification results indicate that the proposed approach can obtain more accurate inversion results than the above individual models. Compared with these models, the inversion errors using ISM are reduced by 8.45%, 3.93% and 20.85%, respectively for C35 concrete, and by 6.53%, 23.82% and 44.43%, respectively for C40 concrete. Additionally, this approach maintains the powerful computational efficiency of surrogate-based optimization, and compared to the methods that directly invert using swarm intelligence algorithms, the analysis efficiency is improved by about 111.7 times.  
concrete dam; temperature field; thermal parameters inversion; integrated surrogate model; Jaya optimization algorithm 2023
###### Abstract  
The alpine grassland on the Qinghai-Tibet Plateau (AGQTP) has undergone severe climate change. Although the carbon budget of AGQTP proved to have altered significantly, the spatiotemporal dynamics and the driving mechanism of the changes remain debated. This study estimated the gross primary productivity (GPP), ecosystem respiration (ER), and net ecosystem productivity (NEP) of the AGQTP, based on remote sensing models, and analyzed their spatiotemporal dynamics and their climatic driving mechanism. Our results showed that the GPP, ER, and NEP increased at rates of 0.002 Pg C yr\({}^{-2}\), 0.013 Pg C yr\({}^{-2}\), and 0.0007 Pg C yr\({}^{-2}\), respectively, during 2000-2020, with substantial spatiotemporal variability. The changes in GPP were influenced by both temperature and precipitation, while NEP and ER were primarily affected by precipitation and temperature, respectively. However, the primary climatic driver of the carbon budget may have shifted from temperature to precipitation around 2010, and the impact of temperature on carbon sink was limited by local water conditions. Furthermore, we found that climate change, particularly precipitation variation, had notable legacy effects on the carbon budget of the AGQTP. Our findings highlight that the climatic impact on the carbon budget is dynamic and long-lasting, rather than static and short-lived, which should be considered in ecosystem carbon budget simulations and other related studies.  
alpine grassland; carbon budget; climate change; legacy effect; Qinghai-Tibet Plateau 1  
Footnote 1: [[https://www.mdpi.com/journal/remotesensing](https://www.mdpi.com/journal/remotesensing)]([https://www.mdpi.com/journal/remotesensing](https://www.mdpi.com/journal/remotesensing))
## 2 Theoretical Background of the Methods
### Bagging Ensemble  
Ensemble learning is a machine learning paradigm where multiple classifiers are trained and combined to enhance the prediction capability of a model. Different from popular machine learning approaches where one model is built from the training data, ensemble frameworks try to generate a set of sub-datasets from the training data, and then, each sub-dataset is used to construct a classifier, which is also called a based learner. At last, all of the based learners are combined to form the final prediction model using combination techniques i.e., averaging or majority voting [59].  
Different ensemble techniques have been successfully proposed i.e., Bagging, AdaBoost, Multiboost, Stacking, and Rotation forest [60]; however, in landslide modeling, Bagging ensemble has proven robust and better than other ensembles [26; 51; 54], therefore, it is selected for this study.  
Bagging also called Bootstrap aggregating in the full name is one of the earliest procedure for generating sub-datasets and combining based learners proposed by [PERSON] [53]. Using the training dataset, this technique generates bootstrap samples in which some of the samples are replicated and some samples are omitted. These bootstrap samples, which are called bootstrapped sub-datasets, are used to construct based learners using the same classification algorithm i.e., the LMTree in this work. These based learners are then combined using the majority voting strategy.
# Space-Constrained Scheduling Optimization Method for Minimizing the Effects of Stacking of Trades
## 3 Space-Constrained Scheduling Optimization Method
### Defining the Occupation Density Function
After the progress rate of 40%, work area B is occupied at 0.2 density. After 80%, work area B is occupied at a density of 0.2, and simultaneously, work area D is occupied. However, the occupation density of work area D increases at a constant rate from 0.4 to 0.6. Work area C is not occupied, while activity \(i\) is executed. Occupation density functions can be defined in various ways according to the characteristics and work areas of the activities. In order to identify the occupation density function of work areas, the user must define the work areas occupied according to the progress rate of the activity, which requires expert judgment. However, this process can be automated by integrating 4D-BIM, which includes execution schedule and spatial information for activities, into SSO. The definition of execution patterns is the direction in which an activity is to be executed [3]. The strategy of changing execution patterns in SCS has become widely used to minimize workspace interference without penalties such as increases in time and cost [3,14,20]. In this study, the changing activity execution patterns method is used to create alternatives for occupation density function (_f_s). In other words, when different activity execution patterns are used; i.e., when the direction of work progress is different, the variables in Equation (3), such as \(r_{i,h}\), \(e_{i,h}\), \(m_{i,h}\), and \(f_{i,h}\), vary, and the work areas (_h_) that are occupied according to the rate of progress (_p_) differ. As a result, a different occupation density function (_f_s,_h_) is defined. Alternatives for the occupation density function (_f_s) of  
Figure 3: An example of the occupation density function. each work area (\(l\)) obtained by changing the execution pattern of activity (\(i\)) are stored in a matrix \(A_{i}\) (Step 6), as shown in Equation (5). \[A_{i}=\left[ \begin{array}{cc}1&\left\{fs_{i,l,1}\right\}\\ 2&\left\{fs_{i,l,2}\right\}\\ \vdots&\vdots\\ \mathrm{k}&\left\{fs_{i,l,k}\right\}\\ \vdots&\vdots\\ \mathrm{K_{i}}&\left\{fs_{i,k,Ki}\right\}\end{array} \right],\;\forall l \tag{5}\]  
where \(K_{i}\) is the number of alternatives for the occupation density function of activity \(l\), and \(k\) is the index of an alternative.
