esa-sceva/satcom-chunk-collection huggingface dataset:
# Deep-Reinforcement-Learning-Based Joint Energy Replenishment and Data Collection Scheme for WRSN
## 5 Experimental Results
### Convergence and Stability  
After D-JERDG is implemented, we first test the convergence and stability of the proposed model. The episode was set as 400, and the model of the number of different clusters, the number of nodes in each cluster, and the maximum charging radius of the UAV were trained. \"C20N200R30\" means that there are 20 CH nodes and 180 CM nodes, the maximum charging radius of UAV is \(R_{c}=30\) m, and the other sections in this chapter follow the same pattern. Then the functional relationship between episode and reward is shown in Figure 5. As can be seen from Figure 5, the reward of most models increased rapidly before 150 episodes. After 200 episodes, the value of rewards becomes stable and oscillates around a certain value until the training ends. The results indicate that the trained D-JERDG algorithm successfully converges and can make high-reward decisions in a dynamically changing network state. Figure 5a shows the reward curves under different numbers of CH nodes and CM nodes, ranging from \"C20N200R30\" to \"C40N400R30\" experimental scenarios. Figure 5b displays the reward curves for different numbers of CH nodes, with the experimental scenarios ranging from \"C15N200R30\" to \"C30N200R30\". Figure 5c illustrates the reward curves under different UAV charging radii, with the experimental scenarios ranging from \"C20N200R15\" to \"C20N200R30\".  
Additionally, Figure 6 displays the loss curve and temporal difference error (TD error) curve of network training for the three algorithms, with the experimental scenario being \"C20N200R30\". In Figure 6a, the curve trends of the three algorithms are mostly consistent before 150 episodes. After that, due to the increased UAV throughput in D-JERDG, some fluctuations occur. However, overall, the loss curves of the three algorithms converge before 400 episodes. In Figure 6b, we show the TD error curve of network training for the three algorithms. Overall, all three algorithms can converge before 400 iterations. However, before 100 episodes, D-JERDG has a slower convergence rate compared with the other two algorithms. Because we expect D-JERDG to learn the positions of more nodes in the early training episodes, we have implemented a strategy where the set of CH nodes initialized using the K-means algorithm is not fixed. The purpose of this approach is to ensure that D-JERDG can learn the positions of as many nodes as possible. By flexibly adjusting the CH node set, D-JERDG can better adapt to the varying node distributions in different environments, enhancing its performance and training effectiveness. After 100 episodes, as the rewards for D-JERDG start to increase and gradually converge, it also exhibits convergence in the TD error curve.
## 4 Theoretical Optimization of Converter Inductor
### Application to the Three Sets of Inductors
#### 4.2.4 Optimal F-R-Oi  
From the three previous Sections 4.2.1-4.2.3 and according to Section 4.1.4, it is then possible to calculate the total losses in the converter processing 50 W from 30 V to 20 V. It includes the losses in the main inductors, the active components, the output filtering stage and the parasitic DC resistance of the converter. With the values shown on Figure 22, it is possible to conclude the theoretical optimal Frequency-Ripple-Output Inductor (F-R-Oi) and compare the different off-the-shelf inductors and select the one that would offer the best compromise.  
Although the different series have different volumes, it is interesting to note that the losses on the optimum are around the same value for the three inductors series. But the lower the volume is, the lower the optimal inductor value is, and the higher the ripple current is. The optimum can be found around a switching frequency of 100 kHz. Figure 23 presents the same result as the frequency is linked to the ripple current by the inductor value. As the optimum switching frequency is around 100 kHz, the optimum ripple current magnitude is around 1.25 A for an average current of 2.5A. Therefore, the optimal ripple current magnitude seems to be located at about 50% of the average current in this particular design case study. This is significantly higher than what the power electronic design engineer considers, usually more in the range of 10 to 25%. It is even above the standard recommendation of 20 to 40% proposed by manufacturers as in [30; 31; 32]. With these considerations, for each series, an optimal working point has been found with a pair of 22 uH inductors and losses extracted in Table 4.  
Figure 21: Losses in the output filter depending on the main inductor values (coloured curves) with respect to the switching frequency. The filtering inductor values are the minimum discrete standard values complying with the 60 dB\(\mu\)V limits at LSIN resistors as proposed in Section 4.1.  
Figure 23: Model results for the three inductor families. Total losses are represented as a function of the Current Ripple and the inductor values.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Serie** & **MSS1210** & **MSS1260** & **XGL6060** \\ \hline Optimal & 1.38 W & 1.44 W & 1.28 W \\ total losses & & & \\ \hline Main & 2 \(\times\) 0.37 W & 2 \(\times\) 0.40 W & 2 \(\times\) 0.32 W \\ inductor losses & & & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Calculated losses at global optimums for each series and individual main inductor losses at these optimums.  
Figure 22: Total mosses modelling results for the three inductor families. Total losses are represented as a function of the switching frequency and the inductor values.
# A Survey of Machine Learning Methods for Time Series Prediction
## 3 Tree-Based Machine Learning Architectures
### Gradient-Boosted Decision Trees  
Gradient-Boosted Decision Trees (GBDTs) are a machine learning algorithm that aggregates predictions from multiple weak learners, typically decision trees. GBDTs use \"boosting\" to build models iteratively, where each subsequent model focuses on correcting mistakes made by previous models. The algorithm achieves this by optimizing a differentiable loss function using gradient descent. While Scikit-learn provides a general implementation of GBDT, the three most prominent and high-performing implementations discussed in this survey are XGBoost, LightGBM, and CatBoost, each offering distinct advantages.
# Characterization of the 2014 Indus River Flood Using Hydraulic Simulations and Satellite Images
## 5 Conclusions  
This study proposed a modelling approach using GIS and RS to depict the spatial extent of the 2014 flood of the Indus River in Pakistan, comparing the model output with MODIS satellite imagery, and determining the extent of floods of different return periods for the basin, in addition to assessing the damage caused by the flood. The methodology consisted of using Landsat images to identify the various LULC types over the watershed and the approach was subsequently evaluated using Google Earth images and field data with an overall classification accuracy of 85% obtained. Also, the analysis found that the 'crop/agricultural land' LULC type was the most affected by the flood. This research also evaluated the HEC-RAS model to simulate the 2014 flood and using the model to simulate floods of different return periods. The K-S statistical test identified the LP3 probability distribution to be best at simulating the flow regime of the Indus River at Taunsa Barrage, and this distribution was then used to identify the peak river discharge for floods with a 5-, 10-, 50-, 100-, and 150-year return period, which was then used as input into the HEC-RAC model to estimate the areas of the watershed at risk of flooding for each return period. The spatial extent of the 2014 flood, as simulated by the model, was found to agree very well with the extent of the flood as observed on a MODIS satellite image, showing the potential of using the model and the approach presented in this paper for risk mitigation.  
A.T.: conceptualization, writing--review and editing, methodology, software, formal analysis, visualization, data curation, writing--original draft, investigation, validation; H.S.: supervision, visualization, validation, investigation, writing--review and editing; A.K.: visualization, validation, investigation, writing--review and editing, supervision; S.S.: writing--review and editing; A.S.G.: investigation, writing--review and editing; L.L.: investigation, writing--review and editing; N.T.T.L.: writing--review and editing, project management; Q.B.P.: visualization, validation, investigation, supervision, writing--review and editing. All authors have read and agreed to the published version of the manuscript.  
No external funding.  
The data presented in this study are available on request from the first or corresponding authors.  
We are thankful to [PERSON] from the Pakistan Meteorological Department in Islamabad, who provided the flood data necessary for this research. We also thank [PERSON], Head of State Key Laboratory of Hydrology at Wuhan University, China, for his support at various stages of the analysis and writing. Finally, we acknowledge the three anonymous reviewers and editors of the special issue of the journal who provided very helpful comments that helped improved the final version of the manuscript.  
The authors declare no conflict of interest.
## 3 Experimental Setup and Evaluation Indicators
### Experiment Details  
The dataset consists of 340 images with defects, out of which 240 are used for training and 100 for validation. To ensure that the model learns an adequate number of defect features and maintains robustness to varying lighting conditions, image angles, and sizes, data augmentation is applied to the training set. This includes randomly adjusting the brightness with a factor ranging from \(-0.2\) to \(0.2\), adjusting the contrast with a factor ranging from \(-0.2\) to \(0.2\), randomly rotating the image between \(-90\) and \(90\) degrees, and resizing the image to \(512\)\(\times\)\(512\) pixels. These augmentation techniques are applied individually or in random combinations, resulting in an expanded training set of 720 images. The validation set remains unchanged to evaluate the model's training performance and ensure its generalization.  