## 3 Results and Discussion
### Semantic Segmentation Maps
Figure 17: Semantic segmented heat maps for RD annotated dataset for (**a**) image ground truth, (**b**) binarized ground truth, (**c**) CM model, (**d**) AM1 model, (**e**) AM2 model, (**f**) ABM model, (**g**) BM model. to the ground truth (Figure 17_f_g), show the defective pixels concentrated around the center of the maps. This may not have depicted the exactness of the ground truth map, but there is an indication that balancing the data significantly improved the model's performance and the semantic segmentation output, as presented in the earlier discussion. The BM output, in comparison to the ABM model, showed a clearer distinction in the prediction and segmentation of sound and delaminated pixels. This segmentation map output justifies the preference of the FN/R/B model based on its performance metrics, as shown in Table 6. This also further suggests that GA may not be an appropriate metric for evaluating the performance of a model. The maps clearly show that the model distinctly and successfully segmented the background from the deck's boundary but would need further studies to improve the segmentation output map for the delamination. A summary of the results of this study compared to earlier studies using the iterative image-based method [13], carried out on the same dataset, is shown in Table 8. The condition map from past studies is presented in Figure 18. GA was used to choose the best model for comparative analysis with previous studies by [PERSON] et al. [14]. The table shows that the uNet had better metrics than the iterative image-based method. Contrarily, the image-based method shows a better visual condition assessment map. This suggests that combining deep learning with image processing-based models for delamination assessment has prospects of yielding improved model and prediction outcomes.
# Artificial Intelligence to Reshape the Healthcare Ecosystem
## 2 AI Techniques for Healthcare
### Recurrent Neural Networks
#### 2.3.1 Long Short-Term Memory  
LSTM [72] is an RNN variant designed to overcome some limitations of traditional RNNs in learning long-term dependencies. For this purpose, LSTM networks include memory cells with gates to control the flow of information. The LSTM cell is governed by the following equations:  
\[\mathbf{f}_{t} =\sigma(\mathbf{W}_{f}\mathbf{x}_{t}+\mathbf{U}_{f}\mathbf{h}_{t-1}+ \mathbf{b}_{f}) \tag{22}\] \[\mathbf{i}_{t} =\sigma(\mathbf{W}_{i}\mathbf{x}_{t}+\mathbf{U}_{i}\mathbf{h}_{t-1 }+\mathbf{b}_{i})\] (23) \[\mathbf{o}_{t} =\sigma(\mathbf{W}_{o}\mathbf{x}_{t}+\mathbf{U}_{o}\mathbf{h}_{t- 1}+\mathbf{b}_{o})\] (24) \[\mathbf{c}_{t} =\mathbf{f}_{t}\odot\mathbf{c}_{t-1}+\mathbf{i}_{t}\odot\tanh( \mathbf{W}_{c}\mathbf{x}_{t}+\mathbf{U}_{c}\mathbf{h}_{t-1}+\mathbf{b}_{c})\] (25) \[\mathbf{h}_{t} =\mathbf{o}_{t}\odot\tanh(\mathbf{c}_{t}) \tag{26}\]  
where \(\mathbf{f}_{t},\mathbf{i}_{t}\), and \(\mathbf{o}_{t}\) are the forget, input, and output gates, respectively, \(\mathbf{c}_{t}\) is the cell state at time step \(t,\mathbf{W}_{*}\) and \(\mathbf{U}_{*}\) are weight matrices, \(\mathbf{b}_{*}\) is the bias vectors, \(\sigma\) is the sigmoid activation function, and \(\odot\) denotes element-wise multiplication.