The input image size is uniformly set to \(512\)\(\times\)\(512\). Training is conducted for 200 epochs with a batch size of 8, using the SGD optimizer for parameter optimization. The initial learning rate is set to 0.0001, and the cosine annealing learning rate schedule is employed for adjustment. The hardware of the experimental environment is detailed in Table 1.
## 3 Results
### Real Time (RT) Analysis
#### 3.2.2 Speed  
There were significant differences between the conditions only at RT6 (F(2, 18) = 14.137, \(p\) < 0.001). The speed was 8.5% faster in the FC (Velocity = 215.0 \(\pm\) 21.7 m.min\({}^{-1}\)) than in the SC (Velocity = 196.7 \(\pm\) 21.7 m.min\({}^{-1}\), \(p\) = 0.002, d = 0.85) and 6.2% faster than in the NC (Velocity = 201.7 \(\pm\) 23.3 m.min\({}^{-1}\), \(p\) = 0.005, d = 0.60; see Figure 3B).  
Figure 4: Physiological variables. Heart Rate (bpm) in the (**A**) observed Time (OT) and (**B**) real time (RT) analysis. Mean (\(\pm\)SD) data for heart rate for each condition with respect to (**A**) OT (every 10-min period of the observed time) and (**B**) RT (every 10-min period of the real time). Notes: * Significant differences between NC and both FC (\(p\) = 0.032) and SC (\(p\) = 0.043). + Significant differences between FC and SC, in T5 (\(p\) = 0.043) and in T6 (\(p\) = 0.004). Oxygen Consumption (ml/kg/min) in the (**C**) observed Time (OT) and (**D**) real time (RT) analysis. Mean (\(\pm\)SD) data for oxygen consumption for each condition with respect to (**C**) OT (every 10-min period of the observed time), and (**D**) RT (every 10-min period of the real time).
# Properties and Mechanism of Hydration of Fly Ash Belite Cement Prepared from Low-Quality Fly Ash
## 3 Results
### Porosity of Hydration Products  
The porosity analysis of FABC and RC pastes hydrated for different ages is shown in Figure 8. The cumulative porosity analysis revealed that the porosity of FABC pastes hydrated for 3, 28, and 90 days was 41.64%, 34.22%, and 33.16%, respectively, as shown in Figure 8a. At the same age, the porosities of RC pastes were 23.15% and 16.60%, which were lower than that of FABC, indicating that the high porosity was the main cause of low early strength of FABC pastes. The pore radius of RC pastes dropped from 56.16 to 45.10 nm in 3-28 days, whereas the pore radius of FABC paste dropped from 58.83 to 25.12 nm. It showed that the most probable pore radius of RC was more than 1.8 times that of FABC pastes in 28-day curing. The results showed that at early ages, the weaker mechanical properties of FABC hydration pastes were related to its loose microstructure and high porosity. From 3 to 28 days, the curing ages led to the thinning of pore size, compaction of hydration products, and high hydration degree, resulting in the improvement of mechanical properties of FABC pastes.  
Similar to Portland cement pastes, the pastes of FABC had a heterogeneous and multiphase structure; the pores were divided into gel pores, micro pores, and transitional pores. The size distribution of pores could be graded according to the effect of pores on pastes. It was divided into four parts: (I) diameter \(<\) 20 nm, harmless pores; (II) 20-50 nm, less harmful pores; (III) 50-200 nm, harmful pores; and (IV) \(>\) 200 nm, more harmful pores. Generally, the less the harmful pores (d \(>\) 50 nm), the better the mechanical properties of FABC pastes. Hence, the percentage of harmful pores (d \(>\) 50 nm) in pastes described the effect of pore structure characteristics on strength. The results are shown in Figure 8 and Table 4. The results indicated that the ratio of harmful pores was up to 55.04% at the age of 3 days. Then, after 28 days, it dropped to 6.71%, which was lower than 35.72% of RC pastes. In conclusion, although the FABC pastes had high porosity, the ratio of harmful pores with a diameter of larger than 50 nm dropped obviously with curing ages. A large number of pores were less than 20 nm diameter,  
Figure 8: Cumulative porosity (**a**) and pore size distribution (**b**) of pastes hydrated for different ages.  
which was less harmful to the microstructure of pastes. Therefore, the harmful effect of high porosity on strength reduced markedly.
# Gear Fault Diagnosis Based on Kurtosis Criterion VMD and SOM Neural Network
## 4 SOM Neural Network Fault Diagnosis Model
### SOM Neural Network Algorithm  
(1) Weight initialization  
A random value within the interval of \([0,1]\) is given to the initial weight vector \(w_{ij}(0)\).  
(2) Sampling  
Take a random sample from the input vector with a certain probability.  
(3) Determining winning neurons  
Within the set step size N of SOM neural network algorithm, the following criteria are used to determine the winning neuron:  
\[i(x)=\underset{i}{\text{argmax}}\ y_{i}(x) \tag{9}\]  
where: \(y_{i}(x)\) is the steady state output of the neural network without feedback:  
\[y_{i}(x)=\sum_{j=1}^{n}w_{ij}x_{j} \tag{10}\]  
A topological field of winning neuron C is defined as \(N_{c}\), then:  
\[\left\{\begin{array}{l}y_{i}=1,i\in N_{c}\\ y_{i}=0,i\
otin N_{c}\end{array}\right.\]  
Otherwise:  
\[w_{ij}(n+1)=w_{ij}(n)\]  
\(0<\alpha(n)<1\) is the learning factor, and step (2) is returned after weight training until \(N_{c}\) or \(\alpha(n)\) meets the requirements.  
(4) Enter the next vector until all the samples are learned. Training samples were input into SOM neural network for training, and a standard SOM neural network was obtained. Then, input the test samples data into the SOM neural network. If the position of the output neuron in the output layer is the same as that of a sample data, the sample to be tested is of the corresponding type.
## 1 Introduction
It is important to note that energy consumption may be lowered by aggregating data in a single transmission and avoiding contention thanks to optimizing the transmission schedule. We propose a periodic communication framework consisting of the number of frames divided into several time slots to coordinate sensor communication. In each frame, \(J\) sensors are allowed to transmit data. As the number of sensors sending data is \(N-1\) (sensor 0 is base station), it is necessary to have \((N-1)/J\) frames provided \(N-1\) is fully divisible by \(J\). In such a framework, it is possible to precompute sensors to slot allocation in each frame and use it to configure sensors before deployment. If sensor software allows on-the-fly configuration, it is also possible to change allocations during network operation if necessary to accommodate battery discharge rates or sensor malfunction.Such a scheme is beneficial in two ways. First, it allows avoiding contention even if relatively short frames are used. The contention may be a fundamental problem in dense networks as close spacing increase the risk of interference. Short frame, in turn, makes synchronizing slots easier. The second benefit is the possibility to replace a single but formidable allocation task with a larger number of much simpler problems. Lower numbers of active nodes and slots to allocate make this task easier to solve. Taking these into account, the proposed framework may be considered a kind of heuristic, where nodes are allocated to frames using a simple algorithm--e.g., by random picking, and then allocations within the frames are found. As the resulting tasks are simpler to solve, we envisage that, at least for smaller networks, it is possible to use exact methods, i.e., MIP, and obtain high-quality solutions. Such a method was proposed in [8], where the authors presented a novel, highly accurate linear transformation for the MILP approach, aiming at optimal deployment of the switches in a distribution system to enhance its reliability. The main contributions of this paper are: A novel mathematical formulation for determining energy-efficient routing and transmission scheduling in a PSN that takes into account:  
* energy profiles of radio devices;
* performance characteristics of the PSN;
* effects of radio transmission in multi-hop networks. We have introduced two formulations of two mixed integer programming problems and discuss numerical results to illustrate the effectiveness of the proposed optimization approaches. The solution of these problems for sample data is then presented along with conclusions and further research directions. The rest of the paper is organized as follows. In Section 2 we present related work on energy-saving methods used in WSNs. Section 3 describes network and energy models. Section 4 provides the formulation of two mixed integer programming problems. In Section 5 we present and discuss numerical results to illustrate the effectiveness of the proposed optimization approaches. Finally, Section 6 concludes the paper and highlights future research directions.