# A Survey of Autonomous Vehicles: Enabling Communication Technologies and Challenges
## 5 Different VANET Technologies Based on Transmission Range  
An AV can communicate with other vehicles and the network infrastructure by using different wireless technologies. For example, the AV may transmit and receive real-time data (Audio/Video) during V2X communication or in case of emergency situations such as fog or accidents, warning information can be exchanged with neighbouring vehicles in a short time [29]. As discussed above and shown in Figure 4, the wireless technology employed depends on the system architecture and particularly on the kind of sensors employed along with the use case. The wireless technologies used in autonomous vehicles can be differentiated in terms of their transmission range, as shown in Figure 6.  
The long-range technologies allow the vehicles to transmit data while being miles apart; examples are C-V2X and 5G-NR communication. The medium-range communication, using Wireless Local Area Network (WLAN) or DSRC, can provide the coverage for V2V and V2I communication within a range of tens or hundreds of feet. The short-range communication technologies include Bluetooth, UWB and ZigBee. Below, these wireless technologies are discussed in detail.
# Automated Classification of Left Ventricular Hypertrophy on Cardiac MRI
## 2 Materials and Methods
### Dataset  
After the exclusion of patients with poor image quality, we investigated 428 patients (mean age: 49 \(\pm\) 18 years, 262 males) with left ventricular hypertrophy in whom CMR examination was clinically indicated and 234 healthy subjects (mean age: 35 \(\pm\) 15 years; 126 males) without any known cardiovascular diseases as a control group. The patients underwent CMR examination in our tertiary referral center between January 2009 and February 2019. Out of the 428 LVH patients, 346 had HCM (age: 46.9 \(\pm\) 18.2 144 males), 45 patients had cardiac amyloidosis (age: 63.9 \(\pm\) 9.7 years, 26 males), 11 patients had Anderson-Fabry disease (age: 48.3 \(\pm\) 12.9 years, 7 males), 16 patients had endomyocardial fibrosis (age: \(46.4\pm 14.3\), years 9 males), and 10 patients had aortic stenosis (age: 63.4 \(\pm\) 17.5 years, 5 males). Appendix C shows example images. CMR examinations were performed on a 1.5 T magnetic resonance (MR) scanner ([PERSON], Philips Medical Systems) using a cardiac coil. ECG gated balanced steady-state free precession (bSSFP) cine images were acquired in the three standard long-axis views: 2-chamber, 4-chamber, and LV outflow tract views. The protocol used for cine images in the present study was described in detail in a previous publication [39]. Short-axis (SA) images were also acquired with the full coverage of the left ventricle.
## 2 Related Work
### Computational Complexity  
In the case of high-dimensional problems, non-dominated sorting not only suffers from the deterioration in the searchability, but it is also slow. A number of attempts were made to improve its time complexity. Some methods are based on **algorithmic improvements**. For example, efficient non-dominated sorting (ENS) [8] reduces the time complexity by comparing solutions only with those that have already been assigned to a front. Similarly, authors of [28] proposed assigning solutions to the fronts in the ascending order of the sum of objectives. There are also approaches that are based on usage of **alternative data structures**, such as trees [29]. The last group of methods performs **approximate** non-dominated sorting. The first algorithm from this group, ANS, was proposed in 2016 [30]. In this algorithm, the dominance relationship between two solutions is determined by a maximum of three objective comparisons on top of a population sorted according to one of the objectives. It was shown by the authors of [30] and further confirmed in other research works [31] that this approach is not only more computationally efficient, but it also leads to better search performance. This idea was further developed in [32], where no more than two objectives were compared.  
In this paper, we demonstrate that \(k\)-Pareto optimality based on probabilistic calculation, the _PO-prob_ algorithm, allows for substantially improving the computational efficiency of genetic optimization, see the results in Section 5.3. The usage of probability theory makes this approach somewhat similar to approximate methods. However, _PO-prob_ utilizes the values of all objectives to compare solutions.
## 5 Analysis of Numerical Calculation Results on Support Effect for Fractured Loose Rock Slopes
### Characteristic Analysis of Monitoring Points After Implementation of Support Measures  
#### 5.4.1 Displacement Monitoring Curve in Y Direction  
As shown in Figure 22, the trend of the Y-direction displacement monitoring curve before and after the implementation of the support measures essentially remains unchanged, but the cumulative displacement of A-2 decreases by approximately 2.1 cm, the cumulative displacement of A-5 decreases by approximately 10.2 cm, and the final displacement of A-8-10 in the \(Y\) direction decreases by approximately 2.8 cm.