## 3 Related Work
Importantly, decoding of every single packet at relay nodes is vulnerable to security and privacy attacks [30, 31]. Use of clustering may not be possible in all applications of WBSNs, hence the hierarchical clustering-based NC works [24, 27]. Due to the complexity of ARQ [21], NC-integrated ARQ schemes [23, 42] are not suitable for WBSNs. The NC-based MAC proposed in [42] may not be suitable for WBSNs, as it is designed for wireless sensor networks. Moreover, some of the NC-based works [13] in BANs or WBSNs are not adaptive to the network channel conditions or environments of WBSNs. Most existing NC-based works in WBSNs exploit either linear combinations [24, 27] or the XoR [13, 26] operation for coding. Security-wise XoR-based coding is better than linear combinations. In a recent work [43], the authors proposed a cloud-assisted RLNC-based (random linear network coding) MAC protocol (CLNC-MAC). It supports guaranteed packet delivery and collision-free relaying, but suffers due to complexity and delay. In summary, most existing NC-based error recovery or performance improvement mechanisms are not QoS-aware of healthcare applications. Existing works which are QoS-aware do not support QoS in both perspectives, and they could be complex (e.g., [43]). In healthcare applications, inclusion of QoS awareness within these mechanisms is highly necessary.
## 3 Results and Discussion
### Construction of DNA-Protein Conjugates with NanoLuc and Venus for BRET-Based Biosensor  
As shown in Figure 1, we designed a BRET-based sensor with DNA-protein conjugates. For construction of the DNA-protein conjugates, the catalytic domain of PCV2 Rep comprising residues 14-109 (tpRep) was fused to the N-terminus of NanoLuc and the C-terminus of Venus\(\Delta\)10 with a His-tag (Figure 3A). The resultant fusion protein, VtpRNH, was expressed in E. coli and subsequently purified from the soluble fraction with a His-tag located at the C-terminus. After purification, the activities of each domain of VtpRNH were evaluated. First, the DNA binding ability of tpRep in the fusion protein was evaluated (Figure 3B). Even after fusion with NanoLuc and Venus\(\Delta\)10, tpRe retained its DNA binding ability. The conjugation efficiencies were found increase with increasing DNA concentration. The maximum efficiency was around 50% (Figure 3C). We also evaluated the functions of both Venus and NanoLuc. Venus was shown to fluorescence in the fusion protein (Figure 3D). The emission peak was \(\sim\)530 nm at an excitation wavelength of 500 nm [22].  
Bioluminescence of NanoLuc in the fusion protein was also evaluated. The emission spectrum of the fusion protein exhibited a peak at \(\sim\)450 nm without external excitation, which corresponds to the emission of NanoLuc [23]. These results suggest that VtpRNH retained its NanoLuc activity. Moreover, in addition to the emission peak around 450 nm, there was a small emission peak at \(\sim\)530 nm. To identify the origin of this peak, the emission spectra were normalized to the peak emission of NanoLuc at 450 nm, which was set to an intensity of 1.00. As shown in Figure 3E, the emission peak of normalized intensity was observed at \(\sim\)530 nm. The emission peak of Venus occurs at \(\sim\)530 nm. These results suggest that BRET occurred between NanoLuc and Venus. We measured the emission spectrum after ssDNA binding via Rep to the fusion protein. Even after ssDNA binding to the fusion proteins via Rep, the emission peak at \(\sim\)530 nm was observed (data not shown).  
Figure 2: DNA binding abilities of truncated pRep proteins. The flexible region of the catalytic domain of PCV2 Rep comprising amino acids 1-116 was truncated from the full protein (pRep\({}_{1-116}\), abbreviated pRep; MW 15,100); pRep\({}_{1-109}\): pRep lacking the last 7 amino acids in the sequence (MW 14,300); pRep\({}_{14-116}\): pRep lacking the first 13 amino acids in the sequence (MW 13,700); and pRep\({}_{14-109}\), abbreviated tpRep: pRep lacking the flexible regions at both the N- and C-terminals of pRep (MW 12,900); white triangles indicate pRep without DNA and black triangles indicate pRep conjugated with DNA.
## 3 Results and Discussion  
### UV-Vis Spectrophotometry  
The biosynthesis of AgNPs from Ajwa dates seed extract was confirmed by UV-Vis spectroscopy, which is a commonly used method to characterize biosynthesized metal nanoparticles. The maximum absorption was observed at 432 nm (Figure 1), which validated the presence of biosynthesized AgNPs in solution. Similar results emerged from a study by [PERSON] and [PERSON] [3] recorded the peak at 429 nm for AgNPs synthesized from date seed extract [2]. [PERSON] et al. 2017 showed that, with the decrease in the amount of date extract, the size of AgNPs became smaller and temperature was indirectly proportional to the size of AgNPs [2]. A conversion from a pale-yellow color to a tan-brownish color was noticed due to surface plasmon resonance phenomenon, which acts as an indicator that the synthesis of AgNPs in the reaction mixture was successful.